CN117730311A

CN117730311A - Logistics network simulation method and system

Info

Publication number: CN117730311A
Application number: CN202180099408.7A
Authority: CN
Inventors: 刘胜; 吴盛楠; 严良; 王煜; 林建伟
Original assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Current assignee: Beijing Jingdong Zhenshi Information Technology Co Ltd
Priority date: 2021-06-22
Filing date: 2021-06-22
Publication date: 2024-03-19
Also published as: WO2022266827A1

Abstract

The present disclosure relates to a logistics network simulation method and system. There is provided an electronic device for logistics network simulation, the electronic device comprising a processing unit configured to: an event sequence including discrete logistic events characterizing the entire logistic process of the logistic network is obtained, and logistic events in the event sequence are sequentially processed to simulate the operation of the logistic network.

Description

Logistics network simulation method and system

Technical Field

The present disclosure relates generally to logistics networks and, in particular, to simulation and construction of logistics networks.

Background

With the development of electronic commerce, the express logistics industry has rapidly developed in recent years, a large number of logistics clusters are established and operated around the world, and up to now, more than ten million packages are delivered per day in china. The logistics enterprises manage a large logistics network comprising hundreds of warehouses and sorting hubs, thousands or tens of thousands of express stations, thousands of backbone transportation routes and tens of thousands of site transportation routes, and provide end-to-end logistics and express services, which brings great convenience to people.

In recent years, sales of large promotions of 11.11, 6.18, etc. of e-commerce websites reach new heights, which bring great challenges to the express industry, such as warehouse overload, serious delays, package loss, etc. To address these issues, the courier enterprise needs to properly plan the logistics network during the promotion according to billions of package delivery needs. However, in the prior art, the conventional mathematical planning tool is difficult to solve such a large-scale problem, and a planner lacks an effective tool, mainly relies on experience to plan a logistics network, which is time-consuming and laborious and has poor effect.

Accordingly, there remains a need to provide an improved solution for optimizing a planned logistics network to effectively address the problems and challenges of the prior art.

Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Also, unless otherwise indicated, issues identified with respect to one or more methods should not be assumed to be recognized in any prior art based on this section.

Disclosure of Invention

The present disclosure proposes an improved mechanism for simulating an actual logistics network, in particular based on logistics related events during a logistics flow through the actual logistics network.

The present disclosure also proposes an improved mechanism for optimizing planning/scheduling of an actual logistics network, and in particular, planning/scheduling of an actual logistics network may be optimized based on simulation. In this way, the actual logistics network, especially the large-scale logistics network, can be optimized and thus can be operationally smooth and efficient.

An aspect of the present disclosure relates to an electronic device for logistics network simulation, the electronic device comprising a processing circuit configured to obtain a sequence of events comprising discrete logistics events arranged based on a time series and characterizing an entire logistics process of a logistics network, and to sequentially process the logistics events in the sequence of events to simulate operation of the logistics network.

Another aspect of the disclosure relates to an electronic device for scheduling an actual logistics network, the electronic device comprising a processing unit configured to: acquiring package delivery requirements to be processed by an actual logistics network; simulating, by the electronic device as described above, a package transportation process through the actual logistics network to obtain a scheduling schedule for the actual logistics network based on the simulation result, and transmitting information about the scheduling schedule for scheduling the actual logistics network.

Yet another aspect of the present disclosure relates to a method of logistics network simulation, the method comprising the steps of: acquiring an event sequence comprising discrete logistics events arranged based on the time sequence and characterizing the whole logistics process of the logistics network; and sequentially processing the logistics events in the event sequence to simulate the operation of the logistics network.

Yet another aspect of the present disclosure relates to a method of scheduling an actual logistics network, the method comprising the steps of: acquiring package delivery requirements to be processed by an actual logistics network; simulating, by the electronic device as described above, a package transportation process through the actual logistics network to obtain a scheduling for the actual logistics network based on the simulation result; and transmitting information about the scheduling schedule for scheduling the actual logistics network.

Yet another aspect of the present disclosure relates to an apparatus comprising a processor and a storage device, and the storage device stores executable instructions that when executed by the processor implement the method as described above.

Yet another aspect of the disclosure relates to a non-transitory computer-readable storage medium storing executable instructions that, when executed by, for example, one or more processors, implement the method as described above.

Yet another aspect of the present disclosure relates to a computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method as described above.

This section is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This section is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Other aspects and advantages of the present technology will become apparent from the following detailed description of the embodiments and the accompanying drawings.

Drawings

The foregoing and other objects and advantages of the disclosure are further described below in connection with the following detailed description and with reference to the drawings. In the drawings, the same or corresponding technical features or components will be denoted by the same or corresponding reference numerals.

Fig. 1 schematically illustrates the concept of logistics network simulation and planning/scheduling in accordance with an embodiment of the present disclosure.

Fig. 2 schematically illustrates conceptual global parcel handling in a large-scale logistics network in accordance with an embodiment of the present disclosure.

Fig. 3 schematically illustrates exemplary processes in a sorting hub, including unloading, sorting, and loading processes, according to an embodiment of the present disclosure.

Fig. 4 schematically illustrates an exemplary sorting shift cycle and transportation cycle according to an embodiment of the present disclosure.

Fig. 5A schematically illustrates an electronic device for simulating a logistics network in accordance with an embodiment of the present disclosure, and fig. 5B schematically illustrates a method for simulating a logistics network in accordance with an embodiment of the present disclosure.

Fig. 6 schematically shows exemplary event categories and events included in the respective categories.

Fig. 7 schematically illustrates an exemplary flow chart of a simulation of a logistics network in accordance with an embodiment of the present disclosure.

Fig. 8A-8N schematically illustrate example event processing logic, respectively, according to an embodiment of the present disclosure.

Fig. 9 schematically illustrates a logistics network coverage area in accordance with an embodiment of the present disclosure.

Fig. 10 schematically illustrates a relationship of events and the flow of a simulation engine for such events, according to an embodiment of the present disclosure.

FIG. 11 schematically illustrates an exemplary main flow of a logistics network simulation engine, in accordance with an embodiment of the present disclosure.

Fig. 12A schematically illustrates an electronic device for planning/scheduling an actual logistics network in accordance with an embodiment of the present disclosure, and fig. 12B schematically illustrates a method for scheduling an actual logistics network by a manual logistics network model in accordance with an embodiment of the present disclosure.

Fig. 13 schematically illustrates an exemplary framework of a parallel logistics network mechanism, according to an embodiment of the present disclosure.

Fig. 14 schematically illustrates an exemplary flow of a parallel logistics network mechanism, in accordance with an embodiment of the present disclosure.

FIG. 15 illustrates an overview of a computer system in which embodiments in accordance with the present disclosure may be implemented.

Fig. 16 illustrates a master interface of a manual logistics network and an actual logistics network in a parallel logistics network system in accordance with an embodiment of the present disclosure.

The embodiments described in this section are susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the embodiment to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.

Detailed Description

Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings. In the interest of clarity and conciseness, not all features of an embodiment are described in the specification. However, it should be appreciated that many implementation-specific settings must be made during the implementation of the examples in order to achieve the developer's specific goals, such as meeting those constraints related to equipment and business that may vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

It is also noted herein that, in order to avoid obscuring the present disclosure due to unnecessary details, only processing steps and/or apparatus structures that are at least closely related to the solution according to the present disclosure are shown in the drawings, while other details that are not greatly relevant to the present disclosure are omitted.

In recent years, the express logistics industry has been rapidly developed, which brings great convenience to the life of people. However, it brings problems such as long delivery time of the package, high transportation cost, and the like. In addition, for large promotions or emergency scenarios, there are further problems such as overload of sorting centers, lengthy parcel delivery times, even parcel loss, etc.

Specifically, on the one hand, during large promotions, such as 6.18 (6 months 18 days) and 11.11 (11 months 11 days), severe backlog of packages may occur in the logistics network due to the greatly increased number of orders from customers. As a result, some node hands are insufficient, while some node hands are excessive. Some transportation routes still lack vehicles, while some transportation routes occupy too many vehicles. This not only increases the transport costs, but also results in more exhaust emissions. The entire logistics network is difficult to run smoothly and efficiently. On the other hand, the express logistics industry may be faced with some sudden events, in which case the logistics industry suffers from challenges such as hub disruption, and thus cannot operate smoothly and efficiently.

Typically, in a large scale logistics network, hundreds of warehouses and sorting hubs, thousands or tens of thousands of express stations are interconnected by tens of thousands of transportation routes over which hundreds of thousands of trucks, trains and planes are operated in concert. Such large scale logistics networks are obviously a typical complex system. The conventional method has great difficulty in predicting its load state and in proposing an effective resource allocation scheme to schedule or plan a logistics network. In particular, conventional methods have difficulty modeling and optimizing logistics networks.

At present, various simulation-related methods have been provided for researching complex systems, and at present, simulation of logistics industry is mainly focused on internal simulation of sorting centers and warehouses, and simulation of local processes during package transportation, such as package station collection and package station dispatch, but due to the requirements of large data volume, large operation amount and fast operation, simulation of the whole network of a large-scale logistics network is difficult to realize, and at present, no research on simulation of the large-scale logistics network is available.

The present disclosure has been made in view of the foregoing, and proposes an improved simulation concept and thereby programs/schedules logistics networks, especially large-scale logistics networks.

In one aspect, an improved simulation for a logistics network is presented, wherein the logistics network simulation can be used to describe the overall process of all transported objects (packages) in the logistics network from a departure site to a destination site within a certain period of time, and in particular to verify the performance of the logistics network under a certain logistics network configuration. The simulation of the present disclosure may be applied specifically to an entire logistics network, which generally refers to a large-scale logistics network of a certain logistics enterprise nationally or even worldwide.

According to embodiments of the present disclosure, the logistics network simulation is based on appropriate logistics events that are capable of characterizing the entire logistics process throughout the logistics network, so that a scheme or configuration can be efficiently and accurately simulated and validated against the actual logistics network. Specifically, the logistics network simulation is based on a discrete event simulation method, wherein the complex dynamic process of the logistics network can be abstracted into discrete events of certain categories, and the events are sequentially processed according to the sequence of the occurrence time of the events so as to simulate the operation of the logistics network, thereby simplifying the description of the logistics network and realizing the efficient simulation of the logistics network. For example, a simulation engine according to embodiments of the present disclosure ensures that a simulation of a 30 day 4 hundred million package distribution is completed on a personal computer for half an hour, significantly improving efficiency.

In another aspect, the present disclosure presents a solution for scheduling/planning a logistics network based on a simulation of the logistics network, wherein the configuration for the actual logistics network can be optimized based on the simulation, enabling a large scale logistics network to work smoothly and efficiently and achieving improved parcel delivery.

According to embodiments of the present disclosure, an optimized solution may include optimal configuration parameters for an actual logistics network, and in particular, configuration parameters for various elements included in the actual logistics network. For example, these elements may include, for example, warehouse, sorting hubs, courier stations, transportation routes, vehicles, couriers, and other suitable elements in a logistics network. In operation, for each element, configuration information such as quantity, distribution including location information, resource allocation, operation mode including run time, and any other information related to the function of the element, may be optimized by means of simulation and applied accordingly to planning/scheduling such elements in the actual logistics network so that the actual logistics network may be optimized for specific package delivery needs.

Embodiments of the present disclosure are described below with reference to the accompanying drawings. Fig. 1 schematically illustrates the general basic concept of the present disclosure, which relates to logistics network simulation and logistics network planning/scheduling based thereon, and wherein the logistics network simulation interacts with logistics network optimization.

Specifically, a logistics network simulation is performed based on events characterizing the logistics transportation process throughout the network, and the simulation may verify logistics network performance by processing events under predetermined configuration information of the logistics network as described above. Such simulation and/or interaction may be performed iteratively until the desired logistics network metric is met or cannot become better, or a predetermined number of iterations has been reached. For example, the simulation may verify whether the predetermined configuration information enables the logistics network to meet the desired metric, and if not, the predetermined configuration information may be adjusted and the simulation will then be performed based on the adjusted configuration information. When the simulation is completed, the corresponding configuration information can be used as optimized configuration information to plan/schedule the actual logistics network. During simulation, the initial configuration information of the actual logistics network may be presented to the simulation side in any suitable way, e.g. from the planning/scheduling side or from a suitable third party.

Note that fig. 1 is illustrative and exemplary only, and is not limiting of the manner in which it may be implemented. The structure as shown in fig. 1 may be implemented in any suitable way, e.g. the simulation side and the planning/scheduling side may be implemented separately, or they may be integrated together, e.g. as a planning/scheduling entity, in order to obtain logistics network information, perform the simulation, and then feed back the network configuration to the logistics network. Furthermore, the structure as shown in fig. 1 may be incorporated into a logistics network or may be external to the logistics network.

Hereinafter, some embodiments of the present disclosure related to logistics network simulation will be described. According to embodiments of the present application, simulations are performed to describe package transportation processes in a logistics network. In particular, packages will undergo various processes in the logistics network when transported from a departure site to a destination site. Fig. 2-4 schematically illustrate three typical transportation related processes in a large-scale logistics network.

FIG. 2 depicts a global parcel process in a large-scale logistics network. Packages are created by packaging goods from a warehouse or from a customer. The transport equipment then transports them to a sorting hub where they are unloaded and sorted. After being sorted, they will be loaded into a transport facility and transported to other sorting hubs or express stations. In the courier station, packages are unloaded from a transportation facility and delivered to customers. Packages may be transported from one sorting hub to another by road, rail, or air route.

Fig. 3 illustrates the unloading, sorting and loading process in the sorting hub. When a full truck reaches the sort hub, it can be unloaded only if the sort hub has an empty unloading gate. Otherwise, it should be queued. When a truck has been unloaded, it releases a discharge gate and leaves the sorting hub, and the earliest truck in the waiting queue will occupy the released discharge gate and be unloaded. The unloaded package will be immediately sorted. After sorting, the packages will be assigned to a shipping route according to their destination. After a sufficient number of packages have been sorted in the delivery route, if the sorting hub has a free loading gate, an empty truck will be called to load the packages.

Fig. 4 (a) shows a sorting shift cycle in each sorting hub. Sorting hubs typically operate with two or three shifts. At the beginning of a sorting shift in the sorting center, packages will be unloaded from trucks occupying the unloading gate, and the sorter will begin sorting packages. If the sort hub has an empty unloading gate and the waiting queue has a full truck, the full truck will occupy the empty unloading gate and be unloaded. At the end of the sorting shift, unloading of packages from the truck is paused, and the sorter pauses sorting packages. Fig. 4 (b) shows a transportation cycle of each transportation route. If the departure sorting hub of the haul route has an empty unloading gate, the haul route will call the haulage device some time before the departure deadline. The transport facility begins loading sorted packages onto the transport route after its arrival. When the departure deadline of a transport route arrives, all transport apparatuses belonging to the transport route will be sent out.

Note that the process described above is merely exemplary, and that other logistical processes may occur during package shipping, such as processes in other package processing locations, processing stages, processing routes, etc.

Fig. 5A shows an electronic device for simulating an actual logistics network. According to one embodiment, electronic device 500 may include processing circuitry 502, where processing circuitry 502 is configured to obtain a sequence of events including discrete logistic events characterizing an entire logistic process of a logistic network, and to sequentially process logistic events in the sequence of events to simulate operation of the logistic network.

In one embodiment, the simulation may be performed based on event classification/grouping, specifically discrete logistic events generated, which are abstracted from the entire logistic process of package transportation throughout the logistic network from the departure site to the destination site over a certain period of time, and are representative. For example, event categories may correspond to processes that are dominant, or are at the heart, or critical in the logistics network. In some embodiments, the logistic events may be categorized according to any suitable condition in the logistic network, such as conditions regarding packages to be delivered, conditions regarding package handling, conditions regarding package delivery, such as processing stages, processing locations (such as sorting centers, hubs, etc.), conditions regarding package delivery, such as delivery tools, delivery routes, etc. Thus, the present disclosure proposes a discrete event simulation technique, and it uses a small granularity in describing the loading (unloading, sorting, transporting) actions of each package, so that the simulation can be performed accurately and efficiently.

In some embodiments, the logistical events to be performed during logistical transportation may be divided into four categories, including a sort center category, a route category, a parcel category, and a transportation equipment category, wherein the sort center category encompasses some representative events occurring at the sort center and may also be referred to as sort category, the route category encompasses some representative vehicle dispatch events occurring on the route, the parcel category encompasses some representative events related to parcel dispatch, and the transportation equipment category encompasses some representative events related to transportation equipment dispatch.

FIG. 6 clusters exemplary logistic events, which can be categorized into four categories, including 14 events. The four types of events are a sort center event/sort event class, a route event class, a package event class, and a transport event class. Sorting center event classes include a sorting shift start event (shift_begin), a sorting shift end event (shift_end), and a Periodic loading event (periodic_loading_event). Route event classes include route call and load events (route_call_empty_transporter) and route departure events (route_send_full_transporter); the package event classes include a package initial arrival event (package_created), a package unload event (package_unloaded), a package sort event (package_sorted), and a package load event (package_loaded). The transport event classes include a full transport arrival event (full_transporter_authorized), a full transport ready unload event (full_transporter_ready_unloading), an empty transport ready load event (empty_transporter_ready_load), an empty transport departure event (empty_transporter_depacketized), and a full transport departure event (full_transporter_depacketized). These events are listed in table 1.

TABLE 1 event List handled by simulation Engine

Note that these listed events are merely exemplary and not limiting. Alternatively or additionally, other event categories may exist and each event category may include other events, depending on the logistics process in the logistics network and the adapted process objects and process conditions. Any other suitable event categories and event numbers are encompassed in the present disclosure as long as they are capable of properly characterizing the logistics process of the package in the logistics network, in particular the entire logistics process of the package in the logistics network.

In one embodiment of the present application, these events may be processed sequentially in any suitable manner, for example, may be processed in the order of time of occurrence of the events, or may be processed in the order of priority, importance, weight, etc., to implement operation of the logistics network. In one embodiment, events may be generated and added to a sequence of events for simulation, and in the sequence, events may be arranged and simulated in a variety of ways. In one example, events may be arranged in a sequence of events in time and may be processed in a first-in, first-out manner. That is, events may be added to the sequence in the order of the first generation to the last generation, and events that were first added to the sequence will be simulated first, that is, earlier events will be processed first. In another example, events in a sequence of events are ordered and processed based on weights or priorities, which may be set for the events in consideration of importance of the events, priorities of packages related to the events, levels of packages related to the events, etc., and thus when simulated, events with higher weights or priorities will be simulated first.

FIG. 7 illustrates a flow chart of an exemplary logistics network simulation. At the start of the simulation, the start time of the simulation is set and an empty sequence of events ES is generated, wherein any event to be incorporated in the sequence ES will be arranged in chronological order, in particular in ascending order of occurrence time, that is to say the earliest event will be arranged first. Subsequently, a logistic event was generated from the simulation and incorporated into the sequence. For example, sort center events occurring on the start of simulation day are generated for each sort center, including sort start and sort end events for the first sort shift, and first periodic loading events, and route events occurring on the start of simulation day are generated for each route, such events may be incorporated into the sequence. Further, although not shown, other kinds of events such as package handling events, transportation equipment events, etc. may be generated at the start of the simulation, such as those occurring on the day of the start of the simulation, or may be generated together with the logistics transportation during the simulation, and will also be arranged in the sequence ES in the chronological order of occurrence.

Next, the simulation is advanced by cyclically extracting events from the ES, specifically starting from the earliest event, advancing the simulation clock to the occurrence time of the earliest event, and processing the earliest event.

Specifically, it will be determined whether there are still packages in the logistics network that are not shipped to their destination, and if so, the earliest occurring event is extracted from the sequence ES and subsequently processed, while the analog clock is advanced to the time of occurrence of the earliest event. Subsequently, the process will be looped as long as there are any packages in the logistics network that have not been transported to their destination, and the simulation can be completed when all packages have been transported to their destination.

Hereinafter, the state of the actual logistics network corresponding to the occurrence time of each event and its processing logic will be described with reference to fig. 8A to 8N, fig. 8A to 8N showing exemplary processes for each event of each category as described above.

Fig. 8A illustrates processing logic for a sort start event, where the occurrence time of the sort center shift start event corresponds to the start state of a sort shift for a sort center in an actual logistics network.

Fig. 8B illustrates processing logic for a sort end of shift event, where the time of occurrence of the sort end of shift event corresponds to the state of the sort end of shift time of a sort center in an actual logistics network.

Fig. 8C shows processing logic for periodic loading events, meaning events for loading package initial arrival events into a logistics network. Typically, periodic loading events are generated with a period of one day and an occurrence time of 00:00:00 per day. The periodic loading event generates a package initial arrival event at the sorting center within 24 hours after the time of occurrence. To simplify the package arrival process without loss of generality, it is assumed that all packages arrive at periodic discrete points in time within 24 hours. For example, a parcel may be specified to arrive every tenth Zhong Chengpi. Processing logic for each periodic loading event will then generate 144 package initial arrival events.

Fig. 8D shows processing logic for route calls and loading events, which corresponds to a state in which in an actual logistics system, an empty vehicle is called at the loading platform for loading sometime before the vehicle drives off on the route, and the period of time for calling the vehicle ahead should be set appropriately to ensure that the vehicle waits on the platform for a time that is not too long to waste loading and transport resources, and is not too short to complete loading.

Fig. 8E shows processing logic for a route departure event corresponding to a state in an actual logistics system where a particular route arrives at a departure time and vehicles on that route are ready to begin transportation from the origin.

Fig. 8F illustrates processing logic for an initial arrival event for a package, which corresponds to a state in an actual logistics system in which a lot of packages is first collected and awaiting sorting at a certain time.

Fig. 8G illustrates processing logic for a package unloading event, which corresponds to a state of a package just unloaded from a transportation device in an actual logistics system.

Fig. 8H illustrates processing logic for a parcel sort event, which corresponds to the status of the parcel just completed sorting in an actual logistics system.

Fig. 8I illustrates processing logic for a package loading event, which corresponds to a state in an actual logistics system in which a package has just been loaded onto a transportation device.

Fig. 8J illustrates processing logic for a full carrier arrival event, which corresponds to a condition in which a carrier, such as a vehicle, airplane, and other conveyance, filled with packages in an actual logistics system, arrives at a destination sorting center and waits for unloading.

Fig. 8K illustrates processing logic for a full carrier ready to unload event corresponding to a state in which a carrier filled with packages in an actual logistics system is ready to unload a first package.

Fig. 8L shows processing logic for an empty transporter ready for loading event corresponding to a state in which an empty transporter is ready for placement on a loading platform of a sorting center for loading of a first parcel in an actual logistics system.

Fig. 8M shows processing logic for an empty transportation device departure event, which corresponds to an immediate state when the transportation device leaves the sorting center after being completely unloaded in an actual logistics system.

Fig. 8N shows processing logic for a full carrier departure event, which corresponds to an immediate state in an actual logistics system when leaving a sorting center after loading of a carrier.

In some embodiments, additionally, simulations may be performed based on the configuration/structure of the actual logistics network to simulate package transportation throughout the actual logistics network. And such configuration information may be notified as analog parameters. For example, a configuration may refer to a configuration with respect to the entirety of a logistics network, or may refer to a configuration with respect to a portion of a logistics network, so long as such portion may accurately characterize the entire logistics network. For example, the simulation may be performed based on an artificial logistics network capable of covering a portion or even all of the logistics network to mimic an actual logistics network, and may be implemented by software, hardware, firmware, or any suitable combination.

In one embodiment, simulations may be performed in view of package delivery requirements, and in particular to find an optimized logistics network configuration that can meet the requirements. The logistics configuration information may be compiled and in particular may be dynamically established, compiled and updated based on information of the actual logistics network or may be dynamically iteratively updated during the simulation process. Such operations may be performed by an electronic device or even a processing circuit therein.

According to another embodiment, the simulation may be performed specifically under some constraints related to package transportation. For example, such constraints may include, but are not limited to, package delivery requirements, shipping entity constraints such as processing capacity limitations, shipping time constraints, etc., so that appropriate simulations may be performed to properly verify and obtain optimal solutions for scheduling actual logistics networks.

According to some embodiments, the simulation results may correspond to a certain logistics network configuration and may indicate logistics network performance within the simulation phase implemented by the configuration, such that it may be determined whether the logistics network performance under the configuration meets predetermined requirements, constraints, etc., and if not, the configuration of the logistics network may be modified and the simulation may then be re-performed based on the modified logistics network configuration. That is, the simulation is actually performed for a predetermined logistics network configuration, and the optimized logistics network configuration is intended to be verified and finally obtained, so that the actual logistics network can be optimized accordingly.

In another embodiment of the present disclosure, the simulation results may also include simulated operational states of the logistics network, e.g., the workload of individual elements in the logistics network, such as the workload of warehouses, sorting hubs, express stations, routes, and even vehicles in the routes, may be obtained and utilized to adjust and optimize the configuration of the actual logistics network. It is thereby possible to determine whether the element is overloaded or whether the capability of the element has been fully utilized, and the capability of the element can be appropriately adjusted based on the determination result. For example, if an element is overloaded, more resources may be allocated to the element to enhance processing power, and for example, if the element's capability is still redundant, some resources for the element may be reallocated to other elements.

In yet another embodiment, the simulation results may also include information about package delivery costs, which may be derived during the simulation with reference to current configuration statistics of the logistics network. In yet another embodiment, the simulation results may also include information about the load of the logistics network, which may indicate the workload of an element in the network or the overall workload of the network.

In one embodiment of the present disclosure, the simulation may be performed iteratively, and in one iteration, sequentially acquiring events from the event sequence; and performing a corresponding process for the obtained events, and wherein the operation in the round of iterations ends when all events in the sequence have been performed.

More specifically, in one embodiment, in each iteration, the simulation may include: simulating package delivery through the structure of the actual logistics network and the previously given logistics network configuration; judging whether the simulation result satisfies a predetermined condition, if not, the logistics network configuration will be adjusted and used as a basis for executing the next round of iteration, otherwise the simulation is ended, and the simulation result is used as a final simulation result to derive a scheduling, and in this case, the scheduling may correspond to configuration parameters of the logistics network used for obtaining the final simulation result.

According to one embodiment, the predetermined condition can indicate a predetermined package delivery requirement, which can be indicated by a threshold value related to package delivery performance, such as a threshold value for at least one of package delivery duration, distance, sorting frequency, loss rate, etc., and the simulation result, i.e., simulated package delivery performance, can be compared to the threshold value, indicating that the current package delivery is not satisfactory when the simulated package delivery performance exceeds the threshold value, possibly resulting in poor user experience, and should be further adjusted. For example, when the package delivery time is less than a specified time threshold and/or the package loss rate is less than a specified loss rate threshold, the simulation results may be deemed satisfactory.

According to another embodiment, the predetermined condition may indicate a predetermined number of iterative rounds, and when the simulation has been performed for the predetermined number of rounds, the simulation may be terminated, and an optimal result may be selected from the results of the predetermined number of rounds as a final simulation result.

According to one embodiment, the optimal result may correspond to the most cost-effective simulated result under the constraints of package delivery requirements, that is, the lowest cost of operation for package delivery through the logistics network corresponding to the optimal result, such that an optimal schedule for the actual logistics network may be obtained, which is cost-optimal, while the actual logistics network is able to meet package delivery requirements at the time of package delivery according to the schedule. According to one embodiment, the optimal results may correspond to simulation results with the appropriate workload under the constraints of package delivery requirements, e.g., a score may be generated for each iteration to indicate workload status and may be represented by a margin score, the greater the score, the higher the network margin, and the higher the reliability of the network, so the simulation result with the highest score may be selected. According to another embodiment, the optimal results may correspond to the simulation results closest to the package delivery requirements, and the simulation results closest to the package delivery requirements may be selected even if all of the simulation results do not meet the requirements. Closest here means that the difference between the package delivery results and the package delivery requirements is minimal. For example, the difference between the simulated package delivery time and the desired package delivery time is minimal, or the difference between the simulated package loss rate and the desired package loss rate is minimal.

According to another embodiment, the two conditions described above may be combined, and in this case, the simulation may be performed iteratively, wherein the simulation iteration may be terminated when the simulation result is able to meet the predetermined package delivery requirements, even if the predetermined number of rounds is not reached. Otherwise, the simulation will be performed until a predetermined number of rounds is reached and the optimal result is selected.

The processing circuit 502 may be implemented in a variety of ways in accordance with the present disclosure, and in particular may be in the form of a general purpose processor, or may be a special purpose processing circuit such as an ASIC. For example, the processing circuit 502 may be configured by a circuit (hardware) or a central processing device such as a Central Processing Unit (CPU). Further, the processing circuit 502 may carry a program (software) for operating the circuit (hardware) or the central processing apparatus. The program may be stored in a memory or an external storage medium connected from the outside, or downloaded via a network (e.g., the internet).

According to some embodiments, the processing circuitry of the electronic device can include various units to implement embodiments according to the present disclosure. For example, the processing circuitry of the electronic device may comprise various units to implement various operations performed on the control device side described herein. In one embodiment, the processing circuitry 502 may include: an acquisition unit 504 for acquiring an event sequence of discrete logistics events comprising an entire logistics process characterizing a logistics network; and a simulation unit 506 for sequentially processing the logistics events in the sequence of events to simulate the operation of the logistics network. Furthermore, in performing simulation iteratively, the simulation unit 506 may further include: an adjusting/updating unit for adjusting/updating the simulation information for the next round of simulation; and a judging unit for judging whether the iteration is terminated. Note that such a unit may be external to the analog unit 506. Although not shown, a communication unit may be included in the processing circuit 502, or even in the electronic device 500 but outside the processing circuit 502, and used to receive information as a basis of the simulation and transmit the simulation result.

It should be noted that while these units are shown in the processing circuit 502, they are merely exemplary, and that at least one of these units may also be external to the processing circuit, even external to the electronic device. The above units are merely logic modules divided according to specific functions implemented therein, not for limiting specific implementations, and for example, such units and processing circuits and even electronic devices may be implemented in software, hardware or a combination of software and hardware. In a practical implementation, the above units may be implemented as separate physical entities or may be implemented by a single entity, e.g. a processor (CPU or DSP etc.), an integrated circuit etc. Furthermore, the various units described above are shown in dashed lines in the figures to indicate that these units may not actually be present, and that the operations/functions they implement may be implemented by the processing circuitry itself.

It should be understood that fig. 5A is merely a schematic structural configuration of the electronic device, and that the electronic device may alternatively include other possible components not shown, such as a memory, a radio frequency stage for communication, a baseband processing unit, a network interface, a controller, etc. The processing circuitry may be associated with the memory and/or the antenna. For example, the processing circuitry may be directly or indirectly (e.g., other components may be connected in-between) connected to the memory for data access. The memory may be volatile memory and/or nonvolatile memory. For example, the memory 510 may include, but is not limited to, random Access Memory (RAM), dynamic Random Access Memory (DRAM), static Random Access Memory (SRAM), read Only Memory (ROM), and flash memory, and the memory may store various information such as analog information generated by the processing circuit 502, logistics network performance information, logistics network configuration parameters, package delivery requirement information, and the like, as programs and data operated by the electronic device. The memory may also be located in the electronic device for simulation but outside the processing circuitry, or even outside the electronic device for simulation.

According to one embodiment, a method for simulating a logistics network is provided, and fig. 5B shows a flowchart of a method 600, including step S602: acquiring an event sequence comprising discrete logistics events characterizing the entire logistics process of the logistics network; and step S604: the logistic events in the sequence of events are processed in turn to simulate the operation of the logistic network. It is noted that the method according to the present disclosure may further comprise operational steps corresponding to the operations performed by the processing circuitry of the electronic device described above, which are not described in detail herein. It is noted that the individual operations of the method according to the present disclosure may be performed by the aforementioned electronic device, in particular by a processing circuit or a corresponding unit, which will not be described in detail here.

Hereinafter, an exemplary simulation operation will be described based on a skeleton network of the entire logistics network consisting of approximately 1000 sorting hubs and thousands of transportation routes as shown in fig. 9. Note that this is merely an example, and the simulation may be implemented on more or fewer elements of an actual logistics network.

In one embodiment, a simulation model for a manual logistics network may be appropriately created based on some information of the infrastructure of the actual logistics network and some constraints. Some of the necessary definitions for the logistics network model are listed in tables 2-4, which may correspond to configuration information about the logistics network, and the package shipping requirements are listed in table 5, which may correspond to package delivery requirements. Note that such tables are merely exemplary, and that other suitable tables may exist that similarly define other logistics structures or participants, such as depending on their operational characteristics.

TABLE 2 definition of class hub

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TABLE 3 definition of routes

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TABLE 4 relation of sorting hub to route

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TABLE 5 package transportation requirements

And some constraints on the logistics network may be as follows. Note that such constraints are merely exemplary, and that other constraints may be set according to actual demands of the logistics network.

One route can only specify one type of default vehicle. Then

A route has only one origin and one destination. And the origin of a route cannot be the same as its destination. So that the number of the parts to be processed,

hub_route_origin _mn +hub_route_destination _mn ≤1

m＝1,2,…,M；n＝1,2,…,N ⑷

one route may have the same origin and the same destination as another route. Then the first time period of the first time period,

the total volume of all packages in the vehicle cannot exceed the maximum allowable volume of the vehicle. Let Q denote the number of packages in the kth type of vehicle. Let pack_vol _q (q=1, 2, …, Q) represents the volume of the Q-th parcel in the vehicle.

Thus, a model for the actual logistics network can be appropriately created, and based on such model, simulation of the actual logistics network can be performed.

Fig. 10 schematically illustrates the relationship of events to the flow of a simulation engine for such events, wherein each event will be triggered and processed by a respective process call, and an exemplary process flow of a logistics network simulation scheme logistic network formulation () according to the present disclosure will be described with reference to fig. 11, which illustrates the main flow of a parallel logistics network, according to an embodiment of the present disclosure.

As part of the experiment, the experiment design module generates multiple schemes, and the experiment evaluation module evaluates the schemes and picks out the better scheme through the simulated network load. The plan includes transportation equipment arrangements for all transportation routes, staff arrangements in all sorting hubs, etc. The simulation engine obtains the simulated network load through the operation scheme. Billions of events are typically created and handled during the operation of large scale logistics network simulation instances during a large promotion. To accelerate simulation, some techniques such as asynchronous simulation, parallel simulation, and distributed simulation may be applied to develop a simulation engine.

At the beginning of the simulation instance, a set of initial events is created and inserted into the event queue. The types of initial events include shift_begin, line_call_empty_transporter, and package_created. The simulation engine cyclically fetches the earliest event from the event queue and processes by calling HandleEvent () as shown in fig. 10 until the event queue does not contain any event. The input parameters of the logic network formulation () are defined in tables 1-5, and the events and their corresponding processing may be implemented as described with reference to fig. 8A-8N and will not be described in detail herein.

According to embodiments of the present disclosure, the simulation results may include information about package delivery, in particular, the information may be package delivery duration, distance, sorting frequency, package delivery loss rate, etc. As an example, the simulation result is composed of values defined in formulas (7) - (9).

Let PACK (N) = { p ₁ ,p ₂ ,…,p _N -a set of packages, where p _n Is the nth parcel in the collection. Let the created_time _n And extended_time _n Respectively represent p _n Creation time and p at its origin _n At the time when its destination is sorted. The average transition time of PACK (N) is expressed as:

note that the delivery duration, i.e. the transition time, can be calculated by dividing the distance by the speed of the transport device, as shown in equation (7). Note that additionally, the transition time may also include some redundant time, including but not limited to waiting time during sorting, unloading, loading, and some other redundant time, and such redundant time may be set by default or empirically. Generally, the shorter the transition time, the better the package delivery performance.

Let the distance _n Represents p _n Is a transport distance of (a). The average transport distance of PACK (N) is expressed as:

let the sounding_frequency _n Represents p _n Sorting frequency from its origin to its destination. The average sorting frequency of PACK (N) is expressed as:

generally, the smaller the distance or sorting frequency, the better the package delivery performance.

Hereinafter, planning/scheduling of a logistics network according to an embodiment of the present disclosure will be described. In the present disclosure, a logistics network, particularly a large-scale logistics network, may be planned/scheduled based on a simulation as described above, and in particular the simulation may verify and optimize the configuration for the logistics network, such that the logistics network may be planned/scheduled based on the simulation.

Fig. 12A illustrates an electronic device for scheduling an actual logistics network in accordance with some embodiments of the present disclosure. According to one embodiment, the electronic device 1200 may include a processing circuit 1202, the processing circuit 1202 configured to obtain package delivery requirements to be handled by an actual logistics network; and simulating the package transportation process through the actual logistics network to obtain scheduling arrangement for the actual logistics network based on the simulation result, and scheduling the actual logistics network based on the scheduling arrangement. Note that the simulation may be performed as described above, so that an optimal planning/scheduling scheme for the logistics network may be obtained. In addition to this, the simulation may be performed in any other suitable way as long as the simulation results can be properly communicated and used for planning/scheduling of the logistics network.

Hereinafter, details of the process according to some embodiments of the present disclosure will be described.

In the present disclosure, such planning/scheduling schemes may be implemented by way of a parallel logistics network mechanism in which a manual logistics network and an actual logistics network can cooperate with each other in order to improve planning/scheduling of the actual logistics network. Specifically, a manual logistics network corresponding to the actual logistics network can be established, and simulation is performed based on the manual logistics network, so that a planning/scheduling scheme for the actual logistics network is verified and optimized, and the actual logistics network can be optimized based on a simulation result. Parallel logistics networks greatly enhance the ability of the logistics network to handle dramatic increases in package delivery, such as large promotions, and/or various emergency situations.

Fig. 13 illustrates an exemplary parallel logistics network mechanism in accordance with the present disclosure, and as illustrated in fig. 13, the framework may include an actual logistics network, a manual logistics network, a logistics data extraction module, and a logistics scheme transmission module.

The actual logistics network may include/provide information about logistics infrastructure, logistics participants and package delivery needs. The logistics infrastructure may indicate various elements/nodes that make up the actual logistics network, such as sorting hubs, warehouses, routes, etc. as described above. The logistics participants may indicate, for example, couriers, sorters, packagers, drivers, carriers, packaging machines, sorters, and other persons or things providing services for transporting packages, which may also be considered elements of the actual logistics network. In a sense, the logistics infrastructure and participants may belong to the configuration of the logistics network as described above.

The package delivery requirements may indicate information about package delivery throughout the logistics network, including the number of packages, package shipping requirements, and the like. The package delivery demand data may be derived from historical package delivery demand and real-time package delivery demand. In one embodiment, the package delivery demand may be a package delivery demand to be performed by an actual logistics network, and thus may be predicted from current package delivery demand or historical package delivery demand. Such prediction may be performed by any suitable party in the system, and in particular by the planning/scheduling electronics themselves. In this case, the electronic device may include a learning engine/unit that predicts future package delivery needs from current and historical package delivery needs data. In another example, package delivery requirements may be obtained from other entities, such as third parties predicting package delivery requirements, and presented to the electronic device. From this point on, the electronic device may comprise an acquisition unit for acquiring package delivery requirements.

Note that the actual logistics network as shown in fig. 13 may in fact refer to an entity capable of obtaining and providing such information about the network structure and may be implemented in any suitable way, such as a database, a computer/processor/controller for managing the actual logistics network, etc.

The artificial logistics network actually refers to a virtual logistics network that can be established based on information about the structure of the actual logistics network to have a network structure equivalent to the actual logistics network and to mathematically describe and simulate the functions/operations of the actual logistics network. Note that the expression "artificial logistics network" is merely illustrative and utilized to facilitate understanding, and that artificial logistics networks may be implemented in a variety of ways. For example, the manual logistics network may be implemented as an apparatus/device that may operate to simulate an actual logistics network, which may be implemented by hardware such as various chips, firmware, etc., such as software running on a computer, for example. In particular, the simulation device as described above may be implemented as part or all of, or even include, a manual logistics network as shown in fig. 13.

The logistics data extraction module may obtain network structure and package data, such as network configuration and package delivery requirements, from the actual logistics network and transmit them to the manual logistics network. Note that the logistics data extraction module may be implemented in various ways, for example, may be included in an actual logistics network or an artificial logistics network, such as in the above-described simulation device, as a communication unit, an interface unit, or the like, or may be a third party forwarding information about the actual logistics network and package delivery requirements to the artificial logistics network.

And the logistics scheme transmission module pushes the network load and the processing scheme obtained by simulation/experiment at the manual logistics network to the actual logistics network. Note that the logistics scheme transmission module may be implemented in various ways, for example, may be combined with a manual logistics network, such as an analog device, as a communication unit, an interface unit, etc., or may be a third party forwarding information about the logistics scheme to the manual logistics network.

Therefore, through interaction between the manual and actual logistics networks, the whole process continuously runs to drive the optimal configuration for the actual logistics network, so that stable and efficient running of the actual logistics network is maintained. In particular, the simulation will run to verify and obtain a configuration of the logistics network that is capable of meeting package delivery requirements and/or achieving cost effectiveness, and thus the actual logistics network can be planned/scheduled based on the obtained configuration.

According to some embodiments of the present disclosure, the interaction between the artificial network and the actual network may be implemented in a variety of ways. According to one embodiment, the simulation may be performed iteratively, and the artificial logistics network may be updated dynamically as the simulation iterates. Such updating may be performed in a variety of ways. In one example, when one round of iteration ends, parameters of the logistics network, in particular some configuration parameters of elements in the network, may be adjusted appropriately as a basis for the next round of iteration, e.g., elements having a much higher workload or approaching their workload limits, such as shifting workload, enhancing workload, etc. In another example, even the artificial logistics network may be adapted to add new elements or to delete inappropriate elements, for example warehouses, sorting hubs, routes or even vehicles may be newly added or deleted, and from this point of view such an update may be used in particular for establishing an actual logistics network.

In one embodiment, the scheduling of the actual logistics network may be performed periodically or on demand. As one example, scheduling operations may be performed at predetermined time intervals so that the arrangement/configuration of the actual logistics network may be updated accordingly and periodically. As another example, the scheduling operation may be performed in response to an operator request, which is triggered by a predetermined event, such as a large promotion or emergency. That is, when such an event occurs, a scheduling operation may be performed accordingly.

In one embodiment, the electronic device may include a network constructor that creates an artificial network structure based on the logistics network data, and such a network constructor may even be included in the processing circuitry, or external to the processing circuitry. Such constructors may belong to a modeling module for modeling the actual logistics network.

In accordance with the present disclosure, the processing circuit 1202 may be implemented in a variety of ways, similar to the processing circuit 1202, and thus will not be described in detail herein. In one embodiment, the processing circuit 1202 may include an acquisition unit 1204 for acquiring package delivery requirements, a simulation unit 1206 for performing simulations, and a scheduling unit 1208 for performing scheduling based on scheduling information. Of course, as noted above, the processing circuitry, and even the electronic device, may include other elements, such as communication units, memory, etc., and will not be described in detail herein.

According to one embodiment, a method of scheduling an actual logistics network by a manual logistics network model is presented, wherein the manual logistics network model is created based on information about the configuration of the actual logistics network, and fig. 12B illustrates a flow chart of a method 1300. In step S1302, acquiring package delivery requirements to be processed by an actual logistics network; in step S1304, parcel delivery is simulated through the actual logistics network based on the artificial logistics network model to acquire a scheduling schedule for the actual logistics network based on the simulation result, and in step S1306, the actual logistics network is scheduled based on the scheduling schedule. It should be noted that the method may further include operation steps corresponding to the operations performed by the processing circuitry of the electronic device for scheduling, which are not described in detail herein. It should be noted that the respective operations of the method according to the present disclosure may be performed by the aforementioned electronic device, in particular, by a processing circuit or a corresponding unit, and will not be described in detail herein.

Hereinafter, an exemplary process flow of the parallel logistics network scheme according to the present disclosure will be described with reference to fig. 14 showing a main flow of the parallel logistics network.

The network structure is created by a network constructor from the network data. Future package delivery needs are obtained by the learning engine based on the historical package delivery needs. The simulation model is created based on the network structure and future package delivery requirements. The artificial logistics network invokes the logistics network formulation () (defined in process 1 as described above) to run the logistics network simulation. As part of the experiment, the experiment design module generates multiple protocols, and the experiment evaluation module evaluates the protocols and picks out better protocols through the simulated network load. The plan includes the arrangement of transportation personnel for all transportation routes, the arrangement of staff in all sorting hubs, etc. The simulation engine obtains the simulated network load by running the model and the scheme. Billions of events are typically created and handled during the operation of large scale logistics network simulation instances during large promotions. To accelerate simulation, some techniques, such as asynchronous simulation, parallel simulation, and distributed simulation, are applied to develop a simulation engine. In one embodiment, the main routine of the logistics network simulation engine may be performed as described above and will be omitted herein.

It should be noted that the above description is merely exemplary. Embodiments of the present disclosure may be performed in any other suitable manner and still achieve the advantageous effects achieved by embodiments of the present disclosure. Moreover, embodiments of the present disclosure may also be applied to examples of other similar applications, and still achieve the advantageous effects obtained by the embodiments of the present disclosure. It should be understood that machine-executable instructions in a machine-readable storage medium or program product according to embodiments of the present disclosure may be configured to perform operations corresponding to the above-described apparatus and method embodiments. Embodiments of a machine-readable storage medium or program product will be apparent to those skilled in the art upon reference to the above-described embodiments of the apparatus and method, and thus the description will not be repeated. Machine-readable storage media and program products for carrying or comprising the foregoing machine-executable instructions are also within the scope of the present disclosure. Such a storage medium may include, but is not limited to, floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, and the like.

Furthermore, it should be understood that the series of processes and apparatuses described above may also be implemented by software and/or firmware. In the case of implementation by software and/or firmware, respective programs constituting the respective software are stored in a storage medium of the relevant device, and when the programs are executed, various functions can be implemented. As one example, a program constituting software may be installed from a storage medium or a network to a computer having a dedicated hardware structure, such as a general-purpose computer 1100 shown in fig. 15, which is capable of performing various functions and the like when various programs are installed. Fig. 15 is a block diagram showing an exemplary structure of a computer as an example of an information processing apparatus that can be employed in the embodiment according to the present disclosure. In one example, the computer may correspond to the above-described exemplary electronic device on the intelligent service provider side or electronic device on the application side according to the present disclosure.

In fig. 15, a Central Processing Unit (CPU) 1101 executes various processes according to a program stored in a Read Only Memory (ROM) 1102 or a program loaded from a storage section 1108 into a Random Access Memory (RAM) 1103. The RAM 1103 also stores data necessary for the CPU 1101 to execute various processes and the like as necessary.

The CPU 1101, ROM 1102, and RAM 1103 are connected to each other via a bus 1104. An input/output interface 1105 is also connected to the bus 1104.

The following components are connected to the input/output interface 1105: an input section 1106 including a keyboard, a mouse, and the like; an output portion 1107 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1108 including a hard disk or the like; and a communication section 1109 including a network interface card such as a LAN card, a modem, and the like. The communication section 1109 performs communication processing via a network such as the internet.

The drive 1110 may also be connected to the input/output interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed on the drive program 1110 as needed, so that a computer program read out therefrom is installed into the storage section 1108 as needed.

In the case of implementing the above-described series of processes by software, a program constituting the software is installed from a network such as the internet or a storage medium such as the removable medium 1111.

It will be understood by those skilled in the art that the storage medium is not limited to the removable medium 1111 storing the program shown in fig. 15, and it is distributed separately from the device to provide the program to the user. Examples of the removable medium 1111 include magnetic disks (including floppy disks), optical disks (including CD-ROMs and Digital Versatile Disks (DVDs)), magneto-optical disks (including mini-disks (MDs) (TM)), and semiconductor memories. Alternatively, the storage medium may be a ROM 1102, a hard disk included in the storage section 1108, or the like, in which the program is stored, and distributed to users together with the device containing them.

Exemplary application

Hereinafter, some exemplary applications in which the disclosed solution may be employed will be described, and in such applications, the disclosed solution may effectively cope with large promotions as well as any possible emergency situations, and may improve the configuration/planning of the actual logistics network to obtain advantageous parcel delivery results.

Through the method and the framework, a Java programming language can be used for realizing the parallel logistics network system. The main interface of the parallel logistics network system is illustrated in fig. 16. Fig. 16 (a) is a manual logistics network subsystem, and fig. 16 (b) is an actual logistics network subsystem.

To illustrate the accuracy of the logistics network simulation engine, we first run the countermeasures taken by the parallel logistics network system on the logistics network simulation engine. Then we compare the simulation results with the results of an actual logistics network treated with the same countermeasures. The static wrap route compliance for both results was 99.7%, while the dynamic wrap route compliance for both results was 94.2%. The latter is lower than the former because countermeasures are slightly modified to deal with accidents (such as traffic accidents, bad weather) during a large promotion. Thus, we can say that the parallel logistics network system is trusted.

In one aspect, an example of applying the present disclosure to an emergency situation will be described, and in particular, logistical data related to the emergency situation may be obtained as exemplary data for evaluating the performance of a solution according to an embodiment of the present disclosure.

In particular, during an emergency, several sorting hubs are closed. By means of the parallel logistics network, countermeasures can be designed, verified and optimized through the manual logistics network. And then forwarding the optimized countermeasures to the actual logistics network. Table 6 lists the average transition times and average sorting frequency of emergency cases for packages in the affected OD (origin to destination). During an emergency, the average transit time (from origin to destination) of packages in the affected OD was 2.88 days, while the average sorting frequency of packages in these OD was 3.23. The average conversion time for the packages in these OD was 2.79 days before emergency, while the average sorting frequency for the packages in these OD was 3.26. The parallel logistics network minimizes the impact on the logistics network during an emergency. The average transit time of the packages in the affected OD was increased by only 3.2%.

TABLE 6 comparison of mean conversion time to mean sorting frequency for parcels

	Before emergency	During an emergency situation
			Average transition time	2.79 days	2.88 days
Average sorting frequency	3.26	3.23

On the other hand, application of the present disclosure during a large promotion will be described as an example of a sharp increase in package orders.

Specifically, during the large promotions 6.18 (day 6 month 18) and 11.11 (day 11 month 11), any scenario of the logistics network can be optimized with the parallel logistics network. The manual logistics network obtains a logistics network structure and package delivery requirements from the actual logistics network. And then designing, verifying and optimizing countermeasures through a manual logistics network. And forwarding the optimized countermeasures to an actual logistics network.

In the prior art, for such large promotions, conventional techniques may empirically generate package delivery countermeasures for the large promotions. Before each large promotion period, tens of people are required to take days to make countermeasures, the work efficiency is low, and the effectiveness of the countermeasures may be insufficient. In contrast, by the scheme according to the embodiment of the present disclosure, countermeasures can be efficiently and accurately made, for example, one person can make countermeasures in one day, while improved parcel delivery results can be obtained.

As an example, a parallel logistics network system runs a logistics network based on data from 2020/10/24 to 2020/11/24, during which 11.11 promotions are open and over 4 hundred million packages are delivered in total. To illustrate the efficiency of the parallel logistics network system for large promotions, we first run 11.11 large promotions countermeasure (abbreviated as CE) obtained empirically and 11.11 large promotions countermeasure (abbreviated as CP) obtained by the parallel logistics network system on the logistics network simulation engine, respectively. The CP here is obtained by 18 iterations of manual intervention simulation and optimization on a parallel logistics network system. One iteration of the simulation of the PC (CPU: i7 8700hq,32gb memory at 4.6 GHz) running the artificial logistics network takes less than about half an hour. We divide all packages into three categories according to their delivery distance. These three classes are called short distance (. Ltoreq.600 km), medium distance (600 km-1500 km) and long distance (. Gtoreq.1500 km), respectively. To evaluate simulation results in steady state we only analyzed the simulation data of 2020/11/1 to 2020/11/15 and not 2020/10/24 to 2020/11/24. The average conversion times for packages from CE and CP from 2020/11/1 to 2020/11/15 are listed in Table 7.

Tables 7.2020/11/1 to 2020/11/15 average conversion time of packages by CE and CP

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As can be seen from Table 7, the average transition times for short-, medium-, and long-range parcels 2020/11/1 to 2020/11/15 by CP were 19.3%, 7.2%, and 5.7% lower than CE, respectively.

The average shipping distance and sorting time for packages from 2020/11/1 to 2020/11/15 by CE and CP are listed in table 8.2020/11/1 to 2020/11/15 mean transport distance and sort time from CP are 6.6% and 2.1% lower than CE, respectively.

Tables 8.2020/11/1 to 2020/11/15 average shipping distance of packages from CE and CP

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By applying the countermeasures obtained by the parallel logistics network system, undeliverable packages are significantly reduced. The number of undeliverable packages by CE and CP is listed in Table 9 from 2020/11/1 to 2020/11/15. The average undeliverable package for CP was 67.8% lower than CE.

Tables 9.2020/11/1 to 2020/11/15 number of undeliverable packages by CE and CP

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The parallel logistics network system is suitable for 6.18 and 11.11 large sales promotion activities. The parallel logistics network system brings great convenience to a logistics network planning part. In summary, for large logistics companies, a parallel logistics network system can improve its customer experience by reducing package conversion time and curtailing undeliverable packages in its logistics network. The parallel logistics network system can also reduce conversion cost and sorting cost by reducing package conversion distance and package sorting times. Shorter package changeover distance also means lower air pollution from trucks and other vehicles.

Although the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the disclosure as defined by the appended claims. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises such elements.

Although some specific embodiments of the present disclosure have been described in detail, it will be understood by those skilled in the art that the above embodiments are merely exemplary and do not limit the scope of the present disclosure. It will be appreciated by those skilled in the art that combinations, modifications or substitutions of the above-described embodiments may be made without departing from the scope and spirit of the disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. An electronic device for logistics network simulation, the electronic device comprising a processing unit configured to:

acquiring an event sequence comprising discrete logistics events characterizing the entire logistics process of the logistics network;

and sequentially processing the logistics events in the event sequence to simulate the operation of the logistics network.

2. The electronic device of claim 1, wherein the discrete logistic events are arranged in order of time of occurrence of the events, the processing unit further configured to process logistic events in the sequence in such a way that first processing occurs at earliest.

3. The electronic device of claim 1, wherein the events in the sequence of events are arranged based on respective weights, wherein a weight of an event is set according to at least one of importance of the event, priority of packages related to the event, and a ranking of packages related to the event,

wherein the processing unit is further configured to process the logistic events in the sequence according to a high to low weight.

4. The electronic device of claim 1, wherein the processing unit is further configured to: and executing the corresponding processing of the events in the sequence until all the events in the sequence are processed.

5. The electronic device of claim 1, wherein the simulation results relate to package delivery performance including at least one of package sort frequency, package delivery duration, and package loss rate.

6. The electronic device of claim 1, wherein the processing unit is further configured to process a logistics event based on a configuration of the logistics network, the simulation result being used to verify or modify a configuration of the logistics network, the configuration of the logistics network being used to plan or schedule the logistics network.

7. The electronic device of claim 1, wherein the processing unit is further configured to:

iteratively performing a simulation of parcel delivery; and

and terminating the iteration when a preset condition is met, and determining a specific simulation result for extracting the scheduling.

8. The electronic device of claim 7, wherein the predetermined condition indicates a package delivery requirement threshold, and the processing unit is further configured to:

iteratively performing the logistic network simulation; and

once the simulation result for a round of iterations is less than or equal to the package delivery requirement threshold, the iteration is terminated and the simulation result is obtained as the particular simulation result.

9. The electronic device of claim 7, wherein the predetermined condition indicates a predetermined number of iterations of the round, and the processing unit is further configured to:

performing a simulation of parcel delivery at the predetermined number of iterations; and

and selecting an optimal simulation result from simulation results of the preset iteration round number as the specific simulation result.

10. The electronic device of claim 9, wherein the optimal simulation result is one of:

the simulation results of the package delivery requirements are met while being most cost effective;

the simulation result of the package delivery requirement is met while the optimal network load is provided;

in the event that the package delivery requirements are not met during the iteration, the simulation result of the package delivery requirements is closest.

11. An electronic device for scheduling an actual logistics network, the electronic device comprising a processing unit configured to:

acquiring package delivery requirements to be processed by an actual logistics network;

simulating, by the simulation apparatus of any one of claims 1 to 10, a parcel delivery process through the actual logistics network to obtain a schedule for the actual logistics network based on the simulation result; and

Scheduling the actual logistics network based on the information about the scheduling.

12. The electronic device of claim 11, wherein the simulation is performed based on a manual logistics network model, wherein the manual logistics network model is created based on configuration-related information of the actual logistics network.

13. The electronic device of claim 11, wherein the scheduling is performed periodically or on demand.

14. The electronic device of claim 11, wherein the package delivery demand is predicted based on current package delivery demand or historical package delivery demand of the actual logistics network.

15. The electronic device of claim 11, wherein the schedule for the actual logistics network corresponds to configuration parameters of the manual logistics network upon obtaining the simulation result.

16. The electronic device of claim 11, wherein the scheduling for the actual logistics network includes configuration information for each element included in the network, the configuration information including at least one of a number of elements, a distribution of elements, an operational mode of elements, an operational capability of elements.

17. A method for logistics network simulation, the method comprising:

acquiring an event sequence comprising discrete logistics events characterizing the entire logistics process of the logistics network; and

18. A method for scheduling an actual logistics network, the method comprising:

simulating, by the simulation apparatus of any one of claims 1 to 10, a package transportation process through the actual logistics network to obtain a schedule for the actual logistics network based on the simulation result; and

19. An apparatus, comprising:

one or more processors; and

one or more storage media storing instructions that, when executed by the one or more processors, cause performance of the method recited in any one of claims 17-18.

20. A computer-readable storage medium storing instructions that, when executed by one or more processors, cause performance of the method recited in any one of claims 17-18.

21. A computer program product comprising instructions which, when executed by a computer, cause the method of any of claims 17 to 18 to be performed.