CN112330247B - Order summarizing method, intelligent warehouse system, computer equipment and storage medium - Google Patents
Order summarizing method, intelligent warehouse system, computer equipment and storage medium Download PDFInfo
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- G06Q10/00—Administration; Management
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- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
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Abstract
The present invention relates to the field of warehousing technologies, and in particular, to an order summarizing method, an intelligent warehousing system, a computer device, and a storage medium. The method comprises the steps of obtaining warehouse operation data corresponding to current order processing wave numbers; wherein the warehouse operational data includes warehouse operational parameters and customer orders; acquiring a pre-established order summarization model; the order summarizing model is obtained through mixed integer nonlinear programming modeling; based on the warehouse operation parameters, an order summarizing model is adopted to process the customer orders, and the shortest order summarizing result in warehouse operation is output; wherein the order summary result is used for indicating the pickers to pick. The method can comprehensively consider the picking operation and the packing operation to realize global optimization of warehouse operation, and improves the warehouse-out operation efficiency.
Description
Technical Field
The present invention relates to the field of warehousing technologies, and in particular, to an order summarizing method, an intelligent warehousing system, a computer device, and a storage medium.
Background
With the rapid development of modern electronic commerce and logistics industry, various goods are various in types, large in quantity, heavy in work load and low in warehouse-out efficiency. At present, when in delivery operation, a batch of customer orders to be delivered are summarized together according to a certain standard or rule in order wave summarization mode, so that delivery operation is performed, and the operation efficiency is improved. However, the traditional order wave-order summarizing mode mainly depends on the condition of on-site operation of a bill producer, orders meeting the same screening conditions are summarized into a picking bill according to system screening conditions (customer requirements, different express delivery and other delivery rules and the like) and printed, the picking bill is manufactured, then the picking producer guides the picking according to the picking bill, the screening conditions summarized by the orders are not comprehensive, the picking bill is unreasonable in distribution, the picking efficiency of the subsequent picking producer is low, and the delivery efficiency is affected.
Disclosure of Invention
The embodiment of the invention provides an order summarizing method, an intelligent warehousing system, computer equipment and a storage medium, which are used for solving the problem of low efficiency of the conventional ex-warehouse operation at present.
An order summarization method, comprising:
acquiring warehouse operation data corresponding to the current order processing wave number; wherein the warehouse operational data includes warehouse operational parameters and customer orders;
acquiring a pre-established order summarization model; the order summarizing model is obtained through mixed integer nonlinear programming modeling;
based on the warehouse operation parameters, an order summarizing model is adopted to process the customer orders, and the shortest order summarizing result in warehouse operation is output; wherein the order summary result is used for indicating the pickers to pick.
An intelligent warehousing system comprising:
the operation data acquisition module is used for acquiring warehouse operation data corresponding to the current order processing wave number; wherein the warehouse operational data includes warehouse operational parameters and customer orders;
the model acquisition module is used for acquiring a pre-established order summarization model; the order summarizing model is obtained through mixed integer nonlinear programming modeling;
the order summarizing module is used for processing the customer orders by adopting an order summarizing model based on the warehouse operation parameters and outputting the shortest order summarizing result in the warehouse operation; wherein the order summary result is used for indicating the pickers to pick.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the order aggregation method described above when the computer program is executed.
A computer storage medium storing a computer program which, when executed by a processor, performs the steps of the order aggregation method described above.
According to the invention, the time of warehouse operation is taken as an optimization object, and the influence of the warehouse operation events in the packing operation on the time of warehouse operation is considered at the same time by fully considering the constraint of each warehouse operation event in the picking operation, so that the accuracy of a model is improved, and the efficiency of warehouse operation (namely, warehouse-out operation) is effectively ensured. In addition, the invention also provides effective linearization treatment strategies for nonlinear factors in the order summarization model, converts the original MINLP model into the MI LP model, and adopts a definition algorithm to realize efficient solution of the model by means of a Gurobi optimizer, thereby further reducing the time for warehouse operation and improving the warehouse operation efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an order summarization method according to an embodiment of the invention;
FIG. 2 is a flow chart of an order summarization method according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a warehouse job timeline in accordance with an embodiment of the present invention;
FIG. 4 is a flowchart showing step S102 in FIG. 2;
FIG. 5 is a flowchart showing step S103 in FIG. 1;
FIG. 6 is a schematic diagram of a smart warehousing system according to an embodiment of the invention;
FIG. 7 is a schematic diagram of a computer device in accordance with an embodiment of the invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are implemented
Examples are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In one embodiment, as shown in fig. 1, an order summarizing method is provided, which includes the following steps:
s101: acquiring warehouse operation data corresponding to the current order processing wave number; wherein the warehouse operational data includes warehouse operational parameters and customer orders.
The warehouse operation data is actual warehouse operation data obtained from a WMS (intelligent warehouse management system). The warehouse operational data includes warehouse operational parameters and customer orders.
The order structure includes multiple categories such as single (each customer order that the pick contains 1 SKU, each SKU contains 1 item), multiple (each customer order that the pick contains 1 SKU, each SKU contains at least 2 items), multiple (each customer order that the pick contains multiple SKUs, each SKU contains 1 item), multiple (each customer order that the pick contains multiple SKUs, each SKU contains at least 2 items), and wake wave (a small number of customer orders that are not ultimately summarized into the pick may contain any of the categories described above) further, the multiple single structure may be further refined to contain 2-5 SKUs per customer order that the pick contains 1 item. The multiple items may be divided into two sets of policies, one set containing 2-5 SKUs for each customer order contained in the pick-up order, each SKU containing 2-20 items; another group contains at least 5 per customer order for the pick order, and at least two items per SKU. It can be understood that by refining the decision strategy of each category in the order structure, the generalization of order processing is ensured, orders with different structures can be processed in a compatible manner, and the accuracy of model training is further ensured.
The warehouse operating parameters include, but are not limited to, warehouse pick zone size, pick zone number of lanes, upper limit on the number of customer orders processed per wave, upper limit on SKU capacity in each pick slip, vertical travel distance of pick path, average length of pick zone pick lanes, preparation time of each pick slip, speed of pick person walking, speed of pick person seeking for goods, time required for pick person to pick each good, speed of pick person sorting, packing speed of packer, upper limit on packing wait time, and average packing time of each good.
S102: acquiring a pre-established order summarization model; the order summarizing model is obtained through mixed integer nonlinear programming modeling.
The order summarizing model is used for solving according to a customer order to obtain an order summarizing result. In this embodiment, the order summary model may be obtained by mixed integer nonlinear programming modeling.
S103: based on the operation parameters of the warehouse, an order summarizing model is adopted to process the customer order, and the shortest order summarizing result in the warehouse operation is output; the order summary result is used for indicating the pickers to pick goods.
The order summary result is used for describing a plurality of order picking lists, and the order picking list is used for indicating pickers to pick. The order includes, but is not limited to, the name of the merchandise, the number of the merchandise required for each customer order, and the location of each merchandise.
In one embodiment, as shown in fig. 2, there is provided an order summarizing method, including the steps of:
s201: establishing a warehouse operation time axis according to a warehouse operation flow; the warehouse operation time shaft comprises a plurality of time nodes corresponding to the warehouse operation events.
As shown in the warehouse operation timeline of fig. 3, the timeline may intuitively describe the entire warehouse operation flow to establish corresponding time nodes through multiple warehouse operation events in the warehouse operation flow to obtain a warehouse operation timeline of continuous time representation.
Wherein the warehouse job time axis includes, but is not limited to, a pick job time axis and a package job time axis. The time axis of the picking operation comprises time nodes corresponding to warehouse operation events such as picking start, picking walking, picking searching, picking taking, picking sorting, picking finishing and the like. The packing operation time axis comprises time nodes corresponding to warehouse operation events such as packing start, packing start and packing end.
S202: and establishing a mathematical model of the mixed integer nonlinear programming according to the warehouse operation time axis, and carrying out linearization processing on the mathematical model to obtain an order summarization model.
Specifically, according to a warehouse operation time axis, modeling a warehouse operation time optimization problem, establishing a mathematical model of mixed integer nonlinear programming, and carrying out linearization treatment on the mathematical model, so as to convert the original highly non-convex mixed integer nonlinear programming model into a mixed integer linear programming model, and obtain an order summarizing model.
In one embodiment, as shown in fig. 4, in step S202, that is, including establishing a mathematical model of mixed integer nonlinear programming according to a warehouse operation time axis, the method specifically includes:
s301: decision variables of the mathematical model are determined.
S302: and constructing an objective function and model constraint conditions according to the warehouse operation time axis.
S303: and respectively carrying out linearization processing on the objective function and the model constraint condition to obtain a linear objective function and a linear constraint condition.
S304: and constructing an order summarization model based on the linear objective function and the linear constraint condition.
Specifically, as can be seen from fig. 3, the time axis of the order picking operation is composed of two operations of the order picking operation and the packing operation in series, so in this embodiment, the specific implementation of steps S301-S304 includes, but is not limited to, two types, one type is to optimize only the order picking operation; the other is to optimize the picking operation and the packing operation simultaneously; to build a mathematical model of the corresponding mixed integer nonlinear programming by either embodiment.
In the embodiment, the picking operation and the packing operation can be optimized simultaneously to construct an optimizing function by comprehensively considering the picking operation and the packing operation, so as to realize global optimization on warehouse operation time. In addition, the corresponding optimization function can be constructed only by optimizing the picking operation, and the picking bill can be distributed for the packing operation in a random distribution packing table mode later so as to ensure the flexibility of warehouse operation.
For ease of understanding, each embodiment is described one by one below.
In one embodiment, for the purpose of optimizing the picking operation, a mathematical model of the mixed-integer nonlinear programming is built, i.e., the decision variables of the mathematical model of the mixed-integer nonlinear programming can be determined as x ij And y jpk Wherein x is ij Indicating whether customer order I e I is assigned to pick order J e J (if so, x ij =1, if not, x ij ＝0)；y jpk Order pick list storage K e K indicating whether order pick list J e J is assigned to order pick person P e P, (if so, y) jpk =1, if not, y jpk ＝0)。
Specifically, the objective function of the mathematical model of the mixed integer nonlinear programming is
The goal is to minimize the sum of order processing times and the span of order processing times; the sum of order processing times refers to the sum of all customer order processing times in the current order wave, including pick-up preparation or waiting times. The order processing time span refers to the time span from the beginning of the pick of the first customer order to the end of the pick of the last customer order in the current order wave.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a sum of order processing times; />
Representing a pick completion time for the last customer order; t is t enter-pick A pick start time representing a first customer order; j represents a pick slip; p represents a picker; k denotes the last pick-up storage unit of the picker.
Specifically, the model constraint conditions corresponding to the objective function are as follows:
it will be appreciated that, as can be appreciated from the above objective function and model constraints, the objective function is a nonlinear objective function, so that linearization of the objective function and constraints is required to convert the nonlinear objective function into a linear objective function.
Specifically, the nonlinear objective function is linearly transformed, i.e. the nonlinear objective function
Equivalent to
And updating constraints, i.e. adding to the constraints
In another embodiment, a mathematical model of a mixed integer nonlinear program is built for the purpose of optimizing the outbound job and the inbound job simultaneously, i.e., the decision variables of the mathematical model of the mixed integer nonlinear program can be determined as x ij 、y jpk And z jrl Wherein x is ij Indicating whether customer order I e I is assigned to pick order J e J (if so, x ij =1, if not, x ij ＝0)；y jpk Order pick list storage K e K indicating whether order pick list J e J is assigned to order pick person P e P, (if so, y) jpk =1, if notIs y jpk ＝0)；z jrl Order picking list storage location L e L indicating whether order picking list J e J is assigned to packing station R e R (if yes, z) jrl =1, if not, z jrl ＝0)。
The objective function of the mathematical model of the mixed integer nonlinear programming is
The goal is to minimize the sum of order processing times and the span of order processing times. The sum of order processing times refers to the sum of all customer order processing times, including the waiting time for pick. The order processing time span refers to a time span from a first customer order pick beginning to a last customer order pick ending.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a sum of order processing times; />
Representing the packing completion time of the last customer order; t is t enter-pick A pick start time representing a first customer order; j represents a pick slip; r represents a packing table; l denotes the last pick-order storage location of the packed bed.
Specifically, the model constraint conditions corresponding to the objective function are as follows:
specifically, the meaning of the parameters corresponding to the model constraint conditions in the above two embodiments is as follows:
it will be appreciated that, as can be appreciated from the objective function and model constraints described above, the objective function is a nonlinear objective function, and the model constraints include nonlinear constraints and linear constraints, so that linearization of the objective function and the constraints is required to convert the nonlinear objective function into a linear objective function and the nonlinear constraints into linear constraints, so that the model constraints include only linear constraints.
Specifically, the nonlinear objective function is linearly transformed, i.e. the nonlinear objective function
Equivalent to
This is again equivalent to->
Min td makesapn -pack And updating constraints, i.e. adding to the constraints
Further, the nonlinear constraint condition is subjected to linearization, namely according to theorem
Can be->
Conversion of l.epsilon.L.1 to
a rl e {0,1}, where a rl Is a binary variable 0 or 1.
Further, the nonlinear constraint condition is subjected to linearization treatment, namely, the nonlinear constraint condition is as follows
The absolute value term in l.epsilon.L.1 is developed, and ∈1>
l.epsilon.L.1
l∈L\{1}。
In one embodiment, as shown in fig. 5, in step S103, that is, based on the warehouse operation parameters, the customer order is processed by using an order summary model, and the shortest order summary result in the warehouse operation is output, which specifically includes the following steps:
s401: and initializing an order summarization model by adopting warehouse operation parameters.
S402: and solving the order summarization model by adopting a definition algorithm, and outputting the shortest order summarization result used in warehouse operation.
Specifically, as known from the above mathematical model, some parameters in the model, such as the SKU capacity upper limit in each order picking list, need to be initialized to perform model solving subsequently, so in this embodiment, the warehouse operation parameters are adopted to initialize the order summary model, and by means of the Gurobi optimizer, a definition algorithm is adopted to realize efficient solving of the model, and the shortest order summary result and the shortest warehouse operation time are output.
Further, when the shortest order summary result is output during warehouse operation, the operation index corresponding to the current order wave number can be output at the same time, wherein the operation index includes, but is not limited to, an order processing time sum (including time of job preparation or waiting) corresponding to the current order processing wave number, an order processing time span (time span from starting of first customer order processing to ending of last order processing) corresponding to the current order processing wave number, personnel (i.e. workload/working time) of a pick-up person and a packer, and pick-up number and packing number suggested to be used by the current order processing wave number.
Specifically, the warehouse operating parameters include, but are not limited to, warehouse pick zone size, pick zone number of lanes, upper limit of customer order number processed per wave, upper limit of SKU capacity in each pick slip, vertical travel distance of pick path, average length of pick zone pick lanes, preparation time of each pick slip, speed of pick person walking, speed of pick person searching for goods, time required for pick person to pick each good, speed of pick person sorting goods, packing speed of packer, upper limit of packing waiting time, and average packing time of each good.
In this embodiment, the time of warehouse operation is used as an optimization object, and by fully considering the constraints of all warehouse operation events in the picking operation, the influence of the warehouse operation events in the packing operation on the time of warehouse operation is also considered, so that the accuracy of the model is improved, and the efficiency of warehouse operation (i.e. warehouse-out operation) is effectively ensured. In addition, the invention also provides effective linearization treatment strategies for nonlinear factors in the order summarization model, converts the original MINLP model into the MI LP model, and adopts a definition algorithm to realize efficient solution of the model by means of a Gurobi optimizer, thereby further reducing the time for warehouse operation and improving the warehouse operation efficiency.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
In an embodiment, an intelligent warehousing system is provided, where the intelligent warehousing system corresponds to the order summarizing method in the above embodiment one by one. As shown in fig. 6, the intelligent warehousing system includes an operational data acquisition module 10, a target operational environment determination module 20, and an order aggregation module 30. The functional modules are described in detail as follows:
the operation data acquisition module 10 is used for acquiring warehouse operation data corresponding to the current order processing wave number; wherein the warehouse operational data includes warehouse operational parameters and customer orders.
The model obtaining module 20 is configured to obtain a pre-created order summary model.
The order summarizing module 30 is used for processing the customer orders by adopting an order summarizing model based on the operation parameters of the warehouse and outputting the shortest order summarizing result in the operation of the warehouse; the order summary result is used for indicating the pickers to pick goods.
Specifically, the intelligent warehousing system further comprises a time axis creation module and an order summary model construction module.
The time axis creation module is used for creating a warehouse operation time axis according to the warehouse operation flow; the warehouse operation time shaft comprises a plurality of time nodes corresponding to the warehouse operation events.
And the order summarization model construction module is used for establishing a mathematical model of the mixed integer nonlinear programming according to the warehouse operation time axis, and carrying out linearization processing on the mathematical model to obtain an order summarization model.
Specifically, the order summary model construction module comprises a decision variable acquisition unit, an initial model construction unit, a linearization processing unit and an order summary model construction unit.
And the decision variable acquisition unit is used for determining the decision variable of the mathematical model.
And the initial model construction unit is used for constructing a nonlinear objective function and model constraint conditions according to the warehouse operation time axis.
The linearization processing unit is used for linearizing the nonlinear objective function to obtain a linear objective function; or respectively carrying out linearization processing on the nonlinear objective function and the model constraint condition to obtain a linear objective function and a linear constraint condition.
And the order summarization model construction unit is used for constructing an order summarization model based on the linear objective function and the linear constraint condition.
Specifically, the order summarization module comprises a model initialization unit and a model solving unit.
And the model initializing unit is used for initializing an order summarizing model by adopting warehouse operation parameters.
And the model solving unit is used for solving the order summarizing model by adopting a definition algorithm and outputting the shortest order summarizing result in warehouse operation.
For specific definitions of the intelligent warehousing system, reference may be made to the definition of the order summarizing method hereinabove, and the description thereof will not be repeated here. The various modules in the intelligent warehousing system described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a computer storage medium, an internal memory. The computer storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the computer storage media. The database of the computer device is used for storing data, such as a display industry track map, generated or acquired during the process of executing the order summarization method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an order aggregation method.
In one embodiment, a computer device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the order aggregation method in the above embodiments when the computer program is executed, such as steps S201-S203 shown in fig. 2, or steps shown in fig. 3-5. Alternatively, the processor may implement the functions of each module/unit in this embodiment of the intelligent warehousing system when executing the computer program, such as the functions of each module/unit shown in fig. 6, which are not described herein again to avoid repetition.
In an embodiment, a computer storage medium is provided, and a computer program is stored on the computer storage medium, where the computer program when executed by a processor implements the steps of the order summarizing method in the above embodiment, for example, steps S201-S203 shown in fig. 2, or steps shown in fig. 3-5, which are not repeated herein. Alternatively, the computer program when executed by the processor implements the functions of each module/unit in this embodiment of the foregoing smart warehousing system, for example, the functions of each module/unit shown in fig. 6, which are not repeated herein.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of each functional unit and module is illustrated, and in practical application, the above-mentioned functional allocation may be performed by different functional units and modules according to needs, i.e. the internal structure of the intelligent storage system is divided into different functional units or modules, so as to perform all or part of the above-mentioned functions.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (7)
1. An order summarization method, comprising:
acquiring warehouse operation data corresponding to the current order processing wave number; wherein the warehouse operational data includes warehouse operational parameters and customer orders;
acquiring a pre-established order summarization model; the order summarizing model is obtained through mixed integer nonlinear programming modeling;
based on the warehouse operation parameters, the order summarizing model is adopted to process the customer orders, and the shortest order summarizing result in warehouse operation is output; the order summarizing result is used for indicating a picker to pick goods;
before the warehouse operation data corresponding to the current order processing wave number is acquired, the order summarizing method further comprises the following steps:
establishing a warehouse operation time axis according to a warehouse operation flow; the warehouse operation time shaft comprises a plurality of time nodes corresponding to warehouse operation events;
establishing a mathematical model of mixed integer nonlinear programming according to the warehouse operation time axis, and carrying out linearization processing on the mathematical model to obtain the order summarization model;
the establishing a mathematical model of mixed integer nonlinear programming according to the warehouse operation time axis comprises the following steps:
determining decision variables of the mathematical model;
constructing a nonlinear objective function and model constraint conditions according to the warehouse operation time axis;
respectively carrying out linearization processing on the nonlinear objective function and the model constraint condition to obtain a linear objective function and a linear constraint condition;
constructing the order summary model based on the linear objective function and the linear constraint condition;
the order summary result comprises a plurality of order picking notes;
the objective function is
The goal is to minimize the sum of order processing times and the span of order processing times; the sum of the order processing time refers to the sum of all customer order processing time in the current order wave, including the time of picking preparation or waiting; wherein (1)>
Representing a sum of order processing times; />
Representing a pick completion time for the last customer order; />
A pick start time representing a first customer order; j represents a pick slip; p represents a picker; k represents the last pick-up storage unit of the picker.
2. The order summary method as claimed in claim 1 wherein said customer orders are processed using an order summary model based on said warehouse operating parameters, outputting a shortest order summary result for warehouse operation, comprising:
initializing the order summary model by adopting the warehouse operation parameters;
and solving the order summarization model by adopting a definition algorithm, and outputting the shortest order summarization result used in warehouse operation.
3. An order summarization method, comprising:
acquiring warehouse operation data corresponding to the current order processing wave number; wherein the warehouse operational data includes warehouse operational parameters and customer orders;
acquiring a pre-established order summarization model; the order summarizing model is obtained through mixed integer nonlinear programming modeling;
based on the warehouse operation parameters, the order summarizing model is adopted to process the customer orders, and the shortest order summarizing result in warehouse operation is output; the order summarizing result is used for indicating a picker to pick goods;
before the warehouse operation data corresponding to the current order processing wave number is acquired, the order summarizing method further comprises the following steps:
establishing a warehouse operation time axis according to a warehouse operation flow; the warehouse operation time shaft comprises a plurality of time nodes corresponding to warehouse operation events;
establishing a mathematical model of mixed integer nonlinear programming according to the warehouse operation time axis, and carrying out linearization processing on the mathematical model to obtain the order summarization model;
the establishing a mathematical model of mixed integer nonlinear programming according to the warehouse operation time axis comprises the following steps:
determining decision variables of the mathematical model;
constructing a nonlinear objective function and model constraint conditions according to the warehouse operation time axis;
respectively carrying out linearization processing on the nonlinear objective function and the model constraint condition to obtain a linear objective function and a linear constraint condition;
constructing the order summary model based on the linear objective function and the linear constraint condition;
the objective function is
The goal is to minimize the sum of order processing timesThe order processing time span is minimum; the sum of the order processing time refers to the sum of all the customer order processing time, including waiting time when picking goods; wherein (1)>
Representing a sum of order processing times;
representing the packing completion time of the last customer order; />
A pick start time representing a first customer order, j representing a pick slip; r represents a packing table; l represents the last pick-order storage position of the packing station.
4. A method of order aggregation as set forth in claim 3 wherein said customer orders are processed using an order aggregation model based on said warehouse operational parameters to output a shortest order aggregation result for a warehouse operation, comprising:
initializing the order summary model by adopting the warehouse operation parameters;
and solving the order summarization model by adopting a definition algorithm, and outputting the shortest order summarization result used in warehouse operation.
5. An intelligent warehousing system for use in the order aggregation method of any one of claims 1 to 2 or the order aggregation method of any one of claims 3 to 4, the intelligent warehousing system comprising:
the operation data acquisition module is used for acquiring warehouse operation data corresponding to the current order processing wave number; wherein the warehouse operational data includes warehouse operational parameters and customer orders;
the model acquisition module is used for acquiring a pre-established order summarization model; the order summarizing model is obtained through mixed integer nonlinear programming modeling;
the order summarizing module is used for processing the customer orders by adopting an order summarizing model based on the warehouse operation parameters and outputting the shortest order summarizing result in the warehouse operation; wherein the order summary result is used for indicating the pickers to pick.
6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the order summarization method according to any one of claims 1 to 2 or the order summarization method according to any one of claims 3 to 4.
7. A computer storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the order aggregation method of any one of claims 1 to 2 or the order aggregation method of any one of claims 3 to 4.
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