CN110097234B - Intelligent dispatching method and system for industrial cigarette transportation - Google Patents
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Abstract
Aiming at the practical characteristics of industrial cigarette transportation, the invention considers the connectivity and dynamic unloading time of the commercial company and the like to construct the path optimization method suitable for industrial cigarette transportation, so that the total industrial enterprise transportation cost is minimized on the basis of meeting the marketing order of the commercial company. According to the invention, the specificity of the industrial cigarette pricing model is considered, key factors such as actual connectivity and collage rules among business companies are objectively analyzed through analyzing and mining historical logistics data, so that the influence of artificial subjective factors and the hidden collage rules in the found data are avoided. According to the invention, a multi-target intelligent evolution algorithm with reinforcement learning capability based on a primary and secondary hierarchical framework is constructed, so that the transportation cost of a carrier is reduced as much as possible on the premise of not increasing the logistics cost of an industrial enterprise, an intelligent dispatching system of the industrial enterprise is established, the logistics efficiency is greatly improved, and the logistics cost of the industrial enterprise is reduced.
Description
Technical Field
The invention relates to the technical field of logistics transportation scheduling optimization, in particular to an intelligent scheduling method and system for industrial cigarette transportation.
Background
The cigarette transportation cost of the industrial enterprise is the largest single cost in logistics cost, the cigarette transportation cost accounts for about 75% of the total cost of the cigarette logistics of the industrial enterprise, wherein the cigarette transportation is mainly road transportation, and the road transportation cost accounts for more than 90% of the transportation cost. Therefore, for industrial enterprises, the method reduces the transportation cost of cigarettes and is an important field of enterprise cost reduction and synergy; the road transportation cost of cigarettes is reduced, and the cigarettes are more important, so that the cigarettes have a large digging space.
The current industrial enterprise finished product logistics transportation has multiple motorcycle types and numerous to goods points, and the allocation of order and transportation route selection all rely on manual experience, and the organization of logistics transportation lacks scientific and reasonable planning, has the problem that the vehicle satisfaction rate is low and unnecessary freight is extravagant, mainly shows in:
1. the current logistics transportation scheduling of finished products adopts a manual mode, the vehicle scheduling and loading depends on personal experiences of schedulers, along with the development of traffic industry in China, a road network is more complex, the route selection mode carried out by the manual experiences is difficult to keep pace with the development pace of the external environment, the optimal allocation of goods, vehicles and transportation routes is difficult to realize, the scientificity and rationality are difficult to judge, and unnecessary transportation cost is easy to generate.
2. The manual scheduling occupies large workload, in the scattered scheduling mode, each cigarette factory needs to arrange a plurality of schedulers to take charge of the work, and when the cigarettes are delivered in a centralized mode in a large batch, continuous overtime operation is needed, so that the labor cost is high.
3. The interior of the delivery point has more links such as warehouse moving and dumping, so that resource waste and indirect logistics cost increase are caused, and the on-site dispatching work of the vehicles in the factory lacks effective management and informatization support.
4. The finished cigarette carrying route is mainly divided by virtue of provincial domain boundaries, and the problems of high transportation cost and low efficiency are caused by unbalanced order quantity in the carrying route and lack of scientific and reasonable route planning, which results in resource waste.
Reasonable logistics transportation scheduling optimization is an important way for reducing the transportation cost of cigarettes in industrial enterprises, and is currently a subject for common exploration inside and outside the industry. The logistics transportation dispatching optimization has a plurality of successful cases in the fields of domestic automobile spare and accessory part industry, quick sales industry, electronic commerce logistics and the like, the enterprises face the challenges of emphasizing the quick response market demand, providing a high-level personalized service mode, timely dispatching of multiple batches and small batches for customers and the like, intelligent logistics dispatching optimization research is conducted in a dispute mode, theoretical research such as planning methods, optimization algorithms and the like is conducted, and the technologies such as GIS, information network and the like are combined, so that intelligent and decision-making support is provided for vehicle type selection, vehicle loading and path optimization in logistics transportation, and a remarkable effect is achieved.
Research on the aspects of cigarette logistics transportation by domestic scholars is mostly focused on the two aspects of optimizing a transportation line and dispatching transportation vehicles so as to achieve the purposes of improving transportation efficiency and reducing logistics cost. Hu Gongchun et al (2007) are mainly directed to the problem of mass distribution vehicle route optimization with real road environments, in-city distribution multiple demand points. The related thought of game theory is introduced, and a cluster algorithm is applied to carry out multi-objective optimization of logistics distribution vehicle routes on nearly thirty thousand cigarette retailers in the Jinan tobacco distribution center [1]. Wang Yong (2009) and the like have mainly studied the problem of dividing tobacco logistics distribution areas into backgrounds. And (3) planning a distribution area for a research object according to a distribution mode adopted in the tobacco industry, establishing a cost-based operation study model, and providing an application genetic algorithm for coding a solving function model to determine respective distribution areas [2] of a distribution center and a transfer station. Ice fire and grass wood (2014) mainly aim at the problem to be solved urgently in tobacco logistics, and the problems that an industry logistics intelligent scheduling management and control platform is built, various industry logistics resources are integrated, configuration is optimized, intelligent scheduling is achieved, and the supply chain logistics operation cost is reduced by utilizing information technologies based on cloud computing, big data, BI (business intelligence) and the like are emphasized. Xu Zhi (2014) and the like propose that dynamic path optimization is performed by adopting a heuristic tabu search algorithm according to daily orders and considering constraint conditions such as vehicles, cost, orders, roads, time and the like, and the problems that distribution lines are unreasonable, vehicle loading capacity cannot be reasonably utilized and a distribution in-transit abnormality monitoring and processing mechanism is lacked in a GPS positioning technology are solved by means of wireless networks such as GPRS (general packet radio service) and 3G (third generation telecommunication). Huang Gewen (2015) mainly emphasizes that information sensing and transmitting equipment such as cloud computing, internet of things, big data, GIS (geographic information System), GPS (Global positioning System), video sensors and the like are integrated, a set of tobacco logistics transportation scheduling system based on cloud computing is developed according to the characteristics of cigarette transportation and distribution, intelligent recognition, positioning, tracking, monitoring and management of tobacco distribution vehicles are achieved, distribution lines are optimized through intelligent algorithm solving, and a network providing comprehensive service is formed to improve logistics efficiency, intelligent optimization and other management [5]. Zhang Huimin (2018) is mainly aimed at optimizing delivery area routes of Fujian Zhangzhou tobacco logistics companies, and an improved flexible line interception optimization algorithm is researched and provided for establishing a new flexible delivery mode by combining the specific situation of the tobacco logistics artificial experience delivery routes, so that the purposes of maximizing the utilization rate of tobacco transportation vehicles and minimizing delivery mileage are achieved [6].
With respect to the study of vehicle transportation scheduling algorithms, liu and Shen (1999) developed an economical construction heuristic and an improved heuristic, the first time a vehicle path problem with time windows and heterogeneous fleets was proposed [14]. Belfiore et al (2007) propose to solve such problems by a decentralized search [15]. Taojiang et al (2008) comprehensively consider the problem that different vehicle types have different marginal cost and driving cost in the problem of the vehicle path of the multiple vehicle types, consider the compatibility of the vehicle types and tasks at the same time, and build a mathematical model [7] aiming at minimizing the total cost for the problem of the vehicle path of the multiple vehicle types with time window constraint and multiple cost non-full load. Shi Chaochun et al (2009) on the basis of analyzing the penalty function of the time window, establish a multi-distribution center vehicle scheduling model with the time window, solve the model design by a scanning algorithm and an improved genetic algorithm successively, and finally verify the algorithm effectiveness by simulation [8]. Wang Zheng et al (2013) have built a mathematical model of the problem and its solution algorithm on the standard example proposed by Solomon with the goal of minimizing the customer time window deviation and the delivery cost [9]. Ma Yugong et al (2013) propose a new multi-segment chromosome hybrid coding algorithm [10] for the problem of large-scale multi-distribution center multi-vehicle type vehicle scheduling. Adelzadeh et al (2014) devised a mathematical model with fuzzy time windows and heterogeneous vehicles taking into account different capacities, speeds and costs, applied a three-stage algorithm to resolve this problem into several common vehicle path problems, and proposed a solution using an improved simulated annealing algorithm [16]. Sun Zhuangzhi et al (2014) use a plurality of professional algorithms such as center clustering, tabu search, local search and the like to study the problem of cigarette distribution and scheduling by taking the Beijing city cigarette market as a background [11]. The Sho-in et al (2017) converts the problem into a single-distribution center vehicle scheduling problem by adopting a boundary distribution method aiming at cross-regional multi-distribution center vehicle scheduling, solves a cross-regional distribution optimal scheduling scheme by combining a genetic algorithm and an ant colony algorithm, and carries out simulation analysis by taking the distribution of tobacco company logistics center cigarettes in Guizhou, qian, southeast China as a background to show the effectiveness of the algorithm [12]. Li Ming et al (2017) model the vehicle path problem with time windows and heterogeneous fleets, while considering the multiple attributes of time windows, heterogeneous fleets and vehicle number limitations, propose an improved tabu search algorithm to solve the problem [13].
At present, more commercial enterprises in the tobacco industry have firstly realized intelligent scheduling in the cigarette distribution link, and a better effect is achieved through dynamic optimization of the urban delivery line. Part of industrial enterprises also start to develop dispatching optimization research, and the automation and the intellectualization of transportation dispatching are explored by combining the implementation of logistics informatization projects, but the existing literature researches on the dispatching problem of cigarette logistics transportation vehicles to the cigarette dispatching of commercial enterprises mostly, and the dispatching algorithm mostly adopts fixed heuristic rules and does not consider the mining application of historical logistics data. And because of the inconsistency of influence factors such as storage layout, management and control modes and the like of industrial enterprises, the whole is still in a starting stage at present, and a mature and stable intelligent optimal scheduling model and algorithm research scheme for cigarette transportation with strong popularization applicability are not formed yet.
Disclosure of Invention
Therefore, the technical scheme is that the industrial finished cigarette transportation constraint and constraint are subjected to theoretical analysis according to the logistics transportation characteristics of industrial enterprises and the historical logistics big data of the industrial enterprises, a vehicle dispatching model for industrial cigarette transportation is established, an intelligent evolution algorithm is designed to carry out optimal configuration on transportation dispatching link resources, and a delivery dispatching system which replaces manual experience with intelligent processing is constructed on the basis of the existing medium smoke comprehensive management platform, so that logistics operation efficiency is further improved, logistics cost is reduced, and the integration and intelligent level of supply chain logistics are improved.
In order to achieve the above purpose, the invention provides an intelligent dispatching method for industrial cigarette transportation, which comprises the following steps:
step 1: constructing a commercial company set V (V= {2,3, … i, … j, …, n }), each commercial company being in the commercial company set, expressed as i epsilon V or j epsilon V, and a point of shipment being represented by 1, the point of shipment and the commercial company union V 0 (V 0 =v {1 }), and an undirected connected graph g= (V) 0 E) E is the edge of every two nodes i and jEach edge { i, j } ∈E, and the distance corresponding to each edge { i, j } is dist ij One path starts at delivery point 1, approaches one or more business companies, and ends at delivery point 1, i.e. one path is an access sequence {1, v i ,V j …,1}, initialize n paths: v (V) 1 →V i →V 1 I.e. each path is transported in a single point, and finally returned to the delivery point V 1 Calculating the minimum single-point transportation cost and/or split transportation cost of all commercial companies;
step 2: calculating pairing priority of business companies, and sequentially fusing the two associated paths on the premise of meeting the split loading rationality constraint to complete the construction of a scheduling scheme;
the pairing priority calculation formula of the business company is as follows:
dist max d is the furthest distance travelled in the marketing contract max The heaviest order in the marketing contract;all are pairing priorities.
Two commercial companies V i And V is equal to j Distance of travel dist between ij The shorter the pairing priority ∈>The higher. Wherein dist 1i ,dist j1 Respectively, distribution center DC to commercial company V i And V j Is a real travel distance of (2);
pairing priority +.>The higher, where d i ,d j Order weights, q, for business companies i, j, respectively 1 The bottom protection tonnage is used for single-point transportation;
the higher the splice confidence, the pairing priority +.>Higher, therein->The value range of the spelling carrying confidence is 0-1;
step 3: repeating the step 2 until all commercial companies are configured to obtain a feasible scheduling scheme,
step 4: the initial scheme is optimized by adopting a double-layer local search strategy, so that the transportation cost of a carrier is reduced as much as possible while the expenditure of an industrial enterprise is not increased, and the method is as follows:
the loop performs the following search strategy on routes with single points in the feasible solution and lower than the bottom-guard tonnage: and each sequence is a feasible solution.
First layer local search: based on the mileage saving sequence, inserting the line into other optimal mileage saving lines meeting the constraint, and if successful, jumping out of the local search of the layer; if not, continuing the local search of the second layer;
Second layer local search: based on the mileage saving sorting, business company warehouses of other spliced lines are inserted into the un-spliced line at the time, and various constraints of the original line cannot be destroyed;
step 5: based on the commercial customer collage scheme, the LKH algorithm (Lin-Kernighan Heuristic) is adopted to optimize the access sequence, and the total travel distance of the carrier is reduced on the premise of meeting a time window.
Further, in step 1, the specific steps of calculating the minimum single point transportation cost and/or the split transportation cost for transporting all commercial companies are as follows:
the following decision variables are defined:
wherein i epsilon V and J epsilon J k J epsilon M, K epsilon K, K= {1,2, …, M } is a vehicle set, and the method for constructing the cigarette transportation scheduling is as follows:
Minimize：
s.t.
a i ≤T i ≤b i ,i∈V (10)
wherein, the transportation expense consists of two types of pricing expense of single-point transportation and split transportation together:
cost of single point transportation:
cost of collage transportation:
equation (1) ensures that each customer is accessible to only one vehicle;
equation (2) specifies that the number of each type of vehicle from the shipment point does not exceed the number of that type;
formula (3) ensures that the total number of supplier materials per vehicle service does not exceed the maximum number of carriers;
equation (4) ensures that the total amount of provider material weight per vehicle service does not exceed the maximum load weight;
Formula (5) is a connectivity constraint between business companies;
formula (6) is a passability constraint between the vehicle model and the commercial company;
equation (7) removes constraints for the sub-path;
equation (8) states that vehicles originate and terminate at the delivery point, and each vehicle returns to the delivery point.
Equation (9) is a time expression of the arrival of the vehicle at each customer;
formula (10) is a business corporate time window constraint;
equations (11) and (12) are model boolean decision variables.
By the above method, the goal is to minimize the cost of transporting all commercial customers, i.e., to ensure that the sum of the two types of transportation costs, single-point transportation and split transportation, is minimized. Wherein, the bottom protection tonnage of single-point transportation is q1, and the bottom protection tonnage of split transportation is q2. due to q2<q1. to a business company fragmented order (i.e., d) with model constraints satisfied j <q 2) carrying out split transportation, the industrial cigarette transportation cost is reduced to the greatest extent.
Wherein d j Total number of materials of commercial company, w j For the weight of commercial company material, D k Maximum number of load-bearing vehicles, W k Vehicle model k maximum load weight, N k The maximum available number of vehicle models k,vehicle model k can access commercial company connectivity parameters, dist 1j Real travel distance of delivery point to different business companies, dist i,j True distance travelled between different business companies, c j Shipping unit price from shipping point to different business company, a ij Linking between business companiesGeneral parameters, u j Commercial company j discharge time, [ a ] j ,b j ]Business company j has a latest hit time window, each business company has a limit on the latest arrival time, and thus each business company has its corresponding time window [ a ] j ,b j ]Time window up to a j Defining the earliest start time of the vehicle service business company j, generally without strict limitation; lower run of b j The latest end time of the vehicle service business company j is defined.
Further, considering the carpooling limit, introducing the connectivity a between business companies ij Metric of connectivity between business companies, a ij Which is a boolean variable that allows connectivity to be set to 1, otherwise to 0, a of the inter-business connectivity ij It is meant that whether commercial company arrival warehouses in the area are connected above the route collage is directly related to whether the vehicle can effectively collage and control of cost. If a candidate collage point with connectivity of 0 is encountered, even though the collage scheme may satisfy weight, time, etc. constraints, such scheme will not be output because the connectivity constraint is not satisfied. It can be seen that reasonable connectivity settings have a key role in both control of logistic costs and rationality of the collage scheme. Since connectivity between business companies involves the profit of the carrier, in a real-world scenario, the collage scheme determination given by the industry enterprise is a process of repeated gaming. Therefore, the results of the investigation of the carrier may vary greatly from the actual. The invention can more objectively reflect the actual connectivity among business companies by carrying out statistical analysis on the historical waybill data. There are two scenarios for business inter-company connectivity decisions: case one is commercial company V i And V is equal to j The historical behavior of the carrier, which appears in the same waybill data, accepts the collage of the two, and can be regarded as the communication of the business company; case two, commercial company V i And V is equal to j Not appearing on the same waybill, such as setting up a new business company or not appearing on the same pool of contracts, based on the classical shipping algorithm and sweep algorithm, by considering the furthest distance and the greatest angle accepted based on the carrier history as connected thresholds, specifically as follows:
1) Historical waybillsEach manifest in the data represents a path, i.e., a carrier haul route to a commercial company collage sequence: route= {1, v i ,V j ,….,1}。V i And V is equal to j The geographical distance between them is dist ij Azimuth angle (Azimuth angle) of azim ij ；
2) If V i With commercial company V j Contract set of same history scheduling waybill with ever presentIn which a can be set, i.e. depending on the communication between the business companies ij ＝1；
3) Based on historical waybill connectivity, statistics is made for each business company V i Is the furthest communicating distance of (2)And the maximum communication azimuth angle thereof
4) If commercial company V j Not previously with V i In the same waybill but with a geographic distance less than V i Historical farthest distance of communicationAnd the azimuth angle is smaller than the historical maximum connection azimuth angle +>Judging that the connectivity of the two is 1;
5) And (3) circulating the steps until the connectivity of each business company is judged to be finished, and forming a connectivity basic data matrix as follows:
6) As shown in table 1, connectivity matrix of five business companies, 0 indicates that no collage is possible between two routes, and 1 indicates that collage is possible.
7) Table business company connectivity matrix example
| Commercial Co Ltd | 1 | 2 | 3 | 4 | 5 |
| 1 | 1 | 0 | 1 | 0 | 0 |
| 2 | 0 | 1 | 0 | 1 | 0 |
| 3 | 1 | 0 | 1 | 0 | 0 |
| 4 | 0 | 1 | 0 | 1 | 1 |
| 5 | 0 | 0 | 0 | 1 | 1 |
Further, in step 2, the premise of the splice rationality constraint is as follows:
business companies i and j do not appear on the loaded path at the same time, i.e., at least 1 point is not loaded;
the non-loaded business company i does not appear on the loaded in-path points, i.e. the non-loaded points must be directly connected to the cigarette factory on the planned path;
the weight of the order of the commercial company i which is not loaded is not greater than the residual capacity of the vehicles of the to-be-fused path;
the fusion of the unassigned business company i does not lead to delays in arrival times of other business companies;
the number of newly loaded path points must not exceed the maximum loadable point;
the total logistics weight of the newly loaded paths must not exceed the maximum passable vehicle type of the corresponding commercial company;
the newly loaded path must meet connectivity constraints;
further, through commercial company V i Will be associated with commercial company V with a high probability j Collage, i.e. there are collage association rulesThe split loading confidence degree->The calculation steps are as follows:
the marketing contracts that the carrier needs to transport daily are: contact= { V 1 ,V 2 ,…,V i ,…,V n -a }; each waybill record in the historical waybill is the collage sequence of the business company: route= {1, v i ,V j …, 1), the collage sequence route is a non-empty subsequence of its marketing contract;
calculating the spelling confidence of the 2-item set association rule in each spelling sequence one by one: spelling ruleConfidence of +.>Defined as including business companies in a historical collage sequence<V i ,V j >The number of (2) and only include<V i >A ratio of the number of (3); note that excluding only commercial company V from historical collage schemes i Date marketing contract of (V) avoiding j Contract absence affecting commercial company V i Calculating the split loading confidence coefficient of (2);
and (5) circulating the steps to calculate a 2-item set of each collage sequence route. Attention to the symmetry of the rules, it is easy to knowThe split-loading confidence of (2) is equal to rule +.>The calculation amount of the split carrying confidence can be saved.
Association rulesThe value range of the splicing confidence coefficient of the (B) is a number between 0 and 1. If->Indicating that only commercial company V i And V j Marketing contracts on the same date are all carried in a sharing way; if 0, it indicates that both are never spelled. The intelligent evolution algorithm provided by the invention also carries out construction and optimization of the collage scheme based on the value.
Further, the LKH algorithm specifically includes the following steps:
5) And acquiring the access path of the acquired client collage scheme.
6) Let S and E be blank edge set to be cancelled and blank edge set to be supplemented
7) Let i=1, randomly select side S1 and side E1 to enter S and E, respectively
8) Increasing r and continuously selecting si and ei into S and E until access paths with lower mileage and meeting time window constraints are available, and 3) turning until there are no unselected edges.
The invention also provides an intelligent dispatching system for industrial cigarette transportation, which comprises a transportation dispatching unit, a site dispatching unit, a basic data management unit, an operation capacity resource management unit, an Internet of things application unit and a report display unit, wherein the transportation dispatching unit acquires data of the data management unit and the operation capacity resource management unit, invokes an intelligent transportation dispatching algorithm, automatically generates a plurality of intelligent allocation schemes in a one-key dispatching mode according to a contract pushed by a marketing batch, each scheme comprises key information elements such as contract number, freight list number, total transportation cost, transportation mileage and the like, sends the generated allocation scheme to the site dispatching unit, facilitates a dispatcher to select a proper allocation scheme, simultaneously sends the allocation scheme to a carrier through the Internet of things application unit, and carries out vehicle dispatching confirmation or abnormal condition feedback by the carrier dispatcher, the vehicle type requires to arrange vehicles and drivers, and automatically sends message reminding to the drivers, and after the carrier carries out vehicle dispatching confirmation, the system automatically generates a complete transportation allocation list, and enters the intelligent reservation queuing system to carry out transportation shipment.
Compared with the prior art, the technical scheme has the following beneficial effects:
first, a path optimization problem model suitable for industrial cigarette transportation is constructed by considering the connectivity of a commercial company, the dynamic unloading time and the like according to the actual characteristics of industrial cigarette transportation, so that the total industrial enterprise transportation cost is minimized on the basis of meeting the marketing order of the commercial company.
Secondly, considering the specificity of the industrial cigarette pricing model, through analyzing and mining the historical logistics data, key factors such as actual connectivity and collage rules among business companies are objectively analyzed, and the influence of artificial subjective factors and the hidden collage rules in the found data are avoided.
Thirdly, a multi-target intelligent evolution algorithm with reinforcement learning capability based on a primary and secondary hierarchical framework is constructed, so that the transportation cost of a carrier is reduced as much as possible on the premise of not improving the logistics cost of an industrial enterprise, an intelligent dispatching information system of the industrial enterprise is established based on the theoretical result, the logistics efficiency is greatly improved, and the logistics cost of the industrial enterprise is reduced.
Drawings
FIG. 1 is a frame diagram of an intelligent dispatching system for industrial cigarette transportation in an embodiment of the invention.
Fig. 2 is a schematic diagram of an intelligent scheduling scheme in an embodiment of the present invention.
Fig. 3 is a detailed scheme of intelligent scheduling and a load circuit diagram in an embodiment of the invention.
Fig. 4 is a schematic diagram of carrier vehicle adjustment in accordance with an embodiment of the present invention.
Fig. 5LIO algorithm main level evolution framework.
Detailed Description
In order to describe the technical content, constructional features, achieved objects and effects of the technical solution in detail, the following description is made in connection with the specific embodiments in conjunction with the accompanying drawings.
The cigarette transportation cost of the industrial enterprise is the largest single cost in logistics cost, the cigarette transportation cost accounts for about 75% of the total cost of the cigarette logistics of the industrial enterprise, wherein the cigarette transportation is mainly road transportation, and the road transportation cost accounts for more than 90% of the transportation cost. Therefore, for industrial enterprises, the method reduces the transportation cost of cigarettes and is an important field of enterprise cost reduction and synergy; the road transportation cost of cigarettes is reduced, and the cigarettes are more important, so that the cigarettes have a large digging space.
Reasonable logistics transportation scheduling optimization is an important way for reducing the transportation cost of cigarettes in industrial enterprises, and is currently a subject for common exploration inside and outside the industry. The logistics transportation dispatching optimization has a plurality of successful cases in the fields of domestic automobile spare and accessory part industry, quick sales industry, electronic commerce logistics and the like, the enterprises face the challenges of emphasizing the quick response market demand, providing a high-level personalized service mode, timely dispatching of multiple batches and small batches for customers and the like, intelligent logistics dispatching optimization research is conducted in a dispute mode, theoretical research such as planning methods, optimization algorithms and the like is conducted, and the technologies such as GIS, information network and the like are combined, so that intelligent and decision-making support is provided for vehicle type selection, vehicle loading and path optimization in logistics transportation, and a remarkable effect is achieved.
According to the embodiment, aiming at the logistics transportation characteristics of cigarettes of industrial enterprises, the historical logistics big data of the industrial enterprises are combined, theoretical analysis is carried out on transportation constraint and constraint of industrial finished cigarettes, a vehicle dispatching model for industrial cigarette transportation is established, an intelligent evolution algorithm is designed to carry out optimal configuration on transportation dispatching link resources, and a delivery dispatching system which replaces manual experience with intelligent processing is constructed on the basis of a medium smoke comprehensive management platform, so that logistics operation efficiency is further improved, logistics cost is reduced, and integration and intelligent level of supply chain logistics are improved.
Based on the above analysis, the tobacco industry enterprise logistics scheduling can be categorized as a vehicle path problem VRP (Vehicle Routing Problem), which was first proposed by Dantzig and Ramser in 1959, which refers to a certain number of customers, each having a different number of cargo demands, the distribution center providing the customers with cargo, being responsible for distributing the cargo by different numbers and types of vehicles, organizing appropriate driving routes, aiming at enabling the customer demands to be satisfied, and achieving the purposes such as shortest distance, minimum cost, least time consumption, etc. under certain constraints.
This problem can be defined as a undirected connectivity graph g= (V 0 E), where v= {1,2, …, n } is the set of nodes, E is the edge of every two nodes,the business companies are denoted 1,2,3, …, n+1. For convenience, the shipping point is denoted by node 1. V (V= {2,3, …, n }) represents the business company set. V 0 (V 0 =v {1 }) represents the point of shipment and the union of business companies. For each business company j e V, there is a determined demand d j The vehicles are heterogeneous, and the distribution capacity of each vehicle type is D k The maximum bearing weight of each vehicle model is W k Here, it is required that the number of single orders from the commercial company does not exceed the maximum delivery capacity of the vehicle, and that the weight of the orders should not be greater than the maximum load-carrying weight of the vehicle model. If the total order number of the commercial company exceeds the maximum distribution capacity of the vehicle, pre-splitting the order is needed in the early stage. Each vehicle type has a limit N of the number of vehicles k And the car type access connectivity +_ of each business company is set in consideration of the business company dock restrictions>Only->The distance between business companies j and j' represents the vehicle model k accessible to business company j, the shortest travel distance of hundred degrees is adopted, but the triangle inequality is not satisfied. One path starts at the delivery point, approaches some business companies (at least one), and ends at the delivery point, i.e. one path is an access sequence {1, V 1 ,V 2 …,1}, all V j Each edge { i, j } ∈E is different, corresponding to a distance of dist ij Each business company has a limit on the latest arrival time, and therefore each business company has its corresponding time window [ a ] j ,b j ]Time window up to a j Defining the earliest start time of the vehicle service business company j, generally without strict limitation; lower run of b j The latest end time of the vehicle service business company j is defined. Discharge time u for business company j j . In addition, considering the carpooling limit, introducing inter-business connectivity a {i,j} See below for details. For ease of reference, the symbols and parameter variables used in the present invention are organized as shown in Table 1 below
The following is shown:
table 1: model parameter variable table
First, to facilitate solving the HVRPTW problem, the following decision variables are defined:
wherein i epsilon V and J epsilon J k J epsilon M and K epsilon K, and constructing a model of the cigarette transportation scheduling problem as follows:
Minimize:
s.t.
a i ≤T i ≤b i ,i∈V (10)
the optimization objective of this mathematical model is to minimize the cost of shipping all commercial customers. Wherein, the objective function transportation cost is composed of two kinds of pricing cost of single-point transportation and split transportation together: wherein the method comprises the steps of
Cost of single point transportation:
cost of collage transportation:
formula 1) ensures that each customer is accessible and only by one vehicle; equation 2) specifies that the number of each type of vehicle from the shipping point does not exceed the number of that type; formula 3) ensures that the total number of supplier materials per vehicle service does not exceed the maximum number of loads; formula 4) ensures that the total amount of provider material weight per vehicle service does not exceed the maximum load weight; equation 5) is a connectivity constraint between business companies; formula 6) is a passability constraint between the vehicle model and the business company; equation 7) removes constraints for the sub-path; equation 8) states that vehicles originate and terminate at a delivery point, and each vehicle returns to the delivery point. Equation 9) is a time expression of the arrival of the vehicle at each customer; formula 10) is a business corporate time window constraint; equations 11) and 12) are model boolean decision variables.
In the past, the research is mostly carried out from a theoretical model, and the manual scheduling result in the historical logistics data is less considered or even if the manual scheme is summarized, the experience is difficult to quantify through specific numerical values due to the dynamic change and randomness of the environment, so that the scheduling scheme constructed by the theoretical model cannot directly meet the actual needs due to the lack of partial model constraint or inaccurate input data. According to the invention, by analyzing and mining the long-term collage scheme in the historical waybill big data, main factors influencing the scheduling scheme are proposed so as to objectively reflect the actual connectivity and hidden collage rules and constraints among business companies. And through the promotion of the intelligent scheduling algorithm, the reasonable fine adjustment of the scheduling scheme is combined with the carrier, and the final actual spelling scheme feeds back key influencing factors, so that the regulation and control of the intelligent scheduling algorithm are formed, and the autonomous learning and optimizing capability of the algorithm is realized.
Industrial enterprise transportation is mainly in an outsourcing mode, and commercial companies are usually divided into different segments by geographical areas, and on the premise that the arrival time of commercial clients is met, the same carrier allows for trans-segment collaging. In general, the loading mileage at any two points must be smaller than the single-point transportation mileage of two vehicles, even if two different segments are located at the business company to be loaded, if the factors such as weight, time and the like are satisfied, the carrier should tend to adopt the loading scheme. However, as previously described for the pricing model, carrier billing fees are charged in the ton kilometer model, and the collages have a different bottom retention tonnage than single point shipping (single point shipping bottom retention tonnage is higher), which makes carriers generally more prone to single point shipping, averting long distance collages such as far lighter, near heavier, etc. empty rates or collages schemes with greater business company azimuth differences, even though multiple single point shipments will increase their total driving range.
For this feature, the present embodiment proposes a measure of the connectivity a between business companies ij Which is a boolean variable, allows the connectivity to be set to 1,otherwise, set to 0. Connectivity refers to whether business company arrival warehouses in an area communicate over route collages, directly related to whether vehicles can effectively collage and cost control. If a candidate collage point with connectivity of 0 is encountered, even though the collage scheme may satisfy weight, time, etc. constraints, such scheme will not be output because the connectivity constraint is not satisfied. It can be seen that reasonable connectivity settings have a key role in both control of logistic costs and rationality of the collage scheme. Since connectivity between business companies involves the profit of the carrier, in a real-world scenario, the collage scheme determination given by the industry enterprise is a process of repeated gaming. Therefore, the results of the investigation of the carrier may vary greatly from the actual.
The invention can more objectively reflect the actual connectivity among business companies by carrying out statistical analysis on the historical waybill data. There are two scenarios for business inter-company connectivity decisions: case one is commercial company V i And V is equal to j What was presented in the same manifest data, i.e., the carrier history accepted both spellings, can be considered business company connectivity at this time. Case two, commercial company V i And V is equal to j Not present in the same waybill, such as setting up a new business company or not present in the same pool of contracts, etc. This case has no historical data to be referred to, and is determined here based on classical saving and sweep algorithms, by considering the furthest distance and the greatest angle accepted based on the carrier history as connectivity thresholds. The method comprises the following steps:
1) Each manifest in the historical manifest data represents a path, i.e., a carrier haul route-to-business company collage sequence: route= {1, v i ,V j ,…,1}。V i And V is equal to j The geographical distance between them is dist ij Azimuth angle (Azimuth angle) of azim ij 。
2) If V i With commercial company V j Contract set of same history scheduling waybill with ever presentIn which a can be set, i.e. depending on the communication between the business companies ij ＝1。
3) Based on historical waybill connectivity, statistics is made for each business company V i Is the furthest communicating distance of (2)And the maximum communication azimuth angle thereof
4) If commercial company V j Not previously with V i In the same waybill but with a geographic distance less than V i Historical farthest distance of communicationAnd the azimuth angle is smaller than the historical maximum connection azimuth angle +>Then it is determined that the connectivity is also 1.
5) And (3) circulating the steps until the connectivity of each business company is judged to be finished, and forming a connectivity basic data matrix as follows:
As shown in table 2, connectivity matrices for five business companies, 0 for non-collable between two routes, and 1 for collable.
Table 2 business company connectivity matrix example
| Commercial Co Ltd | 1 | 2 | 3 | 4 | 5 |
| 1 | 1 | 0 | 1 | 0 | 0 |
| 2 | 0 | 1 | 0 | 1 | 0 |
| 3 | 1 | 0 | 1 | 0 | 0 |
| 4 | 0 | 1 | 0 | 1 | 1 |
| 5 | 0 | 0 | 0 | 1 | 1 |
Before industrial enterprises adopt intelligent algorithms to carry out cigarette transportation, a large number of historical manual collage schemes are accumulated, and a plurality of implicit collage rules are very important but difficult to simply induce. One of the regularity of historical vehicle collages is represented by certain business companies that are always scheduled to the same vehicle, the same model, or even the same driver. The reason for this may be the proximity of geographical locations between business companies, or the fact that certain routes are more suitable for the vehicle model or drivers are more familiar, and the collage rules are difficult to manually summarize and calculate by formulas. With the wide use of logistics systems, a great amount of logistics data is continuously collected and stored, and the hidden internal knowledge is extracted by a data mining association rule method.
The business corporate collage law can be described as: through commercial company V i Will be associated with commercial company V with a high probability j Collage, i.e. there are collage association rulesThe invention carries out the calculation of the splicing confidence coefficient based on the method of the association rule, and comprises the following steps:
1) The marketing contracts that the carrier needs to transport daily are: contact= { V 1 ,V 2 ,…,V i ,…,V n -a }; each waybill record in the historical waybill is the collage sequence of the business company: route= {1, v i ,V j …,1}. The collage sequence route is a non-empty subsequence of its marketing contracts.
2) Calculating the spelling confidence of the 2-item set association rule in each spelling sequence one by one: spelling ruleConfidence of +.>Defined as inclusion in a historical collage sequenceCommercial Co Ltd<V i ,V j >The number of (2) and only include<V i >Is a ratio of the number of (3). Note that excluding only commercial company V from historical collage schemes i Date marketing contract of (V) avoiding j Contract absence affecting commercial company V i And (3) calculating the split loading confidence of the model (C).
3) And (5) circulating the steps to calculate a 2-item set of each collage sequence route. Attention to the symmetry of the rules, it is easy to knowThe split-loading confidence of (2) is equal to rule +.>The calculation amount of the split carrying confidence can be saved.
Association rulesThe value range of the splicing confidence coefficient of the (B) is a number between 0 and 1. If->Indicating that only commercial company V i And V j Marketing contracts on the same date are all carried in a sharing way; if 0, it indicates that both are never spelled. The intelligent evolution algorithm provided by the invention also carries out construction and optimization of the collage scheme based on the value.
Based on the factor analysis of the industrial enterprise cigarette distribution model and the logistics history data, the invention designs an intelligent evolution algorithm (Logistics Intelligent Optimization, LIO for short) for scheduling the transportation and delivery of cigarettes by considering a plurality of factors such as the response time of clients, reasonable car pooling connectivity, benefits of industrial enterprises and carriers and the like. On the basis of theoretical research, an intelligent logistics optimization scheduling system is developed, historical data analysis and intelligent optimization self-feedback are realized, and intelligent scheduling of industrial enterprise cigarette delivery is realized.
The intelligent evolution algorithm provided by the embodiment is divided into a primary level and a secondary level for optimization: learning from the iterative process by adopting a probability-based evolution algorithm in the aspect of the main hierarchy, and improving the optimization effect; and constructing and evaluating a solution to the pairing sequence by combining the analysis results of the basic data and the historical logistics data at the sub-level. The algorithm framework is characterized by primary and secondary level distinction, has strong universality, and can be applicable only by fine adjustment of the sub-level even if external constraint changes.
The Saving algorithm proposed by Clarke and Write has been widely applied to the problem of VRP optimization, and has the advantages of simplicity, high efficiency and strong applicability. The basic flow is as follows: (1) Calculating the distance between each cargo point and constructing a distance matrix; (2) recursively collage calculating the saving mileage between each node; (3) sorting the amounts of savings in order; (4) routing in conjunction with node demand.
The most central factor of the Saving algorithm is the pairing priority calculation between business companies. The classical saving method only considers the distance factor between goods points, and the priority calculation formula is as follows:
/>
wherein dist 1i ,dist j1 Respectively, distribution center DC to commercial company V i And V j Dist of the actual travel distance of (a) ij Is the distance travelled between two commercial companies. The formula will promote pairing priorities of adjacent business companies, which is beneficial for the carrier to reduce transportation cost.
The invention designs an improved business company pairing priority calculation formula aiming at an industrial enterprise pricing model and various realistic constraints. As previously mentioned, the core affecting the cost of an industrial enterprise is the efficient collage of sporadic contracts, i.e., preferential pairing for commercial companies with lower bottom retention tonnage, to minimize the extra bottom retention expenditure of the industrial enterprise. Accordingly, the pairing priority of increasing the order quantity of the consideration business company is as follows. Wherein q 1 The bottom protection tonnage is used for single-point transportation. Pairing priority in the build process increases as the weight average of the business company is smaller.
The abovementioned spelling carrying confidence of the embodimentThe method can effectively reflect the historical collage law which is difficult to induce, and is beneficial to improving the operability of a collage scheme, and based on the method, the higher the collage confidence is, the higher the pairing priority is, so that the expert collage experience is fully utilized.
The three pairing priority influencing factors are comprehensively calculated from the angles of mileage saving, bottom conservation tonnage, split loading rule and the like respectively. However, the dimensions of the factors are different, in order to increase the robustness of the priority calculation, the normalization processing is performed on the factors, and the final formula is as follows:
wherein dist max The furthest driving distance in the marketing contract; d, d max Is the heaviest order in the marketing contract. Because the value range of the split bearing confidence is [0,1 ]]No adjustment is required.
Based on the above-described business company pairing priorities, construction of solutions and evaluation of target fees may be performed. The method comprises the following steps:
step 1: initializing n paths: v (V) 1 →V i →V 1 I.e. each path is transported in a single point, and finally returned to the delivery point V 1 。
Step 2: construction of a scheduling scheme: commercial company pairing priority calculated based on formula (x) fuses the two associated paths in turn on the premise that the following splice rationality constraints are satisfied:
a) Business corporate warehouses i and j do not appear on loaded paths at the same time, i.e., at least 1 point is not loaded.
b) The non-loaded commercial company warehouse i does not appear at the loaded in-path points, i.e., the non-loaded points must be directly connected to the cigarette factory on the scheduled path.
c) The weight of the non-loaded commercial company warehouse i must not be greater than the remaining capacity of the path vehicles to be fused.
d) The fusion of the unassigned business company warehouse i does not present delays in arrival times of other business companies.
e) The number of newly loaded path points must not exceed the maximum loadable point.
f) The total weight of the newly loaded route must not exceed the maximum trafficable vehicle type of the corresponding commercial company.
g) The newly loaded path must meet connectivity constraints.
Step 3: and (3) repeating the step (2) until all the commercial company storerooms are configured, and obtaining a feasible scheduling scheme.
Based on the business company pairing priorities and the above construction steps, a viable scheduling scheme can be obtained. But it may still have a single point of load and lower than the bottom tonnage line, indicating that there is still room for optimization of the feasible solution. Therefore, the invention proposes to perform double-layer local search on the feasible solution on the premise of meeting the conditions of weight, connectivity, time window and the like so as to optimize the initial solution. Furthermore, the objective function of the model of the present invention is only related to the collage scheme, the order of access does not affect the overall logistics cost of the industrial enterprise, but it affects the carrier's total driving range and shipping costs. There is therefore a need to order the order of access to the goods-commerce company, which is again a typical combination optimization problem for the traveller. The method takes time window factors into consideration, and the quantity of the commercial companies spliced in the same line is limited, so that the method adopts the efficient heuristic Lin-Kernighan algorithm [18] to optimize the line, and reduces the transportation cost of the carrier as much as possible while not increasing the expenditure of industrial enterprises.
The method comprises the following specific steps:
step 4: the loop performs the following search strategy on routes with single points in the feasible solution and lower than the bottom-guard tonnage:
a) A first layer: based on the mileage-saving ranking, the line is inserted into other optimal mileage-saving lines that meet the constraint. If successful, jumping out of the LS cycle; if not, the second layer local search is continued.
b) A second layer: based on the mileage saving sorting, business company warehouses of other spliced lines are inserted into the un-spliced line at this time, and various constraints of the original line cannot be destroyed.
Step 5: based on the commercial customer loading scheme, the LKH algorithm is adopted to optimize the access sequence, and the total driving range of the carrier is reduced on the premise of meeting a time window.
Based on the construction process, a single feasible solution can be obtained, but the optimization effect of the solution under different scenes cannot be ensured due to the lack of diversity of the solution. The invention introduces a probability-based evolution algorithm (Evolutionary Algorithm) framework, and through evolution learning of a group of feasible solutions, the diversity of LIO algorithm solutions is increased, and the optimal solution is gradually forced, so that the optimization quality of the algorithm under different scenes is ensured.
The LIO algorithm herein will decompose into a primary and a secondary two interrelated sub-optimization hierarchy components: the main hierarchy is based on Maria Battarra et al proposed evolutionary algorithm framework [19], generating a set of business company pairing priority sequences (chromosomes); the secondary level uses the solution construction algorithm proposed in the section above to construct the pairing sequence and evaluate the fitness function. The evolution process can promote the search experience accumulated by the LIO algorithm in the evolution process, ensure that the population can adaptively adjust the pairing priority of the commercial company, ensure that the commercial company is focused in an ideal solution space for searching, achieve the balance of global and local optimization, and ensure that a satisfactory scheduling solution is constructed within acceptable time. Its main level evolution framework is as follows in fig. 5:
The LIO intelligent optimization framework is flexible and high in practicability, single-point direct sending in transportation operation is combined into multi-point collage based on a constructability strategy in sequence by preprocessing geographic information and basic parameter data of a business company and combining marketing order data and historical logistics data analysis results, and total transportation cost after combination is reduced as much as possible; meanwhile, the influences of charging rules are considered, the constructed dispatching route carries out maximum collage under the limiting conditions of meeting vehicle capacity, latest arrival time, maximum collage point, maximum passable vehicles in a commercial company warehouse and the like, and meanwhile, the optimal arrival sequence is given to the collage route, so that the transportation cost of a carrier is reduced on the premise of not increasing the transportation cost of the industrial enterprise. Notably, are: in the LIO intelligent algorithm, the distances from the delivery points to the warehouse of each commercial company are considered, meanwhile, the real high-speed distances among all the commercial companies are obtained through hundred-degree API interfaces, the real running time of the vehicle can be accurately estimated by combining the average running speed of the vehicle, the arrival time of the commercial company, which is later than the marketing given in the split loading scheme, is ensured not to appear, and accurate data support is provided for the split loading of the multi-point vehicle. In addition, because the actual running time is also influenced by uncertain factors such as weather, road conditions and the like, the maximum splice point constraint and the like can be further limited according to the requirement in the LIO intelligent algorithm, so that the service quality of the client is further ensured.
On the basis of LIO intelligent evolution algorithm research, an intelligent logistics optimization scheduling system is developed to realize intelligent scheduling of cigarette delivery, a plurality of intelligent transportation allocation schemes are automatically generated by one key of the system, the system consists of 4 modules of contract management, plan management, intelligent scheduling and vehicle adjustment, and a series of complete automatic, normalized and programmed intelligent transportation scheduling processing flows from contract receiving- > transportation planning- > intelligent scheduling- > transportation allocation are formed.
As shown in fig. 1, the intelligent dispatching system for industrial cigarette transportation is positioned as a service execution system in the logistics comprehensive management platform, and intelligent dispatching optimization is performed on the schedule dispatching operation links in the factory. The system realizes service cooperation and high-efficiency integration with all systems in the logistics, and forms an organic whole with all systems in the logistics. The system receives contract order data from a logistics comprehensive management platform and acquires basic data such as a carrier, a delivery point, transport capacity resources and the like; and issuing the waybill formed by intelligent transportation optimization scheduling to a logistics comprehensive management platform. The system is integrated with the relevant internet of things equipment of each factory shipment point, comprises a park access control system (license plate recognition), a large screen display system and a voice call system, supports the field operation scheduling and management of the factory shipment points,
The intelligent logistics optimization scheduling system calls an intelligent transportation scheduling algorithm, and according to the contracts pushed by marketing batches, a plurality of intelligent allocation schemes can be automatically generated in a one-key scheduling mode, each scheme comprises key information elements such as contract number, transportation list number, total transportation cost, transportation mileage and the like, and a dispatcher can conveniently select an appropriate allocation scheme, as shown in fig. 2.
The system also supports manual fine tuning of the intelligent load scheme. Each loading line presents the transportation lines and the number of the loading lines in a graphical, concise and visual manner, as shown in fig. 3, so that the carrier can conveniently conduct vehicle transportation planning.
After the system generates an intelligent allocation scheme by a key, a scheduler selects an appropriate allocation scheme and issues the appropriate allocation scheme to a carrier scheduler for vehicle scheduling confirmation, as shown in fig. 4. The carrier looks up the relevant scheme of the unit, confirms the scheme of carrying or feedback of abnormal situation. The vehicle and the driver are arranged according to the vehicle type requirement, and message reminding is automatically sent to the driver, and after the vehicle scheduling confirmation is carried out by the carrier, the system automatically generates a complete transportation allocation sheet and enters an intelligent reservation queuing system for transportation and delivery.
All the shipping notes of the three cigarette factories A, B, C are respectively compared and analyzed according to the order data of a tobacco company in 2017 (2017.2-2017.11) and the distribution situation of a warehouse of a commercial company. And solving algorithm parameters serving as input data through an LIO intelligent evolution algorithm, comparing and analyzing an LIO scheduling scheme and a manual scheduling scheme, and comparing total logistics cost and total driving mileage of the LIO intelligent evolution algorithm to verify the effectiveness of the LIO intelligent scheduling algorithm.
The transportation cost of cigarettes in 2017 of certain tobacco companies is about 2.6 hundred million yuan in the whole year (1-12 months), wherein the transportation cost of self-produced cigarettes is 1.9 hundred million yuan, and the transportation cost of cooperative processing cigarettes is about 7000 ten thousand yuan. Of the self-produced cigarette transportation costs, the cost of the A cigarette factory accounts for 36.4% of the highest cost; b and C are 31.9% and 31.8%, respectively. The actual comparison adopts order data in 2017 (2017.2-2017.11), the transportation cost of self-produced cigarettes is about 1.43 hundred million yuan, the rate of the self-produced cigarettes is about 5656 ten thousand yuan in the A cigarette factory, the rate of the self-produced cigarettes is about 40%, the rate of the self-produced cigarettes in the B cigarette factory is 4155 ten thousand, the rate of the self-produced cigarettes in the B cigarette factory is 29%, and the rate of the self-produced cigarettes in the C cigarette factory is 4480 ten thousand, and the rate of the self-produced cigarettes in the C cigarette factory is 31%.
And (3) comparing and analyzing the LIO intelligent optimization algorithm with the manual scheduling result, and preprocessing the data due to the fact that the zero-point action time period is special in transportation, so that the waybills in two special time periods of 1 month and 12 months in 2017 are eliminated. The comparative results are analyzed as shown in the following table: the LIO scheme saves 858 tens of thousands of yuan in terms of logistics cost compared with a manual scheme, and compared with 2017, the logistics cost of 2-11 months can be reduced by about 6.01%; in addition, the manual scheduling scheme can be reduced by 248 ten thousand kilometers (9.22%) compared with 2017, 2-11 months on carrier mileage. Therefore, the LIO intelligent scheduling algorithm not only can effectively reduce the logistics cost, but also can reduce the total driving mileage of the carrier.
Table 2: LIO protocol versus Manual protocol
Table 3 results of comparison
| Cost comparison | Reduced number of | Reduced proportion% |
| Freight contrast (Yuan) | 8582444.59 | 6.01 |
| Mileage contrast (kilometer) | 2479978.775 | 9.22 |
The embodiment closely surrounds the current situation of industrial cigarette transportation and delivery scheduling, applies theoretical foundations such as operation research, intelligent optimization algorithm, data mining and the like, adopts a qualitative and quantitative combination method, takes mathematical modeling, algorithm design and system factory as means to study and practice the transportation scheduling and other businesses, converts theoretical study results through informatization means, and develops and implements an intelligent transportation scheduling system. The intelligent transportation scheduling system automatically selects the optimal allocation and transportation route for the order, and generates a transportation allocation scheme by one key, so that the integrated and intelligent operation of logistics scheduling treatment is formed, the logistics efficiency is greatly improved, and the cost is reduced.
The test point operation is carried out on the delivery schedule of the cigarettes of certain middle period, and the following results are realized through intelligent scheduling optimization research based on an LIO intelligent evolution algorithm:
1) The transportation expense caused by the reasons of unscientific and reasonable transportation scheduling and loading and the like is reduced, and the annual transportation expense is reduced by 1 to 2 percent.
2) The scheduling rules are solidified into the system through operation theory and intelligent algorithm research, so that dependence of transportation scheduling on manual experience is reduced, and influence of human factors on scheduling result quality is reduced.
3) The transportation dispatching work efficiency is greatly improved, the logistics response speed is further improved, the dispatching work of a plurality of hours per day can be reduced to be completed within a few minutes, and certain labor cost is saved.
The LIO intelligent evolution algorithm is designed aiming at the industry characteristics of industrial tobacco logistics, is different from common logistics optimization software (most commercial solvers) in the market, is a set of data-driven intelligent scheduling algorithm which is independently developed and designed on the basis of logistics big data analysis by combining specific constraint of industrial enterprise cigarette transportation and delivery scheduling aiming at the logistics characteristics in the tobacco field. The intelligent scheduling algorithm is different from a classical operation planning optimization technology adopted by a business solver, is based on the latest data mining and evolution learning technology in the artificial intelligence field, and has the intelligent scheduling technology with autonomous evolution and deep learning capability, and reaches the leading level in China and even internationally. In addition, the LIO intelligent evolution algorithm is embedded with a plurality of engineering optimization technologies, so that even facing massive scheduling tasks, rapid operation can be completed within a minute level, the solving speed is far higher than that of manual and common commercial solvers, and the LIO intelligent evolution algorithm has inherent ultrahigh efficiency.
In the early planning and design stage, the industrial popularization capability of the subject achievement is fully considered, an expansion space is reserved for the intelligent optimization scheduling system, and higher flexibility and adaptability of the intelligent optimization scheduling system are ensured, so that the intelligent optimization scheduling system can be efficiently applied and popularized in other industrial enterprises in industry through primary basic data configuration and simple parameter constraint adjustment.
Some problems are limited by time and research means, and are not studied deeply, and the problems remain deeply in the future research and mainly comprise:
(1) The intelligent transportation scheduling algorithm optimization model established by the invention only comprehensively considers static constraint conditions at present, and does not fully consider dynamic constraint conditions.
(2) The model established by the invention depends on the actual problem, but some idealized factors exist in the modeling process, and the model needs to be verified and continuously improved by combining with the actual service operation.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. 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 terminal 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 terminal. Without further limitation, an element defined by the statement "comprising … …" or "comprising … …" does not exclude the presence of additional elements in a process, method, article or terminal device comprising the element. Further, herein, "greater than," "less than," "exceeding," and the like are understood to not include the present number; "above", "below", "within" and the like are understood to include this number.
While the embodiments have been described above, other variations and modifications will occur to those skilled in the art once the basic inventive concepts are known, and it is therefore intended that the foregoing description and drawings illustrate only embodiments of the invention and not limit the scope of the invention, and it is therefore intended that the invention not be limited to the specific embodiments described, but that the invention may be practiced with their equivalent structures or with their equivalent processes or with their use directly or indirectly in other related fields.
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Claims (7)
1. The intelligent dispatching method for industrial cigarette transportation is characterized by comprising the following steps:
step 1: constructing a commercial company set V, wherein each commercial company is in the commercial company set and expressed as i epsilon V or j epsilon V, and the delivery point is expressed by 1, and the delivery point and the commercial company union set V 0 Wherein V= {2,3, … i, … j, …, n }, V 0 =v {1}, constructing an undirected connected graph g= (V) 0 E) E is the edge of every two nodes i and jCalculating a minimum single point shipping cost and/or a split shipping cost for shipping all commercial companies;
step 2: calculating pairing priority of business companies, and sequentially fusing the two associated paths on the premise of meeting the split loading rationality constraint to complete the construction of a scheduling scheme;
the pairing priority calculation formula of the business company is as follows:
dist max d is the furthest distance travelled in the marketing contract max The heaviest order in the marketing contract;all are pairing priorities;
in step 2, in the calculation process of the pairing priority:two commercial companies V i And V is equal to j Distance of travel dist between ij The shorter the pairing priority ∈>The higher, wherein dist 1i ,dist j1 Respectively, shipping point to business company V i And V j Is a real travel distance of (2);
pairing priority +. >The higher, where d i ,d j Order weights, q, for business companies i, j, respectively 1 The bottom protection tonnage is used for single-point transportation;
the higher the splice confidence, the pairing priority +.>Higher, therein->The value range of the spelling carrying confidence is 0-1;
the splice rationality constraint preconditions are as follows:
business companies i and j do not appear on the loaded path at the same time, i.e., at least 1 point is not loaded;
the non-loaded business company i does not appear on the loaded in-path points, i.e. the non-loaded points must be directly connected to the cigarette factory on the planned path;
the weight of the order of the commercial company i which is not loaded is not greater than the residual capacity of the vehicles of the to-be-fused path;
the fusion of the unassigned business company i does not lead to delays in arrival times of other business companies;
the number of newly loaded path points must not exceed the maximum loadable point;
the total logistics weight of the newly loaded paths must not exceed the maximum passable vehicle type of the corresponding commercial company; the newly loaded path must meet connectivity constraints;
step 3: repeating the step 2 until all commercial companies are configured to obtain a feasible scheduling scheme,
step 4: the double-layer local search strategy is adopted to optimize the initial scheme, so that the transportation cost of the carrier is reduced as much as possible while the expenditure of an industrial enterprise is not increased;
Step 5: based on a commercial customer collage scheme, an LKH algorithm is adopted to optimize the access sequence, and the total driving range of a carrier is reduced on the premise of meeting a time window.
2. The intelligent dispatching method for industrial cigarette transportation according to claim 1, wherein in step 1, the specific steps of calculating the minimum single-point transportation cost and/or split transportation cost for transporting all commercial companies are as follows:
the following decision variables are defined:
wherein i epsilon V and J epsilon J k J epsilon M, K epsilon K, K= {1,2, …, M } is a vehicle set, and the method for constructing the cigarette transportation scheduling is as follows:
Minimize:
s.t.
wherein, the transportation expense consists of two types of pricing expense of single-point transportation and split transportation together:
cost of single point transportation:cost of collage transportation: />
Equation (1) ensures that each customer is accessible to only one vehicle;
equation (2) specifies that the number of each type of vehicle from the shipment point does not exceed the number of that type;
formula (3) ensures that the total number of supplier materials per vehicle service does not exceed the maximum number of carriers;
equation (4) ensures that the total amount of provider material weight per vehicle service does not exceed the maximum load weight;
formula (5) is a connectivity constraint between business companies;
formula (6) is a passability constraint between the vehicle model and the commercial company;
Equation (7) removes constraints for the sub-path;
expression (8) states that vehicles all originate and terminate at a delivery point, and each vehicle is returned to the delivery point;
equation (9) is a time expression of the arrival of the vehicle at each customer;
formula (10) is a business corporate time window constraint;
formulas (11) and (12) are model boolean decision variables;
by the above method, the aim is to minimize the cost of transporting all commercial customers, i.e. to ensure that the sum of the two transportation costs of single-point transportation and split transportation is minimized, wherein the bottom tonnage of single-point transportation is q1, and the bottom tonnage of split transportation is q2. due to q2<q1., d, is a fragmented order for a business company, with model constraints satisfied j <q2, carrying out split transportation to maximally reduce the transportation cost of industrial cigarettes;
wherein d j Commercial publicTotal number of sauce materials, w j For the weight of commercial company material, D k Maximum number of load-bearing vehicles, W k Vehicle model k maximum load weight, N k The maximum available number of vehicle models k,vehicle model k can access commercial company connectivity parameters, dist 1j Real travel distance of delivery point to different business companies, dist i,j True distance travelled between different business companies, c j Shipping unit price from shipping point to different business company, a ij Connectivity parameters between business companies, u j Commercial company j discharge time, [ a ] j ,b j ]Business company j has a latest hit time window, each business company has a limit on the latest arrival time, and thus each business company has its corresponding time window [ a ] j ,b j ]Time window upper bound a j Defining an earliest start time of the vehicle service business company j; lower boundary b j The latest end time of the vehicle service business company j is defined.
3. The intelligent scheduling method for industrial cigarette transportation according to claim 2, wherein the inter-business connectivity a is introduced in consideration of carpooling restriction ij Metric of connectivity between business companies, a ij Which is a boolean variable that allows connectivity to be set to 1, otherwise to 0, a of the inter-business connectivity ij It is meant whether business company arrival warehouses within an area are connected above route collage, there are two scenarios for business inter-company connectivity decisions: case one is commercial company V i And V is equal to j The historical behavior of the carrier, which appears in the same waybill data, accepts the collage of the two, and can be regarded as the communication of the business company; case two, commercial company V i And V is equal to j Not appearing on the same waybill, such as setting up a new business company or not appearing on the same pool of contracts, based on the classical shipping algorithm and sweep algorithm, by considering the furthest distance and the greatest angle accepted based on the carrier history as connected thresholds, specifically as follows:
1) Each manifest in the historical manifest data represents a path, i.e., a carrier haul route-to-business company collage sequence: route= {1, v i ,V j ,....,1}，V i And V is equal to j The geographical distance between them is dist ij Azimuth angle (Azimuth angle) of azim ij ；
2) If V i With commercial company V j Contract set of same history scheduling waybill with ever presentIn which a can be set, i.e. depending on the communication between the business companies ij ＝1；
3) Based on historical waybill connectivity, statistics is made for each business company V i Is the furthest communicating distance of (2)And the maximum communication azimuth angle thereof
4) If commercial company V j Not previously with V i In the same waybill but with a geographic distance less than V i Historical farthest distance of communicationAnd the azimuth angle is smaller than the historical maximum connection azimuth angle +>Judging that the connectivity of the two is 1;
5) And circulating the steps until the connectivity of each business company is judged to be finished, and forming a connectivity basic data matrix.
4. The intelligent dispatching method for industrial cigarette transportation according to claim 1, wherein the industrial cigarette transportation is carried out by commercial company V i Will be associated with commercial company V with a high probability j Collage, i.e. there is collageAssociation rulesThe split loading confidenceThe calculation steps are as follows:
the marketing contracts that the carrier needs to transport daily are: contact= { V 1 ,V 2 ,…,V i ,…,V n -a }; each waybill record in the historical waybill is the collage sequence of the business company: route= {1, v i ,V j ,...1 }, the collage sequence route is a non-empty subsequence of its marketing contract;
calculating the spelling confidence of the 2-item set association rule in each spelling sequence one by one: spelling rule Confidence of +.>Defined as including business companies in a historical collage sequence<V i ,V j >The number of (2) and only include<V i >A ratio of the number of (3); note that excluding only commercial company V from historical collage schemes i Date marketing contract of (V) avoiding j Contract absence affecting commercial company V i Calculating the split loading confidence coefficient of (2);
the steps are circulated to calculate the 2-item set of each collage sequence route, and the symmetry of the rule is noted, so that the user can easily knowThe split-loading confidence of (2) is equal to rule +.>Can save the calculated amount of the split carrying confidence,
association rulesThe value range of the splicing confidence coefficient of the (B) is a number between 0 and 1.
5. The intelligent scheduling method for industrial cigarette transportation according to claim 4, wherein the step 4 is specifically as follows:
the loop performs the following search strategy on routes with single points in the feasible solution and lower than the bottom-guard tonnage:
first layer local search: based on the mileage saving sequence, inserting the line into other optimal mileage saving lines meeting the constraint, and if successful, jumping out of the local search of the layer; if not, continuing the local search of the second layer;
Second layer local search: based on the mileage saving sorting, business company warehouses of other spliced lines are inserted into the un-spliced line at the time, and various constraints of the original line cannot be destroyed;
and each sequence is a feasible solution.
6. The intelligent dispatching method for industrial cigarette transportation according to claim 1, wherein the LKH algorithm comprises the following specific procedures:
1) Aiming at the acquired client collage scheme, acquiring an access path of the client collage scheme;
2) Finding out that all edge sets in the access path are blank edge sets S to be canceled, and all edge sets outside the access path are blank edge sets to be supplemented;
3) Let i=1, randomly select one edge from the blank edge set S to be cancelled and the blank edge set E to be supplemented;
4) And i is continuously increased, new blank edges to be cancelled and new blank edges to be supplemented which meet access path constraint are continuously and randomly selected to enter a blank edge set S to be cancelled and a blank edge set E to be supplemented respectively until access paths which are lower in driving mileage and meet time window constraint can be obtained, and 3) until no unselected edges exist.
7. The system for applying the intelligent dispatching method for industrial cigarette transportation according to claim 1 is characterized by comprising a transportation dispatching unit, a site dispatching unit, a basic data management unit, an operation capacity resource management unit, an Internet of things application unit and a report display unit, wherein the transportation dispatching unit acquires data of the data management unit and the operation capacity resource management unit, invokes an intelligent transportation dispatching algorithm, automatically generates a plurality of intelligent allocation schemes in a one-key dispatching mode according to a contract pushed by marketing batches, each scheme comprises the contract number, the freight list number, the total transportation cost and the transportation mileage, sends the generated allocation schemes to the site dispatching unit, facilitates a dispatcher to select a proper allocation scheme, simultaneously sends the allocation scheme to a carrier through the Internet of things application unit, and carries out vehicle dispatching confirmation or abnormal condition feedback by the carrier dispatcher, the vehicle type requires to arrange vehicles and drivers, and automatically sends message reminding to the driver, and after the carrier carries out vehicle dispatching confirmation, the system automatically generates a complete transportation allocation list, enters the intelligent reservation queuing system to carry out transportation and delivery.
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