CN113393111A - Cross-border transportation bilateral connection vehicle scheduling method based on variable neighborhood tabu search algorithm - Google Patents
Cross-border transportation bilateral connection vehicle scheduling method based on variable neighborhood tabu search algorithm Download PDFInfo
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Abstract
The invention discloses a cross-border transportation bilateral connection vehicle scheduling method based on a variable neighborhood tabu search algorithm, which comprises the following steps of: 1. constructing a cross-border transportation bilateral transfer vehicle scheduling model containing transportation link sequence constraint and vehicle partition constraint; 2. generating a cross-border transportation bilateral transfer vehicle scheduling scheme by adopting a greedy algorithm according to the model established in the step 1; 3. and (3) taking the scheduling scheme generated in the step (2) as an initial feasible solution, and outputting the optimized cross-border transportation bilateral transfer vehicle scheduling scheme by adopting a variable neighborhood tabu search algorithm. The method considers the actual conditions of transportation link sequence constraint, vehicle partition constraint and the like in the cross-border transportation bilateral transfer vehicle scheduling process, improves the basic calculation method, and is beneficial to logistics enterprises to reasonably schedule bilateral transfer vehicles in the cross-border transportation process.
Description
Technical Field
The invention belongs to the field of distribution vehicle scheduling research, and particularly relates to a cross-border transportation bilateral connection vehicle scheduling method based on a variable neighborhood tabu search algorithm.
Background
With the continuous promotion and development of economic globalization, international trade is developed vigorously, and the demand of international logistics transportation is vigorous. The method not only provides a new development opportunity for logistics enterprises serving cross-border transportation, but also makes the logistics enterprises face wider market challenges. In order to meet the dual requirements of a shipper on cargo transportation timeliness and transportation safety, logistics enterprises adopt a road transportation mode in many cross-border transportation. How to realize cost reduction and efficiency improvement of road cross-border transportation becomes a problem to be solved urgently by numerous logistics enterprises.
Because the door-to-door direct transportation mode is restricted by the direct vehicle license, the road conditions of different countries, inconsistent traffic regulations and the like, the logistics company mostly adopts a transportation organization mode of yard connection between border lines of two countries. A complete transfer transportation task can be split into two transportation links, an export transportation link where the delivery country vehicle delivers the goods from the delivery point to the transfer yard, and an import transportation link where the receiving country vehicle delivers the goods from the transfer yard to the receiving point.
In order to better develop a cross-border connection transportation mode and facilitate the coordination of bilateral vehicle dispatching, many logistics companies in China have already built subsidiary companies and own fleets in many areas at home and abroad. In the actual transportation process, logistics companies usually further specify that vehicles equipped in each area provide services for customer points in the area, and do not perform cross-area vehicle dispatching. Compared with the general vehicle scheduling problem, the complexity of the regional bilateral connection vehicle scheduling process is greatly increased, and a logistics company is difficult to make a reasonable bilateral connection vehicle scheduling scheme for the problem.
At present, although scholars at home and abroad form a relatively mature theoretical system in the aspect of vehicle scheduling, research on simultaneously scheduling locally and abroad bilateral connection vehicles is relatively deficient, and a new method is necessary to be created to meet the vehicle scheduling requirement of road cross-border transportation.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the problem that the scheduling decision of the conventional cross-border transportation bilateral transfer vehicle is unreasonable, the invention provides a cross-border transportation bilateral transfer vehicle scheduling method based on a variable neighborhood tabu search algorithm.
The technical scheme is as follows: a cross-border transportation bilateral connection vehicle scheduling method based on a variable neighborhood tabu search algorithm comprises the following steps:
(1) constructing a cross-border transportation bilateral transfer vehicle scheduling model containing transportation link sequence constraint and vehicle partition constraint;
(2) generating an initial cross-border transportation bilateral transfer vehicle scheduling scheme by adopting a greedy algorithm according to the model established in the step (1);
(3) and (3) taking the scheduling scheme generated in the step (2) as an initial feasible solution, and outputting the optimized cross-border transportation bilateral barge vehicle scheduling scheme by adopting a variable neighborhood tabu search algorithm.
The step (1) specifically comprises the following steps:
(1.1) according to the actual operation condition, constructing a model constraint condition comprising transportation link sequence constraint; the transportation link sequence constraint means that each transportation task is divided into an export transportation link and an import transportation link which are in one-to-one correspondence, and each import transportation link from a transfer yard to a receiving point can be started only after the corresponding export transportation link from a delivery point to a transfer yard is completed; the mathematical model is expressed as follows:
wherein u is the total number of transportation tasks; (r-u) and r are transport link numbers which respectively represent an export transport link and an import transport link of a certain transport task; v is a vehicle number;
the decision variables are defined as follows:
(1.2) according to the actual operation condition, constructing a model constraint condition comprising vehicle partition constraint; vehicle zone restriction refers to a vehicle performing a transportation link being allowed to travel within an area; the vehicle constraint is divided into two types of mathematical models for executing an export transportation link and an import transportation link, and the two types of mathematical models are expressed as follows:
wherein v is a vehicle number; m is an area number; s is the number of equipped vehicles of each area, and the number of the equipped vehicles of each area is the same; i. j is a node number, and the node number 0 represents a transfer yard; k is the total number of nodes covered by each area, the total number of the nodes in each area is the same, and the total number of the nodes in each area comprises 1 garage and (k-1) customer nodes; r is a transport link number; u is the total number of transportation tasks;
the decision variables are defined as follows:
(1.3) constructing a mathematical model by taking the minimum total transportation cost as an objective function, taking relevant parameters of a scheduling scheme x as variables, and calculating the total transportation cost f (x) by the following formula:
wherein r is a transport link number; u is the total number of transportation tasks; a is a unit time penalty coefficient of the vehicle arriving earlier than the lower limit of the time window required by the client; erA lower time window limit required for the client; t is trTo schedule the completion time of the transport link r in scheme x,
tr-uis the finish time of an import transportation link (r-u) corresponding to the transportation link r in the scheduling scheme x; t'rThe time of the vehicle for executing the transportation link r in the scheduling scheme x reaching the starting point of the transportation link; i. j is a node number; n is the total number of the regions; k isThe number of nodes in each area is the same; t is tijTime taken for the vehicle to travel from location node i to location node j; t is thThe time spent for a single container exchange operation is constant; b is a unit time punishment coefficient of the vehicle arriving later than the upper limit of the time window required by the client; l isrAn upper time window limit required for the client; c. C1The single fixed use cost of a unit vehicle; s is the number of equipped vehicles of each area, and the number of the equipped vehicles of each area is the same; c. C2The unit vehicle travel cost per unit time;
the decision variables are defined as follows:
The vehicle dispatching scheme in the step (2) specifically comprises the following steps:
(2.1) representing a solution of the cross-border transportation bilateral transfer vehicle scheduling problem by adopting two lines of vectors; wherein the first line vector represents an execution order of the transport links; the second row vector corresponds to the first row vector and represents a vehicle executing the transportation link;
(2.2) incorporating export transportation links of all transportation tasks into an optional transportation link set, wherein the optional transportation link set represents a transportation link set which can be served currently; after a transportation link is selected from the optional transportation link set according to a transportation link selection strategy, removing the link from the optional transportation link set; if the removed link is an export transportation link, adding an import transportation link corresponding to the export transportation link into the optional transportation link set;
(2.2.1) selecting a transportation link selection strategy refers to selecting a transportation link with the earliest nominal delivery time; the nominal delivery time of the export transportation link is the delivery time of the transportation task; the nominal delivery time of the import transportation link is the actual completion time of the corresponding export transportation link;
(2.3) selecting a transport vehicle from vehicles in an area to which a customer point of transport link service belongs according to a vehicle selection strategy;
(2.3.1) the vehicle selection strategy refers to selecting the vehicle which can reach the starting point of the transportation link most quickly.
The neighborhood taboo search algorithm in the step (3) specifically comprises the following steps:
(3.1) emptying a tabu table, wherein the tabu table is used for recording neighborhood transformation types and vector neighborhood transformation positions, and information in the tabu table is not searched or selectively searched in neighborhood transformation, so that global optimization is facilitated; initializing a tabu length and a maximum iteration number; initializing the iteration number NC to be 1; taking the vehicle scheduling scheme generated in the step (2) as an initial feasible solution xinitialInitial feasible solution xinitialThe model constraint is satisfied and the initial basis of the neighborhood transformation is adopted, so that a good initial feasible solution is beneficial to obtaining a better optimization result; let xinitialFor the current solution xnowAnd an optimal solution xbestI.e. xnow＝xinitial，xbest＝xinitial；
(3.2) construction of transportation Link exchange neighborhood transformation N1Transport link insertion neighborhood transformation N2And vehicle variance neighborhood transform N33 neighborhood structures to facilitate expanding the search range of the solution; transport link exchange neighborhood transformation N1Expressing the positions of two transport links in the first row of transport link vectors with the solution exchanged, exchanging vehicles at corresponding positions in the second row of vehicle vectors, specifically, randomly selecting one transport link i in the first row of transport link vectors, and determining a transport link set S capable of being exchanged with the transport link i according to the transport link sequence constraint in the step (1.1)1When S is1Not being empty, at S1Randomly selecting a transportation link j to exchange with a transportation link i, or randomly selecting a transportation link i' again to carry out transportation link exchange neighborhood transformation N1(ii) a Transport link insertion neighborhood transformation N2Indicates that the first row is to beBefore the position of a certain transport link in the transport link vector is inserted into the position of another transport link, vehicles at the corresponding position in the second vehicle vector are also inserted, the specific operation is to randomly select a transport link i in the first transport link vector, and a position set S into which the transport link i can be inserted is determined according to the transport link sequence constraint in the step (1.1)2When S is2Not being empty, at S2Randomly selecting a position j to insert into the current transportation link, otherwise, randomly selecting a transportation link i' again to perform transportation link insertion neighborhood transformation N2(ii) a Vehicle variant neighborhood transformation N3Representing that under the condition that the first row of transportation link vectors are kept unchanged, a certain vehicle in the second row of transportation link vectors is changed into another vehicle, specifically, one transportation vehicle i is randomly selected from the second row of transportation link vectors, and according to the vehicle partition constraint in the step (1.2), a vehicle set S with the changeable transportation vehicle i is determined3When S is3Not being empty, at S3Randomly selecting one vehicle j for variation, or randomly selecting one transport vehicle i' again for vehicle variation neighborhood transformation N3；
The variable neighborhood search rule of the step (3.3) specifically comprises;
(3.3.1) solving for the current solution xnowPerforming transport link exchange neighborhood transformation N1From N1(xnow) Selecting an optimal solution x of evaluation function values of neighborhood transformation without tabu table recording1(ii) a The evaluation function value f (x) of the solution is compared1) And f (x)now) If f (x) is satisfied1)＜f(xnow) Then let xnow＝x1And then the step (3.3.3) is carried out, otherwise, the step (3.3.2) is carried out;
(3.3.2) solving for the current solution xnowCarry out transportation link insertion neighborhood transformation N2From N2(xnow) Selecting an optimal solution x of evaluation function values of neighborhood transformation without tabu table recording2The evaluation function values f (x) of the solution are compared2) And f (x)now) If f (x) is satisfied2)＜f(xnow) Then let xnow＝x2And then the step (3.3.3) is carried out, otherwise, the step (3.3.1) is carried out;
(3.3.3) solving for the current solution xnowPerforming vehicle variant neighborhood transformation N3From N3(xnow) Selecting an optimal solution x of evaluation function values of neighborhood transformation without tabu table recording3The evaluation function values f (x) of the solution are compared3) And f (x)now) If f (x) is satisfied3)＜f(xnow) Then let xnow＝x3And go to step (3.4); otherwise, the step (3.3.1) is carried out;
(3.4) recording the neighborhood transformation operation performed in the step (3.2) into a tabu table; when the tabu table reaches the tabu length, releasing the neighborhood transformation operation which enters the tabu table at the earliest time once per iteration;
(3.5) selecting the total transportation cost f (x) in the step (1.3) as a solution evaluation function, and comparing the current solution xnowAnd the optimal solution xbestIf the evaluation value satisfies the evaluation function f (x)now)＜f(xbest) Then let xbest＝xnow；
(3.6) judging whether the maximum iteration number is reached, if not, judging that the iteration number NC is NC +1, and going to the step (3.3); otherwise, outputting the optimal value xbest。
Has the advantages that: compared with the prior art, the technical scheme disclosed by the invention has the following beneficial effects:
1. transportation sequence constraint and vehicle partition constraint are considered, and the method is closer to the actual situation under the cross-border transportation scene;
2. by applying the variable neighborhood tabu search algorithm instead of the traditional calculation method, the problems that the algorithm is low in solving speed, poor in calculation result and difficult to calculate when the data dimension is large can be effectively solved.
Drawings
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a flow diagram of a greedy algorithm for generating an initial solution;
fig. 3 is a flow chart of a varied neighborhood tabu search algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are further described below with reference to the accompanying drawings.
The invention discloses a cross-border transportation bilateral connection vehicle scheduling method based on a variable neighborhood tabu search algorithm, and the flow is shown in figure 1. In this embodiment, Shenzhen is taken as an example of a certain logistics company in Shenzhen to explain the method in detail.
The business scope of a logistics company in Shenzhen city comprises transshipment of containers across middle roads. The company builds branch companies and own fleets in Vietnam, and provides third-party cross-border logistics service for a plurality of famous electronic products and shoe and clothes enterprises by adopting a transfer transportation mode for a long time. In this embodiment, the selected research objects are 30 transportation tasks shown in table 1 of the company, the regional information is shown in table 2, and a cross-border transportation bilateral connection vehicle scheduling method based on a variable neighborhood search algorithm is adopted to generate a vehicle scheduling scheme, which includes the following main steps:
table 1 details of transportation tasks
TABLE 2 regional information
(1) Constructing a cross-border transportation bilateral transfer vehicle scheduling model containing transportation sequence constraint and vehicle partition constraint; the method specifically comprises the following steps:
(1.1) setting the total number u of the transportation tasks to be 30 according to the actual operation condition; the total number n of the regions is 5; the equipped vehicle number s of each zone is 2; the number k of nodes per area is 5; the penalty coefficient per unit time a of the vehicle arriving earlier than the lower limit of the time window required by the client is 30 yuanTime/day; the penalty coefficient b per unit time of the vehicle arriving later than the upper limit of the time window required by the client is 120 yuan/hour; time t of single container exchange operationh0.5 hour; vehicle starting cost c180 yuan/vehicle; unit transportation cost c25 yuan/km;
(1.2) according to the actual operation condition, constructing a model constraint condition comprising transportation link sequence constraint; the transportation link sequence constraint means that each transportation task is divided into an export transportation link and an import transportation link which are in one-to-one correspondence, and each import transportation link from a transfer yard to a receiving point can be started only after the corresponding export transportation link from a delivery point to a transfer yard is completed; the mathematical model is expressed as follows:
wherein u is the total number of transportation tasks; (r-u) and r are transport link numbers which respectively represent an export transport link and an import transport link of a certain transport task; v is the vehicle number.
The decision variables are defined as follows:
(1.3) according to the actual operation condition, constructing a model constraint condition comprising vehicle partition constraint; vehicle zone restriction refers to a vehicle performing a transportation link being allowed to travel within an area; the vehicle constraint is divided into two types of mathematical models for executing an export transportation link and an import transportation link, and the two types of mathematical models are expressed as follows:
wherein v is a vehicle number; m is an area number; s is the number of equipped vehicles of each area, and the number of the equipped vehicles of each area is the same; i. j is a node number, and the node number 0 represents a transfer yard; k is the total number of nodes covered by each area, the total number of the nodes in each area is the same, and the total number of the nodes in each area comprises 1 garage and (k-1) customer nodes; r is a transport link number; u is the total number of shipping tasks.
The decision variables are defined as follows:
(1.4) constructing a mathematical model by taking the minimum total transportation cost as an objective function, taking relevant parameters of a scheduling scheme x as variables, and calculating the total transportation cost f (x) by the following formula:
wherein r is a transport link number; u is the total number of transportation tasks; a is a unit time penalty coefficient of the vehicle arriving earlier than the lower limit of the time window required by the client; erA lower time window limit required for the client; t is trTo schedule the completion time of the transport link r in scheme x,
tr-uis the finish time of an import transportation link (r-u) corresponding to the transportation link r in the scheduling scheme x; t'rThe time of the vehicle for executing the transportation link r in the scheduling scheme x reaching the starting point of the transportation link; i. j is a node number; n is the total number of the regions; k is the number of nodes of each region, and the number of the nodes of each region is the same; t is tijTime taken for the vehicle to travel from location node i to location node j; t is thThe time spent for a single container exchange operation is constant; b is a unit time punishment coefficient of the vehicle arriving later than the upper limit of the time window required by the client; l isrAn upper time window limit required for the client; c. C1The single fixed use cost of a unit vehicle; s is the number of equipped vehicles of each area, and the number of the equipped vehicles of each area is the same; c. C2The unit vehicle travel cost per unit time;
the decision variables are defined as follows:
(2) Generating a vehicle dispatching scheme by adopting a greedy algorithm according to the model established in the step (1); the method specifically comprises the following steps:
(2.1) representing a solution of the cross-border transportation bilateral transfer vehicle scheduling problem by adopting two lines of vectors; wherein the first line vector represents an execution order of the transport links; the second row vector corresponds to the first row vector and represents a vehicle executing the transportation link;
(2.2) incorporating export transportation links of all transportation tasks into an optional transportation link set, wherein the optional transportation link set represents a transportation link set which can be served currently; after a transportation link is selected from the optional transportation link set according to a transportation link selection strategy, removing the link from the optional transportation link set; if the removed link is an export transportation link, adding an import transportation link corresponding to the export transportation link into the optional transportation link set;
(2.2.1) selecting a transportation link selection strategy refers to selecting a transportation link with the earliest nominal delivery time; the nominal delivery time of the export transportation link is the delivery time of the transportation task; the nominal delivery time of the import transportation link is the actual completion time of the corresponding export transportation link;
(2.3) selecting a transport vehicle from vehicles in an area to which a customer point of transport link service belongs according to a vehicle selection strategy;
(2.3.1) the vehicle selection strategy refers to selecting the vehicle which can reach the starting point of the transportation link most quickly.
The vehicle dispatching scheme generated in step (2) is shown in table 3:
TABLE 3 greedy Algorithm Generation scheduling scheme
(3) Taking the scheduling scheme generated in the step (2) as an initial feasible solution, and outputting an optimized bilateral barge vehicle scheduling scheme by adopting a variable neighborhood tabu search algorithm; the method specifically comprises the following steps:
(3.1) emptying a tabu table, wherein the tabu table is used for recording neighborhood transformation types and vector neighborhood transformation positions, and information in the tabu table is not searched or selectively searched in neighborhood transformation, so that global optimization is facilitated; setting the taboo length to be 50 and the maximum iteration number to be 1000; setting the iteration number NC as 1; taking the vehicle scheduling scheme generated in the step (2) as an initial feasible solution xinitialInitial feasible solution xinitialThe model constraint is satisfied and the initial basis of the neighborhood transformation is adopted, so that a good initial feasible solution is beneficial to obtaining a better optimization result; let xinitialFor the current solution xnowAnd an optimal solution xbestI.e. xnow＝xinitial，xbest＝xinitial；
(3.2) construction of transportation Link exchange neighborhood transformation N1Transport link insertion neighborhood transformation N2And vehicle variance neighborhood transform N33 neighborhood structures to facilitate expanding the search range of the solution; transport link exchange neighborhood transformation N1Expressing the positions of two transport links in the first row of transport link vectors with the solution exchanged, exchanging vehicles at corresponding positions in the second row of vehicle vectors, specifically, randomly selecting one transport link i in the first row of transport link vectors, and determining a transport link set S capable of being exchanged with the transport link i according to the transport link sequence constraint in the step (1.1)1When S is1Not being empty, at S1Randomly selecting a transportation link j to exchange with a transportation link i, or randomly selecting a transportation link i' again to carry out transportation link exchange neighborhood transformation N1(ii) a Transport link insertion neighborhood transformation N2Indicating that before the position of a certain transport link in the first row of transport link vectors is inserted into the position of another transport link, the vehicles at the corresponding position in the second row of vehicle vectors are also inserted, and the specific operation is to randomly select a transport link i in the first row of transport link vectors according to the conditionsStep (1.1) transportation link sequence constraint, determining a position set S into which a transportation link i can be inserted2When S is2Not being empty, at S2Randomly selecting a position j to insert into the current transportation link, otherwise, randomly selecting a transportation link i' again to perform transportation link insertion neighborhood transformation N2(ii) a Vehicle variant neighborhood transformation N3Representing that under the condition that the first row of transportation link vectors are kept unchanged, a certain vehicle in the second row of transportation link vectors is changed into another vehicle, specifically, one transportation vehicle i is randomly selected from the second row of transportation link vectors, and according to the vehicle partition constraint in the step (1.2), a vehicle set S with the changeable transportation vehicle i is determined3When S is3Not being empty, at S3Randomly selecting one vehicle j for variation, or randomly selecting one transport vehicle i' again for vehicle variation neighborhood transformation N3；
Step (3.3) variable neighborhood searching; the method specifically comprises the following steps:
(3.3.1) solving for the current solution xnowPerforming transport link exchange neighborhood transformation N1From N1(xnow) Selecting an optimal solution x of evaluation function values of neighborhood transformation without tabu table recording1(ii) a The evaluation function value f (x) of the solution is compared1) And f (x)now) If f (x) is satisfied1)＜f(xnow) Then let xnow＝x1And then the step (3.3.3) is carried out, otherwise, the step (3.3.2) is carried out;
(3.3.2) solving for the current solution xnowCarry out transportation link insertion neighborhood transformation N2From N2(xnow) Selecting an optimal solution x of evaluation function values of neighborhood transformation without tabu table recording2The evaluation function values f (x) of the solution are compared2) And f (x)now) If f (x) is satisfied2)＜f(xnow) Then let xnow＝x2And then the step (3.3.3) is carried out, otherwise, the step (3.3.1) is carried out;
(3.3.3) solving for the current solution xnowPerforming vehicle variant neighborhood transformation N3From N3(xnow) One of them is selected not to pass throughOptimal solution x of evaluation function values of neighborhood transformation recorded by tabu table3The evaluation function values f (x) of the solution are compared3) And f (x)now) If f (x) is satisfied3)＜f(xnow) Then let xnow＝x3And go to step (3.4); otherwise, the step (3.3.1) is carried out;
(3.4) recording the neighborhood transformation type and the vector neighborhood transformation position in the step (3.3) into a tabu table; when the tabu table reaches the tabu length, releasing the neighborhood transformation operation which enters the tabu table at the earliest time once per iteration;
(3.5) selecting the total transportation cost f (x) in the step (1.3) as a solution evaluation function, and comparing the current solution xnowAnd the optimal solution xbestIf the evaluation value satisfies the evaluation function f (x)now)＜f(xbest) Then let xbest＝xnow；
(3.6) judging whether the maximum iteration number is reached, if not, judging that the iteration number NC is NC +1, and going to the step (3.3); otherwise, outputting the optimal value xbest。
In order to compare the effects of the scheduling optimization method, the embodiments are scheduled by respectively adopting a basic tabu search algorithm and the variable neighborhood tabu search algorithm proposed herein, and the obtained operating conditions are shown in table 4:
TABLE 4 comparison of results obtained by basic tabu search algorithm and neighborhood variant tabu search algorithm
The result comparison shows that the total transportation cost of the variable neighborhood tabu search algorithm provided by the method is obviously reduced and the operation efficiency is improved on the premise that the number of the vehicles used in the two methods is the same. In general, the methods presented herein are able to efficiently solve the bilateral docked vehicle scheduling problem.
The foregoing is a predictive effect of one embodiment of the invention, which may be adapted not only to the specific embodiments described above, but also to various modifications thereof without departing from the basic inventive concept and without exceeding the scope of the invention.
Claims (7)
1. A cross-border transportation bilateral connection vehicle scheduling method based on a variable neighborhood tabu search algorithm is characterized by comprising the following steps:
(1) constructing a cross-border transportation bilateral transfer vehicle scheduling model containing transportation link sequence constraint and vehicle partition constraint;
(2) generating an initial cross-border transportation bilateral transfer vehicle scheduling scheme by adopting a greedy algorithm according to the model established in the step (1);
(3) and (3) taking the scheduling scheme generated in the step (2) as an initial feasible solution, and outputting the optimized cross-border transportation bilateral barge vehicle scheduling scheme by adopting a variable neighborhood tabu search algorithm.
2. The cross-border transportation bilateral docked vehicle scheduling method based on the variable neighborhood tabu search algorithm as claimed in claim 1, wherein the step (1) specifically comprises:
(1.1) according to the actual operation condition, constructing a model constraint condition comprising transportation link sequence constraint; the transportation link sequence constraint means that each transportation task is divided into an export transportation link and an import transportation link which are in one-to-one correspondence, and each import transportation link from a transfer yard to a receiving point can be started only after the corresponding export transportation link from a delivery point to a transfer yard is completed; the mathematical model is expressed as follows:
wherein u is the total number of transportation tasks; (r-u) and r are transport link numbers which respectively represent an export transport link and an import transport link of a certain transport task; v is a vehicle number;
the decision variables are defined as follows:
(1.2) according to the actual operation condition, constructing a model constraint condition comprising vehicle partition constraint; vehicle zone restriction refers to a vehicle performing a transportation link being allowed to travel within an area; the vehicle constraint is divided into two types of mathematical models for executing an export transportation link and an import transportation link, and the two types of mathematical models are expressed as follows:
wherein v is a vehicle number; m is an area number; s is the number of equipped vehicles of each area, and the number of the equipped vehicles of each area is the same; i. j is a node number, and the node number 0 represents a transfer yard; k is the total number of nodes covered by each area, the total number of the nodes in each area is the same, and the total number of the nodes in each area comprises 1 garage and (k-1) customer nodes; r is a transport link number; u is the total number of transportation tasks;
the decision variables are defined as follows:
(1.3) constructing a mathematical model by taking the minimum total transportation cost as an objective function, taking relevant parameters of a scheduling scheme x as variables, and calculating the total transportation cost f (x) by the following formula:
wherein r is a transport link number; u is the total number of transportation tasks; a is a unit time penalty coefficient of the vehicle arriving earlier than the lower limit of the time window required by the client; erA lower time window limit required for the client; t is trTo schedule the completion time of the transport link r in scheme x,
tr-uis the finish time of an import transportation link (r-u) corresponding to the transportation link r in the scheduling scheme x; t'rThe time of the vehicle for executing the transportation link r in the scheduling scheme x reaching the starting point of the transportation link; i. j is a node number; n is the total number of the regions; k is the number of nodes of each region, and the number of the nodes of each region is the same; t is tijTime taken for the vehicle to travel from location node i to location node j; t is thThe time spent for a single container exchange operation is constant; b is a unit time punishment coefficient of the vehicle arriving later than the upper limit of the time window required by the client; l isrAn upper time window limit required for the client; c. C1The single fixed use cost of a unit vehicle; s is the number of equipped vehicles of each area, and the number of the equipped vehicles of each area is the same; c. C2The unit vehicle travel cost per unit time;
the decision variables are defined as follows:
3. The cross-border transportation bilateral docked vehicle scheduling method based on the variant neighborhood tabu search algorithm as claimed in claim 2, wherein the step (2) specifically comprises:
(2.1) representing a solution of the cross-border transportation bilateral transfer vehicle scheduling problem by adopting two lines of vectors; wherein the first line vector represents an execution order of the transport links; the second row vector corresponds to the first row vector and represents a vehicle executing the transportation link;
(2.2) incorporating export transportation links of all transportation tasks into an optional transportation link set, wherein the optional transportation link set represents a transportation link set which can be served currently; after a transportation link is selected from the optional transportation link set according to a transportation link selection strategy, removing the link from the optional transportation link set; if the removed link is an export transportation link, adding an import transportation link corresponding to the export transportation link into the optional transportation link set;
and (2.3) selecting a transport vehicle from vehicles in the area to which the customer point of the transport link service belongs according to a vehicle selection strategy.
4. The method for scheduling a transshipment bilateral barge vehicle based on a variable neighborhood tabu search algorithm as claimed in claim 3, wherein the transportation link selection strategy in the step (2.2) is to select a transportation link with the earliest nominal shipment time, wherein the nominal shipment time is the earliest time at which the transportation link can start; the nominal delivery time of the export transportation link is the delivery time of the transportation task; the nominal delivery time of the import transportation link is the actual completion time of the corresponding export transportation link.
5. The method for scheduling a transborder transportation bilateral docked vehicle based on a variable neighborhood tabu search algorithm as claimed in claim 4, wherein the vehicle selection strategy in step (2.3) is to select a vehicle which can reach the starting point of the transportation link most quickly.
6. The method for scheduling a transshipment bilateral docked vehicle based on a variant neighborhood tabu search algorithm according to claim 2, wherein the variant neighborhood tabu search algorithm in the step (3) specifically comprises:
(3.1) emptying a tabu table, wherein the tabu table is used for recording neighborhood transformation types and vector neighborhood transformation positions, and information in the tabu table is selectively searched in neighborhood transformation, so that global optimization is realized; initializing a tabu length and a maximum iteration number; initializing the iteration number NC to be 1; taking the vehicle scheduling scheme generated in the step (2) as an initial feasible solution x of a variable neighborhood tabu search algorithminitialInitial feasible solution xinitialSatisfying the model constraints and serving as the initial basis for the neighborhood transformation; let xinitialFor the current solution xnowAnd an optimal solution xbestI.e. xnow＝xinitial，xbest＝xinitial；
(3.2) construction of transportation Link exchange neighborhood transformation N1Transport link insertion neighborhood transformation N2And vehicle variance neighborhood transform N33 neighborhood structures to extend the search range of the solution; transport link exchange neighborhood transformation N1Expressing the positions of two transport links in the first row of transport link vectors with the solution exchanged, exchanging vehicles at corresponding positions in the second row of vehicle vectors, specifically, randomly selecting one transport link i in the first row of transport link vectors, and determining a transport link set S capable of being exchanged with the transport link i according to the transport link sequence constraint in the step (1.1)1When S is1Not being empty, at S1Randomly selecting a transportation link j to exchange with a transportation link i, or randomly selecting a transportation link i' again to carry out transportation link exchange neighborhood transformation N1(ii) a Transport link insertion neighborhood transformation N2Indicating that before the position of a certain transport link in the first row of transport link vectors is inserted into the position of another transport link, vehicles at the corresponding position in the second row of vehicle vectors are also inserted, specifically, selecting a transport link i randomly in the first row of transport link vectors, and determining a position set S into which the transport link i can be inserted according to the transport link sequence constraint in the step (1.1)2When S is2Not being empty, at S2Randomly selecting a position j to insert into the current transportation link, otherwise, randomly selecting a transportation link i' again to perform transportation link insertion neighborhood transformation N2(ii) a Vehicle variant neighborhood transformation N3Representing that under the condition that the first row of transportation link vectors are kept unchanged, a certain vehicle in the second row of transportation link vectors is changed into another vehicle, specifically, one transportation vehicle i is randomly selected from the second row of transportation link vectors, and according to the vehicle partition constraint in the step (1.2), a vehicle set S with the changeable transportation vehicle i is determined3When S is3Not being empty, at S3Randomly selecting one vehicle j for variation, or randomly selecting one transport vehicle i' again for vehicle variation neighborhood transformation N3；
(3.3) searching rule in N according to variable neighborhood1、N2、N3Transforming the current solution x within the neighborhood structurenowTo obtain a new current solution xnow；
(3.4) recording the neighborhood transformation type and the vector neighborhood transformation position in the step (3.3) into a tabu table; when the tabu table reaches the tabu length, releasing the neighborhood transformation operation which enters the tabu table at the earliest time once per iteration;
(3.5) selecting the total transportation cost f (x) in the step (1.3) as a solution evaluation function, and comparing the current solution xnowAnd the optimal solution xbestIf the evaluation value satisfies the evaluation function f (x)now)＜f(xbest) Then let xbest＝xnow；
(3.6) judging whether the maximum iteration number is reached, if not, judging that the iteration number NC is NC +1, and going to the step (3.3); otherwise, outputting the optimal value xbest。
7. The cross-border transportation bilateral docked vehicle scheduling method based on variable neighborhood tabu search algorithm of claim 6, wherein the variable neighborhood search rule in step (3.3) is specifically:
(3.3.1) solving for the current solution xnowPerforming transport link exchange neighborhood transformation N1From N1(xnow) Selecting an optimal solution x of evaluation function values of neighborhood transformation without tabu table recording1(ii) a The evaluation function value f (x) of the solution is compared1) And f (x)now) If f (x) is satisfied1)＜f(xnow) Then let xnow＝x1And then the step (3.3.3) is carried out, otherwise, the step (3.3.2) is carried out;
(3.3.2) solving for the current solution xnowCarry out transportation link insertion neighborhood transformation N2From N2(xnow) Selecting an optimal solution x of evaluation function values of neighborhood transformation without tabu table recording2Is toThe evaluation function value f (x) of the solution is compared2) And f (x)now) If f (x) is satisfied2)＜f(xnow) Then let xnow＝x2And then the step (3.3.3) is carried out, otherwise, the step (3.3.1) is carried out;
(3.3.3) solving for the current solution xnowPerforming vehicle variant neighborhood transformation N3From N3(xnow) Selecting an optimal solution x of evaluation function values of neighborhood transformation without tabu table recording3The evaluation function values f (x) of the solution are compared3) And f (x)now) If f (x) is satisfied3)＜f(xnow) Then let xnow＝x3And go to step (3.4); otherwise, go to step (3.3.1).
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117455199A (en) * | 2023-12-21 | 2024-01-26 | 聊城大学 | Method for solving AGV scheduling of matrix manufacturing workshop based on variable neighborhood search algorithm |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101739655A (en) * | 2009-12-17 | 2010-06-16 | 浙江工业大学 | Method for scheduling public slow system dynamically based on rolling horizon scheduling algorithm |
| CN102880798A (en) * | 2012-09-20 | 2013-01-16 | 浪潮电子信息产业股份有限公司 | Variable neighborhood search algorithm for solving multi depot vehicle routing problem with time windows |
| CN104036333A (en) * | 2014-06-26 | 2014-09-10 | 广东工业大学 | Algorithm for solving single-depot time-varying associated logistics transportation vehicle routing problem |
| US20190079975A1 (en) * | 2017-09-11 | 2019-03-14 | Hefei University Of Technology | Scheduling method and system based on hybrid variable neighborhood search and gravitational search algorithm |
| US20190080271A1 (en) * | 2017-09-11 | 2019-03-14 | Hefei University Of Technology | Coordinated Production and Transportation Scheduling Method and System Based on Improved Tabu Search Algorithm |
| CN109800910A (en) * | 2019-01-10 | 2019-05-24 | 浙江工业大学 | A kind of vehicle routing optimization method of the meta-heuristic algorithms based on TABU search |
| CN111382947A (en) * | 2020-03-17 | 2020-07-07 | 郑州天迈科技股份有限公司 | Vehicle shift scheduling algorithm based on greedy tabu search |
| CN111882099A (en) * | 2020-05-11 | 2020-11-03 | 武汉理工大学 | Logistics distribution path planning method based on variable neighborhood parallel annealing algorithm |
-
2021
- 2021-06-09 CN CN202110642019.6A patent/CN113393111A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101739655A (en) * | 2009-12-17 | 2010-06-16 | 浙江工业大学 | Method for scheduling public slow system dynamically based on rolling horizon scheduling algorithm |
| CN102880798A (en) * | 2012-09-20 | 2013-01-16 | 浪潮电子信息产业股份有限公司 | Variable neighborhood search algorithm for solving multi depot vehicle routing problem with time windows |
| CN104036333A (en) * | 2014-06-26 | 2014-09-10 | 广东工业大学 | Algorithm for solving single-depot time-varying associated logistics transportation vehicle routing problem |
| US20190079975A1 (en) * | 2017-09-11 | 2019-03-14 | Hefei University Of Technology | Scheduling method and system based on hybrid variable neighborhood search and gravitational search algorithm |
| US20190080271A1 (en) * | 2017-09-11 | 2019-03-14 | Hefei University Of Technology | Coordinated Production and Transportation Scheduling Method and System Based on Improved Tabu Search Algorithm |
| CN109800910A (en) * | 2019-01-10 | 2019-05-24 | 浙江工业大学 | A kind of vehicle routing optimization method of the meta-heuristic algorithms based on TABU search |
| CN111382947A (en) * | 2020-03-17 | 2020-07-07 | 郑州天迈科技股份有限公司 | Vehicle shift scheduling algorithm based on greedy tabu search |
| CN111882099A (en) * | 2020-05-11 | 2020-11-03 | 武汉理工大学 | Logistics distribution path planning method based on variable neighborhood parallel annealing algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 蔡延光;汤雅连;朱君;: "混合禁忌搜索算法求解关联运输调度问题", 计算机科学, no. 04, 15 April 2015 (2015-04-15) * |
| 郝群茹;潘帅;: "基于禁忌搜索算法的物流系统车辆路径优化", 科学技术与工程, no. 34, 8 December 2019 (2019-12-08) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117455199A (en) * | 2023-12-21 | 2024-01-26 | 聊城大学 | Method for solving AGV scheduling of matrix manufacturing workshop based on variable neighborhood search algorithm |
| CN117455199B (en) * | 2023-12-21 | 2024-03-22 | 聊城大学 | Method for solving AGV scheduling of matrix manufacturing workshop based on variable neighborhood search algorithm |
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