CN110097313B - Method for acquiring a delivery vehicle path with a time window and a first-in last-out limit - Google Patents
Method for acquiring a delivery vehicle path with a time window and a first-in last-out limit Download PDFInfo
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- CN110097313B CN110097313B CN201910272354.4A CN201910272354A CN110097313B CN 110097313 B CN110097313 B CN 110097313B CN 201910272354 A CN201910272354 A CN 201910272354A CN 110097313 B CN110097313 B CN 110097313B
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- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
A method of acquiring a pick-up vehicle path with a time window and a first-in last-out limit is disclosed. In order to quickly solve the problems under a large-scale customer order, after an initial solution is obtained by using a greedy insertion algorithm, a decomposition-combination framework is introduced to decompose the initial solution into a plurality of partial solutions, then a tabu search algorithm is used for carrying out iterative optimization solution on the partial solutions in parallel, after all parallel solution processes are finished, all the partial solutions are reassembled into a complete solution, the obtained complete solution is re-put into the decomposition-combination framework for optimization, and the optimal solution is output after repeated decomposition-combination framework optimization for a plurality of times. In order to solve the above problems at high quality, the present invention uses a variety of domain operators to compute the domain of the solution in a parallel optimization process in a decomposition-combination framework. The invention can quickly solve the problem of the path of the picking and delivering vehicle with time window and first-in last-out limit under the large-scale customer order with high quality, so as to optimize the preset target.
Description
Technical Field
The invention belongs to the field of logistics scheduling, and particularly relates to a method for acquiring a delivery vehicle path with a time window and a first-in last-out limit.
Background
Under the strong pushing of economic development, logistics becomes an important component of enterprise management at present, not only because logistics occupies a higher proportion in the total cost of enterprises, but also because the influence of logistics activities on the service level of the enterprises, excellent logistics service directly influences the loyalty of customers to the enterprises, so that the logistics consumption cost is reduced, and the improvement of the logistics operation efficiency becomes one of the accepted important ways capable of effectively improving the competitiveness of the enterprises. With the rapid development of electronic commerce, the number of logistics orders is increased day by day, and when the problem of picking up and delivering goods vehicles with large-scale order numbers is faced, logistics companies need to rapidly and high-quality give out vehicle driving routes so as to carry out vehicle dispatching and reduce logistics distribution cost.
In the logistics distribution way, for the goods with large weight and volume, if the goods are just at the outermost layer of the carriage when being discharged, the extra discharging cost is not needed, the loading and unloading time in logistics distribution is reduced, and the logistics distribution experience is greatly improved. At present, the research on the first-in last-out limiting conditions of the goods in the order is few in logistics scheduling at home and abroad, and the defects of overlong solving time and the like exist.
Disclosure of Invention
In order to quickly solve the problem of large-scale pick-up vehicle paths with time windows and first-in-last-out constraints, the present invention proposes a method of acquiring pick-up vehicle paths with time windows and first-in-last-out constraints.
The object of the invention is achieved by at least one of the following technical solutions.
The method for acquiring the delivery vehicle path with the time window and the first-in and last-out limit comprises the steps of rapidly solving the delivery vehicle path problem with the time window and the first-in and last-out limit under a large-scale customer order with high quality, so that a preset target is optimized; it comprises the following steps: for a group of customer orders and a series of vehicles with the same specification, an initial solution is obtained by using a greedy insertion algorithm, and then the initial solution is put into a decomposition-combination framework for multiple optimization, and finally the optimal solution is output.
Further, each customer order includes a volume, a weight, and a category of goods; each order contains a pick-up request and a delivery request; the goods taking request corresponds to a goods delivering address for a goods taking address, and the goods taking address and the goods delivering address belong to different addresses; both the pick-up request and the delivery request of the customer order must begin to be serviced within a time window; the time window is a continuous period of time consisting of an earliest starting time and a latest starting time and points in time in between; if the vehicle arrives at the pick-up point or the delivery point earlier than the time window, waiting for the earliest starting time to arrive; for each customer order pick-up request and delivery request, the pick-up action must occur before the delivery action.
Further, the acquired vehicle driving route must meet the limitation condition of the first and last delivery of the goods, wherein the limitation condition of the first and last delivery of the goods means that the goods of the order must be at the outermost layer of the carriage of the vehicle when the vehicle is servicing the delivery request of the order; all vehicles have the same specifications and return to the yard after all customer orders are serviced according to the scheduled travel route from the same yard.
Further, the greedy insertion algorithm includes the steps of:
(1) For the first customer order, inserting it directly into the travel route of the first vehicle;
(2) For a second starting customer order, attempting to insert the order according to the vehicle sequence of the ordered order, and if the customer order can be inserted in the current vehicle driving route, processing the next order; if the order cannot be inserted in all the running routes of the vehicles with the scheduled orders, inserting the order into a running route of a new vehicle, and adding the vehicle into the vehicles with the scheduled orders;
(3) All orders are processed as described in (2) and the initial solution is returned.
Further, the initial solution must be calculated such that the time window of all customer orders is not violated and the loaded weight and volume during vehicle travel does not exceed the maximum load and volume limits of the vehicle itself.
Further, a decomposition-combination framework is introduced, which is divided into three parts, namely a decomposition part, an optimization part and a recombination part; the decomposition part uses a segmentation algorithm to segment a complete solution into a plurality of partial solutions; the optimization part uses a tabu search algorithm to optimize the local solution in parallel; the reorganization part assembles the partial solutions after optimizing the part into a new complete solution.
Further, the scan-segmentation algorithm segments a complete solution into a plurality of partial solutions, the scan-segmentation algorithm comprising the steps of:
(1) Taking a complete solution and a map as a two-dimensional plane, taking a parking lot as a circle center, randomly starting clockwise scanning from an angle of 0-360 degrees until the number of vehicles in a scanned interval reaches one fourth of the total number of vehicles in the initial solution, and returning a scanning segmentation algorithm if the total number of vehicles is less than a set value; taking the vehicles in the scanning interval and the customer orders arranged on the vehicles as a local solution;
(2) Continuing to scan clockwise from the stop in the previous step, stopping scanning as required in (1) until all vehicles are segmented into different local solutions;
(3) One or more local solutions are returned.
Further, the tabu search algorithm is executed as follows:
1) For a local solution S, calculating a cost value f (S) of the local solution;
2) Initializing a tabu table T and historic optimal path cost best_f;
3) Judging whether the termination condition is met or not, if not, stopping;
4) Constructing a neighborhood of S by using a domain function, which is represented by N (S);
5) Calculating cost values of all domain solutions in the domain N (S);
6) Finding the best tabu or non-tabu movement with the minimum path cost value;
7) Executing the optimal tabu movement if the wish condition is met, otherwise executing the optimal non-tabu movement;
8) Updating the current local solution, the cost value f of the local solution, the historical optimal local solution cost_f and a tabu table;
9) And (3) executing the step (3).
Further, the domain function comprises three domain operators, namely an ejection chain operator, a multi-order repositioning operator in a vehicle driving route and a multi-order exchanging operator among the vehicle driving routes.
Further, the cost value is composed of a weighted sum of three elements, namely the total number of vehicles, all vehicle driving distances and all vehicle waiting time, which are contained in the local solution, wherein the weight of the total number of vehicles is the largest, the weight of all vehicle driving distances is the second time, and the weight of all vehicle waiting time is the smallest; the predetermined target is that the weighted sum of the total number of vehicles and the travel distances of all vehicles is minimum, wherein the total number of vehicles is weighted much more than the travel distances of all vehicles.
Compared with the prior art, the invention has the following advantages and technical effects: the invention can quickly solve the problem of the path of the picking and delivering vehicle with time window and first-in last-out limit under the large-scale customer order. In order to be able to solve quickly, the invention uses a decomposition-combination framework to divide a complete solution into a plurality of partial solutions, and then uses a tabu search algorithm to iteratively optimize the partial solutions in parallel; in order to obtain a high-quality solution in the iterative optimization process using a tabu search algorithm, the invention uses the fields of ejection chain operators, multiple order repositioning operators in a vehicle driving route and multiple order exchanging operators among vehicle driving routes to generate the solution.
Drawings
FIG. 1 is a general flow chart of a method of acquiring a pick-up vehicle path with a time window and a first-in last-out limit.
Fig. 2 is a schematic diagram of a scan segmentation algorithm.
FIG. 3 is a schematic diagram of whether the vehicle driving route meets the first-in last-out limitation condition.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings, but the embodiments and protection of the present invention are not limited thereto.
Integral solving process
FIG. 1 is a general flow diagram of a method of acquiring a pick-up vehicle path with a time window and a first-in last-out limit. As shown in the figure, for a group of customer orders and a series of vehicles with the same specification, an initial solution is obtained by using a greedy insertion algorithm, and then the initial solution is put into a decomposition-combination framework for multiple optimization, and finally an optimal solution is output. The detailed working of the greedy insertion algorithm and the decomposition-combination framework is described below.
Greedy insert algorithm solving step
(1) For the first customer order, inserting it directly into the travel route of the first vehicle;
(2) For the second starting customer order, the insertion of the order is attempted in the order of the ordered vehicles, and if the customer order can be inserted in the current vehicle travel route, the next order is processed. If the order cannot be inserted in all the running routes of the vehicles with the scheduled orders, inserting the order into a running route of a new vehicle, and adding the vehicle into the vehicles with the scheduled orders;
(3) All orders are processed as described in (2) and the initial solution is returned.
Decomposition-composition framework workflow
The decomposition-combination framework is divided into three parts, namely a decomposition part, an optimization part and a recombination part. The decomposition part is responsible for dividing a complete solution into a plurality of partial solutions; the optimization part is responsible for parallel iterative optimization of a plurality of local solutions by using a tabu search algorithm through a parallel programming technology; the reorganization part is responsible for assembling a plurality of optimized partial solutions output by the optimization part into a complete solution. After completing a complete decomposition, optimizing, after the recombination process, putting the complete solution obtained after recombination into a decomposition-combination frame for optimizing for a plurality of times, and finally outputting an optimal solution.
Wherein the decomposition section uses a scan-segmentation algorithm to segment a complete solution into a plurality of partial solutions, fig. 2 is a schematic diagram of a complete solution having five vehicle travel routes into three partial solutions, the scan-segmentation algorithm is performed as follows:
(1) And taking the complete solution and the map as a two-dimensional plane, taking a parking lot as a circle center, randomly starting clockwise scanning from a certain angle of 0-360 degrees until the number of vehicles in a scanned interval reaches one fourth of the total number of vehicles in the initial solution, and returning a scanning segmentation algorithm if the total number of vehicles is less than 4. Taking the vehicles in the scanning interval and the customer orders arranged on the vehicles as a local solution;
(2) Continuing to scan clockwise from the stop in the previous step, stopping scanning as required in (1) until all vehicles are segmented into different local solutions;
(3) One or more local solutions are returned.
The optimization part uses a parallel programming technology to carry out iterative optimization on a plurality of local solutions by using a tabu search algorithm, wherein the tabu search algorithm is executed as follows:
1) For a local solution S, calculating a cost value f (S) of the local solution;
2) Initializing a tabu table T and historic optimal path cost best_f;
3) Judging whether the termination condition is met or not, if not, stopping;
4) Constructing a neighborhood of S by using a domain function, which is represented by N (S);
5) Calculating cost values of all domain solutions in the domain N (S);
6) Finding the best tabu or non-tabu movement with the minimum path cost value;
7) Executing the optimal tabu movement if the wish condition is met, otherwise executing the optimal non-tabu movement;
8) Updating the current local solution, the cost value f of the local solution, the historical optimal local solution cost_f and a tabu table;
9) And (3) executing the step (3).
The domain functions used by the algorithm comprise three domain operators, namely an ejection chain operator, a multi-order repositioning operator in a vehicle driving route and a multi-order exchanging operator among the vehicle driving routes, and each operator has the following working contents:
the ejection chain operator randomly selects one vehicle driving route in the local solution, randomly deletes one order in the vehicle driving route, randomly inserts the order into the other vehicle driving route, then based on the inserted vehicle driving route, and repeatedly selects the other vehicle driving route insertion step until all vehicle routes in the local solution are accessed once. If the order cannot be inserted into a certain vehicle driving route in the operation process, selecting another vehicle driving route for insertion, and if all other vehicle driving routes cannot be inserted into the order, restoring the vehicle driving route of the order, and returning the ejection chain operator.
The multiple order repositioning operator in the vehicle driving route is responsible for randomly selecting one vehicle driving route in the local solution, randomly deleting one third of orders in the vehicle driving route, and then reinserting the deleted orders into the vehicle driving route in an out-of-order manner, wherein the order insertion position is the position with the minimum driving distance which causes the whole driving route to increase after inserting the orders into a certain position in the vehicle driving route.
The multiple order exchange operator among the vehicle driving routes is responsible for randomly selecting two vehicle driving routes in the local solution and marking the two vehicle driving routes as R1 and R2, randomly deleting one-fourth of orders in the R1 and the R2, then trying to insert the orders deleted in the R1 into the R2, and inserting the orders deleted in the R2 into the R1. The order insertion position is a position where the travel distance that causes the entire travel route to increase after the order is inserted to a certain position in the travel route of the vehicle is smallest. If any one of the vehicle driving routes R1 and R2 cannot insert the order deleted by the other vehicle driving route, the R1 and R2 are restored to the original state.
All three operator working processes involve the order insertion step requiring that the order in the vehicle travel route meet the first-in-last-out limit, fig. 3 is a schematic diagram of the order first-in-last-out limit, the left travel route meeting the first-in-last-out limit and the right travel route not meeting the first-in-last-out limit.
In addition, the tabu search algorithm calculates the cost value of the local solution from the weighted sum of three elements of the total number of vehicles, all vehicle travel distances, and all vehicle waiting times contained in the local solution. Wherein the total number of vehicles is weighted the largest, the total distance travelled by all vehicles is weighted the second time, and the waiting time of all vehicles is weighted the smallest.
The reorganization part simply splices the optimized partial solutions into a complete solution, then judges whether the decomposition-combination frame can be exited, and if the exiting condition is not reached, the complete solution after reorganization is put into a new decomposition-combination frame again for continuous optimization. And if the exit condition is met, exiting the decomposition-combination framework, outputting the optimal solution, and ending the whole solving process of the path problem of the delivery vehicle with the time window and the first-in and last-out limiting conditions. The exit condition of the decomposition-combination framework in the present invention is whether the number of times of running the decomposition-combination framework reaches 3.
Claims (6)
1. The method for acquiring the delivery vehicle path with the time window and the first-in and last-out limit comprises the steps of rapidly solving the delivery vehicle path problem with the time window and the first-in and last-out limit under a large-scale customer order with high quality, so that a preset target is optimized; the method is characterized by comprising the following steps: for a group of customer orders and a series of vehicles with the same specification, firstly, obtaining an initial solution by using a greedy insertion algorithm, then, putting the initial solution into a decomposition-combination framework for multiple optimization, and finally, outputting an optimal solution;
the decomposition-combination framework is divided into three parts, namely a decomposition part, an optimization part and a recombination part; the decomposition part uses a segmentation algorithm to segment a complete solution into a plurality of partial solutions; the optimization part uses a tabu search algorithm to optimize the local solution in parallel; the recombination part assembles the partial solutions after optimizing the part into a new complete solution; the reorganization part simply splices the optimized partial solutions into a complete solution, then judges whether the decomposition-combination frame can be exited, and if the exiting condition is not reached, the reorganized complete solution is put into a new decomposition-combination frame again for continuous optimization;
the greedy insertion algorithm includes the steps of:
(1) For the first customer order, inserting it directly into the travel route of the first vehicle;
(2) For a second starting customer order, attempting to insert the order according to the vehicle sequence of the ordered order, and if the customer order can be inserted in the current vehicle driving route, processing the next order; if the order cannot be inserted in all the running routes of the vehicles with the scheduled orders, inserting the order into a running route of a new vehicle, and adding the vehicle into the vehicles with the scheduled orders;
(3) Returning to the initial solution after processing all orders according to the method described in (2);
the segmentation algorithm segments a complete solution into a plurality of partial solutions, the segmentation algorithm comprising the steps of:
(1) Taking a complete solution and a map as a two-dimensional plane, taking a parking lot as a circle center, randomly starting clockwise scanning from an angle of 0-360 degrees until the number of vehicles in a scanned interval reaches one fourth of the total number of vehicles in the initial solution, and returning a scanning segmentation algorithm if the total number of vehicles is less than a set value; taking the vehicles in the scanning interval and the customer orders arranged on the vehicles as a local solution;
(2) Continuing to scan clockwise from the stop in the previous step, stopping scanning as required in (1) until all vehicles are segmented into different local solutions;
(3) Returning one or more local solutions;
the tabu search algorithm is executed as follows:
1) For a local solution S, calculating a cost value f (S) of the local solution;
2) Initializing a tabu table T and historic optimal path cost best_f;
3) Judging whether the termination condition is met or not, if not, stopping;
4) Constructing a neighborhood of S by using a neighborhood function, which is represented by N (S);
5) Calculating cost values of all neighborhood solutions in the neighborhood N (S);
6) Finding the best tabu or non-tabu movement with the minimum path cost value;
7) Executing the optimal tabu movement if the wish condition is met, otherwise executing the optimal non-tabu movement;
8) Updating the current local solution, the cost value f of the local solution, the historical optimal local solution cost_f and a tabu table;
9) Executing the step (3);
the neighborhood function comprises three neighborhood operators, namely an ejection chain operator, a multi-order repositioning operator in a vehicle driving route and a multi-order exchanging operator among the vehicle driving routes.
2. The method of acquiring a pick-up vehicle path with a time window and a first-in-last-out limit of claim 1, wherein each customer order includes a volume, a weight, and a category of goods; each order contains a pick-up request and a delivery request; the goods taking request corresponds to a goods delivering address for a goods taking address, and the goods taking address and the goods delivering address belong to different addresses; both the pick-up request and the delivery request of the customer order must begin to be serviced within a time window; the time window is a continuous period of time consisting of an earliest starting time and a latest starting time and points in time in between; if the vehicle arrives at the pick-up point or the delivery point earlier than the time window, waiting for the earliest starting time to arrive; for each customer order pick-up request and delivery request, the pick-up action must occur before the delivery action.
3. A method of retrieving a delivery vehicle path with a time window and a first-in-last-out limit as claimed in claim 1, wherein the retrieved vehicle travel route must meet the first-in-last-out limit of the goods, which means that the goods of the order must be at the outermost layer of the vehicle's compartment when the vehicle is servicing the delivery request of the order; all vehicles have the same specifications and return to the yard after all customer orders are serviced according to the scheduled travel route from the same yard.
4. The method of claim 1, wherein the greedy insertion algorithm is used to find an initial solution that must be such that the time window for all customer orders is not violated and the loaded weight and volume during vehicle travel do not exceed the maximum load and maximum volume limits of the vehicle itself.
5. The method of claim 1, wherein the multiple order repositioning operator in the vehicle travel path is responsible for randomly selecting a vehicle travel path from the partial solutions, randomly deleting three orders in the vehicle travel path, and then randomly reinserting the deleted orders into the vehicle travel path, wherein the order insertion location is the location of least travel distance that causes an increase in the overall travel path after inserting the order into one of the vehicle travel paths;
the multi-order exchange operator among the vehicle driving routes is responsible for randomly selecting two vehicle driving routes in a local solution and marking the two vehicle driving routes as R1 and R2, randomly deleting one-fourth order in the R1 and the R2, then trying to insert the order deleted in the R1 into the R2, and inserting the order deleted in the R2 into the R1; the order insertion position is a position where a travel distance that causes an increase in the entire travel route after an order is inserted into one position in the travel route of the vehicle is minimum; if any one of the vehicle driving routes R1 and R2 cannot insert the order deleted by the other vehicle driving route, the R1 and R2 are restored to the original state.
6. The method of claim 1, wherein the cost value consists of a weighted sum of three elements, a total number of vehicles, all vehicle travel distances, all vehicle waiting times, and all vehicle waiting times, contained in the local solution, wherein the total number of vehicles is the largest, all vehicle travel distances are weighted the next largest, and all vehicle waiting times are weighted the smallest; the predetermined target is that the weighted sum of the total number of vehicles and the travel distances of all vehicles is minimum, wherein the total number of vehicles is weighted much more than the travel distances of all vehicles.
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| CN116523430A (en) * | 2022-09-06 | 2023-08-01 | 西安电子科技大学广州研究院 | Routing method for pick-up and delivery vehicle meeting last-in first-out constraint |
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