Constructing initial solutions for the multiple vehicle pickup and delivery problem with time windows

Hosny, Manar; Mumford, Christine L.

doi:10.1016/j.jksuci.2011.10.006

Cited by 18 publications

(18 citation statements)

References 16 publications

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“…(2) for = 1 to sr // Generating CRV (4) generate CRV ( , PLDT, PUDT, STIL, STIU, ECPUT) randomly (5) endfor (6) for = 1 to sr // Generating OV (7) generate GR vector (ADR, Dist., RN, SR) randomly and calculate Gr using (1) (8) generate CRK vector (VIC, IC, RC, CC) randomly and assign weight according to ranks (9) calculate ST using (2) (10) calculate ERT using (3) (11) endfor (12) Partition ( , , , V ) into sub-networks as = ⋃ =1 (13) for = 1 to (14) Divide time horizon into time seeds as = 1, + 2, + 3, ⋅ ⋅ ⋅ + , (15) for each time seed , (16) dr = rand(0 − ) (17) for = 1 to dr // Generating Customer Request Vector for Dynamic Requests (18) generate CRV ( , PLDT, PUDT, STIL, STIU, ECPUT) randomly (19) endfor (20) for = 1 to dr // Generating Customer Order Vector for Dynamic Requests (21) generate GR vector (ADR, Dist., RN, SR) randomly and calculate Gr using (1) (22) generate CRK vector (VIC, IC, RC, CC) and assign weight according to ranks (23) calculate ST using (2) (24) calculate ERT using (3) (25) endfor (26) = 0 (27) Generate position ( ) and velocity ( ) for th particle in th generation from COV (28) while (| best ( ) − best ( − 1) < |) do (29) = + 1 (30) for each particle ( ( ), ( )) of the search space (31) evaluate fitness using objective function (5) best ( ) = ( ) (34) endfor (35) best ( ) = best 1 ( ) (36) for = 2 to number of particles in the swarm (37) if (Fitt[ ( best ( ), ( ))] > Fitt[ ( best ( ), ( ))]) (38) best ( ) = best ( ) (39) endfor (40) endwhile (41) store best ( ) for th time seed (42) endfor (43) store the set of best ( ) for th partition (44) endfor Algorithm 1: TS-PSO.…”

Section: Results Analysismentioning

confidence: 99%

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Multiobjective Dynamic Vehicle Routing Problem and Time Seed Based Solution Using Particle Swarm Optimization

et al. 2015

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A multiobjective dynamic vehicle routing problem (M-DVRP) has been identified and a time seed based solution using particle swarm optimization (TS-PSO) for M-DVRP has been proposed. M-DVRP considers five objectives, namely, geographical ranking of the request, customer ranking, service time, expected reachability time, and satisfaction level of the customers. The multiobjective function of M-DVRP has four components, namely, number of vehicles, expected reachability time, and profit and satisfaction level. Three constraints of the objective function are vehicle, capacity, and reachability. In TS-PSO, first of all, the problem is partitioned into smaller size DVRPs. Secondly, the time horizon of each smaller size DVRP is divided into time seeds and the problem is solved in each time seed using particle swarm optimization. The proposed solution has been simulated in ns-2 considering real road network of New Delhi, India, and results are compared with those obtained from genetic algorithm (GA) simulations. The comparison confirms that TS-PSO optimizes the multiobjective function of the identified problem better than what is offered by GA solution.

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Section: Results Analysismentioning

confidence: 99%

“…No central depot for the delivery vehicles is required. [37] 2 C -V R P Capacitated VRP It is a simple VRP with vehicles having prespecified and same goods carrying capacity. [38] 3 H -V R P Heterogeneous VRP It is slightly different from C-VRP.…”

Section: Vrp-pd Vrp With Pickup and Deliverymentioning

confidence: 99%

Multiobjective Dynamic Vehicle Routing Problem and Time Seed Based Solution Using Particle Swarm Optimization

et al. 2015

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“…To solve the problem by making the customer slip into the consumer, the method of sequential algorithm is used [11]. Find the initial solution that refers to the first customer selection process entering into route.…”

Section: Methodsmentioning

confidence: 99%

Information System Prediction With Weighted Moving Average (WMA) Method And Optimization Distribution Using Vehicles Routing Problem (VRP) Model for Batik Product

2018

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Distributing the products evenly to the customers is the problem faced by the producer of batik product. The purpose of this research is to optimize product distribution using demand prediction data from each store. The way of distributing products affects products brand and revenue in society. To cover this problem we propose the method to fulfil customers demand and to promote the brand by using Weighted Moving Average (WMA) to forecast product demand in each distributor store. To deliver the product for the customer, we are using the model of Vehicle Routing Problem (VRP) and Sequential Insertion Algorithm. We obtained the result that both distribution cost and travel time of distribution is successfully reduced. The results obtained that the WMA method can predict product demand by generate the small error value with MAPE the result is 0 until 17% which classified as very accurate-good. For distribution optimization using the VRP model and Sequential Insertion algorithms the result of optimal costs is IDR 36244, with a previous cost is IDR 150.000 in one month so can reduce 75%. Besides that, the travel time is 130.05 minutes with the distance 69.7 km, and routes that have been optimized.

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“…In the real world, there are many problems that are similar to VRP but do not quite show the same behavior. Many articles propose various types of VRPs, such as VRP with time windows [23][24][25][26][27][28][29][30][31][32], heterogeneous fleets, the so-called multi-depot heterogeneous vehicle routing problem with time windows [25][26][27][28][29][30][31][32], VRP with pickup and delivery, and the multiple-vehicle pickup and delivery problem [33][34][35][36].…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Differential Evolution Algorithm for a Transportation Software Application

Supattananon

Akararungruangkul

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%.

show abstract

Constructing initial solutions for the multiple vehicle pickup and delivery problem with time windows

Cited by 18 publications

References 16 publications

Multiobjective Dynamic Vehicle Routing Problem and Time Seed Based Solution Using Particle Swarm Optimization

Multiobjective Dynamic Vehicle Routing Problem and Time Seed Based Solution Using Particle Swarm Optimization

Information System Prediction With Weighted Moving Average (WMA) Method And Optimization Distribution Using Vehicles Routing Problem (VRP) Model for Batik Product

Modified Differential Evolution Algorithm for a Transportation Software Application

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