An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery

Kalaycı, Can Berk; Kaya, Can Tansel

doi:10.1016/j.eswa.2016.09.017

Cited by 134 publications

(59 citation statements)

References 41 publications

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“…Dominguez et al (2016) [7] presented a hybrid algorithm that integrated the biased randomized versions of vehicle routing and packing heuristics within a large neighborhood search metaheuristic framework to solve the two-dimensional loading vehicle routing problem with clustered backhauls (2L-VRPB). Can and Can (2016) [8] presented the combination of an ant colony optimization (ACO) and variable neighborhood search (VNS) to solve the vehicle routing problem with simultaneous pickup and delivery. This article focuses on improving the efficiency of the ACO and VNS; therefore, the new combination method has been tested with the standard bench marking problem instances, and the result of the proposed method is outstanding compared with others.…”

Section: Literature Reviewmentioning

confidence: 99%

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

2019

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This research aims to solve the problem of the raw milk collection and transportation system which can be interpreted as a special case of the vehicle routing problem. In the proposed problem, the factory will send the trucks, multiple fleets composed of several compartments, to collect the raw milk from the raw milk farms. The objective of this research is to minimize the total transportation cost and the trucks' and tanks' cleaning costs. The transportation cost directly depends on the fuel usage. The fuel usage occurs during the transportation of the milk and during the waiting times when it arrives at the factory and cannot transfer the raw milk into the production tank. We develop the modified differential evolution algorithm (MDE) to solve the proposed problem. The original process of the Differential Evolution algorithm (DE) has been modified in two folds which are as follows: (1) In the recombination process, the 2nd order of trial vectors has been generated using 3 different strategies and compared with the 1st order trial vector; the better from the 1st and the 2nd order of trial vectors will move to the selection process. (2) The probability function has been used to select the new target vector from one of two sources which are the trial vector and the current target vector so that the worse solution can be accepted in order to increase the diversity of the original DE. The computational result shows that the modified DE (MDE) outperforms the original DE in finding a better solution.

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Section: Literature Reviewmentioning

confidence: 99%

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

2019

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“…In the following, we list the algorithms that are compared in Table and explain how to interpret the corresponding results and run‐times: For ZTK (Zachariadis, Tarantilis, and Kiranoudis ) the number of runs performed to obtain the best solution reported is unknown (marked as ? in the table) and the run‐time is based on the time elapsed when the best solution was found. The results of SDBOF (Subramanian, Drummond, Bentes, Ochi, and Farias ) are based on 50 runs performed by 256 parallel threads and the average time per run. For GKA (Goksal, Karaoglan, and Altiparmak ), VCGP (Vidal, Crainic, Gendreau, and Prins ), SUO (Subramanian, Uchoa, and Ochi ), and KK (Kalayci and Kaya ), the table provides results based on 10 runs and the average time per run. P (Polat ) uses six parallel threads and performs 10 runs. The run‐time is based on the average time required to obtain the best solution reported. For PKKG (Polat, Kalayci, Kulak, and Günther ), the results are based on 10 runs and the time of the best run for benchmarks Salhi‐VRPSPD and Salhi‐VRPSPDTL, and on the average time per run for benchmark Polat‐VRPSPDTL. The results reported for ALNS‐PR are based on 10 runs and the average time per run. …”

Section: Computational Studiesmentioning

confidence: 99%

“…• The results of SDBOF (Subramanian, Drummond, Bentes, Ochi, and Farias [38]) are based on 50 runs performed by 256 parallel threads and the average time per run. • For GKA (Goksal, Karaoglan, and Altiparmak [12]), VCGP (Vidal, Crainic, Gendreau, and Prins [42]), SUO (Subramanian, Uchoa, and Ochi [39]), and KK (Kalayci and Kaya [16]), the table provides results based on 10 runs and the average time per run. • P (Polat [25]) uses six parallel threads and performs 10 runs.…”

Section: 41mentioning

confidence: 99%

An adaptive large neighborhood search with path relinking for a class of vehicle‐routing problems with simultaneous pickup and delivery

Hof

Schneider

2019

Networks

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We study a class of vehicle‐routing problems with simultaneous pickup and delivery (VRPSPD). In VRPSPDs, each customer may require a certain quantity of goods delivered from the depot and a quantity of goods to be picked up and returned to the depot. Besides the standard VRPSPD, we address (1) the VRPSPD with time limit (VRPSPDTL), which imposes a time limit on the routes of the transportation vehicles, (2) the VRPSPD with time windows (VRPSPDTW), which takes customer time windows into account, (3) the VRP with divisible deliveries and pickups (VRPDDP), which allows for fulfilling the delivery and pickup requests of each customer in two separate visits, (4) the previously unstudied VRP with restricted mixing of divisible deliveries and pickups (VRPRMDDP), which accounts for the difficulty of rearranging the vehicle load by additionally requiring that a certain percentage of the vehicle capacity must remain unoccupied when both types of demand are simultaneously loaded, and (5) the previously unstudied VRPDDP with time windows (VRPDDPTW). We develop a hybrid heuristic solution method which combines an adaptive large neighborhood search algorithm with a path relinking approach, called ALNS‐PR, and we demonstrate the competitiveness of our algorithm on benchmark instances proposed in the literature. Especially on VRPSPDTL, VRPSPDTW, and VRPDDP instances, our ALNS‐PR proves to be superior to the majority of comparison algorithms and is able to obtain numerous new best solutions.

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“…The vehicle routing problem with simultaneous pickup and delivery (VRPSPD) [1][2][3][4][5][6][7][8][9], in which customers demanding both delivery and pickup operations have to be visited once by a single vehicle, is one of the main classes of the vehicle routing problem (VRP). This class has attracted research attention due to its applicability in numerous reverse logistic systems.…”

Section: Introductionmentioning

confidence: 99%

A Fast Randomized Algorithm for the Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery

2019

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In the vehicle routing problem with simultaneous pickup and delivery (VRPSPD), customers demanding both delivery and pickup operations have to be visited once by a single vehicle. In this work, we propose a fast randomized algorithm using a nearest neighbor strategy to tackle an extension of the VRPSPD in which the fleet of vehicles is heterogeneous. This variant is an NP-hard problem, which in practice makes it impossible to be solved to proven optimality for large instances. To evaluate the proposal, we use benchmark instances from the literature and compare our results to those obtained by a state-of-the-art algorithm. Our approach presents very competitive results, not only improving several of the known solutions, but also running in a shorter time.

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An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery

Cited by 134 publications

References 41 publications

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

An adaptive large neighborhood search with path relinking for a class of vehicle‐routing problems with simultaneous pickup and delivery

A Fast Randomized Algorithm for the Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery

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