Cooperative Parallel Metaheuristics based Penguin Optimization Search for Solving the Vehicle Routing Problem

2016 ◽  
Vol 7 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Meryem Ammi ◽  
Salim Chikhi
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
New Approach ◽  
Parallel Metaheuristics ◽  
Large Size ◽  
Benchmark Instances ◽  
Parallel Pattern ◽  
Optimization Search ◽  
Capacitated Vehicle

In this work the authors present a new approach based on the cooperation of many variants of metaheuristics in order to solve the large existing benchmark instances of the capacitated vehicle routing problem (CVRP). The proposed method follows the parallel pattern of the generalized island model (GIM). Consequently, the used metaheuristics, namely genetic algorithm (GA), the ant colony optimization (ACO), and the first application of the penguin optimization search (PEO) have been used to handle the large size of the CVRP. These optimization processes have been put over numerous islands that communicate via the process of exchanging solutions. Comparative studies as well as tests over the existing benchmark instances have been reported to prove the efficiency of the proposed approach.

scholarly journals Development Of A New Genetic Algorithm For Solving Capacitated Vehicle Routing Problem In Short Time

2019 ◽  
Vol 8 (11) ◽  
pp. 903-907
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Benchmark Instances ◽  
Full Capacity ◽  
Short Time ◽  
Number Of Customers ◽  
Capacitated Vehicle

In this paper a new genetic algorithm is developed for solving capacitated vehicle routing problem (CVRP) in situations where demand is unknown till the beginning of the trip. In these situations it is not possible normal metaheuristics due to time constraints. The new method proposed uses a new genetic algorithm based on modified sweep algorithm that produces a solution with the least number of vehicles, in a relatively short amount of time. The objective of having least number of vehicles is achieved by loading the vehicles nearly to their full capacity, by skipping some of the customers. The reduction in processing time is achieved by restricting the number of chromosomes to just one. This method is tested on 3 sets of standard benchmark instances (A, M, and G) found in the literature. The results are compared with the results from normal metaheuristic method which produces reasonably accurate results. The results indicate that whenever the number of customers and number of vehicles are large the new genetic algorithm provides a much better solution in terms of the CPU time without much increase in total distance traveled. If time permits the output from this method can be further improved by using normal established metaheuristics to get better solution

scholarly journals A granular tabu search for the refrigerated vehicle routing problem with homogeneous fleet

2022 ◽  
Vol 13 (1) ◽  
pp. 135-150 ◽  
Author(s):  
John Willmer Escobar ◽  
José Luis Ramírez Duque ◽  
Rafael García-Cáceres
Keyword(s):  
Energy Consumption ◽  
Tabu Search ◽  
Vehicle Routing ◽  
Thermodynamic Model ◽  
Vehicle Routing Problem ◽  
High Quality ◽  
Routing Problem ◽  
Benchmark Instances ◽  
Capacitated Vehicle ◽  
Selection Of

The Refrigerated Capacitated Vehicle Routing Problem (RCVRP) considers a homogeneous fleet with a refrigerated system to decide the selection of routes to be performed according to customers' requirements. The aim is to keep the energy consumption of the routes as low as possible. We use a thermodynamic model to understand the unloading of products from trucks and the variables' efficiency, such as the temperature during the day influencing energy consumption. By considering various neighborhoods and a shaking procedure, this paper proposes a Granular Tabu Search scheme to solve the RCVRP. Computational tests using adapted benchmark instances from the literature demonstrate that the suggested method delivers high-quality solutions within short computing times, illustrating the refrigeration system's effect on routing decisions.

New benchmark instances for the Capacitated Vehicle Routing Problem

2017 ◽  
Vol 257 (3) ◽  
pp. 845-858 ◽  
Author(s):  
Eduardo Uchoa ◽  
Diego Pecin ◽  
Artur Pessoa ◽  
Marcus Poggi ◽  
Thibaut Vidal ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Benchmark Instances ◽  
Capacitated Vehicle

scholarly journals CAPACITATED VEHICLE ROUTING PROBLEM

2019 ◽  
Vol 7 (3) ◽  
pp. 310-327
Author(s):  
Ibrahim A.A ◽  
Lo N. ◽  
Abdulaziz R.O ◽  
Ishaya J.A
Keyword(s):  
Reinforcement Learning ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Goods And Services ◽  
Capacitated Vehicle Routing Problem ◽  
Large Size ◽  
The Cost ◽  
Cost Of Transportation ◽  
Capacitated Vehicle

Cost of transportation of goods and services is an interesting topic in today’s society. The  Capacitated vehicle routing problem, which is been consider in this research, is one of the variants of the vehicle routing problem. In this research we develop a reinforcement learning technique to find optimal paths from a depot to the set of customers while also considering the capacity of the vehicles, in order to reduce the cost of transportation of goods and services. Our basic assumptions are; each vehicle originates from a depot, service the customers and return to the depot, the vehicles are homogeneous. We solve the CVRP with an exact method; column generation, goole’s operation research tool and reinforcement learning and compare their solutions. Our objective is to solve a large-size of vehicle routing problem to optimality.

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