Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem

2016 ◽  
Author(s):  
Lei Zhou ◽  
Liang Feng ◽  
Jinghui Zhong ◽  
Yew-Soon Ong ◽  
Zexuan Zhu ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Combinatorial Search ◽  
Routing Problem ◽  
Search Spaces ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle ◽  
Evolutionary Multitasking

scholarly journals Comparative Study of Algorithms Metaheuristics Based Applied to the Solution of the Capacitated Vehicle Routing Problem

2020 ◽  
Author(s):  
Fernando Francisco Sandoya Sánchez ◽  
Carmen Andrea Letamendi Lazo ◽  
Fanny Yamel Sanabria Quiñónez
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Impact Analysis ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Economic Impact Analysis ◽  
Transportation Sector ◽  
Capacitated Vehicle

This chapter presents the best-known heuristics and metaheuristics that are applied to solve the capacitated vehicle routing problem (CVRP), which is the generalization of the TSP, in which the nodes are visited by more than one route. To find out which algorithm obtains better results, there are 30 test instances used, which are grouped into 3 sets of problems according to the position of the nodes. The study begins with an economic impact analysis of the transportation sector in companies, which represents up to 20% of the final cost of the product. This case study focuses on the CVRP for its acronym capacitated vehicle routing problem, analyzing the best-known heuristics such as Clarke & Wright and sweep, and the algorithms GRASP and simulated annealing metaheuristics based.

Towards a Decision Making Support System for the Capacitated Vehicle Routing Problem

2013 ◽  
Vol 5 (4) ◽  
pp. 21-33 ◽  
Author(s):  
Takwa Tlili ◽  
Saoussen Krichen ◽  
Sami Faiz
Keyword(s):  
Decision Making ◽  
Vehicle Routing ◽  
Support System ◽  
Vehicle Routing Problem ◽  
Geographical Information ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle ◽  
Decision Making Support

Distribution of goods is of importance in logistics and supply chain management. The target of a distribution network is to specify the beneficial way for delivering goods and commodities from supply to demand points. In distribution domain, this activity can usually be viewed as a capacitated vehicle routing problem (CVRP). As the CVRP solutions remain computationally intractable, we develop in this paper an interactive routing decision making support system (R-DMSS) that integrates a swarm based approach into a geographical information system (GIS). The empirical experiments include benchmarking instances as well as a case study over the Ezzahra area in Tunisia. The R-DMSS interfaces are presented to better understand the operational aspect of the system.

scholarly journals Simulated annealing algorithm for solving the capacitated vehicle routing problem: a case study of pharmaceutical distribution

2020 ◽  
Vol 4 (1) ◽  
pp. 41-49 ◽  
Author(s):  
Anak Agung Ngurah Perwira Redi ◽  
Fiki Rohmatul Maula ◽  
Fairuz Kumari ◽  
Natasha Utami Syaveyenda ◽  
Nanda Ruswandi ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Pharmaceutical Distribution ◽  
Annealing Algorithm ◽  
Capacitated Vehicle

This study aims to find a set of vehicles routes with the minimum total transportation time for pharmaceutical distribution at PT. XYZ in West Jakarta. The problem is modeled as the capacitated vehicle routing problem (CVRP). The CVRP is known as an NP-Hard problem. Therefore, a simulated annealing (SA) heuristic is proposed. First, the proposed SA performance is compared with the performance of the algorithm form previous studies to solve CVRP. It is shown that the proposed SA is useful in solving CVRP benchmark instances. Then, the SA algorithm is compared to a commonly used heuristic known as the nearest neighborhood heuristics for the case study dataset. The results show that the simulated Annealing and the nearest neighbor algorithm is performing well based on the percentage differences between each algorithm with the optimal solution are 0.03% and 5.50%, respectively. Thus, the simulated annealing algorithm provides a better result compared to the nearest neighbour algorithm. Furthermore, the proposed simulated annealing algorithm can find the solution as same as the exact method quite consistently. This study has shown that the simulated annealing algorithm provides an excellent solution quality for the problem.

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