A heuristic algorithm for the multi-period vehicle routing problem with simultaneous pickup and delivery service

2010 ◽  
Author(s):  
Liangyu Xu
TRANSPORTES ◽  
2010 ◽  
Vol 18 (3) ◽  
Author(s):  
Marcio Tadayuki Mine ◽  
Matheus De Souza Alves Silva ◽  
Luiz Satoru Ochi ◽  
Marcone Jamilson Freitas Souza ◽  
Thaís Cotta Barbosa da Silva

<p><strong>Resumo: </strong>Este trabalho apresenta o algoritmo GENILS para resolver o Problema de Roteamento de Veículos com Coleta e Entrega Simultânea (PRVCES). GENILS é um algoritmo heurístico baseado nas técnicas heurísticas <em>Iterated Local Search</em>, <em>Variable Neighborhood Descent </em>e adaptações das heurísticas Inserção Mais Barata e GENIUS. O algoritmo proposto foi testado em três conjuntos consagrados de problemas-teste da literatura e se mostrou superior aos demais algoritmos da literatura com relação à capacidade de encontrar as melhores soluções conhecidas.</p><strong>Abstract: </strong>This work presents GENILS for solving the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). GENILS is a heuristic algorithm based on Iterated Local Search, Variable Neighborhood Descent and adaptations of the Cheapest Insertion and GENIUS heuristics. The proposed algorithm was tested on three well-known sets of instances found in literature and it overcame other existing algorithms in relation to the ability of finding the best known solutions.


2019 ◽  
Vol 119 (9) ◽  
pp. 2055-2071 ◽  
Author(s):  
Gaoyuan Qin ◽  
Fengming Tao ◽  
Lixia Li ◽  
Zhenyu Chen

Purpose In order to reduce logistics transportation costs and respond to low-carbon economy, the purpose of this paper is to study the more practical and common simultaneous pickup and delivery vehicle routing problem, which considers the carbon tax policy. A low-carbon simultaneous pickup and delivery vehicle routing problem model is constructed with the minimum total costs as the objective function. Design/methodology/approach This study develops a mathematical optimization model with the minimum total costs, including the carbon emissions costs as the objective function. An adaptive genetic hill-climbing algorithm is designed to solve the model. Findings First, the effectiveness of the algorithm is verified by numerical experiments. Second, the research results prove that carbon tax mechanism can effectively reduce carbon emissions within effective carbon tax interval. Finally, the research results also show that, under the carbon tax mechanism, the effect of vehicle speed on total costs will become more obvious with the increase of carbon tax. Research limitations/implications This paper only considers the weight of the cargo, but it does not consider the volume of the cargo. Originality/value Few studies focus on environmental issues in the simultaneous pickup and delivery problem. Thus, this paper constructs a green path optimization model, combining the carbon tax mechanism for the problem. This paper further analyzes the impact of carbon tax value on total costs and carbon emission; at the same time, the effect of vehicle speed on total cost is also analyzed.


Author(s):  
Robin Scanlon ◽  
Qing Wang ◽  
Jie Wang

Reverse logistics is an area that has come under increased scrutiny in recent years as legislators and companies try to increase the amount of goods that businesses reuse and recycle. The vehicle routing problem with simultaneous pickup and delivery arises when firms want to reduce handling costs by dealing with deliveries and returns in one operation. This is a complex problem for planners who aim to minimise the vehicle route length as the vehicle load rises and falls during a tour of facilities. This paper investigates the use of Ant Colony Optimisation to find solutions to this problem. An algorithm combining elements of three different studies is proposed. The algorithm finds results within 0.2% of the best known results and performs well for half of the benchmark problems, but needs further work to reach the same level on the other half. It is found that the proposed changes can have up to a 3.1% improvement in results when compared to previous methods run on this algorithm.


2012 ◽  
Vol 7 (7) ◽  
pp. 1569-1581 ◽  
Author(s):  
Anand Subramanian ◽  
Eduardo Uchoa ◽  
Artur Alves Pessoa ◽  
Luiz Satoru Ochi

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