Solving the Prize-collecting Location Routing Problem in Chinese Rural Logistics Network by a Bi-level Genetic Algorithm

Author(s):  
Jiang Wu ◽  
Yuanhua Jia
2013 ◽  
Vol 756-759 ◽  
pp. 3423-3429
Author(s):  
Xue Feng Wang

The design and optimization of urban-rural dual-directions logistics network is a substantial important issue, which will directly affect the development of the urban-rural integration in China. A reasonable scheme of logistics network will contribute to supply efficient logistics services to customers scattering in urban and rural areas. In this paper, we consider a variant of the Location-Routing-Problem (LRP), namely the LRP with simultaneous pickup and delivery in specially background (LRPSB). The objective of LRPSB is to minimize the total system cost, including depot location cost and vehicle routing cost, and implement and control the effective dual-direct commodity flow to meet customers requirement by simultaneously locating the depots and designing the vehicle routes that satisfy pickup and delivery demand of customer at the same time. A nonlinear mixed integrated programming model is formulated for the problem. Since such integrated logistics network design problems belong to a class of NP-hard problems, we propose a two-phase heuristic approach based on Tabu Search, tp-TS, to solve the large size problem and an initialization procedure to generate an initial solution for the tp-TS. We then empirically evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions or strong lower bounds, and investigate the effectiveness of the proposed heuristic approach. Computational results show that the proposed heuristic approach is computationally efficient in finding good quality solutions for the LRPSB.


2019 ◽  
Vol 29 (3) ◽  
pp. 173-187
Author(s):  
Alena Rybičková ◽  
Denisa Mocková ◽  
Dušan Teichmann

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowen Xiong ◽  
Fan Zhao ◽  
Yundou Wang ◽  
Yapeng Wang

After the earthquake, it is important to ensure the emergency supplies are provided in time. However, not only the timeliness, but also the fairness from different perspectives should be considered. Therefore, we use a multilevel location-routing problem (LPR) to study the fairness of distribution for emergency supplies after earthquake. By comprehensively considering the time window constraints, the partial road damage and dynamic recovery in emergency logistics network, the stochastic driving time of the vehicle, and the mixed load of a variety of emergency materials, we have developed a multiobjective model for the LRP in postearthquake multimodal and fair delivery of multivariety emergency supplies with a limited period. The goal of this model is to minimize the total time in delivering emergency supplies and to minimize the maximum waiting time for emergency supplies to reach demand points. A hybrid heuristic algorithm is designed to solve the model. The example shows that this algorithm has a high efficiency and can effectively realize the supply of emergency supplies after the earthquake within the specified period. This method might be particularly suitable for the emergency rescue scenarios where the victims of the earthquake are vulnerable to mood swings and the emergency supplies need to be fairly distributed.


2014 ◽  
Vol 543-547 ◽  
pp. 2842-2845 ◽  
Author(s):  
Gai Li Du ◽  
Nian Xue

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.


Sign in / Sign up

Export Citation Format

Share Document