Application of the Bi-Level Location-Routing Problem for Post-Disaster Waste Collection

2018 ◽  
pp. 97-115 ◽  
Author(s):  
Cheng Cheng ◽  
Russell G. Thompson ◽  
Alysson M. Costa ◽  
Xiang Huang
2020 ◽  
Vol 147 ◽  
pp. 106632
Author(s):  
Xiaowen Wei ◽  
Huaxin Qiu ◽  
Dujuan Wang ◽  
Jiahui Duan ◽  
Yanzhang Wang ◽  
...  

2019 ◽  
Vol 28 (07) ◽  
pp. 1950024
Author(s):  
Hua Jiang ◽  
Corinne Lucet ◽  
Laure Devendeville ◽  
Chu-Min Li

Location-Routing Problem (LRP) is a challenging problem in logistics, which combines two types of decision: facility location and vehicle routing. In this paper, we focus on LRP with multiple capacitated depots and one uncapacitated vehicle per depot, which has practical applications such as mail delivery and waste collection. We propose a simple iterated variable neighborhood search with an effective perturbation strategy to solve the LRP variant. The experiments show that the algorithm is efficient and can compute better solutions than previous algorithms on tested instances.


2016 ◽  
Vol 34 (4) ◽  
pp. 239-252 ◽  
Author(s):  
Hamed Farrokhi-Asl ◽  
Reza Tavakkoli-Moghaddam ◽  
Bahare Asgarian ◽  
Esmat Sangari

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.


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