Heuristic and lower bound for a stochastic location-routing problem

2007 ◽  
Vol 179 (3) ◽  
pp. 940-955 ◽  
Author(s):  
Maria Albareda-Sambola ◽  
Elena Fernández ◽  
Gilbert Laporte
Keyword(s):  
Lower Bound ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Stochastic Location

Solving a multi-objective multi-depot stochastic location-routing problem by a hybrid simulated annealing algorithm

2009 ◽  
Vol 223 (8) ◽  
pp. 1045-1054 ◽  
Author(s):  
H A Hassan-Pour ◽  
M Mosadegh-Khah ◽  
R Tavakkoli-Moghaddam
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Genetic Operators ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Multi Objective ◽  
Location Routing ◽  
Proposed Model ◽  
Stochastic Location

This paper presents a novel mathematical model for a stochastic location-routing problem (SLRP) that minimizes the facilities establishing cost and transportation cost, and maximizes the probability of delivery to customers. In this proposed model, new aspects of a location-routing problem (LRP), such as stochastic availability of facilities and routes, are developed that are similar to real-word problems. The proposed model is solved in two stages: (i) solving the facility location problem (FLP) by a mathematical algorithm and (ii) solving the multi-objective multi-depot vehicle routing problem (MO-MDVRP) by a simulated annealing (SA) algorithm hybridized by genetic operators, namely mutation and crossover. The proposed SA can find good solutions in a reasonable time. It solves the proposed model in large-scale problems with acceptable results. Finally, a trade-off curve is used to depict and discuss a large-sized problem. The associated results are compared with the results obtained by the lower bound and Lingo 8.0 software.

Simulation-optimization approach for the stochastic location-routing problem

2015 ◽  
Vol 9 (4) ◽  
pp. 296-311 ◽  
Author(s):  
N Herazo-Padilla ◽  
J R Montoya-Torres ◽  
S Nieto Isaza ◽  
J Alvarado-Valencia
Keyword(s):  
Simulation Optimization ◽  
Optimization Approach ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Stochastic Location

scholarly journals An Efficient Algorithm For Solving A Stochastic Location-routing Problem

2014 ◽  
Vol 12 (01) ◽  
pp. 27-38
Author(s):  
H.a. Hassan-pour ◽  
M. Mosadegh-khah ◽  
M. Zareei
Keyword(s):  
Efficient Algorithm ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Stochastic Location

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

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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