The stochastic location-routing-inventory problem of perishable products with reneging and balking

2021 ◽  
Author(s):  
Azam Aghighi ◽  
Alireza Goli ◽  
Behnam Malmir ◽  
Erfan Babaee Tirkolaee
Keyword(s):  
Perishable Products ◽  
Inventory Problem ◽  
Location Routing ◽  
Stochastic Location

scholarly journals A sustainable closed-loop location-routing-inventory problem for perishable products

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Fatemeh Navazi ◽  
Zeinab Sazvar ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Closed Loop ◽  
Perishable Products ◽  
Inventory Problem ◽  
Location Routing

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

scholarly journals Location Routing Inventory Problem with Transhipment Points Using p-center

2016 ◽  
Vol 2016 (1) ◽  
pp. 59-72
Author(s):  
S. S. R. Shariff ◽  
N. S. Kamal ◽  
M. Omar ◽  
N. H. Moin ◽  
◽  
...  
Keyword(s):  
Inventory Problem ◽  
Location Routing

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.

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