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

Abstract

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.

A new integrated mathematical model for a bi-objective multi-depot location-routing problem solved by a multi-objective scatter search algorithm

“…Caballero et al [13] applied a tabu search for a multi-objective location routing problem in a real case. Hassan-Pour et al [14] proposed a simulated annealing algorithm hybridized by genetic operators to solve the multi-objective multi-depot vehicle routing problem. They also presented a novel mathematical model for a stochastic locationrouting problem that minimizes the facilities establishing cost and transportation cost, and maximizes the probability of delivery to customers.…”

“…Constraint (11) shows that a customer may be allocated to a depot only if there is a route assigned to that depot, which serves that customer. Constraints (12)- (14) ensure that the routing variables, location variables, and allocation variables can take on values 0 or 1, respectively. Constraint (15) ensures that the auxiliary variables are nonnegative integers.…”

Credibilistic Location-Routing Model for Hazardous Materials Transportation

“…formulated an integer linear programming model for locating depots, determining the vehicle fleet size, and designing collection routes before the exact level of demand was known. Hassan‐Pour et al . considered a multiobjective problem with the stochastic availability of facilities and transport links.…”

“…r Stochastic LRP: Laporte et al 12 formulated an integer linear programming model for locating depots, determining the vehicle fleet size, and designing collection routes before the exact level of demand was known. Hassan-Pour et al 13 considered a multiobjective problem with the stochastic availability of facilities and transport links. Ahmadi-Javid and Saddish 14 considered stochastic inventory LRP and obtained the linearizations of the formulation under different risk-measurement policies.…”

An Efficient Algorithm For Solving A Stochastic Location-routing Problem

“…Hwang [4,5] considered that facilities are available in a known probability. Hassan-Pour et al [6] developed two new aspects of the LRP considering stochastic availability of facilities and routes. Wu et al [7] divided a multi-depot location-routing problem into a LRP and a VRP.…”