Optimization of the Collaborative Hub Location Problem with Metaheuristics

Abstract

By creating new job opportunities and developing the regional economy, the transport of goods generates significant costs, environmental and sanitary nuisances, and high greenhouse gas (GHG) emissions. In this context, collaboration is an interesting solution that can be used to enable companies to overcome some problems such as globalization, economic crisis, health crisis, issues related to sustainability, etc. This study deals with the design of a multiperiod multiproduct three-echelon collaborative distribution network with a heterogeneous fleet. By applying the mixed integer linear problem (MILP) formulations, it was possible to study the three dimensions of sustainability (economic, environmental, and social/societal). Since the examined problem was NP-hard, it was solved using four metaheuristic approaches to minimize the different logistics costs or CO2 emissions. The social/societal aspect evaluated the accident rate and the noise level generated by the freight transport. Four algorithms were developed to achieve our objectives: a genetic algorithm, a simulated annealing, a particle swarm algorithm, and a vibration damping optimization algorithm. Considering a French distribution network, these algorithms overcame the limits of the exact solution method by obtaining optimal solutions with reasonable execution time.

A Comprehensive Literature Review on Sustainable Horizontal Collaboration

“…In another study, Aloui et al [41] introduced a two-step mixed-integer stochastic programming model to design a distribution network considering the epidemic disturbances and the uncertainty of the demand. Moreover, Gargouri et al [42] dealt with the problem of designing a collaborative distribution network by taking into account the three dimensions of sustainability. The developed MILP was inspired from [39].…”

Multiobjective Optimization of Fuzzy System for Cardiovascular Risk Classification

“…In addition, the use of AI and optimization methods have been used in transportation [13,14] and environmental management [15]. In one study, a genetic algorithm was used to determine the optimal location for traffic control in a smart city.…”