A multi-objective sustainable hub location-scheduling problem for perishable food supply chain. (November 2017)

• Record Type: Journal Article
• Authors: Musavi, MirMohammad; Bozorgi-Amiri, Ali
• Is Part Of: Computers & industrial engineering. Volume 113(2017)
• Page Start: 766
• Page End: 778
• Publication Date: 2017-11
• Subjects: Food supply chain -- Sustainable supply chain -- Hub location -- Vehicle scheduling -- Meta-heuristics; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.07.039
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Scheduling and sequencing of the vehicles at hubs are optimized. Environmental conservation to design a sustainable supply chain is considered. Perishability and freshness of products is considered in a food supply chain. An adopted NSGA-II meta-heuristic is proposed to solve the NP-hard problem. Abstract: Considering responsiveness and environmental impacts, this paper presents a novel sustainable hub location-vehicle scheduling model, in which transportation fleet at hub nodes serving customers are limited in number. Because of this limitation, assignment and sequencing of outbound vehicles at each hub is taken into consideration. Therefore, each hub node performs as a scheduling and sequencing problem. The model considers perishability of products for distribution in a food supply chain and considers total CO2 emission of hub network, simultaneously. The problem is modeled as a multi-objective mixed integer linear programming optimizing the total transportation costs, freshness and quality of foods at the time of delivery and the total carbon emissions of the vehicles to fulfill the sustainability desire of the environment. Due to NP-hardness of the problem, an adopted non-dominated sorting genetic algorithm-II (NSGA-II) Meta heuristic approach is proposed to solve large instance problems. Numerical experiments are performed on CAB and AP datasets. Numerical tests confirms that the proposed meta-heuristic is able to generate proper Pareto solutions considering all of the objectives for decision maker.