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Increasing environmental, legislative, and social concerns are forcing
companies to take a fresh view of the impact of supply chain operations on
environment and society when designing a sustainable supply chain. A
challenging task in today's food industry is distributing high quality
perishable foods throughout the food supply chain. This paper proposes a
multi-objective optimization model by integrating sustainability in
decision-making, on distribution in a perishable food supply chain network
(SCN). It introduces a two-echelon location-routing problem with
time-windows (2E-LRPTW) for sustainable SCN design and optimizing
economical and environmental objectives in a perishable food SCN. The goal
of 2E-LRPTW is to determine the number and location facilities and to
optimize the amount of products delivered to lower stages and routes at
each level. It also aims to reduce costs caused by carbon footprint and
greenhouse gas emissions throughout the network. The proposed method
includes a novel multi-objective hybrid approach called MHPV, a hybrid of
two known multi-objective algorithms: namely, multi-objective particle
swarm optimization (MOPSO) and adapted multi-objective variable
neighborhood search (AMOVNS). MHPV features two strategies for leader
selection procedures (LSP), (i.e. Grids) and crowding distance is compared
to common genetic algorithms based on metaheuristics (i.e. MOGA, NRGA and
NSGA-II). Results indicate that the hybrid approach achieves better
solutions compared to others, and that crowding distance method for LSP
outperforms the former Grids method.
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