Sustainability · 2017 · 56 citations · 27 references
Logistics ProcessesSupply Chain OptimizationEngineeringLogistics OptimizationIndustrial EngineeringSmart ManufacturingInventory TheoryClosed-loop Supply ChainOperations ResearchGenetic AlgorithmSystems EngineeringSupply ChainLogisticsLogistics ModelTransport VehicleManufacturing PlanningManufacturing SystemsSupply Chain DesignSupply Chain ManagementWarehouse ManagementBusinessManufacturing–remanufacturing–transport–warehousing SystemRecovery InventorySupply Chain Analysis
The study focuses on optimizing a closed‑loop supply chain that includes manufacturing and remanufacturing machines, stock, a purchasing warehouse, transport vehicles, and a recovery inventory. The goal is to determine optimal capacities for manufacturing stock, purchasing warehouse, transport vehicle, and the quantity of returned end‑of‑life products. A discrete‑flow model represents the system—including returned products, storage, and transport—and a genetic‑algorithm‑based optimization program selects the decision variables that minimize total cost. Numerical experiments demonstrate how transportation time, remanufacturing cost, and machine configuration influence the optimal decision variables.
This paper deals with the optimization of a manufacturing–remanufacturing–transport–warehousing closed-loop supply chain, which is composed of two machines for manufacturing and remanufacturing, manufacturing stock, purchasing warehouse, transport vehicle and recovery inventory. The proposed system takes into account the return of used end-of-life products from the market. Manufactured and re-manufactured products are stored in the manufacturing stock. The used end-of-life products are stored in the recovery inventory for remanufacturing. The vehicle transports products from the manufacturing stock to the purchasing warehouse. The objective of this work is to simultaneously evaluate the optimal capacities of manufacturing stock, purchasing warehouse and the vehicle, as well as the optimal value of returned used end-of-life products. Those four decision variables minimize the total cost function. A discrete flow model, which is supposed to be the most realistic, is used to describe the system. An optimization program, based on a genetic algorithm, is developed to find the decision variables. Numerical results are presented to study the influence of transportation time, unit remanufacturing cost and configuration of the manufacturing/remanufacturing machines on the decision variables.
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