Closed loop supply chain networks: Designs for energy and time value efficiency
Product recovery has become a viable option for many industries to realize economic gains while protecting the environment. However, insufficient investment and inefficient supply chains have hampered the viability of reuse and/or recycling because of the extended time intervals between the recycling process of recovery and reuse. Manufacturers and distributors face the challenge and necessity to reduce these process delays in order to recover the maximum value of the returned products through an effective, responsive closed loop supply chain (CLSC). This paper quantitatively measures the effective responsiveness of the CLSC model in terms of time and energy efficiency. The proposed multi-objective mixed integer linear programming (MOMILP) model evaluates delay parameters with decision variables that maximize profit, optimize customer surplus and minimize energy use. The model suggests decision makers may achieve an optimal tradeoff among differing objectives in a multiple-objective CLSC scenario. We employed a multi-objective particle swarm optimization (MOPSO) approach to solve the proposed MOMILP model and compared our approach with the Non-Dominated Sorted Genetic Algorithm (NSGA-II) for optimal solution. Results of the comparative evolutionary approaches shows that MOPSO outperforms NSGA-II in almost all cases in achieving the best trade-off solutions. Sensitivity analysis carried out to test the robustness of the model confirms that substantially less cost is feasible through the reduction of return process delays. This paper aims to formulate a multi-objective CLSC problem based on a network-flow model measuring the time value to recover maximum assets lost due to delay at different stages of the recycle process. We also developed a particle swarm approach for a multi-objective CLSC. Our study also offers valuable insights for designers wishing to create a product flow network with an optimal capacity level in case of prioritized objectives scenarios.
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Volume (Year): 183 (2017)
Issue (Month): PB ()
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