Sustainable closed-loop supply chain network under uncertainty considering green technologies: toward the circular economy
研究开发了一个多周期混合整数线性规划模型，在需求、供应、运营成本和退货率不确定的情况下，优化柑橘水果箱网络的成本、环境影响和就业岗位，并比较了多种多目标元启发式算法，其中非洲秃鹫算法表现最佳。
Today, as a result of adopting environmental laws by governments and international organizations to pursue sustainability and the circular economy as goals to observe, Sustainable Closed-Loop Supply Chain (SCLSC) network design, as a cleaner production approach, has become necessary to avoid waste accumulation. The present study develops a multi-period Mixed Integer Linear Programming (MILP) model to optimize the imposed costs, amount of environmental effects and the number of job vacancies in the network of citrus fruits crates under the uncertainty of demand, supply, operational costs and the rate of returned products, simultaneously. In addition, products are manufactured and recycled using different technology levels. A Scenario-Based Robust Optimization (SBRO) approach is employed to tackle the uncertainty. In addition, in small dimensions, LP-metric method and in high dimensions, four well-known and powerful metaheuristic algorithms, i.e., Multi-Objective Simulated Annealing (MOSA), Multi-Objective Whale Optimization Algorithm (MOWOA), Multi-Objective Grey Wolf Optimization (MOGWO) and a novel multi-objective version of the recently published meta-heuristics algorithm, namely, African Vulture Algorithm (MOAVOA), have been utilized to find Pareto solutions. Moreover, the proposed algorithm’s parameters are tuned by the Taguchi technique, and to validate the suggested algorithms, they were compared with various metrics. Furthermore, the validation of the model is examined on a real case study in Iran. The Numerical results showed the efficiency and applicability of the MOAVOA algorithm. Finally, practical insights and implications are provided for the related managers and citrus fruits producer countries.