Efficient flexible long-term capacity planning for optimal sustainability dimensions performance of reverse logistics social responsibility: A system dynamics approach

Abstract

Product lifecycle uncertainties in Closed-Loop Supply Chains (CLSCs) are costly and frequently unavoidable. So the aim of this paper is to develop efficient flexible long-term capacity planning policy for CLSCs that considers social responsibility or a supply chain with Reverse Logistics Social Responsibility (RLSR). This aim is to answer an important research question on how to tackle the lifecycle with its inherited uncertainty to achieve optimal sustainability dimensions performance. Here, a single-product System Dynamics (SD) model of the supply chain with RLSR is used. This SD model considers interrelated sustainability dimensions and adopts the product lifecycle with its inherited uncertainties, such as the length of the product lifecycle, pattern of the product lifecycle, and residence index. Finally, a mathematical model of the developed policy is constructed and a simplified non-linear multi-objective algorithm is proposed to solve this mathematical model. In addition, Taguchi Design is used to minimize the number of simulations needed in the numerical experiment. The findings of this study show that the developed policy could be used to tackle the lifecycle with its inherited uncertainty to optimize the sustainability dimensions performance. These findings have some limitations, however. The findings underscore this paper's contribution to the relatively limited but important academic knowledge on capacity planning development for research on social responsibility issues in CLSCs. In practice, the results will give managers a better understanding of how to tackle product lifecycle uncertainties in RLSR and will therefore lead to better capacity planning to achieve optimal sustainability dimensions performance.