Recent paradigm shifts in production modeling have evolved away from only economic considerations and toward the three pillars of sustainability: economic, environmental, and social, in order to accomplish sustainable development goals. While production modeling research has undergone a radical shift, quite a few studies in the production management literature have addressed sustainability challenges. These classic sustainable production models are only applicable to single-stage production systems and overlook a variety of real-world situations associated with random defective output and resulting shortages during demand fulfillment. To address this concern about developing sustainable strategies for multi-stage production, this paper analyzes a cleaner multi-stage production management system for carbon emissions reduction and active participation in corporate social responsibility activities, while also advancing the system economically. Three scenarios of random imperfect proportion are constructed, and a reworking opportunity is implied to clean the multi-stage production system. A planned backorder policy is advised to address anticipated shortages and improve the system's service level. Analytical optimization techniques are used in the developed situations to determine the appropriate batch size and backorder quantity, hence minimizing the system's overall cost and achieving global optimum solutions. Numerical evaluation and sensitivity analysis are used to conduct a comparative examination of the model scenarios. Significant managerial insights are developed to demonstrate the suggested cleaner multi-stage production model's actual relevance. According to the model results, a lower batch size is the optimal strategy in response to increasing holding and CSR activity costs, but otherwise for the setup cost in a serial production system.
|Journal||Computers and Industrial Engineering|
|Publication status||Published - 2022 Sept|
Bibliographical noteFunding Information:
The work is supported by the National Research Foundation of Korea (NRF) grant, funded by the Korea Government (MSIT) ( NRF2020R1F1A1064460 ).
© 2022 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Computer Science(all)