Optimizing production and remanufacturing strategies for a complex smart single stage production system under uncertainty and waste nullification

Market conditions are highly changeable in today’s competitive environment, leading to uncertain demand and distinctive unit costs. Specifically, the information on market demand for newly launched assembled items (e.g., smart mobiles, laptops, etc.) is highly unpredictable. There is limited data available regarding the expenses involved in producing new products/components. Therefore, unlike previous studies used crisp values of demand and manufacturing costs to synthesize the most effective economic production quantity models for management decision-making, it was attempted here to apply fuzzy theory because of the nature of uncertainty in demand and manufacturing costs. In addition, the autonomated inspection and single-stage manufacturing-remanufacturing were considered such that assembling product can be made better quality. The faulty items are randomly generated in the manufacturing but could be detected through autonomated inspection and remanufactured within a single cycle. Such manufacturing and remanufacturing in a single stage are more cost-effective than traditional manufacturing, which separates them into two stages. This study optimizes the production lot size, backorder, and investment for inspection. Autonomation inspection strategy helps to make this production system 5.95% cost-effective. In addition, a budget and a space constraint is used to make this model realistic. Those constraints help to reduce the cost by 34.51%. A numerical experiment and sensitivity are conducted for different defective rates and conditions.