Abstract
This article addresses the significant issue of job deterioration effects in job-shop scheduling problems and aims to create awareness on its impact within the manufacturing industry. While previous studies have explored deteriorating effects in various production configurations, research on scheduling problems in complex settings, particularly job-shop, is very limited. Thus, we address and optimize the impact of job deterioration in a generic job-shop scheduling problem (JSP). The JSP with job deterioration is harder than the classical JSP as the processing time of an operation is only known when the operation is started. Hence, we propose a biased random key genetic algorithm to find good quality solutions quickly. Through computational experiments, the effectiveness of the algorithm and its multi-population variant is demonstrated. Further, we investigate several deterioration functions, including linear, exponential, and sigmoid. Job deterioration increases operations' processing time, which leads to an increase in the total production time (makespan). Therefore, the management should not ignore deterioration effects as they may lead to a decrease in productivity, to an increase in production time, costs, and waste production, as well to a deterioration in the customer relations due to frequent disruptions and delays. Finally, the computational results reported clearly show that the proposed approach is capable of mitigating (almost nullifying) such impacts. © 2023 IEEE.