Adaptive neighborhood simulated annealing for sustainability-oriented single machine scheduling with deterioration effect

This research considers the realistic phenomenon of cutting tool deterioration in the scheduling of jobs and tool replacement activities to ensure sustainable machining operations. Typically, each job has a basic processing time (independent of tool deterioration) and a tool age-dependent prolongation function. Nevertheless, such a strategy may replace the cutting tools before its useful lifespan to avoid longer processing time as it leads to higher energy consumption and likelihood of tardy jobs. Given the reduced tool operating duration, the prospect of exploiting its capability to limit the processing time prolongation is not considered by prior works. In this research, a single-machine scheduling approach that determines the job processing time based on tool age and operating duration (utilization) is studied. The problem is formulated as a mixed-integer linear program (MILP) with the objective of minimizing the weighted costs of energy consumption, tooling, and job tardiness. Due to its computational complexity, a new variant of simulated annealing (SA) algorithm is proposed to solve large instances, where an adaptive strategy is utilized to determine the neighborhood size for enhanced exploration and exploitation. Computational results for an exhaustive set of instances indicate the proposed scheduling strategy to always outperform the existing method, yielding up to a 43% reduction in total cost. Besides, the proposed variant of SA effectively solves the scheduling problem and consistently dominates the traditional SA method. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This research focuses on sustainable machining operations by considering the impact of cutting tool deterioration in job scheduling and tool replacement activities. A single-machine scheduling approach is studied to determine job processing time based on tool age and operating duration, aiming to minimize weighted costs. A new variant of simulated annealing algorithm is proposed to solve large instances efficiently and consistently outperforms traditional methods.