Pareto‐optimal scheduling for just‐in‐time production systems with energy consideration

Abstract Effective, efficient, and sustainable management of manufacturing processes is a top priority for modern manufacturers. Just‐in‐time (JIT) production systems are widely adopted as a means of achieving efficiency and effectiveness in manufacturing, primarily by eliminating waste and improving customer satisfaction through timely delivery. In this context, the earliness/tardiness (E/T) scheduling problem provides a direct operational implementation of JIT principles in production planning. At the same time, energy consumption has emerged as a global concern due to increasing environmental awareness and sustainability considerations. Several strategies have been proposed to reduce energy consumption in scheduling, including turn‐on/off policies, speed scaling, and peak power management. However, studies that integrate energy considerations into the E/T scheduling problem remain limited. To address this gap, this study identified key properties of optimal schedules and designed efficient solution methods capable of generating high‐quality schedules within reasonable computational time. Two bi‐objective mixed integer linear programming (MILP) models and a heuristic procedure, referred to as the ascending speed‐level (ASL) method, were proposed. Experimental results demonstrated that the ASL method achieves high accuracy in identifying Pareto‐optimal solutions and provides superior computational efficiency, compared with the MILP models. This study opens several avenues for further research, including the consideration of continuous machine speed levels, failure and maintenance planning, dynamic job arrivals, and due‐window scheduling.