Genetic algorithm with normal boundary intersection for multi-objective early/tardy scheduling problem with carbon-emission consideration: a Pareto-optimum solution

Green manufacturing has become an important research topic owing to the dominant role of the manufacturing industry in environmental conservation, global energy consumption, and carbon emissions. Job scheduling is an active research area that supports industrial development and transformation as a part of industrial manufacturing management. Scheduling and just-in-time (JIT) production are complementary concepts that can help organizations optimize their production processes and achieve their goals more efficiently. The objective of these concepts is to reduce waste by focusing on the timely delivery of products or services to meet customer demand without holding excess inventory or wasting resources. Early/tardy job scheduling aligns with the primary goals of JIT production. This study jointly considers the early/tardy scheduling problem and carbon-emission optimization. A speed-scaling strategy is applied, where a machine has the ability to process jobs at discrete machining speeds. A heuristic method based on a genetic algorithm is proposed to solve the above problem. The proposed algorithm integrates a normal boundary intersection to reinforce the generation of a Pareto optimal solution. Numerical experiments show that the proposed approach provides an optimal and satisfactory Pareto solution within a relatively short computational time.