Abstract
This paper studies a challenging optimal scheduling problem considering the raw material with shelf-life constraints in unrelated parallel machines production. The aim is to optimize the assignment and sequencing of jobs to achieve tradeoffs between minimizing total completion time and the minimization of raw material costs. We formulate a bi-objective nonlinear 0–1 integer programming model for it. To solve the bi-objective problem which is NP-hard, we propose an evolutionary discrete particle swarm optimization algorithm (EDPSO) with a hybrid-greedy method (for generating the initial population), a new particle updating strategy, and an SPT-local search method (which has been proven for improving solutions’ quality theoretically and practically), and the Pareto archive updating strategy for storing good solutions. Computational experiments verify the effectiveness of EDPSO and show that it can obtain better solutions compared to other competing algorithms based on four important performance metrics.
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