A New Particle Swarm Optimization Algorithm for Short-Term Scheduling of Single-Stage Batch Plants with Parallel Lines

This is paper proposes a new particle swarm optimization (NPSO) algorithm to short-term scheduling of single-stage batch plants with parallel units using the continuous-time domain representation. The model is formulated as a mixed-integer linear programming (MILP) problem. The key to the improvement of the algorithm is the introduction of mutation operators, crossover operators and some heuristic rules which can get better initialization population and no effect on the optimality of the scheduling problem. Computational examples show that NPSO are clearly more appropriate than GA and PSO algorithm in resolution for batch plants to minimize earliness for scheduling problems with due date constraints, and NPSO becomes more effective after involving heuristic rules