Research and application of multiple constrained hot strip mill scheduling problem based on HPSA

Abstract

This paper proposes a new algorithm combining particle swarm optimization (PSO) and simulated annealing (SA) to solve hot strip mill scheduling problem which is formulated as a multi-objective optimization problem with multi-constraints which process schedule constraints, production means constraints, and energy consumption constraints into account. The constraints in hot mill processes are analyzed in three aspects—process schedule, product means, and energy consumption. According to actual experience, Hybrid Vehicle Routing Problem (HVRP) model is established to solve the scheduling problem which is a combination of variable fleet vehicle routing problem, prize collection vehicle routing, and capacitated vehicle routing problem. In this new algorithm, the PSO algorithm is redefined and modified by introducing metropolis criterion of SA twice, which is used to update two extremes of particle swarm, including the individual optimal solution and the global optimal solution to improve the convergence precision and the convergence rate. The proposed method has been applied in a hot mill line to verify its availability and feasibility by comparing the manual scheduling result.