A hybrid particle swarm optimization with local search for stochastic resource allocation problem

Abstract

Discrete and stochastic resource allocation problems are difficult to solve because of the combinatorial explosion of feasible search space. Resource management is important area and a significant challenge is encountered when considering the relationship between uncertainty factors and inputs and outputs of processes in the service and manufacturing systems. These problems are unavailable in closed-form expressions for objective function. In this paper, we propose $$\hbox {PSO}_{\mathrm{OTL}}$$ , a new approach of the hybrid simulation optimization structure, to achieve a near optimal solution with few simulation replications. The basic search algorithm of particle swarm optimization (PSO) is applied for proper exploration and exploitation. Optimal computing budget allocation combined with PSO is used to reduce simulation replications and provide reliable evaluations and identifications for ranking particles of the PSO procedure. Two-sample t tests were used to reserve good particles and maintain the diversity of the swarm. Finally, trapping in local optimum in the design space was overcome by using the local search method to generate new diverse particles when a similar particle exists in the swarm. This study proposed intelligent manufacturing technology, called the $$\hbox {PSO}_{\mathrm{OTL}}$$ , and compared it with four algorithms. The results obtained demonstrate the superiority of $$\hbox {PSO}_{\mathrm{OTL}}$$ in terms of search quality and computational cost reduction.