PSO with path relinking for resource allocation using simulation optimization

This paper proposes a PSO-based optimization approach with a particular path relinking technique for moving particles. PSO is evaluated for two combinatorial problems. One under uncertainty, which represents a new application of PSO with path relinking in a stochastic scenario. PSO is considered first in a deterministic scenario for solving the Task Assignment Problem (TAP) and hereafter for a resource allocation problem in a petroleum terminal. This is considered for evaluating PSO in a problem subject to uncertainty whose performance can only be evaluated by simulation. In this case, a discrete event simulation is built for modeling a real-world facility whose typical operations of receiving and transferring oil from tankers to a refinery are made through intermediary storage tanks. The simulation incorporates uncertain data and operational details for optimization that are not considered in other mathematical optimization models. Experiments have been carried out considering issues that affect the choice of parameters for both optimization and simulation. The results show advantages of the proposed approach when compared with Genetic Algorithm and OptQuest (a commercial optimization package).

► PSO is evaluated for combinatorial problems in a scenario of randomness.
► We develop a discrete event simulation for transferring oil in a real-world facility.
► Operational details and uncertain data are considered in optimization via simulation.
► Results are focused on tuning of parameters for both optimization and simulation.
► Our approach using PSO has shown better results when compared to a Genetic Algorithm and a commercial package.