Model and analysis of economic-and risk-based objective optimization problem for plant location within industrial estates using epsilon-constraint algorithms

Abstract

In many countries, a number of industrial estates have been established to support the growth of the industrial sector, which is an essential strategy to drive economic growth. Planning for the location of industrial factories within an industrial estate, however, becomes complex, given the various types of industrial plants and the requirements of utilities to support operations within an industrial park. In this research, we model and analyze bi-objective optimization for locating plants within an industrial estate by considering economic-and risk-based cost objectives. Whereas economic objectives are associated with utility distances between plant locations, risk-based cost is a surrogate criterion derived from safety considerations. Next, risk-based data are further generated from Areal Locations of Hazardous Atmospheres (ALOHA), the hazard modeling program, and solutions to the bi-objective model are obtained from the Epsilon-constraint algorithm. Finally, the model is applied to a regional case study in a Thailand industrial estate, and the Pareto frontier is evaluated to demonstrate the trade-off between objectives.