Optimization Model for Industrial Estate Planning under Safety Criterion
Article Sidebar
Main Article Content
Abstract
Many countries, nowadays, had established industrial estates with an aim to operate industrial activities and support the growing industrial sector. In addition, an increasing trend of new industrial estates is arising to support economics and business expansion. However, studies that focus on planning for industrial estates are scarce. In addition, existing studies only cover the cost criterion. In this study, we propose the Mixed-Integer Linear Programming (MILP) model to determine the location of factory plants in an industrial estate with a focus on the safety criterion. The model classifies two types of industrial plants, which are hazardous-material factory and general-type factory. In order to incorporate the safety factor, the model considers the risk and severity in an emergency case simulated using the Areal Locations of Hazardous Atmospheres (ALOHA) program. The simulation results are used as input for the developed MILP model. The results show that location of most industrial plants are located at the outer area of the industrial estate, which are scatteringly located around the industrial estate, given a consideration of the high-risk areas in an emergency situation. Although there are a few areas of high risk chosen for plant assignments, these areas are relatively small in size, allowing minimal impact. The integrated mathematical and simulation model in this research can be further used as a tool to support other design decisions for industrial locations and related locational studies.
Article Details
References
Alves, D. T. S., de Medeiros, J. L., & Ofelia de Queiroz, F. A. (2016). Optimal determination of chemical plant layout via minimization of risk 20.to general public using Monte Carlo and Simulated Annealing techniques. Journal of Loss Prevention in the Process Industries, 41, 202-214.
BOI. (2019, Apr 24). Thailand Board of Investment. https://www.boi.go.th/
Caputo, A. C., Pelagagge, P. M., Palumbo, M., & Salini, P. (2015). Safety-based process plant layout using genetic algorithm. Journal of Loss Prevention in the Process Industries, 34, 139-150.
Chae, J., & Regan, A. C. (2016). Layout design problems with heterogeneous area constraints. Computers & Industrial Engineering, 102, 198-207.
Chaiyaphan, C., & Ransikarbum, K. (2020). Criteria analysis of food safety using the Analytic Hierarchy Process (AHP)-a case study of Thailand’s fresh markets. In E3S Web of Conferences (Vol. 141, p. 02001). EDP Sciences.
Chanthakhot, W., Wattanaseang, N., Wisesla, K., & Ransikarbum, K. (2018, July). Computer Simulation-based Impact Assessment of LPG Storage Tank’s Leakage and Explosion in Split-type Air Conditioner Factory [Paper presentation]. In 2018 IE Network Conference, Bangkok, Thailand.
Chanthakhot, W., & Ransikabum, K. (2019, December). Numerical Simulation for Fire Emergency Planning in a Home Appliances Factory [Paper presentation]. In 2019 Research, Invention, and Innovation Congress (RI2C) IEEE. Bangkok, Thailand.
IEAT. (2020, March 2). Annual Report 2018. https://www.ieat.go.th/annual-report
Jung, S., Ng, D., Laird, C. D., & Mannan, M. S. (2010). A new approach for facility siting using mapping risks on a plant grid area and optimization. Journal of Loss Prevention in the Process Industries, 23(6), 824-830.
Kim, J., Ransikarbum, K., Kim, N., & Paik, E. (2016, May). Agent-based simulation modeling of low fertility trap hypothesis. In Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (pp. 83-86).
Medina-Herrera, N., Jiménez-Gutiérrez, A., & Grossmann, I. E. (2014). A mathematical programming model for optimal layout considering quantitative risk analysis. Computers & Chemical Engineering, 68, 165-181.
Oh, Y., Ransikarbum, K., Busogi, M., Kwon, D., & Kim, N. (2019). Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line. Reliability Engineering & System Safety, 184, 202-212.
Patsiatzis, D. I., Knight, G., & Papageorgiou, L. G. (2004). An MILP approach to safe process plant layout. Chemical Engineering Research and Design, 82(5), 579-586.
Puchongkawarin, C., & Ransikarbum, K. (2020). An Integrative Decision Support System for Improving Tourism Logistics and Public Transportation in Thailand. Tourism Planning & Development, 1-16.
Rahman, S. M. T., Salim, M. T., & Syeda, S. R. (2014). Facility layout optimization of an ammonia plant based on risk and economic analysis. Procedia Engineering, 90, 760-765.
Ransikarbum, K. (2020). Analysis of Traffic Flow at a Red Light Intersection using Computer Simulation technique. Thai Journal of Operations Research: TJOR, 8(2), 1-14.
Ransikarbum, K., & Mason, S. J. (2016). Multiple-objective analysis of integrated relief supply and network restoration in humanitarian logistics operations. International Journal of Production Research, 54(1), 49-68.
Ransikarbum, K., & Mason, S. J. (2016). Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration. International Journal of Production Economics, 182, 324-341.
Ransikarbum, K., Kim, N., Ha, S., Wysk, R. A., & Rothrock, L. (2017). A highway-driving system design viewpoint using an agent-based modeling of an affordance-based finite state automata. IEEE Access, 6, 2193-2205.
Ransikarbum, K., Ha, S., Ma, J., & Kim, N. (2017). Multi-objective optimization analysis for part-to-Printer assignment in a network of 3D fused deposition modeling. Journal of Manufacturing Systems, 43, 35-46.
Ransikarbum, K., Pitakaso, R., & Kim, N. (2020). A Decision-Support Model for Additive Manufacturing Scheduling Using an Integrative Analytic Hierarchy Process and Multi-Objective Optimization. Applied Sciences, 10(15), 5159.
Thai Appraisal. (2018, March 3). The 2018 costs of constructions.http://www.thaiappraisal.org/thai/value/value.php
BSA. (2019, March 1). Building inspection law. http://www.bsa.or.th/
UNCTAD. (2020, February 24). World investment reports 2019: Special Economic Zones. https://unctad.org/
Wang, R., Wu, Y., Wang, Y., & Feng, X. (2017). An industrial area layout design methodology considering piping and safety using genetic algorithm. Journal of cleaner production, 167, 23-31.
Wattanasaeng, N., & Ransikarbum, K. (2019, December). Cost Optimization Model for Plant Assignment in Industrial Estate Planning. In 2019 Research, Invention, and Innovation Congress (RI2C) IEEE, Bangkok, Thailand.
Wattanasaeng, N. & Ransikarbum, K. (2020). Sites Selection of industrial estates for Thailand Border’s Special Economic Zones using Data Envelopment Analysis. UBU Engineering Journal, 13(2), 14-26.
Wattanasaeng, N., & Ransikarbum, K. (2021). Model and Analysis of Economic-and Risk-Based Objective Optimization Problem for Plant Location within Industrial Estates Using Epsilon-Constraint Algorithms. Computation, 9(4), 46.
Wu, Y., Wang, Y., & Feng, X. (2016). A heuristic approach for petrochemical plant layout considering steam pipeline length. Chinese journal of chemical engineering, 24(8), 1032-1037
Yuan, X. U., Zhenyu, W. A. N. G., & Qunxiong, Z. H. U. (2013). An improved hybrid genetic algorithm for chemical plant layout optimization with novel non-overlapping and toxic gas dispersion constraints. Chinese Journal of Chemical Engineering, 21(4), 412-419.