Optimizing a bi-objective reliable facility location problem with adapted stochastic measures using tuned-parameter multi-objective algorithms..pdf
Creators: name: Seifbarghy, Mehdi
name: Jalalia, Sajjad
name: Sadeghi, Javad
name: Ahmadi, Samad
Publisher: name: figshare
Year: 2020
Subjects: subject: 90603 Industrial Electronics
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: 90505 Infrastructure Engineering and Asset Management
subject: FOS: Civil engineering
subject: FOS: Civil engineering
subject: 91599 Interdisciplinary Engineering not elsewhere classified
subject: FOS: Other engineering and technologies
subject: FOS: Other engineering and technologies
subject: 91003 Machine Tools
subject: 91004 Machining
subject: 91099 Manufacturing Engineering not elsewhere classified
subject: 91005 Manufacturing Management
subject: 91006 Manufacturing Processes and Technologies (excl. Textiles)
subject: 91007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)
subject: 91008 Manufacturing Safety and Quality
subject: 91101 Marine Engineering
subject: FOS: Environmental engineering
subject: FOS: Environmental engineering
subject: 91199 Maritime Engineering not elsewhere classified
subject: 91299 Materials Engineering not elsewhere classified
subject: FOS: Materials engineering
subject: FOS: Materials engineering
subject: Mechanical Engineering
subject: FOS: Mechanical engineering
subject: FOS: Mechanical engineering
subject: 91399 Mechanical Engineering not elsewhere classified
subject: 90304 Medical Devices
subject: FOS: Medical engineering
subject: FOS: Medical engineering
subject: 90404 Membrane and Separation Technologies
subject: FOS: Chemical engineering
subject: FOS: Chemical engineering
subject: 91207 Metals and Alloy Materials
subject: 91306 Microelectromechanical Systems (MEMS)
subject: 90604 Microelectronics and Integrated Circuits
subject: 91009 Microtechnology
subject: 91404 Mineral Processing/Beneficiation
subject: 91405 Mining Engineering
subject: 91102 Naval Architecture
subject: 90904 Navigation and Position Fixing
subject: 90405 Non-automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels)
subject: Nuclear Engineering
subject: 91506 Nuclear Engineering (incl. Fuel Enrichment and Waste Processing and Storage)

description: The stochastic process is one the most important tools to overcome uncertainties of supply chain problems. Being a lack of studies on constrained reliable facility location problems (RFLP) with multiple capacity levels, this paper develops a bi-objective RFLP with multiple capacity levels in a three echelon supply chain management while there is a constraint on the coverage levels. Moreover, there is a provider-side uncertainty for distribution-centers (DCs). The aim of this paper is to find a near-optimal solution including suitable locations of DCs and plants, the fraction of satisfied customer demands, and the fraction of items sent to DCs to minimize the total cost and to maximize fill rate, simultaneously. As the proposed model belongs to NP-Hard problems, a meta-heuristic algorithm called multi-objective biogeography-based optimization (MOBBO) is employed to find a near-optimal Pareto solution. Since there is no benchmark in the literature to compare provided solutions, a non-dominated ranking genetic algorithm (NRGA) and a multi objective simulated annealing (MOSA) are used to verify the solution obtained by MOBBO while a two-stage stochastic programming (2-SSP) is employed to capture randomness of DCs. This paper uses the adapted concepts of expected value of perfect information (EVPI) and the value of stochastic solution (VSS) in order to validate 2-SSP. Moreover, the parameters of algorithms are tuned by the response surface methodology (RSM) in the design of experiments. Besides, an exact method, called branch-and-bound method via GAMS optimization software, is used to compare lower and upper bounds of Pareto fronts to optimize two single-objective problems separately.