A multi-objective integrated optimisation model for facility location and order allocation problem in a two-level supply chain network

Abstract

This study proposes a mixed-integer multi-objective integrated mathematical model solving facility location and order allocation optimization problems simultaneously in a two-echelon supply chain network.The proposed problem is motivated by a factoyless concept and, by providing a dynamic decision-making solution under a multi-period time horizon. Within the model, we also determine the optimal replenishment amounts of production facilities by the multi-objective functions. The multi-objective functions include minimization of the total cost, rejected and late delivery units and, maximization of the assessment score of the selected suppliers. The studied dynamic decision model is significant for the cost-efficient management of companies' supply chain networks. The mixed-integer mathematical model is developed by the LP-metric method and it is solved by the GAMS optimization software. Due to the NP-hard structure of the problem, for large-scale instances, we utilize the Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Vibration Damping Optimization (MOVDO) heuristic solution approaches.Numerical results show that for large-scale problems, the MOPSO method performs better in Pareto solutions and decrease run times. However, the MOVDO method performs better regarding the Mean Ideal Distance and the Number of Solutions Cover surface criterion. The developed solution approach by this paper is a generic model which can be applied for any two-level network for simultaneous optimization of supplier selection, location determination of facilities and their replenishment amounts.

A Multi-Objective Model for Designing a Sustainable Closed-Loop Supply Chain Logistics Network

“…They used the weighted sum method to handle the multi-objective model and underlined that improved distribution planning could be regarded as a strategic decision that better aligns the supply chain toward a sustainable one. Other multi-objective functions including minimization of the total cost and rejected and late delivery units and maximization of the assessment score of the selected suppliers were explored in [25] to determine order allocation and facility location of the supply chain network.…”

Web Semantic-Based MOOP Algorithm for Facilitating Allocation Problems in the Supply Chain Domain

“…It involves selecting the locations for warehouses, distribution centers, production plants, and retail outlets to meet customer demand efficiently and effectively (Mahmoudi et al, 2022). The facility allocation problem aims to find the optimal placement of facilities that minimizes costs, maximizes service levels, and improves overall supply chain performance (Amin-Tahmasbi et al, 2023). The problem considers various factors such as facility coverage, facility attractiveness in terms of transportation distance, transportation costs, product availability, inventory costs, facility capacities, customer demand patterns, and service requirements (Jalal et al, 2022), (Chauhan et al, 2019), (Zahraee et al, 2020), (Che et al, 2022).…”

“…The facility's attractiveness consists of multiple factors, such as the size of the market, availability of the product, pricing of the product so on (J. Wang et al, 2020).Numerous elements, including the size of the market, the accessibility of the product, the price of the product, and others, contribute to the facility's attractiveness (Amin-Tahmasbi et al, 2023).We take into account three accurate constraint parameters: facility coverage (C fd ), overall cost ( F f ), which includes setup and transportation expenses, and distance decreasing function ( t fd ).Our proposed algorithm included adequate facilities' availability to minimise the considerable losses in the entire supply chain network. More availability of the facilities can lead to significant losses in utility and finances of a specific supply chain, affecting the overall supply chain network (Taghikhah et al, 2019).…”

Application of Multi-Criteria Optimization Methods in the Calibration Process of Digital Measuring Instruments

“…The scope of their application is very wide. In the literature, there are examples of the use of optimization methods during titanium turning [ 19 ], transport issues [ 20 ], furniture production [ 21 ], traffic [ 22 ], humanitarian aid [ 23 ], energy distribution [ 24 ], logistic [ 25 , 26 , 27 , 28 ], etc. [ 29 , 30 , 31 , 32 ].…”