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Top1. Introduction
The facility allocation problem in the supply chain domain is a complex optimization problem that involves determining the best locations to place facilities such as warehouses, distribution centers, manufacturing plants, and retail stores (Melo et al., 2009). The facility allocation problem usually involves several competing goals, including reducing expenses, increasing service levels, and optimising inventory. It isn't easy to find a trade-off between these goals (Govindan et al., 2017). Hence specialised optimisation approaches are needed. With several tiers of suppliers, intermediary facilities, and end users, modern supply chains can be complicated (Pham & Yenradee, 2017). In such intricate networks, facility placement must take interdependencies and flows between different nodes under consideration (Aldrighetti et al., 2021). Such kind of importance of the facility allocation problem in the supply chain network has a long-term impact on investment. Therefore, this research is focused on the facility allocation problem in supply chain networks. Multi-objective Optimization Facility allocation often involves:
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Balancing multiple conflicting objectives, such as minimizing transportation costs.
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Minimizing inventory holding costs.
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Maximizing customer service levels.
Achieving the right trade-offs among these objectives can be complex. Compared to conventional single-objective approaches, the suggested multi-objective algorithm offers a more thorough and flexible solution to facility allocation problems (Tiwari & Garg, 2022). It enables organizations to make well-informed decisions that balance conflicting aims, deal with complexity, and navigate uncertainties in the supply chain domain by taking numerous objectives under perspective (C & P, 2022), (Sissodia et al., 2022).
The main challenge in the facility allocation problem is the solutions of optimal locations are conflicting in nature.The facility allocation problem's main difficulty is that the best sites for each facility have contradictory solutions. The competitive selection of the facility, where new facilities must compete with old facilities to serve the same demand, is one of the primary causes of this contradictory solution (Yakavenka et al., 2020). This competitive site model with attractive facilities has to be taken into account. According to the competitive location model, there is an intense desire for the closest facilities to guarantee the transportation component. Unfortunately, when facility attractiveness is assessed, the nearest facility might not be the most attractive (Nayeri et al., 2020). The market size, product accessibility, pricing, and other elements contribute to the facility's attractiveness. These factors may lead to demand choosing a different facility over the closest one to meet its needs (Eskandarpour et al., 2017). As a result, it is essential to take into account and evaluate the facility's attractiveness and distance at the same time. The facility's coverage standards were thus introduced. Considerable losses in a specific supply chain's utility and finances, which impact the entire supply chain network, may come from the lack of proper facility availability (S.-C. Wang & Chen, 2017a). 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 (
), overall cost (
), which includes setup and transportation expenses, and distance decreasing function (
).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).