Abstract
In this paper, we present a multi-objective possibilistic programming model to locate distribution centers (DCs) and allocate customers' demands in a supply chain network design (SCND) problem. The SCND problem deals with determining locations of facilities (DCs and/or plants), and also shipment quantities between each two consecutive tier of the supply chain. The primary objective of this study is to consider different risk factors which are involved in both locating DCs and shipping products as an objective function. The risk consists of various components: the risks related to each potential DC location, the risk associated with each arc connecting a plant to a DC and the risk of shipment from a DC to a customer. The proposed method of this paper considers the risk phenomenon in fuzzy forms to handle the uncertainties inherent in these factors. A possibilistic programming approach is proposed to solve the resulted multi-objective problem and a numerical example for three levels of possibility is conducted to analyze the model.
Highlights
One of the primary issues in facility location problem is to locate a set of new facilities such that the transportation cost from various facilities to customers is minimized
There are cases where no limit is assigned to plants and/or distribution centers (DCs) (Kuehn & Hamburger, 1963; Kaufman et al, 1977; Ro & Tcha, 1984; Brimberg et al, 2000) and the resulted models are formulated as uncapacitated facility location problem (UFLP), while there are other cases where some realistic constraints such as production power of plants and storage space
In this paper we develop a fuzzy multi-objective mixed integer linear programming (FMOMILP) model for capacitated DC location and distribution decisions in supply chains where demands of customers and capacities of the DCs are assumed to have some possibility distribution, and risks associated with each potential DC location as well as each arc of the network are considered as fuzzy numbers
Summary
One of the primary issues in facility location problem is to locate a set of new facilities such that the transportation cost from various facilities to customers is minimized. In the SCM context we generally seek the best sites for locating distribution centers (DCs) or warehouses in a discrete solution space such that total fixed cost of locating DCs and variable transportation costs for distributing products (commodities) from manufacturing plants to customers through opened DCs are minimized. This type of problems is normally modeled as mixed integer programming (MIP) formulations. There are cases where no limit is assigned to plants and/or DCs (Kuehn & Hamburger, 1963; Kaufman et al, 1977; Ro & Tcha, 1984; Brimberg et al, 2000) and the resulted models are formulated as uncapacitated facility location problem (UFLP), while there are other cases where some realistic constraints such as production power of plants and storage space
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