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Abstract
The capacitated plant location problem with customer and supplier matching can be modeled as a mixed integer linear program, where the product distribution from plants to customers and the material supply from suppliers to plants are considered together. In order to save allocation cost, distribution trip and a supply trip is merged into one triangular trip. Moreover, vehicles from plants visit a customer and a supplier for each trip. In this paper, we assume interval uncertainties in the demands of costumers. We show that the robust counterpart of the original model with interval uncertainty is equivalent to a larger mixed integer linear program. Finally, the original and robust models are compared on several randomly generated examples showing the impact of uncertainty.
Highlights
The capacitated plant location problem (CPL) consists of locating a set of potential plants with capacities, and assigning a set of customers to these plants
The CPL is modeled as a mixed integer linear programming problems, where several heuristic algorithms are designed to solve it [2,3,4,5,6, 8]
Customers, suppliers and a set of potential plants are mixed in a same network and each open plant holds its fleet of homogenous vehicles
Summary
The capacitated plant location problem (CPL) consists of locating a set of potential plants with capacities, and assigning a set of customers to these plants. A slightly different problem is CLP with customer and supplier matching. In this problem, customers, suppliers and a set of potential plants are mixed in a same network and each open plant holds its fleet of homogenous vehicles. First we describe CLP with customer and supplier matching and present its robust counterpart when interval uncertainties are assumed for demands [1]. It is shown that the robust counterpart of CLP is a larger mixed integer linear program. We consider uncertainty on the demands of customers and show the robust counterpart of (2) is equivalent to a larger mixed integer linear program. The impact of uncertainty is shown using several randomly generated test problems
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