Solving Capacitated p-Median Problem by a New Structure of Neural Network
DOI:
https://doi.org/10.23055/ijietap.2012.19.8.324Keywords:
Location-Allocation, Capacitated p-Median Problem (CPMP), Neural Network, Hopfield NetworkAbstract
One of the most popular and well-known location-allocation problems is the capacitated p-median problem (CPMP), which location of capacitated medians are located to serve a set of customer so that the total of the distance between the customers and medians (or facilities) is minimized. In this paper, first a new formulation (model) for the CPMP is presented based on two type of decision variables and linear constraint, second based on the presented new model (formulation) is proposed a new neural network structure with five layer for solving the CPMP. The proposed neural network consist of two layers of competitive recurrent neural network with process units, location and allocation layer, and other three layers each layer with , , process units respectively, which control (supervise) location and allocation layer. The useful of this proposed network is to provide feasible solutions and since the constraints are united in the neural structure instead of the energy function, therefore tuning parameters will be obviated. According to computational dynamic of new neural network the energy function (objective function) always decreases or remains constant. The effectiveness and efficiency of our algorithm for standard and simulated problems with different sizes are analyzed. The results indicate that the proposed neural network generates good quality and solutions.
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