The existing works considering the flow-based discount factor in the hub and spoke problems, assume that increasing the amount of flow passing through each edge of network continuously decreases the unit flow transportation cost. Although a higher volume of flow allows for using wider links and consequently cheaper transportation, but the unit of flow enjoys more discounts, quite like replacing...

in this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty is characterized by given finite numbers of scenarios. in this model, the objective function minimizes the total expected costs of transportation and opening facilities subject to the robustness constraint. to tackle the problem efficiently and effectively, an effici...

The aim of this study is to present a hybrid algorithm based on two meta-heuristic algorithms named Vibration Damping Optimization (VDO) algorithm and Genetic Algorithm (GA) for solving the stochastic capacitated location-allocation problem. For solving the model more efficiently, the simplex algorithm, stochastic simulation and the proposed hybrid algorithm are integrated to design a powerful ...

INTRODUCTION Facility location decisions are costly and difficult to change. One of the most important issues in facility location problem and specifically supply chain management is locating and allocating the distribution centers. These strategic decisions are critical factors of whether materials will flow efficiently through the chain network. The location of distribution centers are affect...

This article proposes a novel Pareto-based multiobjective meta-heuristic algorithm named non-dominated ranking genetic algorithm (NRGA) to solve multi-facility location-allocation problem. In NRGA, a fitness value representing rank is assigned to each individual of the population. Moreover, two features ranked based roulette wheel selection including select the fronts and choose solutions from ...

The Maximal Covering Location Problem (MCLP) maximizes the population that has a facility within a maximum travel distance or time. Numerous extensions have been proposed to enhance its applicability, like the probabilistic model for the maximum covering location-allocation with constraint in waiting time or queue length for congested systems, with one or more servers per service center. In thi...

Given a sequence of activities and transport modes, for which a framework has been provided in previous work, this paper evaluates the use of a Reinforcement Machine Learning technique. The technique simulates time and location allocation for these predicted sequences and enables the prediction of a more complete and consistent activity pattern. The main contributions of the paper to the curren...

Solution techniques for location-allocation problems usually are not a part of microcomputer-based geoprocessing systems because of the large volumes of data to process and store and the complexity of algorithms. In this paper, it is shown that processing costs for the most accurate, heuristic, location-allocation algorithm can be drastically reduced by exploiting the spatial structure of locat...

The location-allocation problem has been studied over 50 years and recently a new method called “gravity p-median model” is introduced to the public. The key idea is that the probability of customers visit a facility is based on a distance decay function rather than directly choose the nearest one. An empirical test shows that the solution of gravity p-median model with exponential distance dec...

In this paper we consider a generalized class of location-allocation problems, in which N new facilities are to be located in the plane with respect to M objects. We assume each object to be associated with a non-negative convex cost function, specifying the expenses for serving the object from any location in the plane. For the resulting multi-dimensional mixed-integer optimization problem two...