a new guideline for proper allocation of multipoles in the multiple multipole method (mmp) is proposed. in an ‘a posteriori’ approach, subspace fitting (ssf) is used to find the best location of multipole expansions for the two dimensional dielectric scattering problem. it is shown that the best location of multipole expansions (regarding their global approximating power) coincides with the med...

DISCLAIMER This document describes work undertaken as part of a programme of study at the Faculty of Geo-Information Science and Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the Faculty. i ABSTRACT Location allocation is a combinatorial optimization problem. Traditi...

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The problems of facility location and the allocation of demand points to facilities are crucial research issues in spatial data analysis and urban planning. It is very important for an organization or governments to best locate its resources and facilities and efficiently manage resources to ensure that all demand points are covered and all the needs are met. Most of the recent studies, which f...

This paper proposes a bi-objective model for the facility location problem under a congestion system. The idea of the model is motivated by applications of locating servers in bank automated teller machines (ATMS), communication networks, and so on. This model can be specifically considered for situations in which fixed service facilities are congested by stochastic demand within queueing frame...

In this paper, we develop a capacitated location-covering model considering interval values for demand and service parameters. We also consider flexibility on distance standard for covering demand nodes by the servers. We use the satisfaction degree to represent the constraint of service capacity. The proposed model belongs to the class of mixed integer programming models. Our model can be redu...

Article history: Received 1 September 2013 Received in revised form 6 May 2014 Accepted 7 May 2014 Available online 20 May 2014

A large number of heuristic search algorithms are available for function optimization. Each of these heuristics, e.g., simulated annealing, genetic algorithms, tabu search, etc., has been shown to be effective at finding good solutions efficiently. However, little work has been directed at determining what are the important problem characteristics for which one algorithm is more efficient than ...