An Interactive Spatial Decision Support System Enabling Co-Located Collaboration using Tangible User Interfaces for the Multiple Capacitated Facility Location Problem

An Interactive Spatial Decision Support System Enabling Co-Located Collaboration using Tangible User Interfaces for the Multiple Capacitated Facility Location Problem

Nikolaos Ploskas (Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece), Ioannis Athanasiadis (School of Science & Technology, Hellenic Open University, Patras, Greece), Jason Papathanasiou (Department of Business Administration, University of Macedonia, Thessaloniki, Greece) and Nikolaos Samaras (Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece)
Copyright: © 2015 |Pages: 14
DOI: 10.4018/IJDSST.2015040102
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Abstract

The Multiple Capacitated Facility Location Problem (MCFLP) is well-known and studied in the international literature optimization problem. The geographical information data of the enterprises' locations are usually either ignored by the modeler or entered manually in these systems. In this paper, a spatial Decision Support System (DSS) is designed and implemented enabling co-located collaboration using tangible user interfaces through a tabletop. The location of the enterprises and the demand nodes can be added with the use of interactive Google Maps. The DSS extracts the geographical information of the selected locations, find the distances between them and executes a dynamic approximation algorithm for this problem. The interactive spatial DSS has been implemented using Java, TUIO protocol and Google Maps. The tabletop offers a user-friendly interface that can be manipulated with human fingers and fiducials.
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1. Introduction

The facility location (or location-allocation) problem is a well-known operations research problem. The problem consists of a number of enterprises that attempt to find the best location in a specific area in order to install their new facilities while on the same time a number of already established similar facilities exist with known locations (Drezner et al., 2002; Aboolian et al., 2007). New enterprises seek the best location from a set of candidate locations in order to maximize their share and revenue in the specific market. The new enterprises cooperate with each other in order to avoid any overlapping between the market segments they will serve. The facility location problem has many practical applications in different fields, such as supply chain management, air-traffic control, web-server placement, capital investment etc. (Drezner & Hamacher, 2001; Marianov & Serra, 2002; Revelle et al., 2008; Melo et al., 2009).

The international research community offered many variants and extensions of the problem over the years; in this paper, we consider a particular type of the problem, called the Multiple Capacitated Facility Location Problem (MCFLP). In this version of the problem, the market requires a specific quantity/level of a product/service in a determined time period. A set of existing enterprises operate in a specific market producing/offering certain products/services. A set of new cooperating enterprises aim to enter the market and seek the best location from the available candidate locations. The goal of the new enterprises is to obtain the largest possible share of the specific, saturated by the present supply, market by avoiding on the same time any overlapping between the market segments that they will serve. The enterprises should be economically viable in order to enter the market. As such, the production of a new enterprise should be higher than a specified sales threshold level (Shonwiller & Harris, 1996). Existing enterprises should also ensure to be economically viable; if they fail to reach their production thresholds after the entering of the new enterprises, they will be taken off the map (Serra et al., 1999).

Only few software packages exist for the solution of facility location problems (Bender et al., 2002; FLP Spreadsheet Solver, 2014; Sitation, 2014). The geographical information of the enterprises' locations is usually either ignored or entered manually in these systems. Geographical Information Systems (GIS) can assist decision makers to analyze spatial information. GIS technologies have attracted significant attention from researchers. There are a few papers that proposed integration of GIS technologies on DSS for location problems (Lopes et al., 2008; Santos et al., 2011). Google Maps API provides access to read data associated with roads and supplies travel times for each road based on the speed limits.

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