Location Allocation Problem Using Genetic Algorithm and Simulated Annealing: a Case Study Based on School in Enschede

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. Traditional exact method cannot solve location allocation problem efficiently. This problem does not limit itself in a small spectrum rather it has been grown with lots of branches for around a century. Capacity, cost, different type facilities, demands, time, different type of distances and mixing with diverse real world problems have made location allocation problem much complex. Metaheuristic solutions like genetic algorithm and simulated annealing have been exploited for long time to solve location allocation problem. In several researches of location allocation problem, these two algorithms have been bought into one umbrella and have been proved efficient. But location allocation problem with integrated GIS, genetic algorithm and simulated annealing was not much explored. This research has explored location allocation problem by both genetic algorithm and simulated annealing with GIS integration. To achieve this, two case studies based on Enschede schools have been performed. Location allocation problem usually considers nearest distance. Through these case studies, location allocation problem also considers nearest distance with various criteria like capacity, user preference, existing facility etc. ii ACKNOWLEDGEMENTS I would like to thank both of my supervisors for their support and guidance all through this work. I would like to thank my parents and family for their blessings. I would like to thank almighty Allah.