A Sule’s Method initiated genetic algorithm for solving QAP formulation in facility layout design: A real world application

This paper considers the Quadratic Assignment Problem (QAP) as one of the most important issues in optimization. This NP-hard problem has been largely studied in the scientific literature, and exact and approximate (heuristic and meta-heuristic) approaches have been used mainly to optimize one or more objectives. However, most of these studies do not consider or are not tested in real applications. Hence, in this work, we propose the use of Sule’s Method and genetic algorithms, for a QAP (stated as a facility Layout Problem) in a real industry application in Colombia so that the total cost to move the required material between the facilities is minimized. As far as we know, this is the first work in which Sule’s Method and genetic algorithms are used simultaneously for this combinatorial optimization problem. Additionally the proposed approach was tested using well-known datasets from the literature in order to assure its efficiency

Arbeitsbericht (Working Paper) Nr. 2011-07, Dezember 2011

Ilmenauer Beiträge zur Wirtschaftsinformatik Nr. 2011-07 / Technische Universität Ilmenau, Fakultät für Wirtschaftswissenschaften, Institut für Wirtschaftsinformatik ISSN 1861-9223 ISBN 978-3-938940-40-2Abstract: Sub-daily personnel planning, which is the focus of our work offers considerable productivity reserves for companies in certain industries, such as logistics, retail and call centers. However, it also creates complex challenges for the planning software. We compare particle swarm optimisation (PSO), the evolution strategy (ES) and a constructive agentbased heuristic on a set of staff scheduling problems derived from a practical case in logistics. All heuristics significantly outperform conventional manual full-day planning, demonstrating the value of sub-daily scheduling heuristics. PSO delivers the best overall results in terms of solution quality and is the method of choice, when CPU-time is not limited. The approach based on artificial agents is competitive with ES and delivers solutions of almost the same quality as PSO, but is vastly quicker. This suggests that agents could be an interesting method for real-time scheduling or re-scheduling tasks

A Hybrid Lehmer Code Genetic Algorithm and Its Application on Traveling Salesman Problems

Traveling Salesman Problems (TSP) is a widely studied combinatorial optimization problem. The goal of the TSP is to find a tour which begins in a specific city, visits each of the remaining cities once and returns to the initial cities such that the objective functions are optimized, typically involving minimizing functions like total distance traveled, total time used or total cost. Genetic algorithms were first proposed by John Holland (1975). It uses an iterative procedure to find the optimal solutions to optimization problems. This research proposed a hybrid Lehmer code Genetic Algorithm. To compensate for the weaknesses of traditional genetic algorithms in exploitation while not hampering its ability in exploration, this new genetic algorithm will combine genetic algorithm with 2-opt and non-sequential 3-opt heuristics. By using Lehmer code representation, the solutions created by crossover parent solutions are always feasible. The new algorithm was used to solve single objective and multi-objectives Traveling Salesman Problems. A non Pareto-based technique will be used to solve multi-objective TSPs. Specifically we will use the Target Vector Approach. In this research, we used the weighted Tchebycheff function with the ideal points as the reference points as the objective function to evaluate solutions, while the local search heuristics, the 2-opt and non-sequential 3-opt heuristics, were guided by a weighted sum function

Optimization of Heterogeneous UAV Communications Using the Multiobjective Quadratic Assignment Problem

The Air Force has placed a high priority on developing new and innovative ways to use Unmanned Aerial Vehicles (UAVs). The Defense Advanced Research Projects Agency (DARPA) currently funds many projects that deal with the advancement of UAV research. The ultimate goal of the Air Force is to use UAVs in operations that are highly dangerous to pilots, mainly the suppression of enemy air defenses (SEAD). With this goal in mind, formation structuring of autonomous or semi-autonomous UAVs is of future importance. This particular research investigates the optimization of heterogeneous UAV multi-channel communications in formation. The problem maps to the multiobjective Quadratic Assignment Problem (mQAP). Optimization of this problem is done through the use of a Multiobjective Evolutionary Algorithm (MOEA) called the Multiobjective Messy Genetic Algorithm - II (MOMGA-II). Experimentation validates the attainment of an acceptable Pareto Front for a variety of mQAP benchmarks. It was observed that building block size can affect the location vectors along the current Pareto Front. The competitive templates used during testing perform best when they are randomized before each building block size evaluation. This tuning of the MOMGA-II parameters creates a more effective algorithm for the variety of mQAP benchmarks, when compared to the initial experiments. Thus this algorithmic approach would be useful for Air Force decision makers in determining the placement of UAVs in formations

Hochflexibles Workforce Management

It can be observed that companies tend to use a very demand driven personnel scheduling instead of using fixed shifts. In this context the term highly flexible workforce management (WFM) is used. With instruments such as the planning of subdaily workplace rotations, the combination of working time model generation and personnel scheduling or the combination of personnel scheduling and vehicle routing the demand for personnel can be covered very well. Such problems are novel and found little attention by researchers up to now.In this work classical OR-algorithms, metaheuristics and multi-agent systems (MAS) are evaluated on real world problems from logistics, retail and British Telecom. It can be shown, that classical OR-algorithms are not appropriate for these problems of highly flexible WFM, because of impractical CPU-times. On the other hand selected metaheuristics are very suitable. MAS should not be favoured, because selected metaheuristics performed always better. It must point out that a hybrid algorithm (a metaheuristic with a problem-specific repair) is responsible for the success of metaheuristics. MAS lack of a central planning instance that makes major changes for which agents are not able to do. Numerous algorithms of this work where originally developed for continuous problems. The adaption to combinatorial problems is described too. The appropriate adaption of parameters is also addressed.Zunehmend ist bei Unternehmen ein Trend weg von der starren Schicht- oder Dienstplanung hin zu einer auf den Personalbedarf ausgerichteten Planung festzustellen. In diesem Zusammenhang wird der Begriff hochflexibles Workforce Management (WFM) geprägt. Mit Instrumenten wie der Planung untertägiger Arbeitsplatzwechsel, der Kombination aus Arbeitszeitmodellerstellung und Einsatzplanung sowie der kombinierten Personaleinsatz- und Tourenplanung kann der Personaleinsatz sehr gut an den Personalbedarf angepasst werden. Derartige Problemstellungen sind neuartig und fanden in der Forschung bisher wenig Beachtung