Applying the big bang-big crunch metaheuristic to large-sized operational problems

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature

WoLF Ant

Ant colony optimization (ACO) algorithms can generate quality solutions to combinatorial optimization problems. However, like many stochastic algorithms, the quality of solutions worsen as problem sizes grow. In an effort to increase performance, we added the variable step size off-policy hill-climbing algorithm called PDWoLF (Policy Dynamics Win or Learn Fast) to several ant colony algorithms: Ant System, Ant Colony System, Elitist-Ant System, Rank-based Ant System, and Max-Min Ant System. Easily integrated into each ACO algorithm, the PDWoLF component maintains a set of policies separate from the ant colony\u27s pheromone. Similar to pheromone but with different update rules, the PDWoLF policies provide a second estimation of solution quality and guide the construction of solutions. Experiments on large traveling salesman problems (TSPs) show that incorporating PDWoLF with the aforementioned ACO algorithms that do not make use of local optimizations produces shorter tours than the ACO algorithms alone

Ant colony optimization

En los últimos años, la comunidad científica ha realizado una gran cantidad de propuestas de nuevas metaheurísticas que prometían resolver un amplio espectro de problemas de optimización del tipo NP. Sin embargo, en la práctica solamente un grupo pequeño de esas propuestas han logrado consolidarse, demostrando una amplia aplicabilidad sobre problemas de muy diversas características y adquiriendo la madurez necesaria como técnica de optimización para ser una alternativa real al momento de resolver un problema de optimización. Ant Colony Optimization (ACO) es una metaheurística sobre la que se ha trabajado ampliamente en los últimos 15 años. Se ha aplicado con éxito sobre varios de los problemas estándares de optimización demostrando su potencial. El presente reporte es un relevamiento de las diversas variantes de ACO que han sido propuestas en estos 15 años. El eje central de este relevamiento es el estudio de las propuestas existentes para problemas estáticos de optimación combinatoria

Ant Colony Optimization para la resolución del Problema de Steiner Generalizado

Esta tesis presenta un estudio de la metaheurïstica Ant Colony Optimization (ACO) y de la aplicación de técnicas de computación de alto desempeño a dicha metaheurïstica. En particular, se aborda la aplicación de ACO a la resolución del Problema de Steiner Generalizado (GSP). El GSP consiste en el diseño de una subred de costo mínimo que verifique ciertos requerimientos prefijados de conexión entre pares de nodos distinguidos. En el trabajo se presentan versiones ACO con dos enfoques constructivos de la solución distintos. El primero de los enfoques se basa en incorporar aristas hasta completar un camino, mientras que el segundo determina los K caminos más cortos y realiza una selección entre ellos. También se propone una novedosa formulación de un modelo celular aplicado a la metaheurística ACO y su posible paralelización Se incluye los resultados de un estudio experimental exhaustivo de todas las propuestas formuladas en este trabajo, comprendiendo la evaluación de los enfoques basados en aristas y en caminos y el analizas del efecto del tamaño de la población, de la cantidad de caminos y de incorporar operadores de búsqueda local para el enfoque basado en caminos. El estudio permitió comprobar que la utilización de un enfoque basado en caminos con la incorporación del operador de búsqueda local iterado obtiene resultados competitivos con las mejores técnicas disponibles en la actualidad. Asimismo, se evaluaron las versiones secuencial y paralela del modelo celular propuesto, constatándose que el desempeño computacional de la implementación paralela es muy promisoria, aunque se producen leves pérdidas en la calidad de las soluciones con relación a estructurar la población en la forma tradiciona

Coupling order release methods with autonomous control methods – an assessment of potentials by literature review and discrete event simulation

[EN] Production planning and control faces increasing uncertainty, dynamics and complexity. Autonomous control methods proved themselves as a promising approach for coping with these challenges. However, there is a lack of knowledge regarding the interaction between autonomous control and precedent functions of production planning and control. In particular, up to now previous research has paid no attention to the influence of order release methods on the efficiency of autonomous control methods. Thereby, many researchers over the last decades provided evidence that the order release function has great influence on the logistic objective achievement in conventional production systems. Therefore, this paper examines the influence of order release methods on the efficiency of autonomous control methods by both theoretic evaluation and discrete event simulation. The simulation results indicate an overall high influence. Moreover, the logistic performance differs considerably depending on the implemented order release methods and the combinations of order release methods with autonomous control methods. The findings highlight demand for further research in this field.This research was funded by the German Research Foundation (DFG) under the reference number SCHO 540/26-1 “Methods for the interlinking of central planning and autonomous control in production”.Grundstein, S.; Schukraft, S.; Scholz-Reiter, B.; Freitag, M. (2015). Coupling order release methods with autonomous control methods – an assessment of potentials by literature review and discrete event simulation. International Journal of Production Management and Engineering. 3(1):43-56. https://doi.org/10.4995/ijpme.2015.3199SWORD435631Park, H.-S., & Tran, N.-H. (2012). An autonomous manufacturing system based on swarm of cognitive agents. Journal of Manufacturing Systems, 31(3), 337-348. doi:10.1016/j.jmsy.2012.05.002Pinedo, M. L. (2008). Scheduling. theory, algorithms and systems. New York, USA: Springer.Rekersbrink, H. (2012). Methoden zum selbststeuernden Routing autonomer logistischer Objekte. (doctoral disserta-tion). Universität Bremen, Bremen, Germany.Scholz-Reiter, B., Böse, F., Jagalski, T., & Windt, K. (2007a). Selbststeuerung in der betrieblichen Praxis. Ein Framework zur Auswahl der passenden Selbststeuerungsstrategie. Industrie Management, 23(3), 7-10.Scholz-Reiter, B., Freitag, M., de Beer, C., & Jagalski, T. (2006). The influence of production network's complexity on the performance of autonomous control methods. Proceedings of the 5th CIRP International Seminar on Computation in Manufacturing engineering, 317-320.Scholz-Reiter, B., Freitag, M., de Beer, C., & Jagalski, T. (2005b). Modelling and Analysis of Autonomous Shop Floor Control. Proceedings of 38th CIRP International Seminar on Manufacturing Systems, 16-18.Scholz-Reiter, B., & Scharke, H. (2000). Reaktive Planung. Industrie Management, 16(2), 21-26.Weng, M. X., Wu, Z., Qi, G., & Zheng, L. (2008). Multi-agent-based workload control for make-to-order manufacturing. International Journal of Production Research, 46(8), 2197-2213. doi:10.1080/00207540600969758Westphal, J. R. (2001). Komplexitätsmanagement in der Produktionslogistik - ein Ansatz zur flussorientierten Gestal-tung und Lenkung heterogener Produktionssysteme. Wiesbaden, Germany: Deutscher Universitäts Verlag.Wiendahl, H.-P. (Ed.). (1991). Anwendung der belastungsorientierten Auftragsfreigabe. Munich, Germany: Carl Hanser.Wiendahl, H.-P. (1997). Fertigungsregelung. Logistische Beherrschung von Fertigungsabläufen auf Basis des Trich-termodells. Munich, Germany: Carl Hanser.Wiendahl, H.-P. (Ed.). (2005). Betriebsorganisation für Ingenieure. Munich: Hanser.Wyssusek, B. (1999). Grundlagen der Systemanalyse. In Krallmann, H., Frank, H., & Gronau, N. (Eds.), Sytemanalyse im Unternehmen (pp. 19-43). Munich, Germany: Oldenbourg

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation