The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization

This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top of the non-optimizing relational constraint solver Kodkod. We compare the performance of this new algorithm against two algorithms from the literature [Gavanelli 2002, Lukasiewycz et alia 2007, Laumanns et alia 2006]) on three micro-benchmark problems (n-Queens, n-Rooks, and knapsack) and on two aerospace case studies. Results indicate that the new algorithm is better for the kinds of many-objective problems that our aerospace collaborators are interested in solving. The new algorithm returns Pareto-optimal solutions as it computes

Identifying preferred solutions in multiobjective combinatorial optimization problems

We develop an evolutionary algorithm for multiobjective combinatorial optimization problems. The algorithm aims at converging the preferred solutions of a decision-maker. We test the performance of the algorithm on the multiobjective knapsack and multiobjective spanning tree problems. We generate the true nondominated solutions using an exact algorithm and compare the results with those of the evolutionary algorithm. We observe that the evolutionary algorithm works well in approximating the solutions in the preferred regions

Metaheuristics and Their Hybridization to Solve the Bi-objective Ring Star Problem: a Comparative Study

This paper presents and experiments approaches to solve a new bi-objective routing problem called the ring star problem. It consists of locating a simple cycle through a subset of nodes of a graph while optimizing two kinds of cost. The first objective is the minimization of a ring cost that is related to the length of the cycle. The second one is the minimization of an assignment cost from non-visited nodes to visited ones. In spite of its obvious bi-objective formulation, this problem has always been investigated in a single-objective way. To tackle the bi-objective ring star problem, we first investigate different stand-alone search methods. Then, we propose two cooperative strategies that combines two multiple objective metaheuristics: an elitist evolutionary algorithm and a population-based local search. We apply this new hybrid approaches to well-known benchmark test instances and demonstrate their effectiveness in comparison to non-hybrid algorithms and to state-of-the-art methods

Metaheuristics and cooperative approaches for the Bi-objective Ring Star Problem

International audienceThis paper presents and investigates different approaches to solve a new bi-objective routing problem called the ring star problem. It consists of locating a simple cycle through a subset of nodes of a graph while optimizing two kinds of cost. The first objective is the minimization of a ring cost that is related to the length of the cycle. The second one is the minimization of an assignment cost from non-visited nodes to visited ones. In spite of its obvious bi-objective formulation, this problem has always been investigated in a single-objective way. To tackle the bi-objective ring star problem, we first investigate different stand-alone search methods. Then, we propose two cooperative strategies that combine two multi-objective metaheuristics: an elitist evolutionary algorithm and a population-based local search. We apply these new hybrid approaches to well-known benchmark test instances and demonstrate their effectiveness in comparison to non-hybrid algorithms and to state-of-the-art methods

Exact And Representative Algorithms For Multi Objective Optimization

In most real-life problems, the decision alternatives are evaluated with multiple conflicting criteria. The entire set of non-dominated solutions for practical problems is impossible to obtain with reasonable computational effort. Decision maker generally needs only a representative set of solutions from the actual Pareto front. First algorithm we present is for efficiently generating a well dispersed non-dominated solution set representative of the Pareto front which can be used for general multi objective optimization problem. The algorithm first partitions the criteria space into grids to generate reference points and then searches for non-dominated solutions in each grid. This grid-based search utilizes achievement scalarization function and guarantees Pareto optimality. The results of our experimental results demonstrate that the proposed method is very competitive with other algorithms in literature when representativeness quality is considered; and advantageous from the computational efficiency point of view. Although generating the whole Pareto front does not seem very practical for many real life cases, sometimes it is required for verification purposes or where DM wants to run his decision making structures on the full set of Pareto solutions. For this purpose we present another novel algorithm. This algorithm attempts to adapt the standard branch and bound approach to the multi objective context by proposing to branch on solution points on objective space. This algorithm is proposed for multi objective integer optimization type of problems. Various properties of branch and bound concept has been investigated and explained within the multi objective optimization context such as fathoming, node selection, heuristics, as well as some multi objective optimization specific concepts like filtering, non-domination probability, running in parallel. Potential of this approach for being used both as a full Pareto generation or an approximation approach has been shown with experimental studies

Robust scheduling for Berth Allocation and Quay Crane Assignment Problem

[EN] Decision makers must face the dynamism and uncertainty of real-world environments when they need to solve the scheduling problems. Different incidences or breakdowns, for example, initial data could change or some resources could become unavailable, may eventually cause the infeasibility of the obtained schedule. To overcome this issue, a robust model and a proactive approach are presented for scheduling problems without any previous knowledge about incidences. This paper is based on proportionally distributing operational buffers among the tasks. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems. The dynamism and uncertainty are managed by assessing the robustness of the schedules. The robustness is introduced by means of operational buffer times to absorb those unknown incidences or breakdowns. Therefore, this problem becomes a multiobjective combinatorial optimization problem that aims to minimize the total service time, to maximize the buffer times, and to minimize the standard deviation of the buffer times. To this end, a mathematical model and a new hybrid multiobjective metaheuristic is presented and compared with two well-known multiobjective genetic algorithms: NSGAII and SPEA2+.This work has been partially supported by by the Spanish Government under research project MINECO TIN2013-46511-C2-1-P, the project PIRSES-GA-2011-294931 (FP7-PEOPLE-2011-IRSES), and the predoctoral FPU fellowship (AP2010-4405).Rodríguez Molins, M.; Salido Gregorio, MA.; Barber Sanchís, F. (2014). Robust scheduling for Berth Allocation and Quay Crane Assignment Problem. Mathematical Problems in Engineering. 2014(1):1-17. https://doi.org/10.1155/2014/834927S11720141Imai, A., Chen, H. C., Nishimura, E., & Papadimitriou, S. (2008). The simultaneous berth and quay crane allocation problem. Transportation Research Part E: Logistics and Transportation Review, 44(5), 900-920. doi:10.1016/j.tre.2007.03.003Hu, Q.-M., Hu, Z.-H., & Du, Y. (2014). Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels. Computers & Industrial Engineering, 70, 1-10. doi:10.1016/j.cie.2014.01.003Salido, M. A., Rodriguez-Molins, M., & Barber, F. (2011). Integrated intelligent techniques for remarshaling and berthing in maritime terminals. Advanced Engineering Informatics, 25(3), 435-451. doi:10.1016/j.aei.2010.10.001Rodriguez-Molins, M., Salido, M. A., & Barber, F. (2013). A GRASP-based metaheuristic for the Berth Allocation Problem and the Quay Crane Assignment Problem by managing vessel cargo holds. Applied Intelligence, 40(2), 273-290. doi:10.1007/s10489-013-0462-4Stahlbock, R., & Voß, S. (2007). Operations research at container terminals: a literature update. OR Spectrum, 30(1), 1-52. doi:10.1007/s00291-007-0100-9Lim, A. (1998). The berth planning problem. Operations Research Letters, 22(2-3), 105-110. doi:10.1016/s0167-6377(98)00010-8Bierwirth, C., & Meisel, F. (2010). A survey of berth allocation and quay crane scheduling problems in container terminals. European Journal of Operational Research, 202(3), 615-627. doi:10.1016/j.ejor.2009.05.031Kim, K. H., & Moon, K. C. (2003). Berth scheduling by simulated annealing. Transportation Research Part B: Methodological, 37(6), 541-560. doi:10.1016/s0191-2615(02)00027-9Giallombardo, G., Moccia, L., Salani, M., & Vacca, I. (2010). Modeling and solving the Tactical Berth Allocation Problem. Transportation Research Part B: Methodological, 44(2), 232-245. doi:10.1016/j.trb.2009.07.003Liang, C., Guo, J., & Yang, Y. (2009). Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning. Journal of Intelligent Manufacturing, 22(3), 471-479. doi:10.1007/s10845-009-0304-8Diabat, A., & Theodorou, E. (2014). An Integrated Quay Crane Assignment and Scheduling Problem. Computers & Industrial Engineering, 73, 115-123. doi:10.1016/j.cie.2013.12.012Park, Y.-M., & Kim, K. H. (2003). A scheduling method for Berth and Quay cranes. OR Spectrum, 25(1), 1-23. doi:10.1007/s00291-002-0109-zZhang, C., Zheng, L., Zhang, Z., Shi, L., & Armstrong, A. J. (2010). The allocation of berths and quay cranes by using a sub-gradient optimization technique. Computers & Industrial Engineering, 58(1), 40-50. doi:10.1016/j.cie.2009.08.002Lambrechts, O., Demeulemeester, E., & Herroelen, W. (2007). Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. Journal of Scheduling, 11(2), 121-136. doi:10.1007/s10951-007-0021-0Hendriks, M., Laumanns, M., Lefeber, E., & Udding, J. T. (2010). Robust cyclic berth planning of container vessels. OR Spectrum, 32(3), 501-517. doi:10.1007/s00291-010-0198-zHan, X., Lu, Z., & Xi, L. (2010). A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time. European Journal of Operational Research, 207(3), 1327-1340. doi:10.1016/j.ejor.2010.07.018Xu, Y., Chen, Q., & Quan, X. (2011). Robust berth scheduling with uncertain vessel delay and handling time. Annals of Operations Research, 192(1), 123-140. doi:10.1007/s10479-010-0820-0Zhen, L., & Chang, D.-F. (2012). A bi-objective model for robust berth allocation scheduling. Computers & Industrial Engineering, 63(1), 262-273. doi:10.1016/j.cie.2012.03.003Blum, C., Puchinger, J., Raidl, G. R., & Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing, 11(6), 4135-4151. doi:10.1016/j.asoc.2011.02.032Ehrgott, M., & Gandibleux, X. (2008). Hybrid Metaheuristics for Multi-objective Combinatorial Optimization. Studies in Computational Intelligence, 221-259. doi:10.1007/978-3-540-78295-7_8Hanafi, R., & Kozan, E. (2014). A hybrid constructive heuristic and simulated annealing for railway crew scheduling. Computers & Industrial Engineering, 70, 11-19. doi:10.1016/j.cie.2014.01.002Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Kim, M., Hiroyasu, T., Miki, M., & Watanabe, S. (2004). SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2. Parallel Problem Solving from Nature - PPSN VIII, 742-751. doi:10.1007/978-3-540-30217-9_75Rodriguez-Molins, M., Ingolotti, L., Barber, F., Salido, M. A., Sierra, M. R., & Puente, J. (2014). A genetic algorithm for robust berth allocation and quay crane assignment. Progress in Artificial Intelligence, 2(4), 177-192. doi:10.1007/s13748-014-0056-3Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32-49. doi:10.1016/j.swevo.2011.03.001Bandyopadhyay, S., Saha, S., Maulik, U., & Deb, K. (2008). A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA. IEEE Transactions on Evolutionary Computation, 12(3), 269-283. doi:10.1109/tevc.2007.900837While, L., Bradstreet, L., & Barone, L. (2012). A Fast Way of Calculating Exact Hypervolumes. IEEE Transactions on Evolutionary Computation, 16(1), 86-95. doi:10.1109/tevc.2010.207729

Fahrplanbasiertes Energiemanagement in Smart Grids

Die Zunahme dezentraler, volatiler Stromerzeugung im Rahmen der Energiewende führt schon heute zu Engpässen in Stromnetzen. Eine Lösung dieser Probleme verspricht die informationstechnische Vernetzung und Koordination der Erzeuger und Verbraucher in Smart Grids. Diese Arbeit präsentiert einen Energiemanagement-Ansatz, der basierend auf Leistungsprognosen und Flexibilitäten der Akteure spezifische, aggregierte Leistungsprofile approximiert. Hierbei werden Netzrestriktionen berücksichtigt

Fahrplanbasiertes Energiemanagement in Smart Grids

Die Zunahme dezentraler, volatiler Stromerzeugung im Rahmen der Energiewende führt schon heute zu Engpässen in Stromnetzen. Eine Lösung dieser Probleme verspricht die informationstechnische Vernetzung und Koordination der Erzeuger und Verbraucher in Smart Grids. Diese Arbeit präsentiert einen Energiemanagement-Ansatz, der basierend auf Leistungsprognosen und Flexibilitäten der Akteure spezifische, aggregierte Leistungsprofile approximiert. Hierbei werden Netzrestriktionen berücksichtigt

Designing screws for polymer compounding in twin-screw extruders

Tese de doutoramento em Ciência e Engenharia de Polímeros e CompósitosConsidering its modular construction, co-rotating twin screw extruders can be easily adapted to work with polymeric systems with more stringent specifications. However, their geometrical flexibility makes the performance of these machines strongly dependent on the screw configuration. Therefore, the definition of the adequate screw geometry to use in a specific polymer system is an important process requirement which is currently achieved empirically or using a trial-and-error basis. The aim of this work is to develop an automatic optimization methodology able to define the best screw geometry/configuration to use in a specific compounding/reactive extrusion operation, reducing both cost and time. This constitutes an optimization problem where a set of different screw elements are to be sequentially positioned along the screw in order to maximize the extruder performance. For that, a global modeling program considering the most important physical, thermal and rheological phenomena developing along the axis of an intermeshing co-rotating twin screw extruder was initially developed. The accuracy and sensitivity of the software to changes in the input parameters was tested for different operating conditions and screw configurations using a laboratorial Leistritz LSM 30.34 extruder. Then, this modeling software was integrated into an optimization methodology in order to be possible solving the Twin Screw Configuration Problem. Multi-objective versions of local search algorithms (Two Phase Local Search and Pareto Local Search) and Ant Colony Optimization algorithms were implemented and adapted to deal with the combinatorial, discrete and multi-objective nature of the problem. Their performance was studied making use of the hypervolume indicator and Empirical Attainment Function, and compared with the Reduced Pareto Search Genetic Algorithm (RPSGA) previously developed and applied to this problem. In order to improve the quality of the results and/or to decrease the computational cost required by the optimization methodology, different hybrid algorithms were tested. The approaches developed considers the use of local search procedures (TPLS and PLS algorithms) into population based metaheuristics, as MOACO and MOEA algorithms. Finally, the optimization methodology developed was applied to the optimization of a starch cationization reaction. Several starch cationization case studies, involving different screw elements screw lengths and conflicting objectives, were tested in order to validate this technique and to prove the potential of this automatic optimization methodology.Devido à sua construção modular, as extrusoras de duplo-fuso co-rotativas podem ser facilmente adaptadas a sistemas poliméricos que requerem especificações mais rigorosas. No entanto, esta flexibilidade geométrica torna o seu desempenho fortemente dependente da configuração do parafuso. Por isso, a tarefa de definir a melhor configuração do parafuso para usar num determinado sistema polimérico é um requisito importante do processo que é actualmente realizada empiricamente ou utilizando um processo de tentativa erro. O objectivo principal deste trabalho é desenvolver uma metodologia automática de optimização que seja capaz de definir a melhor configuração/geometria do parafuso a usar num determinado sistema de extrusão, reduzindo custos e tempo. Este problema é um problema de optimização, onde os vários elementos do parafuso têm que ser sequencialmente posicionados ao longo do eixo do parafuso de forma a maximizar o desempenho da extrusora. Para isso, foi inicialmente desenvolvido um programa de modelação que considera os mais importantes fenómenos físicos, térmicos e reológicos que ocorrem ao longo da extrusora de duplo fuso co-rotativa. De forma a testar a precisão e a sensibilidade do software às alterações dos parâmetros, diversas condições operativas e configurações de parafuso foram testadas tendo como base uma extrusora laboratorial Leistritz LSM 30.34. Seguidamente, este software de modelação foi integrado numa metodologia de optimização com vista à resolução do problema de configuração da extrusora de duplo-fuso. Para lidar com a natureza combinatorial, discreta e multi-objectiva do problema em estudo, foram adaptadas e implementadas versões multi-objectivas de algoritmos de procura local (Two-Phase Local Search and Pareto Local Search) e Ant Colony Optimization. O desempenho dos diversos algoritmos foi estudado usando o hipervolume e as Empirical Attainment Functions. Os resultados foram comparados com os resultados obtidos com o algoritmo genético Reduced Pareto Search Genetic Algorithm (RPSGA) desenvolvido e aplicado anteriormente a este problema. Com o objectivo de melhorar a qualidade dos resultados e/ou diminuir o esforço computacional exigido pela metodologia de optimização, foram testadas diversas hibridizações. Os algoritmos híbridos desenvolvidos consideram a integração de algoritmos de procura local (TPLS e PLS) noutras metheuristicas, como MOACO e MOEA. Por fim, a metodologia de optimização desenvolvida neste trabalho foi testada na optimização de uma reacção de cationização do amido. Para validar esta técnica e provar o seu potencial, foram realizados vários estudos envolvendo diferentes elementos e comprimentos de parafusos, bem como, a optimização de objectivos em conflito