Exploiting fitness distance correlation of set covering problems

The set covering problem is an NP-hard combinatorial optimization problem that arises in applications ranging from crew scheduling in airlines to driver scheduling in public mass transport. In this paper we analyze search space characteristics of a widely used set of benchmark instances through an analysis of the fitness-distance correlation. This analysis shows that there exist several classes of set covering instances that have a largely different behavior. For instances with high fitness distance correlation, we propose new ways of generating core problems and analyze the performance of algorithms exploiting these core problems.Set covering, iterated local search

Studying Solutions of the p-Median Problem for the Location of Public Bike Stations

The use of bicycles as a means of transport is becoming more and more popular today, especially in urban areas, to avoid the disadvantages of individual car traffic. In fact, city managers react to this trend and actively promote the use of bicycles by providing a network of bicycles for public use and stations where they can be stored. Establishing such a network involves the task of finding best locations for stations, which is, however, not a trivial task. In this work, we examine models to determine the best location of bike stations so that citizens will travel the shortest distance possible to one of them. Based on real data from the city of Malaga, we formulate our problem as a p-median problem and solve it with a variable neighborhood search algorithm that was automatically configured with irace. We compare the locations proposed by the algorithm with the real ones used currently by the city council. We also study where new locations should be placed if the network grows.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research was partially funded by the University of Málaga, Andalucı́a Tech, the Spanish MINECO and FEDER projects: TIN2014- 57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

Algorithm Configuration: Learning policies for the quick termination of poor performers

One way to speed up the algorithm configuration task is to use short runs instead of long runs as much as possible, but without discarding the configurations that eventually do well on the long runs. We consider the problem of selecting the top performing configurations of the Conditional Markov Chain Search (CMCS), a general algorithm schema that includes, for examples, VNS. We investigate how the structure of performance on short tests links with those on long tests, showing that significant differences arise between test domains. We propose a "performance envelope" method to exploit the links; that learns when runs should be terminated, but that automatically adapts to the domain

A metaheuristic multi-criteria optimisation approach to portfolio selection

Portfolio selection is concerned with selecting from of a universe of assets the ones in which one wishes to invest and the amount of the investment. Several criteria can be used for portfolio selection, and the resulting approaches can be classified as being either active or passive. The two approaches are thought to be mutually exclusive, but some authors have suggested combining them in a unified framework. In this work, we define a multi-criteria optimisation problem in which the two types of approaches are combined, and we introduce a hybrid metaheuristic that combines local search and quadratic programming to obtain an approximation of the Pareto set. We experimentally analyse this approach on benchmarks from two different instance classes: these classes refer to the same indexes, but they use two different return representations. Results show that this metaheuristic can be effectively used to solve multi-criteria portfolio selection problems. Furthermore, with an experiment on a set of instances coming from a different financial scenario, we show that the results obtained by our metaheuristic are robust with respect to the return representation used

Multi-objective ant colony optimization for the twin-screw configuration problem

The Twin-Screw Configuration Problem (TSCP) consists in identifying the best location of a set of available screw elements along a screw shaft. Due to its combinatorial nature, it can be seen as a sequencing problem. In addition, different conflicting objectives may have to be considered when defining a screw configuration and, thus, it is usually tackled as a multi-objective optimization problem. In this research, a multi-objective ant colony optimization (MOACO) algorithm was adapted to deal with the TSCP. The influence of different parameters of the MOACO algorithm was studied and its performance was compared with that of a previously proposed multi-objective evolutionary algorithm and a two-phase local search algorithm. The experimental results showed that MOACO algorithms have a significant potential for solving the TSCP.This work has been supported by the Portuguese Fundacao para a Ciencia e Tecnologia under PhD grant SFRH/BD/21921/2005. Thomas Stutzle acknowledges support of the Belgian F.R.S-FNRS of which he is a research associate, the E-SWARM project, funded by an ERC Advanced Grant, and by the Meta-X project, funded by the Scientific Research Directorate of the French Community of Belgium

Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

[EN] Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are ATP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed MD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.Pedro Alfaro-Fernandez and Ruben Ruiz are partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization" (No. RTI2018-094940-B-I00) financed with FEDER funds and under grants BES-2013-064858 and EEBB-I-15-10089. This work was supported by the COMEX project (P7/36) within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stiitzle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director.Alfaro-Fernandez, P.; Ruiz García, R.; Pagnozzi, F.; Stützle, T. (2020). Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems. European Journal of Operational Research. 282(3):835-845. https://doi.org/10.1016/j.ejor.2019.10.004S8358452823Bożejko, W., Gnatowski, A., Niżyński, T., Affenzeller, M., & Beham, A. (2018). Local Optima Networks in Solving Algorithm Selection Problem for TSP. Advances in Intelligent Systems and Computing, 83-93. doi:10.1007/978-3-319-91446-6_9Bożejko, W., Pempera, J., & Smutnicki, C. (2013). Parallel tabu search algorithm for the hybrid flow shop problem. Computers & Industrial Engineering, 65(3), 466-474. doi:10.1016/j.cie.2013.04.007Burke, E. K., Hyde, M. R., & Kendall, G. (2012). Grammatical Evolution of Local Search Heuristics. IEEE Transactions on Evolutionary Computation, 16(3), 406-417. doi:10.1109/tevc.2011.2160401Cahon, S., Melab, N., & Talbi, E.-G. (2004). ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. Journal of Heuristics, 10(3), 357-380. doi:10.1023/b:heur.0000026900.92269.ecCarlier, J., & Neron, E. (2000). An Exact Method for Solving the Multi-Processor Flow-Shop. RAIRO - Operations Research, 34(1), 1-25. doi:10.1051/ro:2000103Chung, T.-P., & Liao, C.-J. (2013). An immunoglobulin-based artificial immune system for solving the hybrid flow shop problem. Applied Soft Computing, 13(8), 3729-3736. doi:10.1016/j.asoc.2013.03.006Cui, Z., & Gu, X. (2015). An improved discrete artificial bee colony algorithm to minimize the makespan on hybrid flow shop problems. Neurocomputing, 148, 248-259. doi:10.1016/j.neucom.2013.07.056Ding, J.-Y., Song, S., Gupta, J. N. D., Zhang, R., Chiong, R., & Wu, C. (2015). An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Applied Soft Computing, 30, 604-613. doi:10.1016/j.asoc.2015.02.006Dubois-Lacoste, J., López-Ibáñez, M., & Stützle, T. (2011). A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems. Computers & Operations Research, 38(8), 1219-1236. doi:10.1016/j.cor.2010.10.008Dubois-Lacoste, J., Pagnozzi, F., & Stützle, T. (2017). An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem. Computers & Operations Research, 81, 160-166. doi:10.1016/j.cor.2016.12.021Gupta, J. N. D. (1988). Two-Stage, Hybrid Flowshop Scheduling Problem. Journal of the Operational Research Society, 39(4), 359-364. doi:10.1057/jors.1988.63Gupta, J. N. D., & Stafford, E. F. (2006). Flowshop scheduling research after five decades. European Journal of Operational Research, 169(3), 699-711. doi:10.1016/j.ejor.2005.02.001Hidri, L., & Haouari, M. (2011). Bounding strategies for the hybrid flow shop scheduling problem. Applied Mathematics and Computation, 217(21), 8248-8263. doi:10.1016/j.amc.2011.02.108Hutter, F., Hoos, H. H., Leyton-Brown, K., & Stuetzle, T. (2009). ParamILS: An Automatic Algorithm Configuration Framework. Journal of Artificial Intelligence Research, 36, 267-306. doi:10.1613/jair.2861Johnson, S. M. (1954). Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, 1(1), 61-68. doi:10.1002/nav.3800010110Khalouli, S., Ghedjati, F., & Hamzaoui, A. (2010). A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop. Engineering Applications of Artificial Intelligence, 23(5), 765-771. doi:10.1016/j.engappai.2010.01.008KhudaBukhsh, A. R., Xu, L., Hoos, H. H., & Leyton-Brown, K. (2016). SATenstein: Automatically building local search SAT solvers from components. Artificial Intelligence, 232, 20-42. doi:10.1016/j.artint.2015.11.002Li, J., Pan, Q., & Wang, F. (2014). A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem. Applied Soft Computing, 24, 63-77. doi:10.1016/j.asoc.2014.07.005Liao, C.-J., Tjandradjaja, E., & Chung, T.-P. (2012). An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem. Applied Soft Computing, 12(6), 1755-1764. doi:10.1016/j.asoc.2012.01.011Lopez-Ibanez, M., & Stutzle, T. (2012). The Automatic Design of Multiobjective Ant Colony Optimization Algorithms. IEEE Transactions on Evolutionary Computation, 16(6), 861-875. doi:10.1109/tevc.2011.2182651López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016). The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives, 3, 43-58. doi:10.1016/j.orp.2016.09.002Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduling Problems. IEEE Transactions on Evolutionary Computation, 18(2), 301-305. doi:10.1109/tevc.2013.2240304Marichelvam, M. K., Prabaharan, T., & Yang, X. S. (2014). Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Applied Soft Computing, 19, 93-101. doi:10.1016/j.asoc.2014.02.005Marichelvam, M. K., Prabaharan, T., Yang, X. S., & Geetha, M. (2013). Solving hybrid flow shop scheduling problems using bat algorithm. International Journal of Logistics Economics and Globalisation, 5(1), 15. doi:10.1504/ijleg.2013.054428Mascia, F., López-Ibáñez, M., Dubois-Lacoste, J., & Stützle, T. (2014). Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools. Computers & Operations Research, 51, 190-199. doi:10.1016/j.cor.2014.05.020Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95. doi:10.1016/0305-0483(83)90088-9Pan, Q.-K., & Dong, Y. (2014). An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation. Information Sciences, 277, 643-655. doi:10.1016/j.ins.2014.02.152Pan, Q.-K., Ruiz, R., & Alfaro-Fernández, P. (2017). Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows. Computers & Operations Research, 80, 50-60. doi:10.1016/j.cor.2016.11.022Pan, Q.-K., Wang, L., Li, J.-Q., & Duan, J.-H. (2014). A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation. Omega, 45, 42-56. doi:10.1016/j.omega.2013.12.004Rajendran, C., & Ziegler, H. (1997). An efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs. European Journal of Operational Research, 103(1), 129-138. doi:10.1016/s0377-2217(96)00273-1Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1-18. doi:10.1016/j.ejor.2009.09.024Sörensen, K. (2013). Metaheuristics-the metaphor exposed. International Transactions in Operational Research, 22(1), 3-18. doi:10.1111/itor.12001Vignier, A., Billaut, J.-C., & Proust, C. (1999). Les problèmes d’ordonnancement de type flow-shop hybride : état de l’art. RAIRO - Operations Research, 33(2), 117-183. doi:10.1051/ro:1999108Wang, S., Wang, L., Liu, M., & Xu, Y. (2013). An enhanced estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machines. The International Journal of Advanced Manufacturing Technology, 68(9-12), 2043-2056. doi:10.1007/s00170-013-4819-yXu, Y., Wang, L., Wang, S., & Liu, M. (2013). An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines. Engineering Optimization, 45(12), 1409-1430. doi:10.1080/0305215x.2012.73778

Me Whita, Kia Whita! Hold Fast to Hasten the Blaze!The Development of an Accelerative Approach toAcquiring te Reo Māori.

This thesis is motivated by an awareness of the key role that Māori second language adult speakers play in the regeneration of the Māori language. The study provides an analytical description of the development of pedagogical materials for a new method of teaching te reo Māori to adults called ‘Kia Whita!’ (Hasten the Blaze!). ‘Kia Whita!’ is designed to rapidly enhance learners’ ability to communicate in te reo Māori while also developing cultural competence, knowledge and understanding. It is modelled on the Accelerative Integrated Method which was pioneered by Wendy Maxwell in Canada for the teaching of French and English to children. The study explains the theoretical foundations on which ‘Kia Whita!’ is built and articulates the special cultural and linguistic considerations that steered its development. This is an applied linguistic thesis drawing on second language acquisition theory and kaupapa Māori methodology. As a result these materials are cognisant of the intertwining issues and needs around second language acquisition, culture, place and the validation of the stated materials by key Māori stakeholders balanced against the varied needs of the second language learner of Te Reo Māori. Adopting this approach to the development of ‘Kia Whita!’ allows the materials to meet the high standards of effective second language pedagogy; and articulate Māori linguistic and cultural content acceptable to Māori experts while being comprehensible to learners of the language

A metaheuristic multi-criteria optimisation approach to portfolio selection

Portfolio selection is concerned with selecting from of a universe of assets the ones in which one wishes to invest and the amount of the investment. Several criteria can be used for portfolio selection, and the resulting approaches can be classified as being either active or passive. The two approaches are thought to be mutually exclusive, but some authors have suggested combining them in a unified framework. In this work, we define a multi-criteria optimisation problem in which the two types of approaches are combined, and we introduce a hybrid metaheuristic that combines local search and quadratic programming to obtain an approximation of the Pareto set. We experimentally analyse this approach on benchmarks from two different instance classes: these classes refer to the same indexes, but they use two different return representations. Results show that this metaheuristic can be effectively used to solve multi-criteria portfolio selection problems. Furthermore, with an experiment on a set of instances coming from a different financial scenario, we show that the results obtained by our metaheuristic are robust with respect to the return representation used

On the impact of the cutoff time on the performance of algorithm configurators

Algorithm conigurators are automated methods to optimise the parameters of an algorithm for a class of problems. We evaluate the performance of a simple random local search conigurator (Param- RLS) for tuning the neighbourhood size k of the RLS k algorithm. We measure performance as the expected number of coniguration evaluations required to identify the optimal value for the parameter. We analyse the impact of the cutof time κ (the time spent evaluat- ing a coniguration for a problem instance) on the expected number of coniguration evaluations required to ind the optimal parameter value, where we compare conigurations using either best found itness values (ParamRLS-F) or optimisation times (ParamRLS-T). We consider tuning RLS k for a variant of the Ridge function class ( Ridge* ), where the performance of each parameter value does not change during the run, and for the OneMax function class, where longer runs favour smaller k . We rigorously prove that ParamRLS- F eiciently tunes RLS k for Ridge* for any κ while ParamRLS-T requires at least quadratic κ . For OneMax ParamRLS-F identiies k = 1 as optimal with linear κ while ParamRLS-T requires a κ of at least Ω ( n log n ) . For smaller κ ParamRLS-F identiies that k > 1 performs better while ParamRLS-T returns k chosen uniformly at random