An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints

[EN] Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 17KJB460018), the Innovation Foundation for Science and Technology of Yangzhou University (No. 2016CXJ020 and No. 2017CXJ018), Science and Technology Project of Yangzhou under (No. YZ2017278), Research Topics of Teaching Reform of Yangzhou University under (No. YZUJX2018-28B), and the Spanish Government (No. TIN2016-80856-R and No. TIN2015-65515-C4-1-R).Dai, M.; Zhang, Z.; Giret Boggino, AS.; Salido, MA. (2019). An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints. Sustainability. 11(11):1-23. https://doi.org/10.3390/su11113085S1231111Wu, X., & Sun, Y. (2018). 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Scheduling flexible job-shops with transportation times: Mathematical models and a hybrid imperialist competitive algorithm. Applied Mathematical Modelling, 41, 667-682. doi:10.1016/j.apm.2016.09.022Liu, Z., Guo, S., & Wang, L. (2019). Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. Journal of Cleaner Production, 211, 765-786. doi:10.1016/j.jclepro.2018.11.231Tang, D., & Dai, M. (2015). Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem. Chinese Journal of Mechanical Engineering, 28(5), 1048-1055. doi:10.3901/cjme.2015.0617.082Hao, X., Lin, L., Gen, M., & Ohno, K. (2013). Effective Estimation of Distribution Algorithm for Stochastic Job Shop Scheduling Problem. Procedia Computer Science, 20, 102-107. doi:10.1016/j.procs.2013.09.246Wang, L., Wang, S., Xu, Y., Zhou, G., & Liu, M. (2012). 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Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29(5), 418-429. doi:10.1016/j.rcim.2013.04.001Beasley, J. E. (1990). OR-Library: Distributing Test Problems by Electronic Mail. Journal of the Operational Research Society, 41(11), 1069-1072. doi:10.1057/jors.1990.166Zhao, F., Shao, Z., Wang, J., & Zhang, C. (2015). A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems. International Journal of Production Research, 54(4), 1039-1060. doi:10.1080/00207543.2015.1041575Van Laarhoven, P. J. M., Aarts, E. H. L., & Lenstra, J. K. (1992). Job Shop Scheduling by Simulated Annealing. Operations Research, 40(1), 113-125. doi:10.1287/opre.40.1.113Wang, L., & Zheng, D.-Z. (2001). An effective hybrid optimization strategy for job-shop scheduling problems. 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Geometric and harmonic means based priority dispatching rules for single machine scheduling problems

[EN] This work proposes two new prority dispatching rules (PDRs) for solving single machine scheduling problems. These rules are based on the geometric mean (GM) and harmonic mean (HM) of the processing time (PT) and the due date (DD) and they are referred to as GMPD and HMPD respectively. Performance of the proposed PDRs is evaluated on the basis of five measures/criteria i.e. Total Flow Time (TFT), Total Lateness (TL), Number of Late Jobs (TNL), Total Earliness (TE) and Number of Early Parts (TNE). It is found that GMPD performs better than other PDRs in achieving optimal values of multiple performance measures. Further, effect of variation in the weight assigned to PT and DD on the combined performance of TFT and TL is also examined which reveals that for deriving optimal values of TFT and TL, weighted harmonic mean (WHMPD) rule with a weight of 0.105 outperforms other PDRs. The weighted geometric mean (WGMPD) rule with a weight of 0.37 is found to be the next after WHMPD followed by the weighted PDT i.e. WPDT rule with a weight of 0.76.Ahmad, S.; Khan, ZA.; Ali, M.; Asjad, M. (2021). Geometric and harmonic means based priority dispatching rules for single machine scheduling problems. International Journal of Production Management and Engineering. 9(2):93-102. https://doi.org/10.4995/ijpme.2021.15217OJS9310292Baharom, M. Z., Nazdah, W., &Hussin, W. (2015). Scheduling Analysis for Job Sequencing in Veneer Lamination Line. Journal of Industrial and Intelligent Information, 3(3). https://doi.org/10.12720/jiii.3.3.181-185Chan, F. T. S., Chan, H. K., Lau, H. C. W., & Ip, R. W. L. (2003). Analysis of dynamic dispatching rules for a flexible manufacturing system. Journal of Materials Processing Technology, 138(1), 325-331. https://doi.org/10.1016/S0924-0136(03)00093-1Cheng, T. C. E., &Kahlbacher, H. G. (1993). Single-machine scheduling to minimize earliness and number of tardy jobs. Journal of Optimization Theory and Applications, 77(3), 563-573. https://doi.org/10.1007/BF00940450da Silva, N. C. O., Scarpin, C. T., Pécora, J. E., & Ruiz, A. (2019). Online single machine scheduling with setup times depending on the jobs sequence. Computers & Industrial Engineering, 129, 251-258. https://doi.org/10.1016/j.cie.2019.01.038Doh, H.H., Yu, J.M., Kim, J.S., Lee, D.H., & Nam, S.H. (2013). A priority scheduling approach for flexible job shops with multiple process plans. International Journal of Production Research, 51(12), 3748-3764. https://doi.org/10.1080/00207543.2013.765074Dominic, Panneer D. D., Kaliyamoorthy, S., & Kumar, M. S. (2004). Efficient dispatching rules for dynamic job shop scheduling. The International Journal of Advanced Manufacturing Technology, 24(1), 70-75.Ðurasević, M., &Jakobović, D. (2018). A survey of dispatching rules for the dynamic unrelated machines environment. Expert Systems with Applications, 113, 555-569. https://doi.org/10.1016/j.eswa.2018.06.053Forrester, P. (2006). Operations Management: An Integrated Approach. International Journal of Operations & Production Management.Geiger, C. D., &Uzsoy, R. (2008). Learning effective dispatching rules for batch processor scheduling. International Journal of Production Research, 46(6), 1431-1454. https://doi.org/10.1080/00207540600993360Hamidi, M. (2016). Two new sequencing rules for the non-preemptive single machine scheduling problem. The Journal of Business Inquiry, 15(2), 116-127.Holthaus, O., & Rajendran, C. (1997). New dispatching rules for scheduling in a job shop-An experimental study. The International Journal of Advanced Manufacturing Technology, 13(2), 148-153. https://doi.org/10.1007/BF01225761Hussain, M. S., & Ali, M. (2019). A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems. Global Journal of Flexible Systems Management, 20(3), 267-290. https://doi.org/10.1007/s40171-019-00214-9Jayamohan, M. S., & Rajendran, C. (2000). New dispatching rules for shop scheduling: A step forward. International Journal of Production Research, 38(3), 563-586. https://doi.org/10.1080/002075400189301Kadipasaoglu, S. N., Xiang, W., &Khumawala, B. M. (1997). A comparison of sequencing rules in static and dynamic hybrid flow systems. International Journal of Production Research, 35(5), 1359-1384. https://doi.org/10.1080/002075497195371Kanet, J. J., & Li, X. (2004). A Weighted Modified Due Date Rule for Sequencing to Minimize Weighted Tardiness. Journal of Scheduling, 7(4), 261-276. https://doi.org/10.1023/B:JOSH.0000031421.64487.95Lee, D.K., Shin, J.H., & Lee, D.H. (2020). Operations scheduling for an advanced flexible manufacturing system with multi-fixturing pallets. Computers & Industrial Engineering, 144, 106496. https://doi.org/10.1016/j.cie.2020.106496Lu, C.C., Lin, S.W., & Ying, K.C. (2012). Robust scheduling on a single machine to minimize total flow time. Computers & Operations Research, 39(7), 1682-1691. https://doi.org/10.1016/j.cor.2011.10.003Krishnan, M., Chinnusamy, T. R., & Karthikeyan, T. (2012). Performance Study of Flexible Manufacturing System Scheduling Using Dispatching Rules in Dynamic Environment. Procedia Engineering, 38, 2793-2798. https://doi.org/10.1016/j.proeng.2012.06.327Munir, E. U., Li, J., Shi, S., Zou, Z., & Yang, D. (2008). MaxStd: A task scheduling heuristic for heterogeneous computing environment. Information Technology Journal, 7(4), 679-683. https://doi.org/10.3923/itj.2008.679.683Oyetunji, E. O. (2009). Some common performance measures in scheduling problems. Research Journal of Applied Sciences, Engineering and Technology, 1(2), 6-9.Pinedo, M. L. (2009). Planning and Scheduling in Manufacturing and Services (2nd ed.). Springer-Verlag. https://doi.org/10.1007/978-1-4419-0910-7Prakash, A., Chan, F. T. S., & Deshmukh, S. G. (2011). FMS scheduling with knowledge based genetic algorithm approach. Expert Systems with Applications, 38(4), 3161-3171. https://doi.org/10.1016/j.eswa.2010.09.002Rafsanjani, M. K., &Bardsiri, A. K. (2012). A New Heuristic Approach for Scheduling Independent Tasks on Heterogeneous Computing Systems. International Journal of Machine Learning and Computing, 371-376. https://doi.org/10.7763/IJMLC.2012.V2.147Tyagi, N., Tripathi, R. P., &Chandramouli, A. B. (2016). Single Machine Scheduling Model with Total Tardiness Problem. Indian Journal of Science and Technology, 9(37). https://doi.org/10.17485/ijst/2016/v9i37/97527Vinod, V., & Sridharan, R. (2008). Dynamic job-shop scheduling with sequence-dependent setup times: Simulation modeling and analysis. The International Journal of Advanced Manufacturing Technology, 36(3), 355-372. https://doi.org/10.1007/s00170-006-0836-4Waikar, A. M., Sarker, B. R., & Lal, A. M. (1995). A comparative study of some priority dispatching rules under different shop loads. Production Planning & Control, 6(4), 301-310. https://doi.org/10.1080/0953728950893028

Genetic algorithms for the scheduling in additive manufacturing

[EN] Genetic Algorithms (GAs) are introduced to tackle the packing problem. The scheduling in Additive Manufacturing (AM) is also dealt with to set up a managed market, called “Lonja3D”. This will enable to determine an alternative tool through the combinatorial auctions, wherein the customers will be able to purchase the products at the best prices from the manufacturers. Moreover, the manufacturers will be able to optimize the production capacity and to decrease the operating costs in each case.This research has been partially financed by the project: “Lonja de Impresión 3D para la Industria 4.0 y la Empresa Digital (LONJA3D)” funded by the Regional Government of Castile and Leon and the European Regional Development Fund (ERDF, FEDER) with grant VA049P17Castillo-Rivera, S.; De Antón, J.; Del Olmo, R.; Pajares, J.; López-Paredes, A. (2020). Genetic algorithms for the scheduling in additive manufacturing. International Journal of Production Management and Engineering. 8(2):59-63. https://doi.org/10.4995/ijpme.2020.12173OJS596382Ahsan, A., Habib, A., Khoda, B. (2015). Resource based process planning for additive manufacturing. Computer-Aided Design, 69, 112-125. https://doi.org/10.1016/j.cad.2015.03.006Araújo, L., Özcan, E., Atkin, J., Baumers, M., Tuck, C., Hague, R. (2015). Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks. 26th Annual International Solid Freeform Fabrication Symposium - an Additive Manufacturing Conference, 401-410.Berman, B. (2012). 3-D printing: The new industrial revolution. Business Horizons, 55: 155-162. https://doi.org/10.1016/j.bushor.2011.11.003Canellidis, V., Dedoussis, V., Mantzouratos, N., Sofianopoulou, S. (2006). Preprocessing methodology for optimizing stereolithography apparatus build performance. Computers in Industry, 57, 424-436. https://doi.org/10.1016/j.compind.2006.02.004Chergui, A., Hadj-Hamoub, K., Vignata, F. (2018). Production scheduling and nesting in additive manufacturing. Computers & Industrial Engineering, 126, 292-301. https://doi.org/10.1016/j.cie.2018.09.048Demirel, E., Özelkan, E.C., Lim, C. (2018). Aggregate planning with flexibility requirements profile. International Journal of Production Economics, 202, 45-58. https://doi.org/10.1016/j.ijpe.2018.05.001Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R., Todisco, V. (2018). A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling. International Journal of Industrial Engineering Computations, 9, 423-438. https://doi.org/10.5267/j.ijiec.2018.1.001Hopper, E., Turton, B. (1997). Application of genetic algorithms to packing problems - A Review. Proceedings of the 2nd Online World Conference on Soft Computing in Engineering Design and Manufacturing, Springer Verlag, London, 279-288. https://doi.org/10.1007/978-1-4471-0427-8_30Ikonen, I., Biles, W.E., Kumar, A., Wissel, J.C., Ragade, R.K. (1997). A genetic algorithm for packing three-dimensional non-convex objects having cavities and holes. ICGA, 591-598.Kim, K.H., Egbelu, P.J. (1999). Scheduling in a production environment with multiple process plans per job. International Journal of Production Research, 37, 2725-2753. https://doi.org/10.1080/002075499190491Lawrynowicz, A. (2011). Genetic algorithms for solving scheduling problems in manufacturing systems. Foundations of Management, 3(2), 7-26. https://doi.org/10.2478/v10238-012-0039-2Li, Q., Kucukkoc, I., Zhang, D. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 157-172. https://doi.org/10.1016/j.cor.2017.01.013Milošević, M., Lukić, D., Đurđev, M., Vukman, J., Antić, A. (2016). Genetic Algorithms in Integrated Process Planning and Scheduling-A State of The Art Review. Proceedings in Manufacturing Systems, 11(2), 83-88.Pour, M.A., Zanardini, M., Bacchetti, A., Zanoni, S. (2016). Additive manufacturing impacts on productions and logistics systems. IFAC, 49(12), 1679-1684. https://doi.org/10.1016/j.ifacol.2016.07.822Wilhelm, W.E., Shin, H.M. (1985). Effectiveness of Alternate Operations in a Flexible Manufacturing System. International Journal of Production Research, 23(1), 65-79. https://doi.org/10.1080/00207548508904691Xirouchakis, P., Kiritsis, D., Persson, J.G. (1998). A Petri net Technique for Process Planning Cost Estimation. Annals of the CIRP, 47(1), 427-430. https://doi.org/10.1016/S0007-8506(07)62867-4Zhang, Y., Bernard, A., Gupta, R.K., Harik, R. (2014). Evaluating the design for additive manufacturing: a process planning perspective. Procedia CIRP, 21, 144-150. https://doi.org/10.1016/j.procir.2014.03.17

Battle of Postdisaster Response and Restoration

[EN] The paper presents the results of the Battle of Postdisaster Response and Restoration (BPDRR) presented in a special session at the first International water distribution systems analysis & computing and control in the water industry (WDSA/CCWI) Joint Conference, held in Kingston, Ontario, Canada, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/nonvisibility of damages. Each team/approach was evaluated against six performance criteria: (1) time without supply for hospital/firefighting, (2) rapidity of recovery, (3) resilience loss, (4) average time of no user service, (5) number of users without service for eight consecutive hours, and (6) water loss. Three main types of approaches were identified from the submissions: (1) general-purpose metaheuristic algorithms, (2) greedy algorithms, and (3) ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably among solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. The effects of damages and repair work were more noticeable during the peak demand periods (morning and noontime) than during the low-flow periods; and tank storage helped to preserve functionality of the network in the first few hours after a simulated event. (C) 2020 American Society of Civil Engineers.Paez, D.; Filion, Y.; Castro-Gama, M.; Quintiliani, C.; Santopietro, S.; Sweetapple, C.; Meng, F.... (2020). Battle of Postdisaster Response and Restoration. Journal of Water Resources Planning and Management. 146(8):1-13. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001239S1131468Balut A. R. Brodziak J. Bylka and P. Zakrzewski. 2018. “Battle of post-disaster response and restauration (BPDRR).” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Bibok A. 2018. “Near-optimal restoration scheduling of damaged drinking water distribution systems using machine learning.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Castro-Gama M. C. Quintiliani and S. Santopietro. 2018. “After earthquake post-disaster response using a many-objective approach a greedy and engineering interventions.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Cimellaro, G. P., Tinebra, A., Renschler, C., & Fragiadakis, M. (2016). New Resilience Index for Urban Water Distribution Networks. Journal of Structural Engineering, 142(8). doi:10.1061/(asce)st.1943-541x.0001433Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. doi:10.1109/tit.1967.1053964Creaco, E., Franchini, M., & Alvisi, S. (2010). Optimal Placement of Isolation Valves in Water Distribution Systems Based on Valve Cost and Weighted Average Demand Shortfall. Water Resources Management, 24(15), 4317-4338. doi:10.1007/s11269-010-9661-5Deb, K., Mohan, M., & Mishra, S. (2005). Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation, 13(4), 501-525. doi:10.1162/106365605774666895Deuerlein J. D. Gilbert E. Abraham and O. Piller. 2018. “A greedy scheduling of post-disaster response and restoration using pressure-driven models and graph segment analysis.” In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Deuerlein, J. W. (2008). Decomposition Model of a General Water Supply Network Graph. Journal of Hydraulic Engineering, 134(6), 822-832. doi:10.1061/(asce)0733-9429(2008)134:6(822)Di Nardo, A., Di Natale, M., Giudicianni, C., Santonastaso, G. F., & Savic, D. (2018). Simplified Approach to Water Distribution System Management via Identification of a Primary Network. Journal of Water Resources Planning and Management, 144(2), 04017089. doi:10.1061/(asce)wr.1943-5452.0000885Eliades D. G. M. Kyriakou S. Vrachimis and M. M. 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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

Production planning in 3D printing factories

[EN] Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure.This research has been partially financed by the project: “Lonja de Impresión 3D para la Industria 4.0 y la Empresa Digital (LONJA3D)” funded by the Regional Government of Castile and Leon and the European Regional Development Fund (ERDF, FEDER) with grant VA049P17.De Antón, J.; Senovilla, J.; González, J.; Acebes, F.; Pajares, J. (2020). Production planning in 3D printing factories. International Journal of Production Management and Engineering. 8(2):75-86. https://doi.org/10.4995/ijpme.2020.12944OJS758682Canellidis, V., Giannatsis, J., & Dedoussis, V. (2013). Efficient parts nesting schemes for improving stereolithography utilization. CAD Computer Aided Design, 45(5), 875-886. https://doi.org/10.1016/j.cad.2012.12.002Chergui, A., Hadj-Hamou, K., & Vignat, F. (2018). Production scheduling and nesting in additive manufacturing. Computers and Industrial Engineering, 126(May), 292-301. https://doi.org/10.1016/j.cie.2018.09.048Cui, Y. (2007). An exact algorithm for generating homogenous T-shape cutting patterns. Computers & Operations Research, 34(4), 1107-1120. https://doi.org/https://doi.org/10.1016/j.cor.2005.05.025Dvorak, F., Micali, M., & Mathieu, M. (2018). Planning and scheduling in additive manufacturing. Inteligencia Artificial, 21(62), 40-52. https://doi.org/10.4114/intartif.vol21iss62pp40-52Gogate, A. S., & Pande, S. S. (2008). Intelligent layout planning for rapid prototyping. International Journal of Production Research, 46(20), 5607-5631. https://doi.org/10.1080/00207540701277002Gupta, M. C., & Boyd, L. H. (2008). Theory of constraints: A theory for operations management. International Journal of Operations and Production Management, 28(10), 991-1012. https://doi.org/10.1108/01443570810903122Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European Journal of Operational Research, 88(1), 165-181. https://doi.org/10.1016/0377-2217(94)00166-9Kucukkoc, I. (2019). MILP models to minimise makespan in additive manufacturing machine scheduling problems. Computers and Operations Research, 105, 58-67. https://doi.org/10.1016/j.cor.2019.01.006Kucukkoc, I., Li, Q., & Zhang, D. Z. (2016). Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times. In 19th International Working Seminar on Production Economics (pp. 195-201). Innsbruck, Austria.Li, Q., Kucukkoc, I., & Zhang, D. Z. (2017). Production planning in additive manufacturing and 3D printing. Computers and Operations Research, 83, 1339-1351. https://doi.org/10.1016/j.cor.2017.01.013López-Paredes, A., Pajares, J., Martín, N., del Olmo, R., & Castillo, S. (2018). Application of combinatorial auctions to create a 3Dprinting market. Advancing in Engineering Network, Castro and Gimenez Eds. Lecture Notes in Management and Industrial Engineering (In Press), 12-13.Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. https://doi.org/10.3390/app9183865Piili, H., Happonen, A., Väistö, T., Venkataramanan, V., Partanen, J., & Salminen, A. (2015). Cost Estimation of Laser Additive Manufacturing of Stainless Steel. Physics Procedia, 78(August), 388-396. https://doi.org/10.1016/j.phpro.2015.11.053Shaffer, S., Yang, K., Vargas, J., Di Prima, M. A., & Voit, W. (2014). On reducing anisotropy in 3D printed polymers via ionizing radiation. Polymer, 55(23), 5969-5979. https://doi.org/10.1016/j.polymer.2014.07.054Singhal, S. K., Pandey, A. P., Pandey, P. M., & Nagpal, A. K. (2005). Optimum Part Deposition Orientation in Stereolithography. Computer-Aided Design and Applications, 2(1-4), 319-328. https://doi.org/10.1080/16864360.2005.10738380Sung‐Hoon, A. (2002). Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal, 8(4), 248-257. https://doi.org/10.1108/13552540210441166Thomas, D. S., & Gilbert, S. W. (2015). Costs and cost effectiveness of additive manufacturing: A literature review and discussion. Additive Manufacturing: Costs, Cost Effectiveness and Industry Economics, 1-96. https://doi.org/10.6028/NIST.SP.1176Toro, E., Garces, A., & Ruiz, H. (2008). Two dimensional packing problem using a hybrid constructive algorithm of variable neighborhood search and simulated annealing. Revista Facultad de Ingeniería Universidad de Antioquia, 119-131.Toro, E., & Granada-Echeverri, M. (2007). Problema de empaquetamiento rectangular bidimensional tipo guillotina resuelto por algoritmos genéticos. Scientia Et Technica.Wang, Y., Zheng, P., Xu, X., Yang, H., & Zou, J. (2019). Production planning for cloud-based additive manufacturing-A computer vision-based approach. Robotics and Computer-Integrated Manufacturing, 58(March), 145-157. https://doi.org/10.1016/j.rcim.2019.03.003Wodziak, J. R., Fadel, G. M., & Kirschman, C. (1994). A Genetic Algorithm for Optimizing Multiple Part Placement to Reduce Build Time. Proceedings of the Fifth International Conference on Rapid Prototyping., (May), 201,210.Zhang, Y., Gupta, R. K., & Bernard, A. (2016). Two-dimensional placement optimization for multi-parts production in additive manufacturing. Robotics and Computer-Integrated Manufacturing, 38, 102-117. https://doi.org/10.1016/j.rcim.2015.11.003Zhao, Z., Zhang, L., & Cui, J. (2018). A 3D printing task packing algorithm based on rectangle packing in cloud manufacturing. Lecture Notes in Electrical Engineering, 460, 21-31. https://doi.org/10.1007/978-981-10-6499-9_3Zhou, L., Zhang, L., Laili, Y., Zhao, C., & Xiao, Y. (2018). 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A Framework for Capacity and Operations Planning in Services Organizations Employing Workers with Intellectual Disabilities

[EN] This paper presents an integrated framework for capacity and operations planning in Spanish sheltered employment centers (SECs). Employment provides socio-economic opportunities for people with disability. Well functioning SECs that provide opportunities for people with and without disability to work alongside each other are an important component of Spain's current labor market. To be economically sustainable, SECs need to satisfy their clients expectations in terms of price, flexibility and performance, whilst taking into account and strengthening the diverse skills and abilities of their workforce like specific learning and forgetting processes. The SEC studied herein, a non-for-profit organization whose mission is to create employment for people with disabilities its workforce is deployed directly on clients' premises. Efficient management across this multi-site environment, whilst supporting the diverse employment needs of its staff, is of paramount importance. This paper contributes an integrated framework to support SEC capacity and operations planning, which prioritizes the explicit training needs of workers with disability at all levels (strategic, tactical, and operational) as a lever for achieving the organization's goals.García Sabater, JP.; Maheut, J.; Ruiz, A.; García Sabater, JJ. (2020). A Framework for Capacity and Operations Planning in Services Organizations Employing Workers with Intellectual Disabilities. Sustainability. 12(22):1-17. https://doi.org/10.3390/su12229713S1171222Fundación Espurnahttps://www.espurna.org/Convention on the Rights of Persons with Disabilities (A/RES/61/106)—Resolution adopted by the General Assembly on 13 December 2006https://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_61_106.pdfJarhag, S., Nilsson, G., & Werning, M. (2009). Disabled Persons and the Labor Market in Sweden. Social Work in Public Health, 24(3), 255-272. doi:10.1080/19371910802595315Mark, B. G., Hofmayer, S., Rauch, E., & Matt, D. T. (2019). Inclusion of Workers with Disabilities in Production 4.0: Legal Foundations in Europe and Potentials Through Worker Assistance Systems. Sustainability, 11(21), 5978. doi:10.3390/su11215978Santos, P. J. de los, & Costa, R. S. (2015). Workers with disabilities in sheltered employment centres: a training needs analysis. International Journal of Inclusive Education, 20(7), 756-769. doi:10.1080/13603116.2015.1111446Miralles, C., Marin-Garcia, J. A., Ferrus, G., & Costa, A. M. (2010). Operations research/management science tools for integrating people with disabilities into employment. A study on Valencia’s Sheltered Work Centres for Disabled. International Transactions in Operational Research, 17(4), 457-473. doi:10.1111/j.1475-3995.2009.00727.xChaves-Avila, R., & Savall-Morera, T. (2019). The Social Economy in a Context of Austerity Policies: The Tension Between Political Discourse and Implemented Policies in Spain. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 30(3), 487-498. doi:10.1007/s11266-018-00075-3López-Penabad, M.-C., Maside-Sanfiz, J. M., Torrelles Manent, J., & Iglesias-Casal, A. (2020). Application of the DEA Double Bootstrap to Analyze Efficiency in Galician Sheltered Workshops. Sustainability, 12(16), 6625. doi:10.3390/su12166625Galeano Revert, A., García-Sabater, J. P., & Miralles Insa, C. (2018). Triple Bottom-line Business Model. Case study on the organic food retailing. WPOM-Working Papers on Operations Management, 9(1), 30. doi:10.4995/wpom.v9i1.9131Chi, C.-F., Dewi, R. S., Jang, Y., & Liu, H.-L. (2018). Workplace accommodation for workers with intellectual or psychiatric disabilities. International Journal of Industrial Ergonomics, 68, 1-7. doi:10.1016/j.ergon.2018.06.001Ellinger, A. E., Naidoo, J., Ellinger, A. D., Filips, K., & Herrin, G. D. (2020). Applying blue ocean strategy to hire and assimilate workers with disabilities into distribution centers. Business Horizons, 63(3), 339-350. doi:10.1016/j.bushor.2020.01.009Lindsay, S., Cagliostro, E., Albarico, M., Mortaji, N., & Karon, L. (2018). A Systematic Review of the Benefits of Hiring People with Disabilities. Journal of Occupational Rehabilitation, 28(4), 634-655. doi:10.1007/s10926-018-9756-zBitencourt, R. S., & de M. Guimarães, L. B. (2012). Inclusion of people with disabilities in the production system of a footwear industry. Work, 41, 4767-4774. doi:10.3233/wor-2012-0027-4767Hübner, A. H., Kuhn, H., & Sternbeck, M. G. (2013). Demand and supply chain planning in grocery retail: an operations planning framework. International Journal of Retail & Distribution Management, 41(7), 512-530. doi:10.1108/ijrdm-05-2013-0104Tuomikangas, N., & Kaipia, R. (2014). A coordination framework for sales and operations planning (S&OP): Synthesis from the literature. International Journal of Production Economics, 154, 243-262. doi:10.1016/j.ijpe.2014.04.026Li, Y., Chen, J., & Cai, X. (2007). An integrated staff-sizing approach considering feasibility of scheduling decision. Annals of Operations Research, 155(1), 361-390. doi:10.1007/s10479-007-0215-zZeng, L., Zhao, M., & Liu, Y. (2017). Airport ground workforce planning with hierarchical skills: a new formulation and branch-and-price approach. Annals of Operations Research, 275(1), 245-258. doi:10.1007/s10479-017-2624-yLlort, N., Lusa, A., Martínez-Costa, C., & Mateo, M. (2018). A decision support system and a mathematical model for strategic workforce planning in consultancies. Flexible Services and Manufacturing Journal, 31(2), 497-523. doi:10.1007/s10696-018-9321-2Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., & De Boeck, L. (2013). Personnel scheduling: A literature review. European Journal of Operational Research, 226(3), 367-385. doi:10.1016/j.ejor.2012.11.029Henderson, J. C., Krajewski, L. J., & Showalter, M. J. (1982). An integrated approach for manpower planning in the service sector. Omega, 10(1), 61-73. doi:10.1016/0305-0483(82)90086-xKomarudin, De Feyter, T., Guerry, M.-A., & Vanden Berghe, G. (2020). The extended roster quality staffing problem: addressing roster quality variation within a staffing planning period. Journal of Scheduling, 23(2), 253-264. doi:10.1007/s10951-020-00654-7Das, D., & Yadav, C. (2020). A framework of hiring strategy for manpower hiring in a hyper-local food delivery organization. Journal of Advances in Management Research, 18(1), 113-135. doi:10.1108/jamr-06-2020-0104Maenhout, B., & Vanhoucke, M. (2013). An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems. Omega, 41(2), 485-499. doi:10.1016/j.omega.2012.01.002Benavides, A. J., Ritt, M., & Miralles, C. (2014). Flow shop scheduling with heterogeneous workers. European Journal of Operational Research, 237(2), 713-720. doi:10.1016/j.ejor.2014.02.012Miralles, C., García-Sabater, J. P., Andrés, C., & Cardós, M. (2008). Branch and bound procedures for solving the Assembly Line Worker Assignment and Balancing Problem: Application to Sheltered Work centres for Disabled. Discrete Applied Mathematics, 156(3), 352-367. doi:10.1016/j.dam.2005.12.012Moreira, M. C. O., Pastor, R., Costa, A. M., & Miralles, C. (2017). The multi-objective assembly line worker integration and balancing problem of type-2. Computers & Operations Research, 82, 114-125. doi:10.1016/j.cor.2017.01.003Bartlo, P., & Klein, P. J. (2011). Physical Activity Benefits and Needs in Adults With Intellectual Disabilities: Systematic Review of the Literature. American Journal on Intellectual and Developmental Disabilities, 116(3), 220-232. doi:10.1352/1944-7558-116.3.220Verdugo, M. A., Jordán de Urríes, F. 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Central European Journal of Operations Research, 28(1), 61-104. doi:10.1007/s10100-018-0594-

A Tabu List-Based Algorithm for Capacitated Multilevel Lot-Sizing with Alternate Bills of Materials and Co-Production Environments

[EN] The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing turns into an increasingly complex set of decisions that requires efficient solution approaches, in response to the time-consuming exact methods (LP, MIP). This paper aims to propose a Tabu list-based algorithm (TLBA) as an alternative to the Generic Materials and Operations Planning (GMOP) model. The algorithm considers a multi-level, multi-item planning structure. It is initialized using a lot-for-lot (LxL) method and candidate solutions are evaluated through an iterative Material Requirements Planning (MRP) procedure. Three different sizes of test instances are defined and better results are obtained in the large and medium-size problems, with minimum average gaps close to 10.5%.This paper shows the results of the project entitled "Algoritmo heuristico basado en listas tabu para la planificacion de la produccion en sistemas multinivel con listas de materiales alternativas y entornos de coproduccion" supported by Universidad de la Costa and Universitat Politecnica de Valencia.Romero-Conrado, AR.; Coronado-Hernandez, J.; Rius-Sorolla, G.; García Sabater, JP. (2019). A Tabu List-Based Algorithm for Capacitated Multilevel Lot-Sizing with Alternate Bills of Materials and Co-Production Environments. Applied Sciences. 9(7):1-17. https://doi.org/10.3390/app9071464S11797Karimi, B., Fatemi Ghomi, S. M. T., & Wilson, J. M. (2003). The capacitated lot sizing problem: a review of models and algorithms. Omega, 31(5), 365-378. doi:10.1016/s0305-0483(03)00059-8Martí, R., & Reinelt, G. (2010). Heuristic Methods. Applied Mathematical Sciences, 17-40. doi:10.1007/978-3-642-16729-4_2Barany, I., Van Roy, T. J., & Wolsey, L. A. (1984). Strong Formulations for Multi-Item Capacitated Lot Sizing. Management Science, 30(10), 1255-1261. doi:10.1287/mnsc.30.10.1255Eppen, G. D., & Martin, R. K. (1987). Solving Multi-Item Capacitated Lot-Sizing Problems Using Variable Redefinition. Operations Research, 35(6), 832-848. doi:10.1287/opre.35.6.832Maes, J., McClain, J. O., & Van Wassenhove, L. N. (1991). Multilevel capacitated lotsizing complexity and LP-based heuristics. European Journal of Operational Research, 53(2), 131-148. doi:10.1016/0377-2217(91)90130-nBuschkühl, L., Sahling, F., Helber, S., & Tempelmeier, H. (2008). Dynamic capacitated lot-sizing problems: a classification and review of solution approaches. OR Spectrum, 32(2), 231-261. doi:10.1007/s00291-008-0150-7Drexl, A., & Kimms, A. (1997). Lot sizing and scheduling — Survey and extensions. European Journal of Operational Research, 99(2), 221-235. doi:10.1016/s0377-2217(97)00030-1Glock, C. H., Grosse, E. H., & Ries, J. M. (2014). The lot sizing problem: A tertiary study. International Journal of Production Economics, 155, 39-51. doi:10.1016/j.ijpe.2013.12.009KUIK, R., SALOMON, M., VAN WASSENHOVE, L. N., & MAES, J. (1993). LINEAR PROGRAMMING, SIMULATED ANNEALING AND TABU SEARCH HEURISTICS FOR LOTSIZING IN BOTTLENECK ASSEMBLY SYSTEMS. IIE Transactions, 25(1), 62-72. doi:10.1080/07408179308964266Standard Price List—AMPLhttps://ampl.com/products/standard-price-list/Seeanner, F., Almada-Lobo, B., & Meyr, H. (2013). Combining the principles of variable neighborhood decomposition search and the fix&optimize heuristic to solve multi-level lot-sizing and scheduling problems. Computers & Operations Research, 40(1), 303-317. doi:10.1016/j.cor.2012.07.002Hung, Y.-F., & Chien, K.-L. (2000). A multi-class multi-level capacitated lot sizing model. Journal of the Operational Research Society, 51(11), 1309-1318. doi:10.1057/palgrave.jors.2601026Kang, Y., Albey, E., & Uzsoy, R. (2018). Rounding heuristics for multiple product dynamic lot-sizing in the presence of queueing behavior. Computers & Operations Research, 100, 54-65. doi:10.1016/j.cor.2018.07.019BERRETTA, R., FRANÇA, P. M., & ARMENTANO, V. A. (2005). METAHEURISTIC APPROACHES FOR THE MULTILEVEL RESOURCE-CONSTRAINED LOT-SIZING PROBLEM WITH SETUP AND LEAD TIMES. Asia-Pacific Journal of Operational Research, 22(02), 261-286. doi:10.1142/s0217595905000510KIMMS, A. (1996). Competitive methods for multi-level lot sizing and scheduling: tabu search and randomized regrets. International Journal of Production Research, 34(8), 2279-2298. doi:10.1080/00207549608905025Sabater, J. P. G., Maheut, J., & Garcia, J. A. M. (2013). A new formulation technique to model materials and operations planning: the generic materials and operations planning (GMOP) problem. European J. of Industrial Engineering, 7(2), 119. doi:10.1504/ejie.2013.052572Maheut, J., & Sabater, J. P. G. (2013). Algorithm for complete enumeration based on a stroke graph to solve the supply network configuration and operations scheduling problem. Journal of Industrial Engineering and Management, 6(3). doi:10.3926/jiem.550Rius-Sorolla, G., Maheut, J., Coronado-Hernandez, J. R., & Garcia-Sabater, J. P. (2018). Lagrangian relaxation of the generic materials and operations planning model. Central European Journal of Operations Research, 28(1), 105-123. doi:10.1007/s10100-018-0593-0Maheut, J., Garcia-Sabater, J. P., & Mula, J. (2012). The Generic Materials and Operations Planning (GMOP) Problem Solved Iteratively: A Case Study in Multi-site Context. IFIP Advances in Information and Communication Technology, 66-73. doi:10.1007/978-3-642-33980-6_8Maheut, J. P. D. (s. f.). Modelos y Algoritmos Basados en el Concepto Stroke para la Planificación y Programación de Operaciones con Alternativas en Redes de Suministro. doi:10.4995/thesis/10251/29290Glover, F. (1989). Tabu Search—Part I. ORSA Journal on Computing, 1(3), 190-206. doi:10.1287/ijoc.1.3.190Glover, F., Taillard, E., & Taillard, E. (1993). A user’s guide to tabu search. Annals of Operations Research, 41(1), 1-28. doi:10.1007/bf02078647Chelouah, R., & Siarry, P. (2000). Tabu Search applied to global optimization. European Journal of Operational Research, 123(2), 256-270. doi:10.1016/s0377-2217(99)00255-6Raza, S. A., Akgunduz, A., & Chen, M. Y. (2006). A tabu search algorithm for solving economic lot scheduling problem. Journal of Heuristics, 12(6), 413-426. doi:10.1007/s10732-006-6017-7Cesaret, B., Oğuz, C., & Sibel Salman, F. (2012). A tabu search algorithm for order acceptance and scheduling. Computers & Operations Research, 39(6), 1197-1205. doi:10.1016/j.cor.2010.09.018Li, X., Baki, F., Tian, P., & Chaouch, B. A. (2014). A robust block-chain based tabu search algorithm for the dynamic lot sizing problem with product returns and remanufacturing. Omega, 42(1), 75-87. doi:10.1016/j.omega.2013.03.003Li, J., & Pan, Q. (2015). Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm. Information Sciences, 316, 487-502. doi:10.1016/j.ins.2014.10.009Hindi, K. S. (1995). Solving the single-item, capacitated dynamic lot-sizing problem with startup and reservation costs by tabu search. Computers & Industrial Engineering, 28(4), 701-707. doi:10.1016/0360-8352(95)00027-xHindi, K. S. (1996). Solving the CLSP by a Tabu Search Heuristic. Journal of the Operational Research Society, 47(1), 151-161. doi:10.1057/jors.1996.13Gopalakrishnan, M., Ding, K., Bourjolly, J.-M., & Mohan, S. (2001). A Tabu-Search Heuristic for the Capacitated Lot-Sizing Problem with Set-up Carryover. Management Science, 47(6), 851-863. doi:10.1287/mnsc.47.6.851.9813Glover, F. (1990). Tabu Search—Part II. ORSA Journal on Computing, 2(1), 4-32. doi:10.1287/ijoc.2.1.4Overview for Create General Full Factorial Designhttps://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/doe/how-to/factorial/create-factorial-design/create-general-full-factorial/before-you-start/overview/Perttunen, J. (1994). On the Significance of the Initial Solution in Travelling Salesman Heuristics. Journal of the Operational Research Society, 45(10), 1131-1140. doi:10.1057/jors.1994.183Elaziz, M. A., & Mirjalili, S. (2019). A hyper-heuristic for improving the initial population of whale optimization algorithm. Knowledge-Based Systems, 172, 42-63. doi:10.1016/j.knosys.2019.02.010Chen, C.-F., Wu, M.-C., & Lin, K.-H. (2013). Effect of solution representations on Tabu search in scheduling applications. Computers & Operations Research, 40(12), 2817-2825. doi:10.1016/j.cor.2013.06.003Tabu List Based Algorithm Datasetshttps://github.com/alfonsoromeroc/tlba-gmo

Un modelo integrado para el enrutamiento de aeronaves y la programación de la tripulación: Relajación lagrangiana y algoritmo metaheurístico

[EN] Airline optimization is a significant problem in recent researches and airline industryl as it can determine the level of service, profit and competition status of the airline. Aircraft and crew are expensive resources that need efficient utilization. This paper focuses simultaneously on two major issues including aircraft maintenance routing and crew scheduling. Several key issues such as aircraft replacement, fairly night flights assignment and long-life aircrafts are considered in this model. We used the flight hours as a new framework to control aircraft maintenance. At first, an integrated mathematical model for aircraft routing and crew scheduling problems is developed with the aim of cost minimization. Then, Lagrangian relaxation and Particle Swarm Optimization algorithm (PSO) are used as the solution techniques. To evaluate the efficiency of solution approaches, model is solved with different numerical examples in small, medium and large sizes and compared with GAMS output. The results show that Lagrangian relaxation method provides better solutions comparing to PSO and also has a very small gap to optimum solution.[ES] La optimización de aerolíneas es un problema importante en investigaciones recientes e industria de aerolíneas, ya que puede determinar el nivel de servicio, el beneficio y el estado de competencia de la aerolínea. Las aeronaves y la tripulación son recursos costosos que necesitan una utilización eficiente. Este artículo se centra simultáneamente en dos cuestiones principales, incluyendo el enrutamiento de mantenimiento de aeronaves y la programación de la tripulación. En este modelo se consideran varios temas clave, como el reemplazo de aeronaves, la asignación de vuelos nocturnos y los aviones envejecidos. Usamos las horas de vuelo como un nuevo marco para controlar el mantenimiento de las aeronaves. Al principio, se desarrolla un modelo matemático integrado para el enrutamiento de aeronaves y los problemas de programación de la tripulación con el objetivo de la minimización de costos. A continuación, se utilizan como técnicas de solución la relajación lagran-giana y el algoritmo “Particle Swarm Optimization” (PSO). Para evaluar la eficiencia de los en-foques de la solución, el modelo se resuelve con diferentes ejemplos numéricos en tamaños pequeños, medianos y grandes y se compara con la salida GAMS. Los resultados muestran que el método de relajación lagrangiana proporciona mejores soluciones en comparación con PSO y también tiene una pequeña diferencia para una solución óptimaMirjafari, M.; Rashidi Komijan, A.; Shoja, A. (2020). An integrated model for aircraft routing and crew scheduling: Lagrangian Relaxation and metaheuristic algorithm. WPOM-Working Papers on Operations Management. 11(1):25-38. https://doi.org/10.4995/wpom.v11i1.12891OJS2538111Al-Thani, Nayla Ahmad, Ben Ahmed, Mohamed and Haouari, Mohamed (2016). A model and optimization-based heuristic for the operational aircraft maintenance routing problem, Transportation Research Part C: Emerging Technologies, Volume 72, Pages 29-44. https://doi.org/10.1016/j.trc.2016.09.004Azadeh, A., HosseinabadiFarahani, M., Eivazy, H., Nazari-Shirkouhi, S., &Asadipour, G. (2013). A hybrid meta-heuristic algorithm for optimization of crew scheduling, Applied Soft Computing, Volume 13, Pages 158-164. https://doi.org/10.1016/j.asoc.2012.08.012Barnhart C. and Cohn, A. (2004). Airline schedule planning: Accomplishments and opportunities, Manufacturing & Service Operations Management, 6(1):3-22, 47, 69, 141, 144. https://doi.org/10.1287/msom.1030.0018Basdere, Mehmet and Bilge, Umit (2014). Operational aircraft maintenance routing problem with remaining time consideration, European Journal of Operational Research, Volume 235, Pages 315-328. https://doi.org/10.1016/j.ejor.2013.10.066Bazargan, Massoud (2010). Airline Operations and scheduling second edition, Embry-Riddle Aeronautical University, USA, Ashgate publishing limite.Belien, Jeroen, Demeulemeester, Eric and Brecht (2010). Integrated staffing and scheduling for an aircraft line maintenance problem, HUB RESEARCH PAPER Economics & Management.Ben Ahmed, M., Zeghal Mansour, Farah and Haouari, Mohamed (2018). Robust integrated maintenance aircraft routing and crew pairing, Journal of Air Transport Management, Volume 73, Pages 15-31. https://doi.org/10.1016/j.jairtraman.2018.07.007Ben Ahmed, M., Zeghal Mansour, F., Haouari, M. (2017). A two-level optimization approach for robust aircraft routing and retiming, Computers and Industrial Engineering, Volume 112, Pages 586-594. https://doi.org/10.1016/j.cie.2016.09.021Borndorfer, R., Schelten, U., Schlechte, T., Weider, S. (2006). A column generation approach to airline crew scheduling, Springer Berlin Heidelberg, Pages 343-348. https://doi.org/10.1007/3-540-32539-5_54Clarke, L., E. Johnson, G. Nemhauser, Z. Zhu. (1997). The Aircraft Rotation Problem. Annals of Operations Research, 69, Pages 33-46. https://doi.org/10.1023/A:1018945415148Deveci, Muhammet and ÇetinDemirel, Nihan (2018). Evolutionary algorithms for solving the airline crew pairing problem, Computers & Industrial Engineering, Volume 115, Pages 389-406. https://doi.org/10.1016/j.cie.2017.11.022Dozic, S., Kalic, M. (2015). Three-stage airline fleet planning model, J. Air Transport. Manag, 43, Pages 30-39. https://doi.org/10.1016/j.jairtraman.2015.03.011Eltoukhy, A.E., Chan, F.T., Chung, S. (2017). Airline schedule planning: a review and future directions, Ind. Manag. Data Syst, 117(6), Pages 1201-1243. https://doi.org/10.1108/IMDS-09-2016-0358Feo, T. A., J. F. Bard. (1989). Flight Scheduling and Maintenance Base Planning. Management Science, 35(12), Pages 1415-1432. https://doi.org/10.1287/mnsc.35.12.1415Goffin, J. L. (1977). On the convergence rates of subgradient optimization methods. Math. Programming, 13, Pages 329-347. https://doi.org/10.1007/BF01584346Gopalakrishnan, B., Johnson, E. L (2005). Airline crew scheduling, State-of-the-art. Ann. Oper. Res, 140(1), Pages 305-337. https://doi.org/10.1007/s10479-005-3975-3Held, M. and Karp, R.M. (1970). The Traveling-Salesman Problem and Minimum SpanningTrees. Operations Research, 18, 1138-1162. https://doi.org/10.1287/opre.18.6.1138Held, M. Wolfe, P., Crowder, H. D. (1974). Validation of subgradient optimization, Math. Programming, 6, 62-88. https://doi.org/10.1007/BF01580223Jamili, Amin (2017). A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling, with consideration of fleet assignment problem, Journal of Air Transport Management, Volume 58, Pages 21-30. https://doi.org/10.1016/j.jairtraman.2016.08.008Jiang, H., Barnhart, C. (2009) Dynamic airline scheduling, Transport. Sci, 43(3), Pages 336-354. https://doi.org/10.1287/trsc.1090.0269Kasirzadeh, A., Saddoune, M., Soumis, F. (2015). Airline crew scheduling: models, algorhitms and data sets, Euro Journal on Transportation and Logistics, 6(2), Pages 111-137. https://doi.org/10.1007/s13676-015-0080-xLacasse-Guay, E., Desaulniers, G., Soumis, F. (2010). Aircraft routing under different business processes, J. Air Transport. Manag, 16(5), Pages 258-263. https://doi.org/10.1016/j.jairtraman.2010.02.001Muter, İbrahim, Birbil, Ş. İlker, Bülbül, Kerem, Şahin, Güvenç,Yenigün, Hüsnü, Taş,Duygu andTüzün, Dilek (2013). 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