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Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimisation for job scheduling. The proposed genetic procedure leads to the Pareto front, and the application of the multiple criteria decision aiding (MCDA) approach allows extraction of the final solution. Definition of the key performance indicator (KPI), reflecting relevant features of the solutions, and the efficiency of the genetic procedure provide the Pareto front comprising the representative set of feasible solutions. The application of chosen MCDA, namely elimination et choix traduisant la réalité (ELECTRE) method, allows for the elicitation of the decision maker (DM) preferences and subsequently leads to the final solution. This solution fulfils all of the DM expectations and constitutes the best trade-off between considered KPIs. The proposed method is an efficient combination of genetic optimisation and the MCDA method.
Rocznik
Strony
57--74
Opis fizyczny
Bibliogr. 68 poz., rys.
Twórcy
  • Department of Logistics, Poznań University of Economics and Business, Poznań, Poland
  • Advanced Analytics Team, PSI Poland, Poznań, Poland
  • nstitute of Computing Science, Faculty of Computing, Poznan University of Technology, Poznań, Poland
  • Advanced Analytics Team, PSI Poland, Poznań, Poland
  • Institute of Medical Biology of Polish Academy of Sciences, Łódź, Poland
Bibliografia
  • [1] Al-Yakoob, S. M., and Sherali, H. D. Multiple shift scheduling of hierarchical workforce with multiple work centers. Informatica 18, 3 (2007), 325–342.
  • [2] Atiquzzaman, M., Liong, S.-Y., and Yu, X. Alternative decision making in water distribution network with NSGA-II. Journal of Water Resources Planning and Management 132, 2 2006, 122–126.
  • [3] Baker, K. R. Workforce allocation in cyclical scheduling problems: A survey. Operational Research Quarterly (1970-1977) 27, 1 (1976), 155–167.
  • [4] Bandyopadhyay, S. Modified NSGA-II for a bi-objective job sequencing problem. Intelligent Information Management 4, 6 (2012), 319–329.
  • [5] Bari, F., and Leung, V. Application of ELECTRE to network selection in a hetereogeneous wireless network environment. In 2007 IEEE Wireless Communications and Networking Conference, IEEE, (2007), pp. 3810-3815.
  • [6] Bisdorff, R., Meyer, P., and Roubens, M. R UBIS: a bipolar-valued outranking method for the choice problem. 4OR 6, 2 (2007), 143–165.
  • [7] Brans, J.-P., and Mareschal, B. Promethee methods. In Multiple Criteria Decision Analysis: State of the Art Surveys, J.Figueira, S. Greco and M. Ehrogott, Eds., vol. 78 of International Series in Operations Research & Management Science. SpringerVerlag, New York, NY, 2005, pp. 163–186.
  • [8] Brans, J. P., and Vincke, P. Note—A preference ranking organisation method. (The PROMETHEE Method for Multiple Criteria Decision-Making). Management Science 31, 6 (1985), 647–656.
  • [9] Brucker, P., Qu, R., and Burke, E. Personnel scheduling: Models and complexity. European Journal of Operational Research 210, 3 (2011), 467–473.
  • [10] Cinelli, M., Kadziński, M., Miebs, G., Gonzalez, M., and Słowiński, R. Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system. European Journal of Operational Research 302, 2 (2022), 633–651.
  • [11] Cinelli, M., Kadziński, M., Gonzalez, M., and Słowiński, R. How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega 96, (2020), 102261.
  • [12] Cinelli, M., Spada, M., Kadziński, M., Miebs, G., and Burgherr, P. Advancing hazard assessment of Energy accidents in the natural gas sector with rough set theory and decision rules. Energies 12, 21 (2019), 4178.
  • [13] Corne, D. W., Knowles, J. D., and Oates, M. J. The Pareto envelope-based selection algorithm for multiobjective optimization. In Parallel problem solving from nature PPSN VI, M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel, Eds., vol. 1917 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2000, pp. 839–848.
  • [14] Cowling, P., Colledge, N., Dahal, K., and Remde, S. The trade off between diversity and quality for multi-objective workforce scheduling. In Evolutionary Computation in Combinatorial Optimization, J. Gottlieb, G. R. Raidl, Eds., vol. 3906 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2006, pp. 13–24.
  • [15] de Almeida, I. D. P., de Pina Corriça, J. V., de Araújo Costa, A. P., de Araújo Costa, I. P., do Nascimento Maêda, S. M., Gomes, C. F. S., and dos Santos, M. Study of the location of a second fleet for the Brazilian navy: Structuring and mathematical modeling using SAPEVO-M and VIKOR methods. In Production Research, D. A. Rossit, F. Tohmé, G. M. Delgadillo, Eds., vol. 1408 of Communications in Computer and Information Science Springer International Publishing, Cham, 2021, pp. 113–124.
  • [16] Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182–197.
  • [17] Deb, K., Rao N. U. B., and Karthik, S. Dynamic multi-objective optimization and decision-making using modified NSGAII: A case study on hydro-thermal power scheduling. In Evolutionary Multi-Criterion Optimization, 4th International Conference, EMO 2007, Matsushima, Japan, March 5-8, 2007, S. Obayashi, K. Deb, C. Poloni, T. Hiroyasu, T. Murata, Eds., vol. 4403 of Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2007, pp. 803–817.
  • [18] Dias, L. C., Antunes, C. H., Dantas, G., de Castro, N., and Zamboni, L. A multi-criteria approach to sort and rank policies based on Delphi qualitative assessments and ELECTRE TRI: The case of smart grids in Brazil. Omega 76 (2018), 100–111.
  • [19] dos Santos, M., de Araújo Costa, I. P., and Gomes, C. F. S. Multicriteria decision-making in the selection of warships: A new approach to the AHP method, International Journal of the Analytic Hierarchy Process 13, 1 (2021), 147–169.
  • [20] Drumond, P., Basílio, M. P., de Araújo Costa, I. P., de Moura Pereira, D. A., Gomes, C. F. S., and dos Santos, M. Multicriteria analysis in additive manufacturing: An ELECTRE-MOr based approach. In Modern management based on big data II and machine learning and intelligent systems III, A. J. Tallón-Ballesteros, Ed., IOS Press: Amsterdam, The Netherlands, 2021, pp. 126–132.
  • [21] Durillo, J. J., and Nebro, A. J. jMetal: A Java framework for multi-objective optimization. Advances in Engineering Software 42, 10 (2011), 760–771.
  • [22] Figueira, J. R., Greco, S., Roy, B., and Słowiński, R. An overview of ELECTRE methods and their recent extensions. Journal of Multi-Criteria Decision Analysis 20, 1-2 (2013), 61–85.
  • [23] French, S. Sequencing and scheduling. An introduction to the mathematics on the job-shop. Ellis Horwood Ltd, 1981.
  • [24] Garey, M. R., Johnson, D. S., and Sethi, R. The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research 1, 2 (1976), 117–129.
  • [25] Gasser, P., Suter, J., Cinelli, M., Spada, M., Burgherr, P., Hirschberg, S., Kadziński, M., and Stojadinović, B. Comprehensive resilience assessment of electricity supply security for 140 countries. Ecological Indicators 110, (2020), 105731.
  • [26] Gezer, V., and Wagner, A. Real-time edge framework (RTEF): task scheduling and realisation. Journal of Intelligent Manufacturing 32, (2021), 2301–2317.
  • [27] Gharaei, A., and Jolai, F. A Pareto approach for the multi-factory supply chain scheduling and distribution problem. Operational Research 21, (2021), 2333–2364.
  • [28] Gilenson, M., Shabtay, D., Yedidsion, L., and Rohit, M. Scheduling in multi-scenario environment with an agreeable condition on job processing times. Annals of Operations Research 307 (2021), 153–173.
  • [29] Glover, F. Tabu search—part I. ORSA Journal on Computing 1, 3 (1989), 190–206.
  • [30] Goldberg, D. E., and Holland, J. H. Genetic algorithms and machine learning. Machine Learning 3 (1988), 95–99.
  • [31] Govindan, K., Kadziński, M., Ehling, R., and Miebs, G. Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega 85 (2019), 1–15.
  • [32] Goyal, S., Luthra, S., and Garg, D. Shifting systematically towards sustainable consumption and production: A solution framework to overcome the impacts of Covid-19. International Journal of Information Technology & Decision Making 21, 03 (2022), 933–968.
  • [33] Greco, S., Kadziński, M., Mousseau, V., and Słowiński, R. ELECTREGKMS: Robust ordinal regression for outranking methods. European Journal of Operational Research 214, 1 (2011), 118–135.
  • [34] Greco, S., Matarazzo, B., and Słowiński, R. Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129, 1 (2001), 1–47.
  • [35] Horn, J., Nafpliotis, N., and Goldberg, D. E. A niched Pareto genetic algorithm for multiobjective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 1994, pp. 82–87.
  • [36] Huang, B., Buckley, B., and Kechadi, T.-M. Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications. Expert Systems with Applications 37, 5 (2010), 3638–3646.
  • [37] Jardim, R. R. J., Santos, M., Neto, E., da Silva, E., and de Barros, F. Integration of the waterfall model with ISO/IEC/IEEE 29148:2018 for the development of military defense system. IEEE Latin America Transactions 18, 12 (2020),2096–2103.
  • [38] Jiang, X., and Li, Y. Improved NSGA-II for the job-shop multi-objective scheduling problem. International Journal of Performability Engineering 14, 5 (2018), 891–898 .
  • [39] Kadziński, M., Tervonen, T., and Figueira, J. R. Robust multi-criteria sorting with the outranking preference model and characteristic profiles. Omega 55, (2015), 126–140.
  • [40] Kadziński, M., Tervonen, T., Tomczyk, M. K., and Dekker, R. Evaluation of multi-objective optimization approaches for solving green supply chain design problems. Omega 68, (2017), 168–184.
  • [41] Kirkpatrick, S., Gelatt Jr., C. D., and Vecchi, M. P. Optimization by simulated annealing. Science 220, 4598 (1983), 671–680.
  • [42] Knowles, J. D., and Corne, D. W. Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary Computation 8, 2 (2000), 149–172.
  • [43] Krzakiewicz, K., Ed., Theoretical foundations of organization and management.. Wydawnictwo Akademii Ekonomicznej w Poznaniu, 2006 (in Polish).
  • [44] Li, C. P., Cui, H. Y., and Wang, G. C. The optimization of flexible job-shop scheduling problem based on NSGA-II. Advanced Materials Research 651, (2013), 684–687.
  • [45] Li, J., Burke, E. K., Curtois, T., Petrovic, S., and Qu, R. The falling tide algorithm: A new multi-objective approach for complex workforce scheduling. Omega 40, 3 (2012), 283–293.
  • [46] Liu, Z., Wang, W., Wang, D., and Liu, P. A modified ELECTRE II method with double attitude parameters based n linguistic z-number and its application for third-party reverse logistics provider selection. Applied Intelligence 52, 13 (2022), 14964–14987.
  • [47] Mousseau, V., and Dias, L. Valued outranking relations in ELECTRE providing manageable disaggregation procedures. European Journal of Operational Research 156, 2 (2004), 467–482.
  • [48] Murata, T., and Ishibuchi, H. Moga: multi-objective genetic algorithms. In Proceedings of 1995 IEEE International Conference on Evolutionary Computation, 1995, IEEE, Perth, WA, Australia, vol. 1, Piscataway, NJ, USA, IEEE, 1995, pp. 289–294.
  • [49] Oliveira, M. D. N. T., Ferreira, F. A. F., Ilander, G. O. P.-B., and Jalali, M. S. Integrating cognitive mapping and MCDA for bankruptcy prediction in small- and medium-sized enterprises. Journal of the Operational Research Society 68, 9 (2017), 985–997.
  • [50] Oppio, A., Dell’Ovo, M., Torrieri, F., Miebs, G., and Kadziński, M. Understanding the drivers of Urban Development Agreements with the rough set approach and robust decision rules. Land Use Policy 96 (2020), 104678.
  • [51] Ott, L. E. Labor utilization in independent Indiana supermarkets. Ph. D thesis, Purdue University, 1959.
  • [52] Radziszewska-Zielina, E., Adamkiewicz, D., Szewczyk, B., and Kania, O. Decision-making support for housing projects in post-industrial areas. Sustainability 14, 6 (2022), 3573.
  • [53] Rahimi, I., Gandomi, A. H., Deb, K., Chen, F., and Nikoo, M. R. Scheduling by NSGA-II: Review and bibliometric analysis. Processes 10, 1 (2022), 98.
  • [54] Reisi, M., Afzali, A., and Aye, L. Applications of analytical hierarchy process (AHP) and analytical network process (ANP) for industrial site selections in Isfahan, Iran. Environmental Earth Sciences 77, 14 (2018), 537.
  • [55] Roy, B. Classement et choix en présence de points de vue multiples. R. I. R. O. 2, 8 (1968), 57–75.
  • [56] Saaty, T. L. What is the analytic hierarchy process? In Mathematical Models for Decision Support, Mitra G., Ed., Springer, Berlin, Heidelberg, 1988, pp. 109–121.
  • [57] Sadeghi, J., Sadeghi, S., and Niaki, S. T. A. A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: An NSGA-II with tuned parameters. Computers & Operations Research 41 (2014), 53–64.
  • [58] Sakr, A. H., Aboelhassan, A., Yacout, S., and Bassetto, S. Simulation and deep reinforcement learning for adaptive dispatching in semiconductor manufacturing systems. Journal of Intelligent Manufacturing (2021), 1–13.
  • [59] Silva, J. D. L., Burke, E. K., and Petrovic, S. An introduction to multiobjective metaheuristics for scheduling and timetabling. In Metaheuristics for Multiobjective Optimisation. X. Gandibleux, M. Sevaux, K. Sörensen, V. T’kindt, Eds., vol. 535 of Lecture Notes in Economics and Mathematical Systems. Springer, Berlin, Heidelberg, 2004, pp. 91–129.
  • [60] Spronk, J., Steuer, R. E., and Zopounidis, C. Multicriteria decision aid/analysis in finance. In Multiple Criteria Decision Analysis. S. Greco, M. Ehrgott, J. Figueira, Eds., vol. 233 of International Series in Operations Research & Management Science, Springer, New York, 2016, pp. 1011–1065.
  • [61] Stoner, J. A. F., Freeman, R. E., and Gilbert jr., D. R. Management. Polskie Wydawnictwo Ekonomiczne, 1997(in Polish).
  • [62] Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., and De Boeck, L. Personnel scheduling: A literature review. European Journal of Operational Research 226, 3 (2013), 367–385.
  • [63] Vansnick, J.-C. On the problem of weights in multiple criteria decision making (the noncompensatory approach). European Journal of Operational Research 24, 2 (1986), 288–294.
  • [64] Wang, S., Zhao, D., Yuan, J., Li, H., and Gao, Y. Application of NSGA-II algorithm for fault diagnosis in power system. Electric Power Systems Research 175 (2019), 105893.
  • [65] Waters, D. Operations management: Producing goods and services. Wydawnictwo Naukowe PWN, 2001 (in Polish).
  • [66] West, S., Gaiardelli, P., and Saccani, N. Overcoming the barriers to service excellence. In Modern Industrial Services. Book series: Springer Texts in Business and Economics. Springer, Cham, 2022, pp. 19–174.
  • [67] Xu, W., Xu, J., He, D., and Tan, K. C. A combined differential evolution and NSGA-II approach for parametric optimization of a cancer immunotherapy model. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (2017), IEEE, Honolulu, HI, USA, pp. 1–8.
  • [68] Zitzler, E., and Lothar, T. An evolutionary algorithm for multiobjective optimization: the strength Pareto approach. vol. 43 of TIK-report, ETH Zurich, Computer Engineering and Networks Laboratory, 1998.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83190e41-0a06-46a2-821d-33634a2c9e14
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