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A hybrid evolutionary approach to job-shop scheduling with generic time lags

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Abstract

This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra (minimum and maximum) delays can be introduced between operations. It belongs to a category of problems known as NP-hard problems due to the large solution space. Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon (IEEE Trans Evol Comput 12:702–713, 2008) to optimize hard combinatorial optimization problems. BBO has successfully solved optimization problems in many different domains and has demonstrated excellent performance. We propose a hybrid biogeography-based optimization (HBBO) algorithm for solving the job shop scheduling problem with additional time lag constraints while minimizing total completion time. In the proposed HBBO, an effective greedy constructive heuristic is adapted to generate the initial habitat population. A local search metaheuristic is investigated in the mutation step in order to improve the solution quality and enhance the diversity of the population. To assess the performance of the HBBO, a series of experiments are performed on well-known benchmark instances for job shop scheduling problems with time lag constraints. The results prove the efficiency of the proposed algorithm in comparison with various other algorithms.

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References

  • Afsar, H. M., Lacomme, P., Ren, L., Prodhon, C. & Vigo, D. (2016). Resolution of a job-shop problem with transportation constraints: A master/slave approach. In IFAC conference on manufacturing modelling, management and control.

  • Al-Roomi, A., & El-Hawary, M. (2016). Metropolis biogeography-based optimization. Information Sciences, 360, 73–95.

    Google Scholar 

  • Artigues, C., Huguet, M., & Lopez, P. (2011). Generalized disjunctive constraint propagation for solving the job shop problem with time lags. Engineering Applications of Artificial Intelligence, 24, 220–231.

    Google Scholar 

  • Bartusch, M., Mohring, R. H., & Rademacher, F. J. (1988). Scheduling project networks with resource constraints and time windows. Annals of Operations Research, 16, 201–240.

    Google Scholar 

  • Bhattacharya, A., & Chattopadhyay, P. (2010). Hybrid differential evolution with biogeography-based optimization for solution of economic load dispatch. IEEE Transactions on Power Systems, 25(4), 1955–1964.

    Google Scholar 

  • Bhattacharya, A., & Chattopadhyay, P. (2011). Hybrid differential evolution with biogeography-based optimization algorithm for solution of economic emission load dispatch problems. Expert Systems With Applications, 38(11), 14001–14010.

    Google Scholar 

  • Botta-Genoulaz, V. (2000). Hybrid flow shop scheduling with precedence constraints and time lags to minimize maximum lateness. International Journal of Production Economics, 64, 101–111.

    Google Scholar 

  • Boussaïd, I., Chatterjee, A., & Siarry, P. (2011). Hybridizing biogeography-based optimization with differential evolution for optimal power allocation in wireless sensor networks. IEEE Transactions on Vehicular Technology, 60(5), 2347–2353.

    Google Scholar 

  • Brinkmann, K., & Neumann, K. (1996). Heuristic procedures for resource-constrained project scheduling with minimal and maximal time lags: The minimum project-duration and resource-levelling problems. Journal of Decision Systems, 5, 129–156.

    Google Scholar 

  • Brucker, P., Hilbig, T., & Hurink, J. (1999). A branch and bound algorithm for a single machine scheduling with positive and negative time-lags. Discrete Applied Mathematics, 120, 77–99.

    Google Scholar 

  • Carlier, J. (1978). Ordonnancements á contraintes disjunctives. RAIRO Recherche operationelle, 12, 333–351.

  • Caumond A. A., Lacomme, P. & Tchernev, N. (2005a). Proposition of genetic algorithm for job-shop with time-lags. ROADEF’05 (pp. 183–200).

  • Caumond, A., Gourgand, M., Lacomme, P., & Tchernev, N. (2004). Metaheuristics for job-shop with time lags. In Proceedings of the conference Mosim’s 04 (pp. 939–946). Nantes, France.

  • Caumond, A., Lacomme, P., & Tchernev, N. (2005b). Feasible schedule generation with extension of the Giffler and Thompson’s heuristic for the job shop problem with time lags. In International conference of industrial engineering and systems management (pp. 489–499).

  • Caumond, A., Lacomme, P., & Tchernev, N. (2008). A memetic algorithm for the job-shop with time-lags. Computers & Operations Research, 35, 2331–2356.

    Google Scholar 

  • Chu, C., & Proth, J. M. (1996). Single machine scheduling with chain structured precedence constraints and separation time windows. IEEE Transactions on Robotics and Automation, 12, 835–843.

    Google Scholar 

  • Deppner, F. (2004). Ordonnancement d’atelier avec contraintes temporelles entre opérations. Ph.D. thesis, Institut National Polytechnique de Lorraine.

  • Deroussi, L., Gourgand, M., & Norre, S. (2006). New effective neighborhoods for the permutation flow shop problem.

  • Dhouib, E., Teghem, J., & Loukil, T. (2013). Lexicographic optimization of a permutation flow shop scheduling problem with time lag constraints. International Transactions in Operational Research, 20(2), 213–232.

    Google Scholar 

  • Du, D., Simon, D., & Ergezer, M. (2009). Biogeography-based optimization combined with evolutionary strategy and immigration refusal. In Proceedings of IEEE international conference on systems, man and cybernetics, San Antonio (pp. 997–1002).

  • Feng, S.-L. Yang, Z.-Q. Huang, M.-X. (2017). Hybridizing adaptive biogeography-based optimization with differential evolution for multi-objective optimization problems. Information, 8, 83.

  • Fisher, H., & Thompson, G. L. (1963). Probabilistic learning combination of local job shop scheduling rules. Industrial scheduling (pp. 225–251). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Fondrevelle, J., Oulamaraa, A., & Portmann, M.-C. (2006). Permutation flowshop scheduling problems with maximal and minimal time lags. Computers & Operations Research, 33, 1540–1556.

    Google Scholar 

  • Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 13(5), 533–549.

    Google Scholar 

  • González, M. A., Oddi, A., Rasconi, R., & Varela, R. (2015). Scatter search with path relinking for the job shop with time lags and set up times. Computer & Operation Research, 60, 37–54.

    Google Scholar 

  • Guo, W., Li, W., Zhang, Q., Wang, L., & Wu, Q. (2013). Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems. Engineering Optimization, 46(11), 1–20.

    Google Scholar 

  • Habib, S., Rahmati, A., & Zandieh, M. (2011). A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 58(9), 1115–1129.

    Google Scholar 

  • Hamdi, I., & Loukil, T. (2011). Minimizing the makespan in the permutation flowshop problem with maximal and minimal time lags. In Communications, computing and control applications (CCCA) (pp. 1–6).

  • Harrabi, M., & Belkahla Driss, O. (2015). MATS-JSTL: A multi-agent model based on Tabu search for the job shop problem with time lags. In International computational collective intelligence technologies and applications ICCCI (pp. 39–46).

  • Harrabi, M., & Belkahla Driss, O. (2016). Tabu Search metaheuristic for job shop scheduling problem with generic time lags. In International conference on decision aid sciences and applications DASA.

  • Harrabi, M., Belkahla Driss, O., & Ghedira, K. (2017a). Competitive agents implementing parallel Tabu searches for job shop scheduling problem with time lags. In IASTED international conference on modelling, identification and control (pp. 848–052).

  • Harrabi, M., Belkahla Driss, O., & Ghedira, K. (2017b). Combining genetic algorithm and Tabu search for job shop scheduling problem with time lags. In IEEE international conference on engineering & MIS.

  • Harrabi, M., Belkahla Driss, O., & Ghedira, K. (2017c). A multi-agent model based on hybrid genetic algorithm for job shop scheduling problem with generic time lags. In ACS/IEEE international conference on computer systems and applications AICCSA.

  • Harrabi, M., Belkahla Driss, O., & Ghedira, K. (2018). A greedy biogeography-based optimization algorithm for job shop scheduling problem with time lags. In M. Graña, et al. (Eds.), International joint conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18. Advances in intelligent systems and computing (Vol. 771). Springer.

  • Harrabi, M., Belkahla Driss, O., & Ghedira, K. (2020). A modified biogeography-based optimization algorithm with improved mutation operator for job shop scheduling problem with time lags. Logic Journal of the IGPL, jzaa037.

  • Heilmann, R. (2003). A branch-and-bound procedure for the multi-mode resource-constrained project scheduling problem with minimum and maximum time lags. European Journal of Operational Research, 144(144), 348–365.

    Google Scholar 

  • Hodson, A., Muhlemann, P., & Price, D. H. R. (1985). A microcomputer based solution to a practical scheduling problem. Journal of the Operational Research Society, 36(10), 903–914.

    Google Scholar 

  • Hurink, J., & Keuchel, J. (2001). Local search algorithms for a single machine scheduling problem with positive and negative time-lags. Discrete Applied Mathematics, 112, 179–197.

    Google Scholar 

  • Johnson, S. M. (1954). Optimal two-and three-stage production schedules with setup times included. Naval Research Logistics, 1, 61–68.

    Google Scholar 

  • Karoui, W., Huguet, M. J., Lopez, P., & Haouari, M. (2010). Méthode de recherche a divergence limitée pour les \(probl\grave{e}mes\) d’ordonnancement avec contraintes de délais. In Conférence Internationale de Modélisation et Simulation (Vol. 8, pp. 10–12).

  • Kim, Y. D., Lim, H. G., & Park, M. W. (1996). Search heuristics for a flowshop scheduling problem in a printed circuit board assembly process. European Journal of Operational Research, 91, 124–143.

    Google Scholar 

  • Lacomme, P., Huguet, MJ., & Tchernev, N. (2011). Dedicated constraint propagation for job-shop problem with generic time-lags. In 16th IEEE conference on emerging technologies and factory automation IEEE catalog number: CFP11ETF-USB.

  • Lacomme, P., & Tchernev, N. (2012). Job-shop with generic time lags: A heuristic based approach. In 9th international conference of modeling, optimization and simulation—MOSIM.

  • Lawrence, S. (1984). Supplement to resource constrained project scheduling: An experimental investigation of heuristic scheduling techniques. Pittsburgh: Graduate School of Industrial Administration, Carnegie Mellon University.

    Google Scholar 

  • Lin, J. (2016). A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem. International Journal of Production Research, 54(16), 1–10.

    Google Scholar 

  • Lu, X., Du, Y., Yang, X., & Zheng, Y. (2018). A biogeography-based memetic algorithm for job-shop scheduling. In International conference on bio-inspired computing: theories and applications (pp. 273–284).

  • Ma, H., Su, S., Simon, D., & Fei, M. (2015). Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling. Engineering Applications of Artificial Intelligence, 44, 79–90.

    Google Scholar 

  • Ma, H., & Simon, D. (2017). Biogeography-based optimization: A 10-year review. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(5), 391–407.

    Google Scholar 

  • MacArthur, R., & Wilson, E. (1967). The theory of biogeography. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Manier, M. A., & Bloch, C. (2003). A classification of hoist scheduling problems. International Journal of Flexible Manufacturing Systems, 15(1), 37–55.

    Google Scholar 

  • Manne, S. (1960). On the job-shop scheduling problem. Operations Research, 8, 219–223.

    Google Scholar 

  • Mitten, L. G. (1958). Sequencing n jobs on two machines with arbitrary time lags. Management Science, 5(3), 293–298.

    Google Scholar 

  • Nawaz, M., Enscore, J. E. E., & Ham, I. (1983). A heuristic algorithm for the n-job, m machine sequencing problem. Management Science, 16/B, 630–637.

    Google Scholar 

  • Neumann, K., Schwindt, C., & Zimmermann, J. (2002). Project scheduling with time windows and scarce resources. Berlin: Springer.

    Google Scholar 

  • Nikbakhsh, J. P., Mohammad, F. M., & Mohammad, K. (2012). An immune algorithm for hybrid flow shop scheduling problem with time lags and sequence dependent setup times. International Journal of Advanced Manufacturing Technology, 63(1–4), 337–348.

    Google Scholar 

  • Nowicki, E., & Smutnicki, C. (2005). An advanced Tabu search algorithm for the job shop problem. Journal of Scheduling, 8, 145–159.

    Google Scholar 

  • Rabiee, M., Jolai, F., Asefi, H., & Fattahi, P. (2016). A biogeography-based optimization algorithm for a realistic no-wait hybrid flow shop with unrelated parallel machines to minimize mean tardiness. International Journal of Computer Integrated Manufacturing, 29, 1007–1024.

    Google Scholar 

  • Rifai, A. P., Nguyen, H., Aoyama, H., Dawal, S. Z., & Masruroh, N. A. (2018). Non-dominated sorting biogeography-based optimization for bi-objective reentrant flexible manufacturing system scheduling. Applied Soft Computing, 62, 187–202.

    Google Scholar 

  • Rajendran, C. (1994). A no-wait flow shop scheduling heuristic to minimize makespan. The Journal of the Operational Research Society, 45, 472–478.

    Google Scholar 

  • Roy, B., & Sussmann, B. (1964). Les problèmes d’ordonnancement avec contraintes disjonctives. SEMA: Technical report.

  • Sheikh, S. (2013). Multi-objective flexible flow lines with due window, time lag and job rejection. International Journal of Advanced Manufacturing Technology, 64(9–12), 1423–1433.

    Google Scholar 

  • Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12, 702–713.

    Google Scholar 

  • Sinha, S., Bhola, A., Panchal, V., Singhal, S., & Abraham, A. (2012). Resolving mixed pixels by hybridization of biogeography based optimization and ant colony optimization. In IIEEE Congress on Evolutionary Computation, Brisbane, QLD (pp. 1–6).

  • Soukhal, A., Oulamara, A., & Martineau, P. (2005). Complexity of flow shop scheduling problems with transportation constraints. European Journal of Operational Research, 161, 32–41.

    Google Scholar 

  • Talbi, E. G. (2009). Metaheuristics: From design to implementation. Hoboken, NJ: Wiley.

    Google Scholar 

  • Wang, X., & Duan, H. (2014). A hybrid biogeography-based optimization algorithm for job shop scheduling problem. Computers & Industrial Engineering, 73, 96–114.

    Google Scholar 

  • Wee, L., Antoni, W., & Mohammad, D. (2016). A biogeography-based optimization algorithm hybridized with Tabu search for the quadratic assignment problem. Computational Intelligence and Neuroscience,. https://doi.org/10.1155/2016/5803893.

    Article  Google Scholar 

  • Wikum, E. D., Llewellyin, D. C., & Nemhauser, G. L. (1994). One-machine generalized precedence constrained scheduling problem. Operations Research Letters, 16, 87–99.

    Google Scholar 

  • Wismer, A. D. (1972). Solution of the flow shop scheduling problem with no intermediate queues. Operations Research, 20, 689–697.

    Google Scholar 

  • Wu, J., Wu, X., Lu, X., Lu, X., Du, Y., & Zhang, M. (2019). Water wave optimization for flow-shop scheduling (Vol. 771–783).

  • Yang, Y. (2015). A modified biogeography-based optimization for the flexible job shop scheduling problem. In Mathematical problems in engineering.

  • Ye, S., Zhao, N., Li, K., & Lei, C. (2017). Efficient heuristic for solving non-permutation flow-shop scheduling problems with maximal and minimal time lags. Computers & Industrial Engineering, 113, 160–184.

    Google Scholar 

  • Zhang, X. (2010). Scheduling with time lags. Ph.D. Thesis, Erasmus Research Institute of Management.

  • Zhao, F., Qin, S., & Zhang, Y. (2019). A hybrid biogeography-based optimization with variable neighborhood search mechanism for no-wait flow shop scheduling problem. Expert Systems with Applications, 126, 321–339.

    Google Scholar 

  • Zhao, N., Ye, S., Li, K., & Chen, S. (2017). Effective iterated greedy algorithm for flow-shop scheduling problems with time lags. Chinese Journal of Mechanical Engineering, 30, 652–662.

    Google Scholar 

  • Zheng, Y., Ling, H., Shi, H., & Chen, H. (2014a). Emergency railway wagon scheduling by hybrid biogeography-based optimization. Computers & Operations Research, 43(1), 1–8.

    Google Scholar 

  • Zheng, Y., Ling, H., & Xue, J. (2014b). Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations. Computers & Operations Research, 50, 115–127.

    Google Scholar 

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Authors and Affiliations

  1. Ecole Nationale des Sciences de l’Informatique, SOIE-COSMOS Laboratory, Université de Manouba, Manouba, Tunisie

    Madiha Harrabi

  2. Ecole Supérieure de Commerce, SOIE-COSMOS Laboratory, Université de Manouba, Manouba, Tunisie

    Olfa Belkahla Driss

  3. Université Centrale de Tunis, Honoris United Universities, Eljaziri, Tunisie

    Khaled Ghedira

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  1. Madiha Harrabi
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Harrabi, M., Driss, O.B. & Ghedira, K. A hybrid evolutionary approach to job-shop scheduling with generic time lags. J Sched 24, 329–346 (2021). https://doi.org/10.1007/s10951-021-00683-w

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