Skip to main content
SpringerLink
Log in

A review and classification of scheduling objectives in unpaced flow shops for discrete manufacturing

  • Published:
Journal of Scheduling Aims and scope Submit manuscript

Abstract

The gap between production scheduling theory and practice still persists as theoretical scheduling models do not cover real-world characteristics such as the multidimensional nature of scheduling decisions appropriately. Moreover, the set of objectives used in classical scheduling does not fully capture multidimensional scheduling decisions. This work therefore reviews which further objectives besides the classical ones have been established so far, which objectives are currently discussed in the literature, and identifies gaps for additional objectives for unpaced flow shops with discrete products. The aim is to provide a clustering of scheduling objectives that covers the multidimensional nature of scheduling decisions holistically. The scheduling objectives are clustered with respect to the respective stakeholders of the supply chain: customer, manufacturer and supplier. Within the objectives of interest for the manufacturer, objectives are further clustered into productivity-, flow- and human-related objectives. The classification serves as basis for the choice of a meaningful subset of objectives in industrial practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Agnetis, A., Billaut, J. C., Gawiejnowicz, S., Pacciarelli, D., & Soukhal, A. (2014). Multiagent scheduling. Springer.

    Book  Google Scholar 

  • Agnetis, A., Hall, N. G., & Pacciarelli, D. (2006). Supply chain scheduling: Sequence coordination. Discrete Applied Mathematics, 154(15), 2044–2063.

    Article  MathSciNet  Google Scholar 

  • Allahverdi, A., Gupta, J. N., & Aldowaisan, T. (1999). A review of scheduling research involving setup considerations. Omega, 27(2), 219–239.

    Article  Google Scholar 

  • Allahverdi, A., Ng, C. T., Cheng, T. E., & Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), 985–1032.

    Article  MathSciNet  Google Scholar 

  • Aloulou, M. A., Bouzaiene, A., Dridi, N., & Vanderpooten, D. (2014). A bicriteria two-machine flow-shop serial-batching scheduling problem with bounded batch size. Journal of Scheduling, 17(1), 17–29.

    Article  MathSciNet  Google Scholar 

  • Anzanello, M. J., & Fogliatto, F. S. (2011). Learning curve models and applications: Literature review and research directions. International Journal of Industrial Ergonomics, 41(5), 573–583.

    Article  Google Scholar 

  • Azizoglu, M., Çakmak, E., & Kondakci, S. (2001). A flexible flowshop problem with total flow time minimization. European Journal of Operational Research, 132(3), 528–538.

    Article  MathSciNet  Google Scholar 

  • Badiru, A. B. (1995). Multivariate analysis of the effect of learning and forgetting on product quality. International Journal of Production Research, 33(3), 777–794.

    Article  Google Scholar 

  • Baker, K. R., & Smith, J. C. (2003). A multiple-criterion model for machine scheduling. Journal of Scheduling, 6(1), 7–16.

    Article  MathSciNet  Google Scholar 

  • Baker, K. R., & Trietsch, D. (2009). Principles of Sequencing and Scheduling (1st ed.). John Wiley & Sons Inc.

    Book  Google Scholar 

  • Bard, J. F., Shtub, A., & Joshi, S. B. (1994). Sequencing mixed-model assembly lines to level parts usage and minimize line length. International Journal of Production Research, 32(10), 2431–2454.

    Article  Google Scholar 

  • Baur, W. (1967). Neue Wege der betrieblichen Planung. Springer. In German.

    Book  Google Scholar 

  • Bautista, J., Alfaro, R., Batalla, C., & Cano, A. (2014). Incorporating the work pace concept into the MMSP-W. In C. Hernandez, A. López-Paredes, & J. M. Pérez (Eds.), Managing complexity (pp. 261–268). Cham: Springer.

    Chapter  Google Scholar 

  • Bautista, J., Alfaro-Pozo, R., & Batalla-García, C. (2015). Consideration of human resources in the mixed-model sequencing problem with work overload minimization: Legal provisions and productivity improvement. Expert Systems with Applications, 42(22), 8896–8910.

    Article  Google Scholar 

  • Benkalai, I., Rebaine, D., & Baptiste, P. (2019). Scheduling flow shops with operators. International Journal of Production Research, 57(2), 338–356.

    Article  Google Scholar 

  • Biskup, D. (1999). Single-machine scheduling with learning considerations. European Journal of Operational Research, 115(1), 173–178.

    Article  Google Scholar 

  • Biskup, D. (2008). A state-of-the-art review on scheduling with learning effects. European Journal of Operational Research, 188(2), 315–329.

    Article  MathSciNet  Google Scholar 

  • Blackstone, J. H., Phillips, D. T., & Hogg, G. L. (1982). A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research, 20(1), 27–45.

    Article  Google Scholar 

  • Blazewicz, J., Ecker, K. H., Pesch, E., Schmidt, G., & Weglarz, J. (2007). Handbook on scheduling: From theory to applications. Springer Science & Business Media.

    Google Scholar 

  • Bodendorf, F., Merkl, P., & Franke, J. (2021). Intelligent cost estimation by machine learning in supply management: A structured literature review. Computers & Industrial Engineering, 160, 107601.

    Article  Google Scholar 

  • Boudreau, J., Hopp, W., McClain, J. O., & Thomas, L. J. (2003). On the interface between operations and human resources management. Manufacturing & Service Operations Management, 5(3), 179–202.

    Article  Google Scholar 

  • Boysen, N. (2005). Variantenfließfertigung. Deutscher Universitätsverlag. In German.

    Book  Google Scholar 

  • Boysen, N., Fliedner, M., & Scholl, A. (2007a). Produktionsplanung bei Variantenfließfertigung: Planungshierarchie und Elemente einer Hierarchischen Planung. Journal of Business Economics, 77(7–8), 759–793. In German.

    Article  Google Scholar 

  • Boysen, N., Fliedner, M., & Scholl, A. (2007b). Level-Scheduling bei Variantenfließfertigung: Klassifikation. Literaturüberblick Und Modellkritik. Journal Für Betriebswirtschaft, 57(1), 37–66. In German.

    Article  Google Scholar 

  • Boysen, N., Fliedner, M., & Scholl, A. (2008). Sequencing mixed-model assembly lines to minimize part inventory cost. Or Spectrum, 30(3), 611–633.

    Article  MathSciNet  Google Scholar 

  • Bozer, Y. A., & McGinnis, L. F. (1992). Kitting versus line stocking: A conceptual framework and a descriptive model. International Journal of Production Economics, 28(1), 1–19.

    Article  Google Scholar 

  • Browne, S., & Yechiali, U. (1990). Scheduling deteriorating jobs on a single processor. Operations Research, 38(3), 495–498.

    Article  Google Scholar 

  • Brucker, P. (2007). Scheduling Algorithms (5th ed.). Springer.

    Google Scholar 

  • Burns, L. D., & Daganzo, C. F. (1987). Assembly line job sequencing principles. International Journal of Production Research, 25(1), 71–99.

    Article  Google Scholar 

  • Buschkühl, L., Sahling, F., Helber, S., & Tempelmeier, H. (2010). Dynamic capacitated lot-sizing problems: A classification and review of solution approaches. Or Spectrum, 32(2), 231–261.

    Article  MathSciNet  Google Scholar 

  • Buxey, G. M., Slack, N. D., & Wild, R. (1973). Production flow line system design: A review. AIIE Transactions, 5(1), 37–48.

    Article  Google Scholar 

  • Buzacott, J. A. (1999). The structure of manufacturing systems: Insights on the impact of variability. International Journal of Flexible Manufacturing Systems, 11(2), 127–146.

    Article  Google Scholar 

  • Cai, X., Wu, X., & Zhou, X. (2014). Optimal stochastic scheduling (4th ed.). Springer.

    Book  Google Scholar 

  • Celano, G., Costa, A., Fichera, S., & Perrone, G. (2004). Human factor policy testing in the sequencing of manual mixed model assembly lines. Computers & Operations Research, 31(1), 39–59.

    Article  Google Scholar 

  • Cesaret, B., Ozguz, C., & Salman, F. S. (2012). A tabu search algorithm for order acceptance and scheduling. Computers and Operations Research, 39(6), 1197–1205.

    Article  Google Scholar 

  • Chandra, P., Mehta, P., & Tirupati, D. (2009). Permutation flow shop scheduling with earliness and tardiness penalties. International Journal of Production Research, 47(20), 5591–5610.

    Article  Google Scholar 

  • Chen, J., Wang, H., & Zhong, R. Y. (2021). A supply chain disruption recovery strategy considering product change under COVID-19. Journal of Manufacturing Systems, 60, 920–927.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen, Z. L., & Hall, N. G. (2007). Supply chain scheduling: Conflict and cooperation in assembly systems. Operations Research, 55(6), 1072–1089.

    Article  MathSciNet  Google Scholar 

  • Cheng, T. C. E. (1988). Optimal common due-date with limited completion time deviation. Computers & Operations Research, 15(2), 91–96.

    Article  ADS  Google Scholar 

  • Cheng, T. C. E., & Wang, G. (2000). Single machine scheduling with learning effect considerations. Annals of Operations Research, 98(1), 273–290.

    Article  MathSciNet  Google Scholar 

  • Cheng, T. C. E., Wu, C. C., & Lee, W. C. (2008). Some scheduling problems with deteriorating jobs and learning effects. Computers & Industrial Engineering, 54(4), 972–982.

    Article  Google Scholar 

  • Conway, R. W., Maxwell, W. L., & Miller, L. W. (1967). Theory of scheduling. Reading: Addison-Wesley.

    Google Scholar 

  • Daniels, R. L., & Chambers, R. J. (1990). Multiobjective flow-shop scheduling. Naval Research Logistics, 37(6), 981–995.

    Article  Google Scholar 

  • Daniels, R. L., & Mazzola, J. B. (1994). Flow shop scheduling with resource flexibility. Operations Research, 42(3), 504–522.

    Article  Google Scholar 

  • Daniels, R. L., Mazzola, J. B., & Shi, D. (2004). Flow shop scheduling with partial resource flexibility. Management Science, 50(5), 658–669.

    Article  Google Scholar 

  • Day, J. E., & Hottenstein, M. P. (1970). Review of sequencing research. Naval Research Logistics Quarterly, 17(1), 11–39.

    Article  Google Scholar 

  • Decker, M. (1993). Capacity smoothing and sequencing for mixed-model lines. International Journal of Production Economics, 30, 31–42.

    Article  Google Scholar 

  • Dessouky, M. I., Moray, N., & Kijowski, B. (1995). Taxonomy of scheduling systems as a basis for the study of strategic behavior. Human Factors, 37(3), 443–472.

    Article  Google Scholar 

  • Deuse, J., Weisner, K., Hengstebeck, A., & Busch, F. (2015). Gestaltung von Produktionssystemen im Kontext von Industrie 4.0. In A. Botthof & E. A. Hartmann (Eds.), Zukunft der Arbeit in Industrie 4.0 (pp. 99–109). Berlin: Springer. In German.

    Chapter  Google Scholar 

  • Dileepan, P., & Sen, T. (1988). Bicriterion static scheduling research for a single machine. Omega, 16(1), 53–59.

    Article  Google Scholar 

  • Dios, M., & Framinan, J. M. (2016). A review and classification of computer-based manufacturing scheduling tools. Computers & Industrial Engineering, 99, 229–249.

    Article  Google Scholar 

  • Dode, P., Greig, M., Zolfaghari, S., & Neumann, W. P. (2016). Integrating human factors into discrete event simulation: A proactive approach to simultaneously design for system performance and employees’ well being. International Journal of Production Research, 54(10), 3105–3117.

    Article  Google Scholar 

  • Domschke, W., Scholl, A., & Voß, S. (1997). Produktionsplanung. Ablauforganisatorische Aspekte (2nd ed.). Berlin: Springer. In German.

    Book  Google Scholar 

  • Dondeti, V. R., & Mohanty, B. B. (1998). Impact of learning and fatigue factors on single machine scheduling with penalties for tardy jobs. European Journal of Operational Research, 105, 509–524.

    Article  Google Scholar 

  • Dörmer, J. (2013). Produktionsprogrammplanung bei variantenreicher Fließproduktion: Untersucht am Beispiel der Automobilendmontage. Springer. In German.

    Book  Google Scholar 

  • Drexl, A., & Kimms, A. (1997). Lot sizing and scheduling—Survey and extensions. European Journal of Operational Research, 99(2), 221–235.

    Article  Google Scholar 

  • Drexl, A., Kimms, A., & Matthießen, L. (2006). Algorithms for the car sequencing and the level scheduling problem. Journal of Scheduling, 9(2), 153–176.

    Article  Google Scholar 

  • Dudek, R. A., Panwalkar, S. S., & Smith, M. L. (1992). The lessons of flowshop scheduling research. Operations Research, 40(1), 7–13.

    Article  Google Scholar 

  • Duplaga, E. A., Hahn, C. K., & Hur, D. (1996). Mixed-model assembly line sequencing at Hyundai motor company. Production and Inventory Management Journal, 37, 20–26.

    Google Scholar 

  • Dyckhoff, H., & Spengler, T. S. (2010). Produktionswirtschaft: Eine Einführung (3rd ed.). Springer. In German.

    Book  Google Scholar 

  • Ehrgott, M. (2005). Multicriteria optimization. Springer Science & Business Media.

  • Ehrgott, M., & Nickel, S. (2002). On the number of criteria needed to decide Pareto optimality. Mathematical Methods of Operations Research, 55(3), 329–345.

    Article  MathSciNet  Google Scholar 

  • Emmons, H., & Vairaktarakis, G. (2013). Flow Shop Scheduling. Springer.

    Book  Google Scholar 

  • Felan, J. T., & Fry, T. D. (2001). Multi-level heterogeneous worker flexibility in a dual resource constrained (DRC) job-shop. International Journal of Production Research, 39(14), 3041–3059.

    Article  Google Scholar 

  • Felbecker, O. (1980). Ein Beitrag zur Reihenfolgeplanung bei Mehrprodukt-Linienfertigung. PhD Thesis, RWTH Aachen. In German.

  • Fox, M. S. (1994). ISIS: A retrospective. Intelligent Scheduling, 1, 3–28.

    Google Scholar 

  • Framinan, J. M., Gupta, J. N., & Leisten, R. (2004). A review and classification of heuristics for permutation flow-shop scheduling with makespan objective. Journal of the Operational Research Society, 55(12), 1243–1255.

    Article  Google Scholar 

  • Framinan, J. M., Leisten, R., & García, R. R. (2014). Manufacturing scheduling systems, an integrated view on models, methods and tools. Springer.

    Book  Google Scholar 

  • Framinan, J. M., Leisten, R., & Rajendran, C. (2003). Different initial sequences for the Heuristic of Nawaz, Enscore and Ham to Minimize Makespan, Idletime or flowtime in the static permutation flowshop sequencing problem. International Journal of Production Research, 41(1), 121–148.

    Article  Google Scholar 

  • Freiheit, T., & Li, W. (2017). The effect of work content imbalance and its interaction with scheduling method on sequential flow line performance. International Journal of Production Research, 55(10), 2791–2805.

    Article  Google Scholar 

  • Fryer, J. S. (1975). Effects of shop size and labor flexibility in labor and machine limited production systems. Management Science, 21(5), 507–515.

    Article  Google Scholar 

  • Gary, K., Uzsoy, R., Smith, S. P., & Kempf, K. (1995). Measuring the quality of manufacturing schedules. In W. Scherer & D. Brown (Eds.), Intelligent scheduling systems (pp. 129–154). New York: Kluwer Academic Publishing.

    Chapter  Google Scholar 

  • Gawiejnowicz, S. (1996). A note on scheduling on a single processor with speed dependent on a number of executed jobs. Information Processing Letters, 57, 297–300.

    Article  MathSciNet  Google Scholar 

  • Gawiejnowicz, S. (2008). Time-dependent Scheduling. Springer.

    Book  Google Scholar 

  • Gawiejnowicz, S. (2020a). Models and algorithms of time-dependent scheduling (2nd ed.). Springer.

    Book  Google Scholar 

  • Gawiejnowicz, S. (2020b). A review of four decades of time-dependent scheduling: Main results, new topics, and open problems. Journal of Scheduling, 23, 3–47.

    Article  MathSciNet  Google Scholar 

  • Geiger, M. J. (2005). Multikriterielle Ablaufplanung. Deutscher Universitätsverlag. In German.

    Book  Google Scholar 

  • Geng, X. N., Wang, J. B., & Bai, D. (2019). Common due date assignment scheduling for a no-wait flowshop with convex resource allocation and learning effect. Engineering Optimization, 51(8), 1301–1323.

    Article  ADS  MathSciNet  Google Scholar 

  • Givi, Z. S. (2014). The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems. PhD Thesis, Ryerson University, Canada.

  • Givi, Z. S., Jaber, M. Y., & Neumann, W. P. (2015). Production planning in DRC systems considering worker performance. Computers & Industrial Engineering, 87, 317–327.

    Article  Google Scholar 

  • Gordon, V., Proth, J. M., & Chu, C. (2002). A survey of the state-of-the-art of common due date assignment and scheduling research. European Journal of Operational Research, 139(1), 1–25.

    Article  MathSciNet  Google Scholar 

  • Graham, R. L., Lawler, E. L., Lenstra, J. K., & Rinooy Kan, A. H. G. (1979). Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics, 5, 287–326.

    Article  MathSciNet  Google Scholar 

  • Graves, S. C. (1981). A review of production scheduling. Operations Research, 29(4), 646–675.

    Article  MathSciNet  Google Scholar 

  • Grimme, C., Lepping, J., & Schwiegelshohn, U. (2013). Multi-criteria scheduling: An agent-based approach for expert knowledge integration. Journal of Scheduling, 16(4), 369–383.

    Article  MathSciNet  Google Scholar 

  • Günther, H. O. (2014). The block planning approach for continuous time-based dynamic lot sizing and scheduling. Business Research, 7(1), 51–76.

    Article  Google Scholar 

  • Gupta, J. N., & Dudek, R. A. (1971). Optimality criteria for flowshop schedules. AIIE Transactions, 3(3), 199–205.

    Article  Google Scholar 

  • Gupta, J. N., & Gupta, S. K. (1988). Single facility scheduling with nonlinear processing times. Computers & Industrial Engineering, 14(4), 387–393.

    Article  Google Scholar 

  • Gupta, J. N., Neppalli, V. R., & Werner, F. (2001). Minimizing total flow time in a two-machine flowshop problem with minimum makespan. International Journal of Production Economics, 69(3), 323–338.

    Article  Google Scholar 

  • Gutenberg, E. (1951). Grundlagen der Betriebswirtschaftslehre Band 1 (1st ed.). Berlin: Springer. (In German).

    Book  Google Scholar 

  • Hall, N. G., & Potts, C. N. (2003). Supply chain scheduling: Batching and delivery. Operations Research, 51(4), 566–584.

    Article  MathSciNet  Google Scholar 

  • Haupt, R. (1989). A survey of priority rule-based scheduling. Or Spectrum, 11(1), 3–16.

    Article  MathSciNet  Google Scholar 

  • Hejazi, R. S., & Saghafian, S. (2005). Flowshop-scheduling problems with makespan criterion: A review. International Journal of Production Research, 43(14), 2895–2929.

    Article  Google Scholar 

  • Hoogeveen, H. (2005). Multicriteria scheduling. European Journal of Operational Research, 167(3), 592–623.

    Article  MathSciNet  Google Scholar 

  • Hopp, W. J., & Spearman, M. L. (2011). Factory physics (3rd ed.). Waveland Press.

    Google Scholar 

  • Hopp, W. J., Tekin, E., & van Oyen, M. P. (2004). Benefits of skill chaining in serial production lines with cross-trained workers. Management Science, 50(1), 83–98.

    Article  Google Scholar 

  • Hottenstein, M. P., & Bowman, S. A. (1998). Cross-training and worker flexibility: A review of DRC system research. The Journal of High Technology Management Research, 9(2), 157–174.

    Article  Google Scholar 

  • Jaber, M. Y., & Bonney, M. (1996). Production breaks and the learning curve: The forgetting phenomenon. Applied Mathematical Modelling, 2(20), 162–169.

    Article  Google Scholar 

  • Jaber, M. Y., & Neumann, W. P. (2010). Modelling worker fatigue and recovery in dual-resource constrained systems. Computers & Industrial Engineering, 59(1), 75–84.

    Article  Google Scholar 

  • Jaehn, F., & Pesch, E. (2014). Ablaufplanung: Einführung in scheduling. Springer. In German.

    Book  Google Scholar 

  • Jain, A. K., & Elmaraghy, H. A. (1997). Production scheduling/rescheduling in flexible manufacturing. International Journal of Production Research, 35(1), 281–309.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Keckl, S., Kern, W., Abou-Haydar, A., & Westkämper, E. (2016). An analytical framework for handling production time variety at workstations of mixed-model assembly lines. Procedia CIRP, 41, 201–206.

    Article  Google Scholar 

  • Kempf, K., Uzsoy, R., Smith, S., & Gary, K. (2000). Evaluation and comparison of production schedules. Computers in Industry, 42(2–3), 203–220.

    Article  Google Scholar 

  • Kilic, H. S., & Durmusoglu, M. B. (2015). Advances in assembly line parts feeding policies: A literature review. Assembly Automation, 35(1), 57–68.

    Article  Google Scholar 

  • Kotani, S., Ito, T., & Ohno, K. (2004). Sequencing problem for a mixed-model assembly line in the Toyota production system. International Journal of Production Research, 42(23), 4955–4974.

    Article  Google Scholar 

  • Koulamas, C., & Kyparisis, G. J. (2021). The no-wait flow shop with rejection. International Journal of Production Research, 59(6), 1852–1859.

    Article  Google Scholar 

  • Kreipl, S., & Pinedo, M. (2004). Planning and scheduling in supply chains: An overview of issues in practice. Production and Operations Management, 13(1), 77–92.

    Article  Google Scholar 

  • Kubiak, W. (1993). Minimizing variation of production rates in just-in-time systems: A survey. European Journal of Operational Research, 66(3), 259–271.

    Article  MathSciNet  Google Scholar 

  • Kuik, R., Salomon, M., & van Wassenhove, L. N. (1994). Batching decisions: Structure and models. European Journal of Operational Research, 75(2), 243–263.

    Article  Google Scholar 

  • Larsen, R., & Pranzo, M. (2018). A framework for dynamic rescheduling problems. International Journal of Production Research, 57, 1–18.

    Google Scholar 

  • Lawler, E. L., Lenstra, J. K., Rinnooy-Kan, A. H. G., & Shmoys, D. B. (1993). Sequencing and scheduling: Algorithms and complexity. In Graves, S.S., Rinnooy-Kan, A. H. G., Zipkin, P. (eds.), Handbooks in operations research and management science, 4th edn. pp 445–522.

  • Lee, C. Y., Lei, L., & Pinedo, M. (1997). Current trends in deterministic scheduling. Annals of Operations Research, 70, 1–41.

    Article  MathSciNet  Google Scholar 

  • Lee, K., Leung, J. Y., Jia, Z. H., Li, W., Pinedo, M. L., & Lin, B. M. (2014). Fast approximation algorithms for bi-criteria scheduling with machine assignment costs. European Journal of Operational Research, 238(1), 54–64.

    Article  MathSciNet  Google Scholar 

  • Lei, D. (2009). Multi-objective production scheduling: A survey. The International Journal of Advanced Manufacturing Technology, 43(9), 926.

    Article  Google Scholar 

  • Li, K., Zhang, X., Leung, J. Y. T., & Yang, S. L. (2016). Parallel machine scheduling problems in green manufacturing industry. Journal of Manufacturing Systems, 38, 98–106.

    Article  Google Scholar 

  • Lodree, E. J., Jr., Geiger, C. D., & Jiang, X. (2009). Taxonomy for integrating scheduling theory and human factors: Review and research opportunities. International Journal of Industrial Ergonomics, 39(1), 39–51.

    Article  Google Scholar 

  • Lodree, E. J., Jr., & Norman, B. A. (2006). Scheduling models for optimizing human performance and well-being. In J. W. Herrmann (Ed.), Handbook of production scheduling (pp. 287–314). New York: Springer Science & Business Media.

    Chapter  Google Scholar 

  • Maassen, K., Perez-Gonzalez, P., & Günther, L. C. (2020). Relationship between common objective functions, idle time and waiting time in permutation flow shop scheduling. Computers & Operations Research, 121, 1–13.

    Article  MathSciNet  Google Scholar 

  • Maccarthy, B. L., & Liu, J. (1993). Addressing the gap in scheduling research: A review of optimization and heuristic methods in production scheduling. The International Journal of Production Research, 31(1), 59–79.

    Article  Google Scholar 

  • McCreery, J. K., & Krajewski, L. J. (1999). Improving performance using workforce flexibility in an assembly environment with learning and forgetting effects. International Journal of Production Research, 37(9), 2031–2058.

    Article  Google Scholar 

  • McKay, K., Pinedo, M., & Webster, S. (2002). Practice-focused research issues for scheduling systems. Production and Operations Management, 11(2), 249–258.

    Article  Google Scholar 

  • McKay, K. N., & Wiers, V. C. (1999). Unifying the theory and practice of production scheduling. Journal of Manufacturing Systems, 18(4), 241.

    Article  Google Scholar 

  • Mellor, P. (1966). A review of job shop scheduling. Journal of the Operational Research Society, 17(2), 161–171.

    Article  Google Scholar 

  • Mensch, G. (1968). Ablaufplanung. Springer Fachmedien. In German.

    Book  Google Scholar 

  • Merengo, C., Nava, F., & Pozzetti, A. (1999). Balancing and sequencing manual mixed-model assembly lines. International Journal of Production Research, 37(12), 2835–2860.

    Article  Google Scholar 

  • Merten, A. G., & Muller, M. E. (1972). Variance minimization in single machine sequencing problems. Management Science, 18(9), 518–528.

    Article  Google Scholar 

  • Miltenburg, J. (1989). Level schedules for mixed-model assembly lines in just-in-time production systems. Management Science, 35(2), 192–207.

    Article  Google Scholar 

  • Minella, G., Ruiz, R., & Ciavotta, M. (2008). A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. Journal on Computing, 20(3), 451–471.

    MathSciNet  Google Scholar 

  • Monden, Y. (1998). Toyota production system: An integrated approach to just-in-time (3rd ed.). Chapmann & Hall.

    Google Scholar 

  • Mor, B., Mosheiov, G., & Shabtay, D. (2021). Minimizing the total tardiness and job rejection cost in a proportionate flow shop with generalized due dates. Journal of Scheduling, 24(6), 553–567.

    Article  MathSciNet  Google Scholar 

  • Müllerklein, D., Fontaine, P., & Ostermeier, F. (2022). Integrated consideration of assembly line scheduling and feeding: A new model and case study from the automotive industry. Computers & Industrial Engineering., 170, 108288.

    Article  Google Scholar 

  • Nagar, A., Haddock, J., & Heragu, S. (1995). Multiple and bicriteria scheduling: A literature survey. European Journal of Operational Research, 81(1), 88–104.

    Article  Google Scholar 

  • Öner-Közen, M., Minner, S., & Steinthaler, F. (2017). Efficiency of paced and unpaced assembly lines under consideration of worker variability: A simulation study. Computers & Industrial Engineering, 111, 516–526.

    Article  Google Scholar 

  • Ostermeier, F. F. (2020). The impact of human consideration, schedule types and product mix on scheduling objectives for unpaced mixed-model assembly lines. International Journal of Production Research, 58(14), 4386–4405.

    Article  Google Scholar 

  • Ostermeier, F. F. (2022). On the trade-offs between scheduling objectives for unpaced mixed-model assembly lines. International Journal of Production Research, 60(3), 866–893.

    Article  MathSciNet  Google Scholar 

  • Panwalkar, S. S., Dudek, R. A., & Smith, M. L. (1973). Sequencing research and the industrial scheduling problem. In M. Beckmann, G. Goos, & H. P. Künzi (Eds.), Symposium on the theory of scheduling and its applications (pp. 29–38). Berlin: Springer.

    Chapter  Google Scholar 

  • Pargar, F., Zandieh, M., Kauppila, O., & Kujala, J. (2018). The Effect of worker learning on scheduling jobs in a hybrid flow shop: A bi-objective approach. Journal of Systems Science and Systems Engineering, 27(3), 265–291.

    Article  Google Scholar 

  • Pinedo, M. (2009). Planning and scheduling in manufacturing and services (2nd ed.). Springer.

    Book  Google Scholar 

  • Pinedo, M. L. (2016). Scheduling: Theory, algorithms, and systems (5th ed.). Springer.

    Book  Google Scholar 

  • Portougal, V., & Robb, D. J. (2000). Production scheduling theory: Just where is it applicable? Interfaces, 30(6), 64–76.

    Article  Google Scholar 

  • Potts, C. N., & Kovalyov, M. Y. (2000). Scheduling with batching: A review. European Journal of Operational Research, 120(2), 228–249.

    Article  MathSciNet  Google Scholar 

  • Potts, C. N., & Strusevich, V. A. (2009). Fifty years of scheduling: A survey of milestones. Journal of the Operational Research Society, 60(1), 41–68.

    Article  Google Scholar 

  • Potts, C. N., & van Wassenhove, L. N. (1992). Integrating scheduling with batching and lot-sizing: A review of algorithms and complexity. Journal of the Operational Research Society, 43(5), 395–406.

    Article  Google Scholar 

  • Rajendran, C. (1995). Heuristics for scheduling in flowshop with multiple objectives. European Journal of Operational Research, 82(3), 540–555.

    Article  MathSciNet  Google Scholar 

  • Rajendran, C., & Holthaus, O. (1999). A comparative study of dispatching rules in dynamic flowshops and jobshops. European Journal of Operational Research, 116(1), 156–170.

    Article  Google Scholar 

  • Rodammer, F. A., & White, K. P. (1988). A recent survey of production scheduling. IEEE Transactions on Systems, Man, and Cybernetics, 18(6), 841–851.

    Article  MathSciNet  Google Scholar 

  • Rossit, D. A., Tohmé, F., & Frutos, M. (2018). The non-permutation flow-shop scheduling problem: A literature review. Omega, 77, 143–153.

    Article  Google Scholar 

  • Roy, R., Souchoroukov, P., & Shehab, E. (2011). Detailed cost estimating in the automotive industry: Data and information requirements. International Journal of Production Economics, 133, 694–707.

    Article  Google Scholar 

  • Ruiz-Torres, A. J., Alomoto, N., Paletta, G., & Pérez, E. (2015). Scheduling to maximise worker satisfaction and on-time orders. International Journal of Production Research, 53(9), 2836–2852.

    Article  Google Scholar 

  • Sabuncuoglu, I., & Goren, S. (2009). Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. International Journal of Computer Integrated Manufacturing, 22(2), 138–157.

    Article  Google Scholar 

  • Sadeh, N. (1993). MICRO-BOSS: A micro-opportunistic factory scheduler. Expert Systems with Applications, 6(3), 377–392.

    Article  Google Scholar 

  • Salum, L., & Araz, Ö. U. (2009). Using the when/where rules in dual resource constrained systems for a hybrid push-pull control. International Journal of Production Research, 47(6), 1661–1677.

    Article  Google Scholar 

  • Sammarco, M., Fruggiero, F., Neumann, W. P., & Lambiase, A. (2014). Agent-based modelling of movement rules in DRC systems for volume flexibility: Human factors and technical performance. International Journal of Production Research, 52(3), 633–650.

    Article  Google Scholar 

  • Schneeweiß, C., & Söhner, V. (2013). Kapazitätsplanung bei moderner Fließfertigung (2nd ed.). Springer-Verlag. In German.

    Google Scholar 

  • Scholl, A. (1999). Balancing and sequencing of assembly lines. PhD Thesis, Technische Universität Darmstadt. In German.

  • Scholl, A., Boysen, N., & Fliedner, M. (2013). The assembly line balancing and scheduling problem with sequence-dependent setup times: Problem extension, model formulation and efficient heuristics. Or Spectrum, 35(1), 291–320.

    Article  MathSciNet  Google Scholar 

  • Schultz, K. L., Juran, D. C., Boudreau, J. W., McClain, J. O., & Thomas, L. J. (1998). Modeling and worker motivation in JIT production systems. Management Science, 44(12), 1595–1607.

    Article  Google Scholar 

  • Seeanner, F. (2013). Multi-stage simultaneous lot-sizing and scheduling: Planning of flow lines with shifting bottlenecks. New York: Springer Science & Business Media.

    Book  Google Scholar 

  • Shabtay, D., Gaspar, N., & Kaspi, M. (2013). A survey on offline scheduling with rejection. Journal of Scheduling, 16, 3–28.

    Article  MathSciNet  Google Scholar 

  • Shabtay, D., & Steiner, G. (2007). A survey of scheduling with controllable processing times. Discrete Applied Mathematics, 155(13), 1643–1666.

    Article  MathSciNet  Google Scholar 

  • Silva, J. D. L., Burke, E. K., & Petrovic, S. (2004). An introduction to multiobjective metaheuristics for scheduling and timetabling. In X. Gandibleux, M. Sevaux, K. Sörensen, & V. T’Kindt (Eds.), Metaheuristics for multiobjective optimisation (pp. 91–129). Berlin: Springer.

    Chapter  Google Scholar 

  • Stadtler, H. (2015). Supply chain management: An overview. In H. Stadtler, C. Kilger, & H. Meyr (Eds.), Supply chain management and advanced planning (5th ed., pp. 195–212). Berlin: Springer.

    Chapter  Google Scholar 

  • Sterna, M. (2011). A survey of scheduling problems with late work criteria. Omega, 39(2), 120–129.

    Article  Google Scholar 

  • Sterna, M. (2021). Late and early work scheduling: A survey. Omega, 104, 120–129.

    Article  Google Scholar 

  • Strusevich, V. A., & Rustogi, K. (2017). Scheduling with time-changing effects and rate-modifying activities. Springer.

    Book  Google Scholar 

  • Sun, Y., Zhang, C., Gao, L., & Wang, X. (2010). Multi-objective optimization algorithms for flow shop scheduling problem: A review and prospects. The International Journal of Advanced Manufacturing Technology, 55(5–8), 723–739.

    Google Scholar 

  • T’Kindt, V. (2011). Multicriteria models for just-in-time scheduling. International Journal of Production Research, 49(11), 3191–3209.

    Article  Google Scholar 

  • T’Kindt, V., & Billaut, J. C. (2001). Multicriteria scheduling problems: A survey. RAIRO-Operations Research, 35(2), 143–163.

    Article  MathSciNet  Google Scholar 

  • T’Kindt, V., & Billaut, J. C. (2006). Multicriteria scheduling: theory, models and algorithms. Berlin: Springer Science & Business Media.

    Google Scholar 

  • Treleven, M. (1989). A review of the dual resource constrained system research. IIE Transactions, 21(3), 279–287.

    Article  Google Scholar 

  • Valledor, P., Gomez, A., Priore, P., & Puente, J. (2018). Solving multi-objective rescheduling problems in dynamic permutation flow shop environments with disruptions. International Journal of Production Research, 56, 1–15.

    Article  Google Scholar 

  • Vickson, R. G. (1980). Choosing the job sequence and processing times to minimize total processing plus flow cost on a single machine. Operations Research, 28(5), 1155–1167.

    Article  MathSciNet  Google Scholar 

  • Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Rescheduling manufacturing systems: A framework of strategies, policies, and methods. Journal of Scheduling, 6(1), 39–62.

    Article  MathSciNet  Google Scholar 

  • Wang, J. B. (2007). Single-machine scheduling problems with the effects of learning and deterioration. Omega, 35(4), 397–402.

    Article  Google Scholar 

  • Wierzchoń, S. T., & Kłopotek, M. A. (2018). Modern algorithms of cluster analysis. Springer.

    Book  Google Scholar 

  • Woodruff, D. L., & Spearman, M. L. (1992). Sequencing and batching for two classes of jobs with deadlines and setup times. Production and Operations Management, 1(1), 87–102.

    Article  Google Scholar 

  • Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the Aeronautical Sciences, 3(4), 122–128.

    Article  MathSciNet  Google Scholar 

  • Xu, J., Xu, X., & Xie, S. Q. (2011). Recent developments in Dual Resource Constrained (DRC) system research. European Journal of Operational Research, 215(2), 309–318.

    Article  Google Scholar 

  • Yagmahan, B., & Yenisey, M. M. (2009). Scheduling practice and recent developments in flow shop and job shop scheduling. In U. Chakraborty (Ed.), Computational intelligence in flow shop and job shop scheduling (pp. 261–300). Berlin: Springer.

    Chapter  Google Scholar 

  • Yelle, L. E. (1979). The learning curve: Historical review and comprehensive survey. Decision Sciences, 10(2), 302–328.

    Article  Google Scholar 

  • Yin, Y., Li, D., Wang, D., & Cheng, T. C. E. (2021). Single-machine serial-batch delivery scheduling with two competing agents and due date assignment. Annals of Operations Research, 298(1), 497–523.

    Article  MathSciNet  Google Scholar 

  • Yue, H. (2005). Worker flexibility in dual resource constrained (DRC) shops. PhD Thesis, University of Groningen.

  • Yue, H., Slomp, J., Molleman, E., & Van Der Zee, D. J. (2008). Worker flexibility in a parallel dual resource constrained job shop. International Journal of Production Research, 46(2), 451–467.

    Article  Google Scholar 

Download references

Acknowledgements

We want to thank the reviewers for their valuable comments.

Author information

Authors and Affiliations

  1. BMW Group, Petuelring 130, 80788, Munich, Germany

    Frederik Ferid Ostermeier

  2. Institute of Production Systems, TU Dortmund University, Leonhard-Euler-Str. 5, 44227, Dortmund, Germany

    Frederik Ferid Ostermeier & Jochen Deuse

  3. Faculty of Engineering and Information Technology, School of Mechanical and Mechatronic Engineering, Centre for Advanced Manufacturing, University of Technology Sydney, Sydney, Australia

    Jochen Deuse

Authors
  1. Frederik Ferid Ostermeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jochen Deuse
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frederik Ferid Ostermeier.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ostermeier, F.F., Deuse, J. A review and classification of scheduling objectives in unpaced flow shops for discrete manufacturing. J Sched 27, 29–49 (2024). https://doi.org/10.1007/s10951-023-00795-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10951-023-00795-5

Keywords

Search

Navigation