Skip to main content
Log in

A Benders decomposition approach for order acceptance and scheduling problem: a robust optimization approach

  • Published:
Computational and Applied Mathematics Aims and scope Submit manuscript

Abstract

In this paper, we consider a simultaneous order acceptance and scheduling problem in a non-identical parallel machines environment. The orders are defined by their due dates, revenues, tardiness penalties, different processing times on the machines, and sequence-dependent setup times. We present an MILP formulation to maximize the profit. Furthermore, we assume that the revenue from an accepted order and the processing times are uncertain and accordingly, develop the robust counterpart of the proposed MILP model. The problem is computationally intractable; therefore, we develop a Benders decomposition approach to solve it. We introduce some valid cuts to accelerate the convergence of the classical Benders algorithm and a heuristic method to obtain the feasible solutions. Through numerical experiments on randomly generated large instances with up to 40 orders and 6 machines, we demonstrate that the proposed Benders decomposition approach outperforms the MILP model.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Azad N, Saharidis GKD, Davoudpour H, Malekly H, Yektamaram SA (2013) Strategies for protecting supply chain networks against facility and transportation disruptions: An improved Benders decomposition approach. Ann Oper Res 210:125–163

    Article  MATH  MathSciNet  Google Scholar 

  • Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust Optimization. Princeton University Press, Princeton and Oxford

  • Ben-Tal A, Nemirovski A (2000) Robust solutions of linear programming problems contaminated with uncertain data. Math Program 88:411–421

    Article  MATH  MathSciNet  Google Scholar 

  • Benders JF (1962) Partitioning procedures for solving mixed integer variables programming problems. Numer Math 4:238–252

    Article  MATH  MathSciNet  Google Scholar 

  • Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52:35–53

    Article  MATH  MathSciNet  Google Scholar 

  • Bertsimas D, Thiele A (2006) Robust and data-driven optimization: modern decision-making under uncertainty Tutorials in Operations Research 95–122

  • Cesaret B, Oğuz C, Salman FS (2012) A tabu search algorithm for order acceptance and scheduling. Comput Oper Res 39:1197–1205

    Article  Google Scholar 

  • Cote G, Laughton M (1984) Large-scale mixed integer programming: Benders type heuristics. Eur J Oper Res 16:327–333

    Article  MATH  MathSciNet  Google Scholar 

  • El-Ghaoui L, Oustry F, Lebret H (1998) Robust solutions to uncertain semidefinite programs. SIAM J Optim 9:33–52

    Article  MATH  MathSciNet  Google Scholar 

  • Gabrel V, Murat C, Thiele A (2014) Recent Advances in Robust Optimization: An Overview. Eur J Oper Res 235:471–483

    Article  MATH  MathSciNet  Google Scholar 

  • Gabrel V, Murat CC, Remli N (2010) Linear programming with interval right hand sides. Int Trans Oper Res 17:397–407

    Article  MATH  MathSciNet  Google Scholar 

  • Ghosh J (1997) Job selection in a heavily loaded shop. Comput Oper Res 24:141–145

    Article  MATH  Google Scholar 

  • Hopp WJ, Spearman ML (2000) Factory Physics, 2nd edn. McGraw-Hill, Columbus

    Google Scholar 

  • Keskinocak P, Tayur S (2004) Due date management policies. Handbook of Quantitative Supply Chain Analysis: Modeling in the E-business Era, Kluwer

  • Lewis HF, Slotnick SA (2002) Multi-period job selection: planning work loads to maximize profit. Comput Oper Res 29:1081–1098

    Article  MATH  Google Scholar 

  • Liaw CF, Lin YK, Cheng CY, Chen M (2003) Scheduling unrelated parallel machines to minimize total weighted tardiness. Comput Oper Res 30:1777–1789

    Article  MATH  MathSciNet  Google Scholar 

  • Magnanti TL, Wong RT (1981) Accelerating Benders decomposition: algorithmic enhancement and model selection criteria. Oper Res 29:464–484

    Article  MATH  MathSciNet  Google Scholar 

  • McDaniel D, Devine M (1977) A modified Benders partitioning algorithm for mixed integer programming. Manag Sci 24:312–319

    Article  MATH  Google Scholar 

  • Mestry S, Damodaran P, Chen CS (2011) A branch-and-price solution approach for order acceptance and capacity planning in make-to-order operations. Eur J Oper Res 211:480–495

    Article  MATH  MathSciNet  Google Scholar 

  • Nicholas JM (1997) Competitive Manufacturing Management: Continuous Improvement. McGraw-Hill, Columbus

    Google Scholar 

  • Oguz C, Salman FS, Yalcin ZB (2010) order acceptance and scheduling decisions in make-to-order systems IntJ Production. Economics 125:200–211

    Google Scholar 

  • Öncan T (2009) Alt\({\i }\)nel K, Laporte G. A comparative analysis of several asymmetric traveling salesman problem formulations. Comput Oper Res 36:637–654

  • Park J, Nguyen S, Zhang M, Johnston M (2013) Genetic Programming for Order Acceptance and Scheduling. Paper presented at the IEEE Congress on Evolutionary Computation, Cancún, México, June 20–23

  • Potts CN, Van Wassenhove LN (1985) A branch and bound algorithm for the total weighted tardiness problem. Oper Res 33:363–377

    Article  MATH  Google Scholar 

  • Rei W, Cordeau JF, Gendreau M, Soriano P (2008) Accelerating Benders decomposition by local branching. INFORMS J Comput 21:333–345

    Article  MATH  MathSciNet  Google Scholar 

  • Rom WO, Slotnick SA (2009) Order acceptance using genetic algorithms. Comput Oper Res 36:1758–1767

    Article  MATH  Google Scholar 

  • Saharidis GKD, Boile M, Theofanis S (2011) Initialization of the Benders master problem using valid inequalities applied to fixed-charge network problems. Expert Syst Appl 38:6627–6636

    Article  Google Scholar 

  • Saharidis GKD, Ierapetritou MG (2010) Improving Benders decomposition using maximum feasible subsystem (MFS) cut generation strategy. Comput Chem Eng 34:1237–1245

    Article  Google Scholar 

  • Saharidis GKD, Ierapetritou MG (2013) Speed-up Benders decomposition using maximization density cut (MDC) generation. Ann Oper Res 210:101–123

    Article  MATH  MathSciNet  Google Scholar 

  • Saharidis GKD, Minoux M, Ierapetritou MG (2010) Accelerating Benders method using covering cut bundle generation. Int Trans Oper Res 17:221–237

    Article  MATH  MathSciNet  Google Scholar 

  • Saito H, Murota K (2007) Benders Decomposition Approach to Robust Mixed Integer Programming. Pac J Optim 3:99–112

    MATH  MathSciNet  Google Scholar 

  • Siddiqui S, Shapour A, Steven G (2011) A modified Benders decomposition method for efficient robust optimization under interval uncertainty. Struct Multidiscip 44:259–275

    Article  Google Scholar 

  • Slotnick SA (2011) Order acceptance and scheduling: a taxonomy and review. Eur J Oper Res 212:1–11

    Article  MathSciNet  Google Scholar 

  • Slotnick SA, Morton TE (1996) Selecting jobs for a heavily loaded shop with lateness penalties. Comput Oper Res 23:131–140

    Article  MATH  Google Scholar 

  • Slotnick SA, Morton TE (2007) Order acceptance with weighted tardiness. Comput Oper Res 34:3029–3042

    Article  MATH  Google Scholar 

  • Soyster AL (1973) Convex programming with set-inclusive constraints and applications to inexact linear programming. Oper Res 21:1154–1157

    Article  MATH  MathSciNet  Google Scholar 

  • Stevenson M, Hendry LC, Kingsman BG (2005) A review of production planning and control: The applicability of key concepts to the make-to-order industry. Int J Prod Res 43:869–898

    Article  Google Scholar 

  • Talla Nobibon F, Leus R (2011) Exact algorithms for a generalization of the order acceptance and scheduling problem in a single-machine environment. Comput Oper Res 38:367–378

  • Tang L, Jiang W, Saharidis GKD (2013) An improved Benders decomposition algorithm for the logistics network design problem with capacity expansions of existing warehouses. Ann Oper Res 210:165–190

  • Wang X, Xie X, Cheng TCE (2013) Order acceptance and scheduling in a two-machine flowshop. Int J Prod Econ 141:366–376

    Article  Google Scholar 

  • Zakeri G, Philpott AB, Ryan DM (1999) Inexact cuts in Benders decomposition SIAM. J Optim 10:643–657

    MATH  Google Scholar 

  • Zhong X, Ou J, Wang G (2014) Order acceptance and scheduling with machine availability constraints. Eur J Oper Res 232:435–441

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saeed Emami.

Appendix

Appendix

See Tables 5 and 6.

Table 5 The computational results of MILP model and the BD algorithm
Table 6 The computational results of MILP model and the BD algorithm

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Emami, S., Moslehi, G. & Sabbagh, M. A Benders decomposition approach for order acceptance and scheduling problem: a robust optimization approach. Comp. Appl. Math. 36, 1471–1515 (2017). https://doi.org/10.1007/s40314-015-0302-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40314-015-0302-8

Keywords

Mathematical Subject Classification

Navigation