Scheduling is one of the most important functions in managing industrial production. This article deals with the scheduling for the flow-shop production system planning by presenting a model that uses the framework of multiple criteria and multiple constraint levels () linear programming, aiming at advancing the adaptation of the model to the situation when there are more than one decision makers in the system. At first, a flow-shop production system planning model with multiple constraint levels is proposed considering fluctuation in resource constraints such as the investment constraint and time constraint. Then, an improved algorithm is proposed. Finally, an example about production scheduling of an actual production is given, and the computational results are presented.
BabuTRRaoKSPMaheshwaranCU (2007) Application of TOC embedded ILP for increasing throughput of production lines. International Journal of Advanced Manufacturing Technology33(7–8): 812–818.
2.
DoganisPSarimveisH (2008) Optimal production scheduling for the dairy industry. Annals of Operations Research159(1): 315–331.
3.
Dogan-SahinerEAltiokT (1998) Blocking policies in pharmaceutical transfer lines. Annals of Operations Research79: 323–347.
4.
JacobsonRESadowskiRP (1997) A scheduling algorithm for the unbalanced production line. IIE Transactions9(1): 36–43.
5.
JohriPK (1987) A linear programming approach to capacity estimation of automated production lines with finite buffers. International Journal of Production Research25(6): 851–867.
6.
KaraYTekinM (2009) A mixed integer linear programming formulation for optimal balancing of mixed-model U-lines. International Journal of Production Research47(15): 4201–4233.
7.
KaurAKumarA (2012a) Fuzzy solid transshipment problems and its fuzzy optimal solution. Applied and Computational Mathematics11: 398–420.
8.
KaurJKumarA (2013a) A new method to find the unique fuzzy optimal value of fuzzy linear programming problems. Journal of Optimization Theory and Applications156(2): 529–534.
9.
KaurMKumarA (2012b) Fuzzy optimal solution for unbalanced fully fuzzy minimal cost flow problems. International Journal of Fuzzy Systems14: 1–10.
KaurMKumarA (2013c) Optimal compromise solution of multi-objective minimal cost flow problems in fuzzy environment. Applied Mathematical Modelling37: 1677–1693.
12.
KumarAKaurM (2011) An algorithm for solving fuzzy maximal flow problems using generalized triangular fuzzy numbers. International Journal of Hybrid Intelligent Systems8: 15–24.
13.
MiltenburgJ (2002) Balancing and scheduling mixed-model U-shaped production lines. International Journal of Flexible Manufacturing Systems14(2): 119–151.
14.
NishiTHiranakaY (2013) Lagrangian relaxation and cut generation for sequence-dependent setup time flowshop scheduling problems to minimise the total weighted tardiness. International Journal of Production Research51(16): 4778–4796.
15.
Paternina-ArboledaCDDasTK (2001) Intelligent dynamic control policies for serial production lines. IIE Transactions33(1): 65–77.
16.
RoanCHuSJ (1995) Monitoring and classification of dimensional faults for automotive body assembly. International Journal of Flexible Manufacturing Systems7(2): 103–125.
17.
SavsarMCogunC (1994) Analysis and modelling of a production line in a corrugated box factory. International Journal of Production Research32(7): 1571–1589.
18.
ShiYPengY (2001) Multiple Criteria and Multiple Constraint Levels Linear Programming: Concepts, Techniques and Applications. River Edge, NJ: World Scientific Publishing Co. Pte. Ltd.
19.
SparlingDMiltenburgJ (1998) The mixed-model U-line balancing problem. International Journal of Production Research36(2): 485–501.
20.
VasantPM (2003) Application of fuzzy linear programming in production planning. Fuzzy Optimization and Decision Making2(3): 229–241.
21.
VenturaJARadhakrishnanS (2002) Sequencing mixed model assembly lines for a Just-In-Time production system. Production Planning & Control13(2): 199–210.
22.
VergaraHAKimDS (2009) A new method for the placement of buffers in serial production lines. International Journal of Production Research47(16): 4437–4456.
23.
YousefiMRosnahMY (2013) Minimizing earliness and tardiness penalties in single machine scheduling against common due date using imperialist competitive algorithm. International Journal of Production Research51(16): 4797–4804.
24.
ZhangYH (2005) Research on harmonious production schedule for multiple product line. Master’s Degree Dissertation, Southeast University, Nanjing, China.
