Scheduling problems — An overview

Abstract

There seems to be a significant gap between the theoretical and the practical aspects of scheduling problems in the job shop environment. Theoretically, scheduling systems are designed on the basis of an optimum approach to the scheduling model. However in the practice, the optimum that is built into the scheduling applications seems to face some challenges when dealing with the dynamic character of a scheduling system, for instance machine breakdown or change of orders. Scheduling systems have become quite complex in the past few years. Competitive business environments and shorter product life cycles are the imminent challenges being faced by many companies these days. These challenges push companies to anticipate a demand driven supply chain in their business environment. A demand-driven supply chain incorporates the customer view into the supply chain processes. As a consequence of this, scheduling as a core process of the demand-driven supply chain must also reflect the customer view. In addition, other approaches to solving scheduling problems, for instance approaches based on human factors, prefer the scheduling system to be more flexible in both design and implementation. After discussion of these factors, the authors propose the integration of a different set of criteria for the development of scheduling systems which not only appears to have a better flexibility but also increased customer-focus.

Keywords

Job shop / scheduling algorithms / scheduling techniques / scheduling support system

References

[1] Abdallah M.H.. A knowledge-based simulation model for job shop. International Journal of Operations & Production Management, 1995, 15(10): 89-102.

[2] Bermudez J.. Supply chain management: more than just technology. Supply Chain Management Review, 2002, 6(2): 15

[3] Bistline Sr., William G., Banerjee S., Banerjee A.. RTSS: an interactive decision support system for solving real time scheduling problems considering customer and job priorities with schedule interruptions. Computers & Operations Research, 1998, 25(11): 981-995.

[4] Dorn J., Kerr R., Thalhammer G.. Reactive scheduling: improving the robustness of schedules and restricting the effects of shop floor disturbances by fuzzy reasoning. Human Computer Studies, 1995, 42: 687-704.

[5] Ecker K., Gupta J.N.D., Schmidt G.. A framework for decision support systems for scheduling problems. European Journal of Operational Research, 1997, 101(3): 452-462.

[6] Jain A.S., Meeran S.. Deterministic job-shop scheduling: past, present and future. European Journal of Operational Research, 1999, 113(2): 390-434.

[7] Kara S., Kayis B., O’Kane S.. The role of human factors in flexibility management: a survey. Human Factors and Ergonomics in Manufacturing, 2002, 12(1): 75-119.

[8] Koh K.-H., Souza R.d., Ho N.-C.. Database driven simulation / simulation-based scheduling of a job shop. Simulation Practice and Theory, 1996, 4: 31-45.

[9] Kuo W.-H., Hwang S.-L.. A prototype of a real-time support system in the scheduling production systems. International Journal of Industrial Ergonomics, 1998, 21: 133-143.

[10] MacCarthy B.L., Wilson J.R., Crawford S.. Human performance in industrial scheduling: a framework for understanding. Human Factors and Ergonomics in Manufacturing, 2001, 11(4): 299-320.

[11] Norbis M., Smith J.M.. An interactive decision support system for the resource constrained scheduling problem. European Journal of Operational Research, 1996, 94(1): 54-65.

[12] Noronha S.J., Sarma V.V.S.. Knowledge-Based approaches for scheduling problems: a survey. IEEE Transactions on Knowledge and Data Engineering, 1991, 3(2): 160-171.

[13] Pereira J., Paulre B.. Flexibility in manufacturing systems: a relational and a dynamic approach. European Journal of Operational Research, 2001, 130(1): 70-82.

[14] Sabuncuoglu I., Bayiz M.. Analysis of reactive scheduling problems in a job shop environment. European Journal of Operational Research, 2000, 126(3): 567-586.

[15] Schniederjans M.J., Carpenter D.A.. A heuristic job scheduling decision support system: a case study. Decision Support Systems, 1996, 18(2): 159-166.

[16] Sellers D.W.. A survey of approaches to the job shop scheduling problem. The 28th Southeastern Symposium on System Theory (SSST’ 96), 1996, Cookeville: IEEE

[17] Speranza M.G., Woerlee A.P.. A decision support system for operational production scheduling. European Journal of Operational Research, 1991, 55: 329-343.

[18] Stoop P.P.M.W., Vincent C.S.. The complexity of scheduling in practice. International Journal of Operations & Production Management, 1996, 16(10): 37.

[19] Suresh V., Chaudhuri D.. Dynamic scheduling — a survey of research. International Journal of Production Economics, 1993, 32: 53-63.

[20] Suryadi K., Muluk A.. Design forced method scheduling algorithms. Agile and Shared Manufacturing System, 1999, Bandung: Production System Laboratory, Industrial Engineering, Bandung Institute of Technology

[21] Wiers V.C.S., Van Der Schaaf T.W.. A framework for decision support in production scheduling tasks. Production Planning & Control, 1997, 8(6): 533-544.

[22] Wortman J.C.E., Euwe M.J., Taal M., Wiers V.C.S.. A review of capacity planning techniques within standard software packages. Production Planning & Control, 1996, 7(2): 117-128.