×

A fuzzy robust scheduling approach for product development projects. (English) Zbl 1044.90038

Summary: Efficient scheduling of a product development project is difficult, since a development project is usually unique in nature and high level of design imprecision exists at the early stages of product development. Moreover, risk-averse project managers are often more interested in estimating the risk of a schedule being late over all potential realizations. The objective of this research is to develop a robust scheduling methodology based on fuzzy set theory for uncertain product development projects. The imprecise temporal parameters involved in the project are represented by fuzzy sets. A measure of schedule robustness based on qualitative possibility theory is proposed to guide the search process to determine the robust schedule; i.e., the schedule with the best worst-case performance. A genetic algorithm approach is developed for solving the problem with acceptable performance. An example of electronic product development project is used to illustrate the concept developed.

MSC:

90B35 Deterministic scheduling theory in operations research
03E72 Theory of fuzzy sets, etc.
90C59 Approximation methods and heuristics in mathematical programming

Software:

Genocop; PSPLIB; FULPAL
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Bell, C.E; Park, K, Solving resource-constrained project scheduling problems by A\(\^{}\{∗\}\) search, Naval research logistics, 37, 61-84, (1990) · Zbl 0684.90054
[2] Chanas, S, Fuzzy optimization in networks, (), 303-327
[3] Chen, S.-J; Hwang, C.-L; Hwang, F.P, Fuzzy multiple attribute decision making: methods and applications, (1992), Springer-Verlag Berlin
[4] Cheng, R; Gen, M, An evolution programme for the resource-constrained project scheduling problem, International journal of computer integrated manufacturing, 11, 3, 274-287, (1998)
[5] Daniels, R.L; Kouvelis, P, Robust scheduling to hedge against processing time uncertainty in single-stage production, Management science, 41, 2, 363-376, (1995) · Zbl 0832.90050
[6] Demeulemeester, E; Herroelen, W, A branch-and-bound procedure for the multiple resource-constrained project scheduling problem, Management science, 38, 12, 1803-1818, (1992) · Zbl 0761.90059
[7] Dubois, D; Prade, H, Possibility theory: an approach to computerized processing of uncertainty, (1988), Plenum Press New York
[8] Dubois, D; Prade, H, Qualitative possibility theory and its applications to constraint satisfaction and decision under uncertainty, International journal of intelligent systems, 14, 45-61, (1999) · Zbl 0942.68717
[9] Dubois, D; Fargier, H; Prade, H, Fuzzy constraints in job-shop scheduling, Journal of intelligent manufacturing, 6, 4, 215-234, (1995)
[10] Elmaghraby, S.E, Activity networks: project planning and control by network models, (1977), John Wiley and Sons New York · Zbl 0385.90076
[11] Fortemps, P, Jobshop scheduling with imprecise durations: A fuzzy approach, IEEE transactions on fuzzy systems, 5, 4, 557-569, (1997)
[12] Fortemps, P; Roubens, M, Ranking and defuzzification methods based on area compensation, Fuzzy sets and systems, 82, 3, 319-330, (1996) · Zbl 0886.94025
[13] Foulds, L; Neumann, K, Temporal analysis, cost minimization, and scheduling of projects with stochastic evolution structure, Asia Pacific journal of operations research, 6, 167-191, (1989) · Zbl 0728.90043
[14] French, S, Decision theory: an introduction to the mathematics of rationality, (1993), Ellis Horwood New York
[15] Giachetti, R.E; Young, R.E; Roggatz, A; Eversheim, W; Perrone, G, A methodology for the reduction of imprecision in the engineering process, European journal of operational research, 100, 277-292, (1997) · Zbl 0920.90083
[16] Goldberg, D.E, Genetic algorithms in search, optimization, and machine learning, (1989), Addison-Wesley Massachusetts · Zbl 0721.68056
[17] Graves, S.C, A review of production scheduling, Operations research, 29, 646-675, (1981) · Zbl 0464.90034
[18] Hapke, M; Slowinski, R, Fuzzy project scheduling system for software development, Fuzzy sets and systems, 67, 101-117, (1994)
[19] Hapke, M; Slowinski, R, Fuzzy priority heuristics for project scheduling, Fuzzy sets and systems, 83, 291-299, (1996)
[20] Herroelen, W; De Reyck, B; Demeulemeester, E, Resource-constrained project scheduling: A survey of recent developments, Computers and operations research, 25, 279-302, (1998) · Zbl 1040.90525
[21] Ishibuchi, H; Yamamoto, N; Misaki, S; Tanaka, H, Local search algorithms for flow shop scheduling with fuzzy due-dates, International journal of production economics, 33, 53-66, (1994)
[22] Ishii, H; Tada, M; Masuda, T, Two scheduling problems with fuzzy due-dates, Fuzzy sets and systems, 46, 339-347, (1992) · Zbl 0767.90037
[23] Klir, G.J; Yuan, B, Fuzzy sets and fuzzy logic: theory and applications, (1995), Prentice Hall New Jersey · Zbl 0915.03001
[24] Kolisch, R; Sprecher, A, PSPLIB–a project scheduling problem library, European journal of operational research, 96, 205-216, (1997) · Zbl 0947.90587
[25] Konno, T; Ishii, H, An open shop scheduling problem with fuzzy allowable time and fuzzy resource constraint, Fuzzy sets and systems, 109, 141-147, (2000)
[26] Krishnan, V, Managing the simultaneous execution of coupled phases in concurrent product development, IEEE transactions on engineering management, 43, 2, 210-217, (1996)
[27] Lee, J.-K; Kim, Y.D, Search heuristics for resource constrained project scheduling, Journal of the operational research society, 47, 678-689, (1996) · Zbl 0863.90090
[28] Liou, T.-S; Wang, M.-J, Ranking fuzzy numbers with integral value, Fuzzy sets and systems, 50, 247-255, (1992) · Zbl 1229.03043
[29] Lootsma, F.A, Stochastic and fuzzy PERT, European journal of operational research, 43, 174-183, (1989) · Zbl 0681.90039
[30] Michalewicz, Z, Genetic algorithms+data structures=evolution programs, (1992), Springer-Verlag Berlin · Zbl 0763.68054
[31] Nasution, S.H, Fuzzy critical path method, IEEE transactions on systems, man, and cybernetics, 24, 48-57, (1994)
[32] Neumann, K, Recent developments in stochastic activity networks, Infor, 22, 219-248, (1984) · Zbl 0545.90058
[33] Ozdamar, L; Ulusoy, G, A survey on the resource-constrained project scheduling problem, IIE transactions, 27, 574-586, (1995)
[34] Ozdamar, L, A genetic algorithm approach to a general category project scheduling problem, IEEE transactions on systems, man, and cybernetics–part C: applications and review, 29, 1, 44-59, (1999)
[35] Rommelfanger, H, FULPAL: an interactive method for solving (multiobjective) fuzzy linear programming problems, (), 279-299 · Zbl 0734.90120
[36] Schaffer, J.D., Caruana, A., Eshelman, L.J., Das, R., 1989. A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Proceedings of the Third International Conference on Genetic Algorithms, George Mason University, pp. 51-60
[37] Wang, J, A fuzzy project scheduling approach to minimize schedule risk for product development, Fuzzy sets and systems, 127, 2, 99-116, (2002) · Zbl 0993.90053
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.