A genetic algorithm approach to a general category project scheduling problem

A genetic algorithm (GA) approach is proposed for the general resource-constrained project scheduling model, in which activities may be executed in more than one operating mode, and renewable as well as nonrenewable resource constraints exist. Each activity's operation mode has a different duration and requires different amounts of renewable and nonrenewable resources. The objective is the minimization of the project duration or makespan. The problem under consideration is known to be one of the most difficult scheduling problems, and it is hard to find a feasible solution for such a problem, let alone the optimal one. The GA approach described in this paper incorporates problem-specific scheduling knowledge by an indirect chromosome encoding that consists of selected activity operating modes and an ordered set of scheduling rules. The scheduling rules in the chromosome are used in an iterative scheduling algorithm that constructs the schedule resulting from the chromosome. The proposed GA is denoted a hybrid GA (HGA) approach, since it is integrated with traditional scheduling tools and expertise specifically developed for the general resource-constrained project scheduling problem. The results demonstrate that HGA approach produces near-optimal solutions within a reasonable amount of computation time.

[1]  Gündüz Ulusoy,et al.  A local constraint based analysis approach to project scheduling under general resource constraints , 1994 .

[2]  Kazuhiko Kawamura,et al.  Managing genetic search in job shop scheduling , 1993, IEEE Expert.

[3]  J. H. Patterson,et al.  An Algorithm for a general class of precedence and resource constrained scheduling problems , 1989 .

[4]  J. C. Bean.  Genetics and random keys for sequencing amd optimization , 1993 .

[5]  Stephen F. Smith,et al.  Using Genetic Algorithms to Schedule Flow Shop Releases , 1989, ICGA.

[6]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[7]  F. Brian Talbot,et al.  Resource-Constrained Project Scheduling with Time-Resource Tradeoffs: The Nonpreemptive Case , 1982 .

[8]  Emanuel Falkenauer,et al.  A genetic algorithm for job shop , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[9]  Gündüz Ulusoy,et al.  A heuristic scheduling algorithm for improving the duration and net present value of a project , 1995 .

[10]  Chung-Yee Lee,et al.  Solving a Class Scheduling Problem with a Genetic Algorithm , 1995, INFORMS J. Comput..

[11]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[12]  R. Storer,et al.  New search spaces for sequencing problems with application to job shop scheduling , 1992 .

[13]  Fayez F. Boctor,et al.  A new and efficient heuristic for scheduling projects with resource restrictions and multiple execution modes , 1996 .

[14]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[15]  B. R. Fox,et al.  Genetic Operators for Sequencing Problems , 1990, FOGA.

[16]  Varghese S. Jacob,et al.  A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts , 1993, IEEE Trans. Syst. Man Cybern..

[17]  Ramón Alvarez-Valdés Olaguíbel,et al.  Chapter 5 – HEURISTIC ALGORITHMS FOR RESOURCE-CONSTRAINED PROJECT SCHEDULING: A REVIEW AND AN EMPIRICAL ANALYSIS , 1989 .

[18]  Gündüz Ulusoy,et al.  Heuristic Performance and Network/Resource Characteristics in Resource-constrained Project Scheduling , 1989 .

[19]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[20]  Jan Karel Lenstra,et al.  Scheduling subject to resource constraints: classification and complexity , 1983, Discret. Appl. Math..

[21]  L. Darrell Whitley,et al.  Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator , 1989, International Conference on Genetic Algorithms.

[22]  RAINER KOLISCH,et al.  Local search for nonpreemptive multi-mode resource-constrained project scheduling , 1997 .

[23]  K. De Jong.  Adaptive System Design: A Genetic Approach , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[24]  Dirk C. Mattfeld,et al.  Evolutionary Search and the Job Shop - Investigations on Genetic Algorithms for Production Scheduling , 1996, Production and Logistics.

[25]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[26]  Arno Sprecher,et al.  Project scheduling with discrete time-resource and resource-resource tradeoffs , 1994 .

[27]  Gündüz Ulusoy,et al.  A constraint-based perspective in resource constrained project scheduling , 1994 .

[28]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[29]  James C. Bean,et al.  Genetic Algorithms and Random Keys for Sequencing and Optimization , 1994, INFORMS J. Comput..

[30]  Gündüz Ulusoy,et al.  A note on an iterative forward/backward scheduling technique with reference to a procedure by Li and Willis , 1996 .

[31]  Rainer Kolisch,et al.  Characterization and generation of a general class of resource-constrained project scheduling problems , 1995 .

[32]  John E. Biegel,et al.  Genetic algorithms and job shop scheduling , 1990 .

[33]  Gündüz Ulusoy,et al.  An iterative local constraints based analysis for solving the resource constrained project scheduling problem , 1996 .

[34]  Takeshi Yamada,et al.  Conventional Genetic Algorithm for Job Shop Problems , 1991, ICGA.

[35]  Andreas Drexl,et al.  Nonpreemptive multi-mode resource-constrained project scheduling , 1993 .

[36]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[37]  F. F. Boctor.  Heuristics for scheduling projects with resource restrictions and several resource-duration modes , 1993 .

[38]  Erwin Pesch,et al.  Evolution based learning in a job shop scheduling environment , 1995, Comput. Oper. Res..