The Design of Memetic Algorithms for Scheduling and Timetabling Problems

There are several characteristics that make scheduling and timetabling problems particularly difficult to solve: they have huge search spaces, they are often highly constrained, they require sophisticated solution representation schemes, and they usually require very time-consuming fitness evaluation routines. There is a considerable number of memetic algorithms that have been proposed in the literature to solve scheduling and timetabling problems. In this chapter, we concentrate on identifying and discussing those strategies that appear to be particularly useful when designing memetic algorithms for this type of problems. For example, the many different ways in which knowledge of the problem domain can be incorporated into memetic algorithms is very helpful to design effective strategies to deal with infeasibility of solutions. Memetic algorithms employ local search, which serves as an effective intensification mechanism that is very useful when using sophisticated representation schemes and time-consumingfitness evaluation functions. These algorithms also incorporate a population, which gives them an effective explorative ability to sample huge search spaces. Another important aspect that has been investigated when designing memetic algorithms for scheduling and timetabling problems, is how to establish the right balance between the work performed by the genetic search and the work performed by the local search. Recently, researchers have put considerable attention in the design of self-adaptive memetic algorithms. That is, to incorporate memes that adapt themselves according to the problem domain being solved and also to the particular conditions of the search process. This chapter also discusses some recent ideas proposed by researchers that might be useful when designing self-adaptive memetic algorithms. Finally, we give a summary of the issues discussed throughout the chapter and propose some future research directions in the design of memetic algorithms for scheduling and timetabling problems.

[1]  Puteh Saad,et al.  Incorporating constraint propagation in genetic algorithm for university timetable planning , 1999 .

[2]  Marco Dorigo,et al.  New Ideas in Optimisation , 1999 .

[3]  P. Preux,et al.  Towards hybrid evolutionary algorithms , 1999 .

[4]  Andreas T. Ernst,et al.  Staff scheduling and rostering: A review of applications, methods and models , 2004, Eur. J. Oper. Res..

[5]  David W. Corne,et al.  Towards Landscape Analyses to Inform the Design of Hybrid Local Search for the Multiobjective Quadratic Assignment Problem , 2002, HIS.

[6]  Edmund K. Burke,et al.  Combining Hybrid Metaheuristics and Populations for the Multiobjective Optimisation of Space Allocation Problems , 2001 .

[7]  Andrzej Jaszkiewicz,et al.  Genetic local search for multi-objective combinatorial optimization , 2022 .

[8]  Wilhelm Erben,et al.  A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling , 2000, PATAT.

[9]  Kathryn A. Dowsland Review of Practice and theory of automated timetabling III (Third international conference, Patat 2000, Konstanz, Germany, August 2000, selected papers) by Edmund Burke and Wilhelm Erben (eds), Springer lecture notes in computer science, vol.2079, 2001 , 2003 .

[10]  Hisao Ishibuchi,et al.  Selection of initial solutions for local search in multiobjective genetic local search , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  Mitsuo Gen,et al.  Cellular Genetic Local Search for Multi-Objective Optimization , 2000, GECCO.

[12]  Andrea Schaerf,et al.  A Survey of Automated Timetabling , 1999, Artificial Intelligence Review.

[13]  Hisao Ishibuchi,et al.  Effectiveness of Genetic Local Search Algorithms , 1997, ICGA.

[14]  Hisao Ishibuchi,et al.  Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling , 2003, IEEE Trans. Evol. Comput..

[15]  Joshua D. Knowles,et al.  M-PAES: a memetic algorithm for multiobjective optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[16]  E.K. Burke,et al.  Hybrid population-based metaheuristic approaches for the space allocation problem , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[17]  Edmund K. Burke,et al.  Hybrid evolutionary techniques for the maintenance scheduling problem , 2000 .

[18]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[19]  Jean-Paul Watson,et al.  The impact of approximate evaluation on the performance of search algorithms for warehouse scheduling , 1999 .

[20]  Andrew J. Davenport,et al.  Cooperative Strategies for Solving the Bicriteria Sparse Multiple Knapsack Problem , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[21]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.

[22]  Ben Paechter,et al.  Extensions to a Memetic Timetabling System , 1995, PATAT.

[23]  Tomohiro Yoshikawa,et al.  Genetic algorithm with the constraints for nurse scheduling problem , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[24]  El-Ghazali Talbi,et al.  A Taxonomy of Hybrid Metaheuristics , 2002, J. Heuristics.

[25]  John J. Grefenstette,et al.  Genetic Search with Approximate Function Evaluation , 1985, ICGA.

[26]  Peter Ross,et al.  Some Observations about GA-Based Exam Timetabling , 1997, PATAT.

[27]  A. Alkan,et al.  Memetic algorithms for timetabling , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[28]  Natalio Krasnogor,et al.  Studies on the theory and design space of memetic algorithms , 2002 .

[29]  B. Freisleben,et al.  Fitness Landscapes and Memetic Algorithm Design 3.1 Introduction 3.2 Fitness Landscapes of Combinatorial Problems , 1999 .

[30]  Marco Dorigo,et al.  Metaheuristics for High School Timetabling , 1998, Comput. Optim. Appl..

[31]  Jim Smith,et al.  A Memetic Algorithm With Self-Adaptive Local Search: TSP as a case study , 2000, GECCO.

[32]  Edmund K. Burke,et al.  A multistage evolutionary algorithm for the timetable problem , 1999, IEEE Trans. Evol. Comput..

[33]  Edmund K. Burke,et al.  A memetic algorithm to schedule planned maintenance for the national grid , 1999, JEAL.

[34]  Pierre Hansen,et al.  Variable Neighbourhood Search , 2003 .

[35]  Javier Ruiz-del-Solar,et al.  Soft computing systems : design, management and applications , 2002 .

[36]  Pablo Moscato,et al.  Memetic algorithms: a short introduction , 1999 .

[37]  Anthony Wren,et al.  Scheduling, Timetabling and Rostering - A Special Relationship? , 1995, PATAT.

[38]  Jacek Blazewicz,et al.  The job shop scheduling problem: Conventional and new solution techniques , 1996 .

[39]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[40]  Edmund K. Burke,et al.  Hyperheuristic Approaches for Multiobjective Optimisation , 2003 .

[41]  Wilhelm Erben,et al.  A Genetic Algorithm Solving a Weekly Course-Timetabling Problem , 1995, PATAT.

[42]  El-Ghazali Talbi,et al.  Design of multi-objective evolutionary algorithms: application to the flow-shop scheduling problem , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[43]  Marcus Randall,et al.  A General Meta-Heuristic Based Solver for Combinatorial Optimisation Problems , 2001, Comput. Optim. Appl..

[44]  Landa Silva,et al.  Metaheuristic and Multiobjective Approaches for Space Allocation , 2003 .

[45]  Edmund K. Burke,et al.  A Memetic Algorithm for University Exam Timetabling , 1995, PATAT.

[46]  Marc Despontin,et al.  Multiple Criteria Optimization: Theory, Computation, and Application, Ralph E. Steuer (Ed.). Wiley, Palo Alto, CA (1986) , 1987 .

[47]  Peter I. Cowling,et al.  A Memetic Approach to the Nurse Rostering Problem , 2001, Applied Intelligence.