Time-varying constraints and other practical problems in real-world scheduling applications

When an evolutionary algorithm is used as an optimizer in a scheduling software application that is destined for use in a real-world commercial setting, a number of time-variability issues are encountered. This paper explores several such issues and other practical problems that arose during the solution of a scheduling application in the area of wine bottling. Each hurdle was addressed by appropriately adjusting the candidate individual representation, the procedure used to decode an individual, or the objective function itself. Addressing these issues is critical when designing and constructing the evolutionary algorithm, in order to ensure that the resulting system is robust enough to meet the demands of day-to-day use. The approach described in this paper has been proven by implementation and vigorous sustained use in a complex business environment.

[1]  Colin R. Reeves,et al.  Permutation flowshop scheduling by genetic local search , 1997 .

[2]  William B. Langdon,et al.  Scheduling Planned Maintenance of the National Grid , 1995, Evolutionary Computing, AISB Workshop.

[3]  E. Nowicki,et al.  A fast tabu search algorithm for the permutation flow-shop problem , 1996 .

[4]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..

[5]  Shengxiang Yang,et al.  Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[6]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[7]  Elena Marchiori,et al.  An Evolutionary Algorithm for Large Scale Set Covering Problems with Application to Airline Crew Scheduling , 2000, EvoWorkshops.

[8]  Tapabrata Ray,et al.  Optimum Oil Production Planning using an Evolutionary Approach , 2007, Evolutionary Scheduling.

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

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

[11]  S. G. Ponnambalam,et al.  A GA-SA Multiobjective Hybrid Search Algorithm for Integrating Lot Sizing and Sequencing in Flow-Line Scheduling , 2003 .

[12]  Mitsuo Gen,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation , 1996 .

[13]  F. Martinelli Stochastic Comparison Algorithm for Discrete Optimization with Estimation of Time-Varying Objective Functions , 1999 .

[14]  Shengxiang Yang,et al.  Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[15]  Ling Wang,et al.  A Modified Genetic Algorithm for Job Shop Scheduling , 2002 .

[16]  Lutz Schönemann,et al.  Evolution Strategies in Dynamic Environments , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[17]  Gerry Dozier,et al.  Adapting Particle Swarm Optimizationto Dynamic Environments , 2001 .