A genetic algorithm for online-scheduling of a multiproduct polymer batch plant
An overview of past and recent work on the application of genetic algorithms (GAs) to scheduling problems can be found in [Corne, D., & Ross, P. (1997). Practical issues and recent advanced in job and open shop scheduling. In: D. Dasgupta, & Z. Michalewiz, Evolutionary algorithms in engineering applications, Springer, Heidelberg.]. There it is stated that GAs performs very well in certain cases, e.g. for minimizing the make-span and/or maximizing resource utilization. On the other hand, applications to real world scheduling examples with numerous constraints are seldom reported. In this contribution, the application of an augmented genetic algorithm to a real-world scheduling problem from the polymer industries is investigated. A nonstandard feature of the problem is a high degree of coupled production. None of the different products can be produced separately and only their relative proportion can be influenced by the choice of the recipes of the polymerizations. The discontinuous and the continuous part of the plant are connected by a mixing stage which gives rise to nonlinear equations. Rigorous mathematical modeling leads to a large, non-convex, mixed integer nonlinear problem (MINLP). In the application of GAs to this problem, special attention must be paid to the handling of constraints on the genetic level to ensure that most individuals represent feasible schedules. The quality of the schedule and the numerical performance or the algorithm are discussed and compared with the mathematical programming algorithm described in [Schulz, C., Engell, S., & Rudolf, R. (1998). scheduling of a multi-product polymer batch plant. In: J.F. Pekny, G.E. Blau, & B. Carnahan, Proceeding for foundations of computer-aided process operations (FOCAPO'98), CACHE Publications, Snowbird.]. (C) 2000 Elsevier Science Ltd. All rights reserved.
genetic algorithm scheduling of batch plants mixed integer nonlinear programming