An ordinal optimization based evolution strategy to schedule complex make-to-order products

Abstract

This paper considers the problem of planning and scheduling a complex make-to-order product with multiple levels of product structure. The work assumes finite capacity constraints and uncertain processing times. For planning such systems we define a schedule as a set of planned operation start times together with a set of priority rules (for individual resources) that are followed in implementing the schedule. An optimal schedule is determined by minimizing the expected total cost (the sum of work in progress holding costs, product earliness costs and product tardiness costs). A Stochastic Discrete-Event Based Evolution Strategy (SDEES) is first introduced to tackle the scheduling problem. However, SDEES is computationally demanding due to the multiplicative effect of the number of search iterations and the size of the evaluation samples required at each stage in the search. To reduce computation and improve search speed, an Ordinal Optimization Based Evolution Strategy (OOES) is developed. Quantitative examples covering a range of uncertainty levels are used to illustrate the effectiveness of the methods. Further, a case study using data from a collaborating company demonstrates the practical effectiveness. The Ordinal Optimization Evolutionary Strategy achieves a performance similar to the SDEES whilst reducing the computational time by around 60%.

Keywords:

• Manufacturing scheduling
• Make-to-order
• Genetic algorithm
• Evolution strategy
• Ordinal optimization
• Stochastic