Heuristics based on MILP for solving a flow shop rescheduling problem simultaneously considering efficiency and stability

To keep a competitive edge, the production firms must quickly react for dealing with uncertainties due to new orders arrivals. In addition, production problems can also disrupt the already established planning. Therefore, the integration of rescheduling process in manufacturing systems is necessary for efficiently revising the initial planning, in preference with little movements. In the literature, many papers consider the efficiency criteria for measuring the schedule performance. However, the stability is also an important aspect for this kind of problems. In this paper, a performance measure considering simultaneously the schedule efficiency and stability is investigated. This one associates the total weighted waiting times and the total weighted completion times deviation. This association of criteria is inspired from real life industrial systems. An iterative predictive-reactive strategy is used for rescheduling the jobs under disruptions caused by new jobs arrival. This problem has first been modeled as a Mixed Integer Linear Programing (MILP) model. Due to its NP-hard complexity, the resolution is only possible for a limited number of jobs. Thereby, two heuristics based on the MILP are designed and discussed in terms of solution quality and computing times.