A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: A case study

In industrial environments, scheduling systems often operate under dynamic and stochastic circumstances. In these conditions, it is inevitable to encounter some disruptions which are inherently stochastic or totally unexpected events. These disruptions may cause the initial schedule to become infeasible and non-optimal. So, appropriate revisions and rescheduling methods are needed to overcome the unfavorable subsequent of these disruptions. In this paper, we address a dynamic flexible flow shop (FFS) environment considering unexpected arrival of new jobs into the process as disruptions. A novel reactive model is proposed based on a classical objective function (total weighted tardiness) and two new surrogate measures, stability and resistance to change. In fact the proposed model is presented to generate a stable reschedule against of any possible occurrences of mentioned disruption. Due to the computational complexity, a variable neighborhood search (VNS) algorithm is implemented to solve the problem. To show the performance of the reactive approach, a case study in petrochemical industry is studied. Computational experiments and comparisons of the proposed algorithm with three dispatching rule and an efficient rescheduling approach show the efficiency of the presented reactive approach to reschedule the jobs. (C) 2016 Elsevier Ltd. All rights reserved.