2019/02/27 by Mehmet Ertem, Feristah Ozcelik, Feriştah Özçelik +2 · 27 citations
Engineering · Mathematics · #Advanced Manufacturing and Logistics Optimization #Assembly Line Balancing Optimization #Computer science #Due date #Dynamic programming #Heuristics #Job shop scheduling #Mathematical optimization #Mathematics #Schedule #Scheduling (production processes) #Scheduling and Optimization Algorithms #Sequence (biology) #Single-machine scheduling #Stochastic programming #Tardiness
paper · doi:10.1080/00207543.2019.1581383
published in International Journal of Production Research 57(10), 3273-3289 (Taylor & Francis)
openalex publication_date 2019/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
In this study, we consider stochastic single machine scheduling problem. We assume that setup times are both sequence dependent and uncertain while processing times and due dates are deterministic. In the literature, most of the studies consider the uncertainty on processing times or due dates. However, in the real-world applications (i.e. plastic moulding industry, appliance assembly, etc.), it is common to see varying setup times due to labour or setup tools availability. In order to cover this fact in machine scheduling, we set our objective as to minimise the total expected tardiness under uncertain sequence-dependent setup times. For the solution of this NP-hard problem, several heuristics and some dynamic programming algorithms have been developed. However, none of these approaches provide an exact solution for the problem. In this study, a two-stage stochastic-programming method is utilised for the optimal solution of the problem. In addition, a Genetic Algorithm approach is proposed to solve the large-size problems approximately. Finally, the results of the stochastic approach are compared with the deterministic one to demonstrate the value of the stochastic solution.