Scheduling a single machine to maintain a dynamic WIP profile in coordinated manufacturing chains

Abstract

This article addresses the problem of selecting and scheduling several jobs on a single machine to sustain the desired dynamic work-in-process profile. We consider sequence-dependent setup times between jobs and strictly enforced time window constraints on the start time of each job. We use working shift production targets to coordinate decentralized local schedulers and make them inline with the manufacturing chain goals. Based on the discretization of scheduling time horizon, we propose a two-step mixed-integer programming model and a new network-based heuristic. The primary objective of these approaches is to maximize the total defined score for jobs while satisfying production targets. The secondary objective is to minimize the maximum completion time of all selected jobs. The effectiveness, efficiency, and robustness of the proposed algorithms are analyzed and compared with two existing approaches over a wide range of simulated scenarios.

Continuous-time algorithms for scheduling a single machine with sequence-dependent setup times and time window constraints in coordinated chains

“…This article targets this gap. With the exception of the studies of Jula and Rafiey (2012) and Jula and Kones (2012) the problem as outlined in this article has not been addressed previously. Jula and Rafiey (2012) introduced two algorithms based on the discretisation of the planning horizon to constant and static intervals, namely the Common Grid Points Network (CGPN) and the Individual Jobs Grid Points Network (IJGPN) algorithms.…”

“…We compared the results with the only existing relevant models in the literature, introduced by Jula and Rafiey (2012) and Jula and Kones (2012). Jula and Rafiey (2012) introduced two network-based algorithms based on the discretisation of the planning horizon to constant and static intervals, namely the Common Grid Points Network (CGPN) and the Individual Jobs Grid Points Network (IJGPN) algorithm.…”

“…Therefore, we do not include CGPN in our analysis in this article. Jula and Kones (2012) introduced a two-step time-indexed goal programming MIP approach (TIMP) that produces an optimal solution when the temporal parameters of the system are multiples of the basic time grid length (a) used in International Journal of Production Research 3661 the model. The authors further introduced the dynamic grid point network (DGPN) heuristic approach, in which the discretisation interval is tailored for each job, and dynamically changes in each iteration of the algorithm.…”

“…Early attempts at scheduling in this domain Kones 2012, Jula andRafiey 2012) relied on the discrete-time approach, in which the time horizon is divided into a number of intervals of uniform duration, and events such as the beginning of a job are associated with the boundaries of these time intervals. To achieve a suitable approximation of the original problem, this approach usually requires the use of a time interval that is sufficiently small.…”

“…\1 sec; 'X' indicates that the algorithm was not able to generate the result in the time limit of 30 minutes in some instances; IJGPN, Individual Jobs Grid Point Network algorithm ofJula and Rafiey (2012); DGPN (Dynamic Grid Point Network) and TIMP (Time Indexed MIP) algorithms ofJula and Kones (2012).…”