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

In this paper we address the problem of selecting and scheduling several jobs on a single machine with sequence-dependent setup times and strictly enforced time window constraints on the start time of each job. We use short-term production targets to coordinate decentralised local schedulers and to...

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Vydané v:International journal of production research Ročník 51; číslo 12; s. 3654 - 3670
Hlavní autori: Jula, Payman, Kones, Ishai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Routledge 01.06.2013
Taylor & Francis LLC
Predmet:
Algorithms
Heuristic
Job shops
Mathematical analysis
Mathematical models
Mathematical programming
operations management
production management
Production scheduling
Robustness
Scheduling
sequence dependent setups
Setup times
Studies
supply chain coordination
Windows (intervals)
Work in process
ISSN:0020-7543, 1366-588X
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Shrnutí:In this paper we address the problem of selecting and scheduling several jobs on a single machine with sequence-dependent setup times and strictly enforced time window constraints on the start time of each job. We use short-term production targets to coordinate decentralised local schedulers and to make the objectives of specific areas in line with the chain objectives by maintaining a desired work in process profile in manufacturing environments. The existing literature in this domain is based on discrete-time approaches. We depart from prior approaches by considering continuous time. We introduce a two-step mathematical programming model based on disjunctive constraints to solve small problems to optimality, and propose an insertion-based heuristic to solve large-scale instances. We provide several variations of the insertion heuristic based on different score functions. The primary objective of these approaches is to maximise the total defined score for jobs while satisfying production targets for families of jobs in each shift. Further, our models minimise the maximum completion time of all selected jobs. The effectiveness, efficiency, and robustness of the proposed algorithms are analysed and compared with the existing literature.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2012.757666