Lot streaming and scheduling heuristics for m-machine no-wait flowshops

Kumar, Subodha; Bagchi, Tapan P.; Sriskandarajah, Chelliah

doi:10.1016/s0360-8352(00)00035-8

Cited by 92 publications

(33 citation statements)

References 16 publications

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“…Other authors extended to the case containing detached (Chen & Steiner, 1997a) and attached (Chen & Steiner, 1998) setup times. For the case of m/1/C/CV, it was extended a previous work (Sriskandarajah & Wagneur, 1999) and it was used genetic algorithm (GA) to solve problems in which fixed and variable numbers of sublots for each product were included (Kumar, Bagchi & Sriskandarajah, 2000).…”

Section: Lot Streaming In M-stage Flow Shopmentioning

confidence: 99%

“…Multi-objective lot streaming problem (minimizing makespan and mean flow time simultaneously) was investigated (Bukchin & Masin, 2004). They also considered setup times such as (Kumar et al, 2000), who considered no-wait condition like (Chen & Steiner, 2003). For the problem of m/N/C/CV a heuristic and the use of GA for sequencing the products and for determining the number of sublots were proposed (Kumar et al, 2000).…”

Section: Lot Streaming In M-stage Flow Shopmentioning

confidence: 99%

“…They also considered setup times such as (Kumar et al, 2000), who considered no-wait condition like (Chen & Steiner, 2003). For the problem of m/N/C/CV a heuristic and the use of GA for sequencing the products and for determining the number of sublots were proposed (Kumar et al, 2000). Bukchin extended his previous work in m/1/C to n jobs, but this time allowing interleaving (Bukchin, Masin & Kirshner, 2010).…”

Section: Lot Streaming In M-stage Flow Shopmentioning

confidence: 99%

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A review of lot streaming in a flow shop environment with makespan criteria

Gómez-Gasquet

Segura-Andrés

Romano

2013

JIEM

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Abstract:Purpose: This paper reviews current literature and contributes a set of findings that capture the current state-of-the-art of the topic of lot streaming in a flow-shop.Design/methodology/approach: A literature review to capture, classify and summarize the main body of knowledge on lot streaming in a flow-shop with makespan criteria and, translate this into a form that is readily accessible to researchers and practitioners in the more mainstream production scheduling community. Findings:The existing knowledge base is somewhat fragmented. This is a relatively unexplored topic within mainstream operations management research and one which could provide rich opportunities for further exploration.Originality/value: This paper sets out to review current literature, from an advanced production scheduling perspective, and contributes a set of findings that capture the current state-of-the-art of this topic.

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Section: Lot Streaming In M-stage Flow Shopmentioning

confidence: 99%

Section: Lot Streaming In M-stage Flow Shopmentioning

confidence: 99%

Section: Lot Streaming In M-stage Flow Shopmentioning

confidence: 99%

See 1 more Smart Citation

A review of lot streaming in a flow shop environment with makespan criteria

Gómez-Gasquet

Segura-Andrés

Romano

2013

JIEM

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“…Sriskandarajah and Wagneur (1999) presented an efficient heuristic for solving the problem of simultaneous lot-streaming and scheduling of multiple products in a two-machine no-wait flow shop. For the m-machine lot-streaming flow shop problem, Kumar, Bagchi and Sriskandarajah (2000) extended the approach presented by Sriskandarajah and Wagneur (1999) to the m-machine case. Later, Kalir and Sarin (2001) proposed a bottleneck minimal idleness heuristic to sequence a set of batches to be processed in equal sub-lots for minimizing makespan.…”

Section: Literature Reviewmentioning

confidence: 99%

An estimation of distribution algorithm for lot-streaming flow shop problems with setup times

Pan

Ruíz

2012

Omega

144

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Lot-streaming flow shops have important applications in different industries including textile, plastic, chemical, semiconductor, and many others. This paper considers an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and no-idling production cases. The objective is to minimize the maximum completion time or makespan. To solve this important practical problem, a novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation. In the proposed EDA, an efficient initialization scheme based on the NEH heuristic is presented to construct an initial population with a certain level of quality and diversity. An estimation of a probabilistic model is constructed to direct the algorithm search towards good solutions by taking into account both job permutation and similar blocks of jobs. A simple but effective local search is added to enhance the intensification capability. A diversity controlling mechanism is applied to maintain the diversity of the population. In addition, a speed-up method is presented to reduce the computational effort needed for the local search technique and the NEH-based heuristics. A comparative evaluation is carried out with the best performing algorithms from the literature. The results show that the proposed EDA is very effective in comparison after comprehensive computational and statistical analyses.

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“…Larger instances were solved by means of evolutionary methods. Kumar et al (2000) using this approach showed that the multi-product problem with continuous sized sublots could be reduced to the traveling salesman problem. Other research in this area includes the work of Yoon and Ventura (2002) who applied a genetic algorithm to find soluitons; Tseng and Liao (2008), who applied a particle swarm optimization approach, and Pan et al (2011) who implemented a discrete bee colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

A non-permutation flowshop scheduling problem with lot streaming: A Mathematical model

Rossit¹,

Tohmé²,

Frutos³

et al. 2016

10.5267/j.ijiec

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In this paper we investigate the use of lot streaming in non-permutation flowshop scheduling problems. The objective is to minimize the makespan subject to the standard flowshop constraints, but where it is now permitted to reorder jobs between machines. In addition, the jobs can be divided into manageable sublots, a strategy known as lot streaming. Computational experiments show that lot streaming reduces the makespan up to 43% for a wide range of instances when compared to the case in which no job splitting is applied. The benefits grow as the number of stages in the production process increases but reach a limit. Beyond a certain point, the division of jobs into additional sublots does not improve the solution.

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Lot streaming and scheduling heuristics for m-machine no-wait flowshops

Cited by 92 publications

References 16 publications

A review of lot streaming in a flow shop environment with makespan criteria

A review of lot streaming in a flow shop environment with makespan criteria

An estimation of distribution algorithm for lot-streaming flow shop problems with setup times

A non-permutation flowshop scheduling problem with lot streaming: A Mathematical model

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