Minimizing makespan on a single burn-in oven with job families and dynamic job arrivals

Sung, Chang Sup; Choung, You In; Hong, Joonki; Kim, Y. H.

doi:10.1016/s0305-0548(00)00098-8

Cited by 47 publications

(5 citation statements)

References 21 publications

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“…Since then, exact and approximate solution methods have been developed for a variety of batch processor problems. These include models where the processing time of the batch is determined by the longest processing time among the jobs in the batch (e.g., Glassey and Weng 1991, Ahmadi et al 1992, Lee et al 1992, Chandru et al 1993a, Avramidis et al 1998, Lee and Uzsoy 1999 and those that schedule multiple incompatible job families (e.g., Chandru et al 1993b, Uzsoy 1995, Webster and Baker 1995, Mehta and Uzsoy 1998, Azizoglu and Webster 2001, Sung et al 2002, Wang and Uzsoy 2002, Monch et al 2004, Perez et al 2005 where jobs of different families cannot be batched together. Note that problems with a single job family fit equally well into both of the above categories.…”

Section: Previous Research On Batch Processor Schedulingmentioning

confidence: 99%

Learning effective dispatching rules for batch processor scheduling

Geiger

Uzsoy

2008

International Journal of Production Research

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Batch processor scheduling, where machines can process multiple jobs simultaneously, is frequently harder than its unit-capacity counterpart because an effective scheduling procedure must not only decide how to group the individual jobs into batches, but also determine the sequence in which the batches are to be processed. We extend a previously developed genetic learning approach to automatically discover effective dispatching policies for several batch scheduling environments, and show that these rules yield good system performance. Computational results show the competitiveness of the learned rules with existing rules for different performance measures. The autonomous learning approach addresses a growing practical need for rapidly developing effective dispatching rules for these environments by automating the discovery of effective job dispatching procedures.

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Section: Previous Research On Batch Processor Schedulingmentioning

confidence: 99%

Learning effective dispatching rules for batch processor scheduling

Geiger

Uzsoy

2008

International Journal of Production Research

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“…Refs. [13][14][15][16][17][18] dealt with constant batch-processing times. This research deals with the varying batch-processing time case.…”

Section: Literature Reviewmentioning

confidence: 99%

Minimizing makespan in a flow shop with two batch-processing machines using simulated annealing

Manjeshwar

Damodaran

Srihari

2009

Robotics and Computer-Integrated Manufacturing

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“…The issue of batch scheduling optimization has attracted significant research attention, since on the one hand, many practical applications are highly related to batch-processing including chemical processes (Seid and Majozi, 2014;Fabre et al, 2011;Liu et al, 2010;Méndez et al, 2006), testing process in burn-in oven in the semiconductor manufacturing (Jula and Leachman, 2010;Sung et al, 2002;Sung and Choung, 2000), heat-treating ovens (Melouk et al, 2004). On the other hand, many batch scheduling problems are NP-hard (Potts and Kovalyov, 2000), even for most singlemachine environment, which makes them difficult to be solved in theoretical perspective.…”

Section: Introductionmentioning

confidence: 99%

Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration

Wang

Liu

Chu

et al. 2016

Journal of Cleaner Production

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With the increasing energy price, the rapid growth of electricity demand and severe challenges for sustainable development, energy-efficient scheduling is becoming more and more important for power-intensive manufacturing industry, especially for batch production companies. This paper investigates a bi-objective single machine batch scheduling problem with non-identical job sizes, the time-of-use (TOU) electricity prices, and different energy consumption rates of the machine. The first objective is to minimize the makespan and the second is to minimize the total energy costs, by considering both the machine utilization and the economic cost. The problem is formulated as an integer programming model. Then, an exact-constraint method is adapted to obtain the exact Pareto front. To deal with large scale problems, based on decomposition ideas, two heuristic methods are developed to obtain approximate Pareto fronts. Computational experiments on randomly generated instances show the effectiveness of the methods. A study case of a real-world glass manufacturing company is also conducted to show that the proposed methods are promising for practical usages.

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Minimizing makespan on a single burn-in oven with job families and dynamic job arrivals

Cited by 47 publications

References 21 publications

Learning effective dispatching rules for batch processor scheduling

Learning effective dispatching rules for batch processor scheduling

Minimizing makespan in a flow shop with two batch-processing machines using simulated annealing

Bi-objective optimization of a single machine batch scheduling problem with energy cost consideration

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