Minimizing total tardiness for scheduling identical parallel machines with family setups

Schaller, Jeffrey

doi:10.1016/j.cie.2014.04.001

Cited by 38 publications

(20 citation statements)

References 20 publications

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“…A pilot experiment is conducted for determining significant settings. To evaluate the performance of the proposed algorithms, we compare them with a similar genetic algorithm [45]. 12 4 × tasks for each problem instance.…”

Section: A Optimal Solutionmentioning

confidence: 99%

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Minimizing the Total Tardiness of a Game Project Considering the Overlap Effect

2020

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There has long been a custom that game development is not the mainstream of engineering and its tardiness brings little or even no harm to this industry. Nowadays, the pendulum of industrial development has swung to another side. A game project may involve hundreds of developers, thousands of jobs, and millions of costs. Situations would be worse if jobs overlap with others. Any delay of a large game project can cause a heavy penalty. Consequently, in this study, we propose two scheduling algorithms to reduce the total tardiness of a game project. If the problem size is small, a branch-and-bound algorithm is employed to provide the optimal schedules; otherwise a genetic algorithm is used to generate near-optimal schedules. The experimental results show that the proposed algorithms reduce the total tardiness significantly.

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Section: A Optimal Solutionmentioning

confidence: 99%

“…Table IIIincludes 1,250 problem instances (i.e., 27 settings × 50 instances) and compares the performance of B&B, GA, GA+SA, and GA(Schaller)[45] for different problem sizes. The NS column of B&B means the number of the nodes is over 150,000,000 nodes, i.e., not solvable; and the related statistics are not taken into account.…”

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confidence: 99%

Minimizing the Total Tardiness of a Game Project Considering the Overlap Effect

2020

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“…Starting with a current population of possible solutions to the scheduling problem, the best solutions are allowed to produce new children by the process of mutation and crossover in the aim of providing better generations that meet the goal or the objective of the scheduling. This approach has been found to quickly generate good solutions for a wide variety of scheduling problems (Schaller, 2014). Some successful applications of GA can be found in Malve & Uzsoy (2007), Zhou et al (2009), Behnamian et al (2009) ,Demirel (2011), Lin, Pfund and Fowler (2011) and Schaller (2014).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This approach has been found to quickly generate good solutions for a wide variety of scheduling problems (Schaller, 2014). Some successful applications of GA can be found in Malve & Uzsoy (2007), Zhou et al (2009), Behnamian et al (2009) ,Demirel (2011), Lin, Pfund and Fowler (2011) and Schaller (2014). In a very recent article, Joo & Kim (2015) developed a hybrid genetic algorithm with the combination of dispatching rule for the unrelated parallel machine and production availability.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving parallel machine scheduling problem with release dates using genetic algorithm

Suppiah¹,

Angusamy²,

Chong³

et al. 2018

IJET

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This research deals with a scheduling problem for parallel machines environment to minimize total weighted tardiness with the consideration of sequence dependent setup times and release dates. There are two research questions that need to be addressed: 1) How to allocate jobs on machines ? 2) How to sequence jobs on each machine? Therefore, this research aims to find an efficient solution method that answers the research questions with the goal of minimizing the total weighted tardiness with the presence of sequence dependent setup times. Due to the complexity of the problem at hand, the authors have developed genetic algorithm to find a solution to this problem. Furthermore, various dispatching rules were used to enhance the performance of the genetic algorithm in terms of the total weighted tardiness value.

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“…They enumerated at least 130 works published in the last 25 years. Almost all of them proposed a solution approach for a particular variant or a very limited class of problems, such as the single machine exact algorithms by van den Akker et al (2000); Sourd and Kedad-Sidhoum (2003); Avella et al (2005); Sourd (2005); Sourd and Kedad-Sidhoum (2008); Tanaka and Fujikuma (2008); Bigras et al (2008); Pan and Shi (2008); Tanaka and Araki (2013) and the parallel machine exact algorithms by Chen and Powell (1999); Liaw et al (2003); Yalaoui and Chu (2006); Shim and Kim (2007a,b); Nessah et al (2008); Tanaka and Araki (2008); Jouglet and Savourey (2011); Schaller (2014); Bülbül and Şen (2017); Kowalczyk and Leus (2018).…”

Section: Introductionmentioning

confidence: 99%

On the exact solution of a large class of parallel machine scheduling problems

Bulhões

Sadykov

Subramanian

et al. 2020

J Sched

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This work deals with a very generic class of scheduling problems with identical/uniform/unrelated parallel machine environment. It considers well-known attributes such as release dates or sequence-dependent setup times and accepts any objective function defined over job completion times. Non-regular objectives are also supported. We introduce a branch-cut-and-price algorithm for such problems that makes use of non-robust cuts, i.e., cuts which change the structure of the pricing problem. This is the first time that such cuts are employed for machine scheduling problems. The algorithm also embeds other important techniques such as strong branching, reduced cost fixing and dual stabilization. Computational experiments over literature benchmarks showed that the proposed algorithm is indeed effective and could solve many instances to optimality for the first time.

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Minimizing total tardiness for scheduling identical parallel machines with family setups

Cited by 38 publications

References 20 publications

Minimizing the Total Tardiness of a Game Project Considering the Overlap Effect

Minimizing the Total Tardiness of a Game Project Considering the Overlap Effect

Solving parallel machine scheduling problem with release dates using genetic algorithm

On the exact solution of a large class of parallel machine scheduling problems

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