Increasing the profitability and competitiveness in a production environment with random and bounded setup times

Aydilek, Asiye; Aydilek, Harun; Allahverdi, Ali

doi:10.1080/00207543.2011.652263

Cited by 24 publications

(11 citation statements)

References 25 publications

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“…completion time fluctuations [16], various methods are used to address this issue. More particularly, studies addressed the issues pertinent to the setup time uncertainty considering processing and setup times within certain intervals [17]- [19], known as lower-and upper-bounds [20], [21], and using fuzzy time variables [22], [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

et al. 2020

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Setup time consists of all the activities that need to be completed before the production process takes place. The extant scheduling predominantly relies on simplistic methods, like the average value obtained from historical data, to estimate setup times. However, such methods are incapable of representing the real industry situation, especially when the setup time is subject to significant uncertainties. In this situation, the estimation error increases proportionally to the problem size. This study proposes a Random-Forest-based metaheuristic to minimize the makespan in an Unrelated Parallel Machines Scheduling Problem (UPMSP) with uncertain machine-dependent and job sequence-dependent setup times (MDJSDSTs). Taking the forging industry as an example, the numerical experiments show that the error percentage for the setup time estimation substantially decreases when the proposed approach is applied. This improvement is particularly significant when large-scale problems are sought. Overall, this study highlights the role of advanced analytics in bridging the gap between scheduling theory and practice. INDEX TERMS Scheduling, unrelated parallel machines, setup times, random-forest, metaheuristic.

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Section: Literature Reviewmentioning

confidence: 99%

Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

et al. 2020

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“…On the other hand, the problem of F3|LBt j,k ≤t j,k ≤UBt j,k |∑C i was investigated by Sotskov et al (2004) where they provided few dominance relations for problem. For some other uncertain scheduling environments, the problem was investigated as well in the literature, e.g., Allahverdi and Allahverdi (2018), Aydilek et al (2013Aydilek et al ( , 2015Aydilek et al ( , 2017, Lai andSotskov (1999), Lai et al (1997).…”

Section: Introductionmentioning

confidence: 99%

An Improved Algorithm for Minimizing Makespan on Flowshops with Uncertain Processing Times

Allahverdi¹,

Allahverdi²

2021

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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We address the four-machine flowshop scheduling problem with the objective of minimizing makespan with uncertain processing times The problem was investigated in the literature (RAIRO Operations Research 54, 529-553) and different algorithms were proposed. In this paper, we propose a new algorithm for the problem. The new proposed algorithm is compared with the best existing algorithm in the literature by using extensive computational experiments. Computational experiments show that the new proposed algorithm performs much better than the best existing algorithm in the literature in terms of error while both have the same computational time. Specifically, the new proposed algorithm reduces the error of the best existing algorithm in the literature about 40%. This result has been confirmed by using hypotheses testing with a significance level of 0.01.

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“…Gonzalez-Neira et al [11] and Wang and Choi [27] remark that a wide range of uncertainties exist in manufacturing environments. In such cases, assuming setup times to be deterministic values may result in poor efficiency, Aydilek et al [8]. Moreover, Aydilek et al [9] state that assuming setup times to follow certain probability distributions may not be accurate.…”

mentioning

confidence: 99%

“…Moreover, Allahverdi ( [3], [5], [4]) establish dominance relations for the problems of F2| LBs i,k ≤ s i,k ≤ UBs i,k | C max , F2| LBs i,k ≤ s i,k ≤ UBs i,k | C j , and F2| LBs i,k ≤ s i,k ≤ UBs i,k | L max , respectively, where L max denotes maximum lateness. Furthermore, some heuristics are provided by Aydilek et al [8] for the problem F2| LBs i,k ≤ s i,k ≤ UBs i,k | C max and some others are provided by Aydilek et al [9] for the same problem where processing times are uncertain variables as well. For uncertain environments, researchers investigate different scheduling problems by modeling setup/processing times within certain intervals including Braun et al [10], Sotskov et al [23], Matsveichuk et al [21], Sotskov and Matsveichuk [25], and Sotskov and Lai [24].…”

mentioning

confidence: 99%

A better dominance relation and heuristics for Two-Machine No-Wait Flowshops with Maximum Lateness Performance Measure

Allahverdi¹,

Aydilek²,

Aydilek³

et al. 2021

JIMO

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In this paper, we consider a manufacturing system with twomachine no-wait flowshop scheduling problem where setup times are uncertain. The problem with the performance measure of maximum lateness was addressed in the literature (Computational and Applied Mathematics 37, 6774-6794) where dominance relations were proposed. We establish a new dominance relation and show that the new dominance relation is, on average, about 90% more efficient than the existing ones. Moreover, since it is highly unlikely to find optimal solutions for problems of reasonable size by utilizing dominance relations and since there exist no heuristic in the literature for the problem, we propose constructive heuristics to solve real life problems. We conduct extensive computational experiments to evaluate the proposed heuristics. Computational experiments indicate that the performance of the worst proposed heuristic is at least 20% better than a benchmark solution. Furthermore, they also indicate that the best proposed heuristic is about 130% better than the worst one. The average CPU time of the heuristics is significantly less than a second. 2010 Mathematics Subject Classification. 90B36.

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Increasing the profitability and competitiveness in a production environment with random and bounded setup times

Cited by 24 publications

References 25 publications

Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

An Improved Algorithm for Minimizing Makespan on Flowshops with Uncertain Processing Times

A better dominance relation and heuristics for Two-Machine No-Wait Flowshops with Maximum Lateness Performance Measure

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