Two meta-heuristics for parallel machine scheduling with job splitting to minimize total tardiness

Sarıçiçek, İnci; Çelik, Cenk

doi:10.1016/j.apm.2011.02.035

Cited by 41 publications

(27 citation statements)

References 15 publications

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“…Each of the related works in Table 1 has advantages and limitations. Some related studies do not target reconfigurable systems ( [7], [10], [38]) and cannot be generalized as the number of tasks increases. Some ( [1], [10], [38]) do not deal with multicore architectures.…”

Section: Nomenclature Mmentioning

confidence: 99%

“…Some related studies do not target reconfigurable systems ( [7], [10], [38]) and cannot be generalized as the number of tasks increases. Some ( [1], [10], [38]) do not deal with multicore architectures. Such architectures represent an advantage in terms of sharing and synchronizing OS tasks, which directly influences the amount of energy consumed by the system.…”

Section: Nomenclature Mmentioning

confidence: 99%

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Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

et al. 2020

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This paper deals with the scheduling of real-time periodic tasks executed on heterogeneous multicore platforms. Each processor is composed of a set of multi-speed cores with limited energy resources. A reconfigurable system is sensible to unpredictable reconfiguration events from related environment, such as the activation, removal or update of tasks. The problem is to handle feasible reconfiguration scenarios under energy constraints. Since any task can finish execution before achieving its worst-case execution time (WCET), the idea is to distribute this execution on different processor cores for meeting related deadlines and reducing energy consumption. The methodology consists in using lower processor speeds first to consume less energy. If the system is still non-feasible after reconfiguration, then we adjust the task periods as a flexible solution or migrate some of them to the least loaded processors. Accordingly, an integer linear program (ILP) is formulated to encode the execution model that assigns tasks to different cores with optimal energy consumption, thereby realizing energy-efficient computing/green computing. The potency and effectiveness of the proposed approach are rated through simulation studies. By measuring the energy consumption cost, our solution offers better than 11% of gain than recently published methods and improves by 85% the overall number of adjusted periods.

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Section: Nomenclature Mmentioning

confidence: 99%

Section: Nomenclature Mmentioning

confidence: 99%

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

et al. 2020

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“…Hsu ve diğ. [11] [13]. Çalışmada ele alınan paralel makina çizelgeleme problemlerine çözüm aramak amacıyla kodlanan yazılım ile karar verici, veri tabanından makina ve işlere ilişkin verileri alabilmekte tavlama benzetimi veya yasaklı arama algoritmalarından biri ile istediği amaç için çizelgeleme problemini çözdürebilmektedir.…”

Section: Paralel Makina çIzelgeleme çAlışmalarıunclassified

A decision support system for non-identical parallel machine scheduling

Sarıçiçek¹

2018

Pamukkale J Eng Sci

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İşletmelerin üretim planlama faaliyetlerinde makina çizelgeleme kısa dönem planlamada sık tekrarlanan bir faaliyet olup kaynakların etkin kullanımı açısından çok önemlidir. Gerek darboğaz problemleri gerek kapasiteyi arttırma amaçlı aynı işi yapabilen birden fazla makina bulanan atölyelerde atölye ortamı ne olursa olsun bir paralel makina çizelgeleme probleminin de çözülmesi gereği ortaya çıkmaktadır. Özellikle özdeş olmayan paralel makinalarda işin hangi makinada işleneceği birçok faktöre bağlı olarak belirlenmek istenmektedir. Bu tür durumlara genel yazılımlar cevap verememektedir. Ayrıca plastik akıtma, fırınlama gibi paralel makina ortamlarında iş sırasına bağlı olarak hazırlık sürelerinin değişmesi durumu söz konusu olduğunda sıraya bağımlı hazırlık sürelerini de dikkate alan çizelgeler hazırlamak planlamacılar için önem arz etmektedir. Bu kapsamda, çalışmada sıraya bağımlı hazırlık süreli özdeş olmayan paralel makina çizelgeleme problemleri için bir karar destek sistemi tasarlanmıştır. Karar verici ilgili sistemi kullanarak, durumsallık yaklaşımıyla ilgili dönemde atölyede hangi amaç için işlerin çizelgelenmesi gerekiyorsa ilgili amaca göre çizelgeyi elde edebilir. Ayrıca, işleri en kısa sürede bitirmek ve geciken iş sayısını en küçüklemek gibi farklı amaç fonksiyonları için çizelgeleri elde etme ve sonuçlarını karşılaştırma imkânı bulabilir. Karar destek sisteminin model temelinde, büyük boyutlu gerçek hayat çizelgeleme problemlerine kısa sürede çözüm üretebilen ileri sezgisel algoritmalar kullanılmıştır. Machine scheduling in the production planning activities of enterprises is an activity that is frequently repeated in short periods and is very important in terms of efficient use of resources. It is necessary to solve a parallel machine scheduling problem regardless of the workshop environment in workshops where more than one machine that can do the same job to increase the capacity and solve the bottleneck problems. Especially in non-identical parallel machines, it is desired to determine which machine will process the job depending on many factors. A general software cannot respond to such situations. In addition, it is important for planners to prepare charts that take into account sequence dependent setup times in parallel machine environments such as plastic injection and oven scheduling problems. In this context, the study is focused on designing a decision support system for non-identical parallel machine scheduling problems with sequence dependent setup times. By using the decision support system, the decision maker can obtain the schedule for the jobs need to be scheduled for the relevant period and purpose according to situation of workshop. The system makes it possible to find and compare schedules for different objective functions such as minimizing makespan and minimizing the number of tardy jobs. On the basis of the model of the decision support system, meta-heuristic algorithms that can produce a solution to large scale real-life scheduling problems in a short time have been used.

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“…This problem is similar to parallel machine scheduling problems but is more complicated in that the residence time of a lot on a test head is dependent on the lots being placed on the other test heads. Recent research on parallel machine scheduling problems solved by simulated annealing, tabu search, and genetic algorithm can be seen in [33][34][35][36][37][38][39][40]. In this paper, three metaheuristic techniques, including simulated annealing, tabu search, and genetic algorithm embedded with lot-specific and configuration-specific information, are proposed and are compared to the traditional approaches.…”

Section: =1mentioning

confidence: 99%

Scheduling Semiconductor Multihead Testers Using Metaheuristic Techniques Embedded with Lot-Specific and Configuration-Specific Information

Hung

Wang

2013

Mathematical Problems in Engineering

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In the semiconductor back-end manufacturing, the device test central processing unit (CPU) is most costly and is typically the bottleneck machine at the test plant. A multihead tester contains a CPU and several test heads, each of which can be connected to a handler that processes one lot of the same device. The residence time of a lot is closely related to the product mix on test heads, which increases the complexity of this problem. It is critical for the test scheduling problem to reduce CPU's idle time and to increase tester utilization. In this paper, a multihead tester scheduling problem is formulated as an identical parallel machine scheduling problem with the objective of minimizing makespan. A heuristic grouping method is developed to obtain a good initial solution in a short time. Three metaheuristic techniques, using lot-specific and configuration-specific information, are proposed to receive a near-optimum and are compared to traditional approaches. Computational experiments show that a tabu search with lot-specific information outperforms all other competing approaches.

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Two meta-heuristics for parallel machine scheduling with job splitting to minimize total tardiness

Cited by 41 publications

References 15 publications

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

A decision support system for non-identical parallel machine scheduling

Scheduling Semiconductor Multihead Testers Using Metaheuristic Techniques Embedded with Lot-Specific and Configuration-Specific Information

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