On solving the unrelated parallel machine scheduling problem: active microrheology as a case study

Orts, Francisco; Ortega, Gloria; Puertas, Antonio M.; Garcı́a, Inmaculada

doi:10.1007/s11227-019-03121-z

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…In [248] the authors consider a GA for the makespan minimisation. The proposed algorithm has been tested for scheduling on several clusters when solving a difficult problem from statistical mechanics (calculating an active microrheology model).…”

Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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Today scheduling problems have an immense effect on various areas of human lives, be it from their application in manufacturing and production industry, transportation, or workforce allocation. The unrelated parallel machines scheduling problem (UPMSP), which is only one of the many different problem types that exist, found its application in many areas like production industries or distributed computing. Due to the complexity of the problem, heuristic and metaheuristic methods are gaining more attention for solving it. Although this problem variant did not receive much attention as other models, recent years saw the increase of research dealing with this problem. During that time, many different problem variants, solution methods, or other interesting research directions were considered. However, no study has until now tried to systematise the research in which heuristic methods are applied for the UPMSP. The goal of this study is to provide an extensive literature review on the application of heuristic and metaheuristic methods for solving the UPMSP. The research was systematised and classified into several categories to enable an easy overview of the different problem and solution variants. Additionally, current trends and possible future research directions are also shortly outlined.

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Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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“…Genetic algorithms are inspired by biological evolution, such as reproduction, mutation, recombination, and selection. In Reference 27, a genetic algorithm for the scheduling of independent sequential tasks onto a heterogeneous cluster is proposed. In genetic algorithms, the schedules are represented by chromosomes on which genetic operators such as crossover or mutation are applied.…”

Section: Related Workmentioning

confidence: 99%

The search‐based scheduling algorithm HP* for parallel tasks on heterogeneous platforms

Dietze

Rünger

2020

Concurrency and Computation

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Scheduling is a widely used method in parallel computing, which assigns tasks to compute resources of a parallel environments. In this article, we consider independent parallel tasks to be scheduled onto a heterogeneous execution platform consisting of a set of multicores of different architecture. Each parallel task has an internal potential parallelism which allows a parallel execution on any multicore processors. However, the execution time may differ due a different computation speed of different multicores. In this article, we propose a new search-based scheduling algorithm HETEROGENEOUS PARALLEL TASK SCHEDULING BASED ON A* (called HP*) to solve the problem of scheduling independent parallel tasks onto heterogeneous multicore platforms. Specifically, we propose a heuristic cost function needed for an informed search. Also, three pruning techniques are proposed, which are shown to significantly reduce the search space of HP*. Performance measurements on a heterogeneous platform are performed and the results of HP* are compared to scheduling results of other popular scheduling methods. The performance results with benchmark tasks from the SPLASH-3 benchmark suite demonstrate the good scheduling results and the improvements achieved by HP*. K E Y W O R D S heterogeneous platforms, parallel tasks, pruning techniques, search-based scheduling This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The objective is to minimise the schedule's completion time (aka the makespan). UPMSP has several real-world applications such as ceramics plant, mail facilities, semiconductor manufacturing, sport tournaments, block erection scheduling in a shipyard, automobile gear manufacturing process, steel making industry, chemical processes, hospital operating rooms management and human resources [7], [8], [9], [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Variable Neighbourhood Search for the Unrelated Parallel Machine Scheduling Problem

et al. 2021

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This paper addresses a challenging industrial problem known as the unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times. In UPMSP, we have a set of machines and a group of jobs. The goal is to find the optimal way to schedule jobs for execution by one of the several available machines. UPMSP has been classified as an NP-hard optimisation problem and, thus, cannot be solved by exact methods. Meta-heuristic algorithms are commonly used to find suboptimal solutions. However, large-scale UPMSP instances pose a significant challenge to meta-heuristic algorithms. To effectively solve a large-scale UPMSP, this paper introduces a two-stage evolutionary variable neighbourhood search (EVNS) methodology. The proposed EVNS integrates a variable neighbourhood search algorithm and an evolutionary descent framework in an adaptive manner. The proposed evolutionary framework is employed in the first stage. It uses a mix of crossover and mutation operators to generate diverse solutions. In the second stage, we propose an adaptive variable neighbourhood search to exploit the area around the solutions generated in the first stage. A dynamic strategy is developed to determine the switching time between these two stages. To guide the search towards promising areas, a diversitybased fitness function is proposed to explore different locations in the search landscape. We demonstrate the competitiveness of the proposed EVNS by presenting the computational results and comparisons on the 1640 UPMSP benchmark instances, which have been commonly used in the literature. The experiment results show that our EVNS obtains better results than the compared algorithms on several UPMSP instances. INDEX TERMS Machine scheduling problem, genetic algorithm, local search algorithm.

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On solving the unrelated parallel machine scheduling problem: active microrheology as a case study

Cited by 10 publications

References 20 publications

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

The search‐based scheduling algorithm HP* for parallel tasks on heterogeneous platforms

An Evolutionary Variable Neighbourhood Search for the Unrelated Parallel Machine Scheduling Problem

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