Scheduling independent tasks to reduce mean finishing time

Bruno, John; Coffman, E. G.; Sethi, Ravi

doi:10.1145/361011.361064

Cited by 444 publications

(246 citation statements)

References 7 publications

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“…Bruno et al (1974) show that the problem with two identical parallel machines is N P-hard in the ordinary sense, while if the weights are equal there is a polynomial time algorithm to minimize C j on any number of unrelated parallel machines. The twomachine flow shop to minimize C j is N P-hard in the strong sense, as proved by Garey et al (1976).…”

Section: Classification Scheme For Schedulingmentioning

confidence: 99%

Fifty years of scheduling: a survey of milestones

Potts

Strusevich

2009

Journal of the Operational Research Society

172

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Scheduling has become a major field within operational research with several hundred publications appearing each year. This paper explores the historical development of the subject since the mid 1950s when the landmark publications started to appear. A discussion of the main topics of scheduling research for the past five decades is provided, highlighting the key contributions that helped shape the subject. The main topics covered in the respective decades are combinatorial analysis, branch and bound, computational complexity and classification, approximate solution algorithms, and enhanced scheduling models.

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Section: Classification Scheme For Schedulingmentioning

confidence: 99%

Fifty years of scheduling: a survey of milestones

Potts

Strusevich

2009

Journal of the Operational Research Society

172

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“…While the hardness result for the minimum makespan problem is straightforward, the hardness for the minimum total flow-time problem with limited budget is complex and a bit surprising -given that the minimum flow-time problem is solvable even on unrelated machines 562 PROCEEDINGS OF THE FEDCSIS. GDAŃSK, 2016 [6], [16], and that the dual variant of achieving an optimal reschedule using minimum budget is also solvable [2]. Our hardness results are valid already for very restricted classes, with a single added machine and no job-extension penalties.…”

Section: Our Contributionmentioning

confidence: 67%

Heuristics for Job Scheduling Reoptimization

Iwanir

Tamir

2016

Annals of Computer Science and Information Systems

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Abstract-Many real-life applications involve systems that change dynamically over time. Thus, throughout the continuous operation of such a system, it is required to compute solutions for new problem instances, derived from previous instances. Since the transition from one solution to another incurs some cost, a natural goal is to have the solution for the new instance close to the original one (under a certain distance measure). We study reoptimization problems arising in scheduling systems. Formally, due to changes in the environment (out-of-order or new machines, modified jobs' processing requirements, etc.), the schedule needs to be modified. That is, jobs might be migrated from their current machine to a different one. Migrations are associated with a cost -due to relocation overhead and machine set-up times. In some systems, a migration is also associated with job extension. The goal is to find a good modified schedule, with a low transition cost from the initial one.We consider reoptimization with respect to the classical objectives of minimum makespan and minimum total flowtime. We first prove that the reoptimization variants of both problems are NP-hard, already for very restricted classes. We then develop and present several heuristics for each objective, implement these heuristics, compare their performance on various classes of instances and analyze the results.

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“…As there is an availability of several resources, which are able to carry out a single job process in the problem for track maintenance scheduling covered in this paper, it develops a correlation with a parallel machine scheduling problem [3]. The parallel machine scheduling problem with non-preemptive jobs for minimizing completion time is known as NP-hard [1], which corresponds with the scheduling problem to solve in this paper.…”

Section: Status Of Related Studiesmentioning

confidence: 99%

Railway Track Maintenance Scheduling using Artificial Bee Colony and Harmony Search

Kim¹,

Kim²,

Nam³

et al. 2016

Journal of Sensor Science and Technology

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The objective of this paper is to propose a heuristic algorithm to optimize the railway track maintenance scheduling, a NP-hard problem, by reflecting conditions of the actual field more quickly and easily. We develop the mechanism based on Binary Artificial Bee Colony (BABC) and Binary Harmony Search (BHS), and verify their performance through simulation experiments. Our proposed BABC and BHS mechanisms were applied to problems composed of 30, 60, 100, and 200 operations for railway track maintenance scheduling to carry out experiments and analysis. On comparing it with the results solved by CPLEX, it is found that the mechanism could present an optimal solution within limited time by user.Keywords: Railway Track Maintenance Scheduling, Artificial Bee Colony, Harmony Search Background and Purpose of the StudyThe most efficient and effective maintenance schedule is established by considering the abilities and availabilities of various resources such as manpower and equipment in the track maintenance field [15]. An efficient and effective maintenance plan can be explained in various ways. The criterion would be varied depending on the purpose or circumstances of the organization carrying out the maintenance. However, performing more jobs with given resources for an appropriate period is the most general and common purpose for optimization of a maintenance schedule.At present, maintenance jobs for railway tracks are carried out through various methods depending on the conditions of the field. Therefore, field conditions reflected in a schedule also show a variety of differences. This paper proposes a methodology applying a heuristic algorithm for flexible handling of such a situation and a quicker establishment of a schedule.Even though a heuristic algorithm could not guarantee optimal solutions, it can present optimal or best solutions within a short time or period given by schedulers. Furthermore, it also has an advantage that it can solve various problems more easily.Therefore, various conditions at the railway track maintenance fields can also be reflected more easily to optimize it.Among them, Artificial Bee Colony (ABC) and Harmony Search (HS) have been actively studied, their performances were proved, and related studies are constantly being carried out even in resource assignment and scheduling fields [2,6,7].The purpose of this paper is to mathematically model the railway track maintenance scheduling problem and propose a mechanism that can be applied to obtain the best solution to the track maintenance schedule with sample data of KORAIL's maintenance history of high-speed railway tracks. Status of Related StudiesPresent methods which are used to solve a resource assignment or scheduling problem in various fields could also be exploited as a method to solve optimization problems for track maintenance scheduling. As there is an availability of several resources, which are able to carry out a single job process in the problem for track maintenance scheduling covered in this paper, it develops a cor...

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Scheduling independent tasks to reduce mean finishing time

Cited by 444 publications

References 7 publications

Fifty years of scheduling: a survey of milestones

Fifty years of scheduling: a survey of milestones

Heuristics for Job Scheduling Reoptimization

Railway Track Maintenance Scheduling using Artificial Bee Colony and Harmony Search

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