An artificial immune based algorithm for parallel-machine scheduling with preference of machines

Huang, Ching‐Jen; Liao, Li-Man

doi:10.1109/ieem.2012.6837783

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…They obtained an UB 1.4 and a LB 1.33. Wu et al [54] tighten the gap between the obtained UB and LB of [48] and obtained a tight bound 1.33 for the setup P 3 |Sum, M ax|C max . GOS.…”

Section: Well-known Results In Semi-online Scheduling (2001-2005)mentioning

confidence: 99%

See 1 more Smart Citation

Semi-online scheduling: A survey

Dwibedy

Mohanty

2022

Computers & Operations Research

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“…They obtained an UB 1.4 and a LB 1.33. Wu et al [54] tighten the gap between the obtained UB and LB of [48] and obtained a tight bound 1.33 for the setup P 3 |Sum, M ax|C max . GOS.…”

Section: Well-known Results In Semi-online Scheduling (2001-2005)mentioning

confidence: 99%

“…Combined Information. Hua et al [48] advanced the work of [28] for 3identical machine setting with known Sum and Max. They obtained an UB 1.4 and a LB 1.33.…”

Section: Well-known Results In Semi-online Scheduling (2001-2005)mentioning

confidence: 99%

Semi-online scheduling: A survey

Dwibedy

Mohanty

2022

Computers & Operations Research

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“…Angelelli [14] achieved a lower bound 1.392 and an upper bound 1.421 on the competitive ratio by considering known Sum. Hua et al [15] introduced multiple EPI in semi-online scheduling on three identical machines settings. They considered Sum and Max as the known EPI and obtained a lower bound 1.33 and an upper bound 1.4 on the competitive ratio.…”

Section: State-of-the-art Resultsmentioning

confidence: 99%

“…For this setting, Wu et al [16] improved the lower bound of Hua e al. [15] to achieve a tight bound 1.33 on the competitive ratio. By considering only known Decr, Cheng et al [17] achieved a tight bound 1.18, which is the best competitive bound known till date in semi-online scheduling on three identical machines settings.…”

Section: State-of-the-art Resultsmentioning

confidence: 99%

New Competitive Semi-online Scheduling Algorithms for Small Number of Identical Machines

Dwibedy¹,

Mohanty²

2021

Preprint

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Design and analysis of constant competitive deterministic semi-online algorithms for the multi-processor scheduling problem with small number of identical machines have gained significant research interest in the last two decades. In the semi-online scheduling problem for makespan minimization, we are given a sequence of independent jobs one by one in order and upon arrival, each job must be allocated to a machine with prior knowledge of some Extra Piece of Information (EPI) about the future jobs. Researchers have designed multiple variants of semi-online scheduling algorithms with constant competitive ratios by considering one or more EPI. In this paper, we propose four new variants of competitive deterministic semi-online algorithms for smaller number of identical machines by considering two EPI such as Decr and Sum. We obtain improved upper bound and lower bound results on the competitive ratio for our proposed algorithms, which are comparable to the best known results in the literature. In two identical machines setting with known Sum, we show a tight bound of 1.33 on the competitive ratio by considering a sequence of equal size jobs. In the same setting we achieve a lower bound of 1.04 and an upper bound of 1.16 by considering Sum and a sequence of jobs arriving in order of decreasing sizes. For three identical machines setting with known Decr and Sum, we show a lower bound of 1.11 on the competitive ratio. In this setting, we obtain an upper bound of 1.5 for scheduling a sequence of equal size jobs and achieves an upper bound of 1.2 by considering a sequence of decreasing size jobs. Further we develop an improved competitive algorithm with an upper bound of 1.11 on the competitive ratio.

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“…Secondly, they obtained a LB of 1.11 with known Sum and Decr. Recent contributions in this setting are due to [108][109][110][111][112][113]. Important results achieved in the literature for online scheduling with combined EPI s are reported in Table 14.…”

Section: Arrival Sequence Of the Jobsmentioning

confidence: 99%

Online Scheduling with Makespan Minimization: State of the Art Results, Research Challenges and Open Problems

Dwibedy,

Mohanty

2020

Preprint

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Online scheduling has been a well studied and challenging research problem over the last five decades since the pioneering work of Graham with immense practical significance in various applications such as interactive parallel processing, routing in communication networks, distributed data management, client-server communications, traffic management in transportation, industrial manufacturing and production. In this problem, a sequence of jobs is received one by one in order by the scheduler for scheduling over a number of machines. On arrival of a job, the scheduler assigns the job irrevocably to a machine before the availability of the next job with an objective to minimize the completion time of the scheduled jobs. This paper highlights the state of the art contributions for online scheduling of a sequence of independent jobs on identical and uniform related machines with a special focus on preemptive and non-preemptive processing formats by considering makespan minimization as the optimality criterion. We present the fundamental aspects of online scheduling from a beginner's perspective along with a background of general scheduling framework. Important competitive analysis results obtained by well-known deterministic and randomized online scheduling algorithms in the literature are presented along with research challenges and open problems. Two of the emerging recent trends such as resource augmentation and semi-online scheduling are discussed as a motivation for future research work.

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An artificial immune based algorithm for parallel-machine scheduling with preference of machines

Cited by 5 publications

References 14 publications

Semi-online scheduling: A survey

Semi-online scheduling: A survey

New Competitive Semi-online Scheduling Algorithms for Small Number of Identical Machines

Online Scheduling with Makespan Minimization: State of the Art Results, Research Challenges and Open Problems

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