An improved earliness–tardiness timing algorithm

Hendel, Yann; Sourd, Francis

doi:10.1016/j.cor.2005.11.004

Cited by 38 publications

(27 citation statements)

References 16 publications

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“…Analyzing the results presented in Table 2, it can be noticed that SH 16 provides the best results. SH 15 and SH 14 have the second and third best results, respectively.…”

Section: Scheduling Heuristic 1 (Sh 1 )mentioning

confidence: 88%

“…In general, this gain increases with the number of jobs and is larger for more restrictive due dates. Table 4 shows the mean CPU time for a problem using SH 16 and Sarper's H3 heuristic for problems with 100 or more jobs. CPU times using SH 16 for problems with n < 100 were shorter than or equal to 0.001 s and were not considered here.…”

Section: Scheduling Heuristic 1 (Sh 1 )mentioning

confidence: 99%

“…Table 4 shows the mean CPU time for a problem using SH 16 and Sarper's H3 heuristic for problems with 100 or more jobs. CPU times using SH 16 for problems with n < 100 were shorter than or equal to 0.001 s and were not considered here. SH 16 has a CPU time significantly shorter than H3 in all cases.…”

Section: Scheduling Heuristic 1 (Sh 1 )mentioning

confidence: 99%

“…For the construction of the sequences, we use procedures based on the NEH heuristic proposed by Nawaz et al [10] and on local search, while for the schedule we present an algorithm that provides the optimal schedule for a given job sequence. More precisely, this algorithm computes the start times for each operation and it is called "timing algorithm", in a reference to similar algorithms for the earliness-tardiness problem with distinct due dates [16].…”

Section: Timing Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Scheduling in a two-machine flowshop for the minimization of the mean absolute deviation from a common due date

Sakuraba¹,

Ronconi²,

Sourd³

2009

Computers & Operations Research

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“…Analyzing the results presented in Table 2, it can be noticed that SH 16 provides the best results. SH 15 and SH 14 have the second and third best results, respectively.…”

Section: Scheduling Heuristic 1 (Sh 1 )mentioning

confidence: 88%

Section: Scheduling Heuristic 1 (Sh 1 )mentioning

confidence: 99%

Section: Scheduling Heuristic 1 (Sh 1 )mentioning

confidence: 99%

Section: Timing Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Scheduling in a two-machine flowshop for the minimization of the mean absolute deviation from a common due date

Sakuraba¹,

Ronconi²,

Sourd³

2009

Computers & Operations Research

Self Cite

View full text Add to dashboard Cite

“…In the context of the one-machine scheduling problem, Hendel and Sourd (2007) proposed an algorithm that can solve the OTSP from scratch in O(Δ(σ k ) log(n k )) time.…”

Section: Piecewise Linear Convex Time Window Cost Functionmentioning

confidence: 99%

Recent progress of local search in handling the time window constraints of the vehicle routing problem

et al. 2010

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Vehicle routing and scheduling problems have a wide range of applications and have been intensively studied in the past half century. The condition that enforces each vehicle to start service at each customer in the period specified by the customer is called the time window constraint. This paper reviews recent results on how to handle hard and soft time window constraints, putting emphasis on its different definitions and algorithms. With these diverse time windows, the problem becomes applicable to a wide range of real-world problems.

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Timing problems and algorithms: Time decisions for sequences of activities

Vidal

Crainic²,

Gendreau

et al. 2015

Networks

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Timing problems involve the choice of task execution dates within a predetermined processing sequence, and under various additional constraints or objectives such as time windows, time-dependent costs, or flexible processing times, among others. Their efficient resolution is critical in branch and bound and neighborhood search methods for vehicle routing, project and machine scheduling, as well as in various applications in network optimization, resource allocation, and statistical inference. Timing-related problems have been studied for years, yet research on this subject suffers from a lack of consensus, and most knowledge is scattered among operations research and applied mathematics domains. This article introduces a classification of timing problems and features, as well as a unifying multidisciplinary analysis of timing algorithms. In relation to frequent application cases within branching schemes or neighborhood searches, the efficient resolution of series of similar timing subproblems is also analyzed. A dedicated formalism of reoptimization "by concatenation" is introduced to that extent. The knowledge developed through this analysis is valuable for modeling and algorithmic design, for a wide range of combinatorial optimization problems with time characteristics, including rich vehicle routing settings and emerging nonregular scheduling applications, among others.

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An improved earliness–tardiness timing algorithm

Cited by 38 publications

References 16 publications

Scheduling in a two-machine flowshop for the minimization of the mean absolute deviation from a common due date

Scheduling in a two-machine flowshop for the minimization of the mean absolute deviation from a common due date

Recent progress of local search in handling the time window constraints of the vehicle routing problem

Timing problems and algorithms: Time decisions for sequences of activities

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