A survey of the state-of-the-art of common due date assignment and scheduling research

Gordon, Valery; Proth, Jean‐Marie; Chen, Chu

doi:10.1016/s0377-2217(01)00181-3

Cited by 399 publications

(157 citation statements)

References 117 publications

Supporting

Mentioning

152

Contrasting

Unclassified

Order By: Relevance

“…on relevant studies of this subject was provided by Gordon et al (2002). Researchers developed polynomially or pseudo-polynomially solvable algorithms for special cases where conditions for i α and i β are imposed (Cheng & Gupta, 1989;Hall et al, 1991;Hoogeveen & van de Velde, 1991;Kahlbacher, 1993).…”

Section: Introductionmentioning

confidence: 99%

Uric acid as a risk factor for progression of non-diabetic chronic kidney disease? The Mild to Moderate Kidney Disease (MMKD) Study

Sturm

Kollerits

Neyer

et al. 2008

Experimental Gerontology

152

109

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Uric acid as a risk factor for progression of non-diabetic chronic kidney disease? The Mild to Moderate Kidney Disease (MMKD) Study

Sturm

Kollerits

Neyer

et al. 2008

Experimental Gerontology

152

109

View full text Add to dashboard Cite

“…  (11) With y, denoting the biggest integer smaller than, or equal to y. That is, for each problem, the common due date d is estimated by multiplying the summation of the processing times of all the n jobs with the restrictive factor h. The lower the value of h the more restrictive is d (the higher the expected percentage of the late jobs).…”

Section: Experimental Analysis and Discussionmentioning

confidence: 99%

A Particle Swarm Optimization Algorithm for Scheduling Against Restrictive Common Due Dates

Nearchou¹,

Omirou²

2013

IJCIS

View full text Add to dashboard Cite

Abstract:Focusing on the just-in-time (JIT) operations management, earliness as well as, tardiness of jobs' production and delivery should be discouraged. In accordance to this philosophy, scheduling problems involving earliness and tardiness penalties are very critical for the operations manager. In this paper, a new population heuristic based on the particle swarm optimization (PSO) technique is presented to solve the single machine early/tardy scheduling problem against a restrictive common due date. This type of scheduling sets costs depending on whether a job finished before (earliness), or after (tardiness) the specified due date. The objective is to minimize a summation of earliness and tardiness penalty costs, thus pushing the completion time of each job as close as possible to the due date. The problem is known to be NP-hard, and therefore large size instances cannot be addressed by traditional mathematical programming techniques. The performance of the proposed PSO heuristic is measured over benchmarks problems with up to 1000 jobs taken from the open literature, and found quite high and promising in respect to the quality of the solutions obtained. Particularly, PSO was found able to improve the 82% of the existing best known solutions of the examined benchmarks test problems.

show abstract

“…Scheduling that incorporates due dates is of permanent interest [46]. The problem has been investigated since 1965 in the work of Jackson [47].…”

Section: Order Due-date Assignmentmentioning

confidence: 99%

“…In the literature, few works treat scheduling problems with the due-date assignment decision being of primary focus. Among the most significant contributions, Cheng and Gupta [48] review due-date assignment approaches up to 1990, whereas Gordon et al [46] review more recent publications (up to 2002). In most works, the due-date assignment problem is treated using benchmarking instances [49], single-resource problems [50,51] or is focused on assembly shops [52].…”

Section: Order Due-date Assignmentmentioning

confidence: 99%

“…Moreover, the calculation of due dates in ETO environments is relatively mistreated. Most studies focus on static problem instances where jobs do not arrive continuously in the system [46], or require significant modelling efforts [53] and simulation experiments [54], which are rarely feasible in actual daily practice for due-date calculation.…”

Section: Order Due-date Assignmentmentioning

confidence: 99%

See 1 more Smart Citation

A mobile application for knowledge-enriched short-term scheduling of complex products

2016

View full text Add to dashboard Cite

The ever-increasing product complexity, especially for the case of engineer-to-order products, highly affects the performance of manufacturing systems. Therefore, a high degree of flexibility is needed during daily decision-making activities, such as production scheduling. For addressing this challenge, this research work proposes a knowledge-enriched short-term job-shop scheduling mechanism, which is implemented into a mobile application. More precisely, it focuses on the short-term scheduling of the resources of the machine shop, through an intelligent algorithm that generates and evaluates alternative assignments of resources to tasks. Based on the requirements of a new order, a similarity mechanism retrieves successfully executed past orders together with a dataset that includes the processing times, the job and task sequence, and the suitable resources. In addition to that, the similarity mechanism is used to calculate the due-date assignments of the orders based on the knowledge stored in past cases. Afterwards, it adapts these parameters to the requirements of the new order so as to evaluate the alternative schedules and identify a good alternative in a timely manner. The deriving schedule can be presented on mobile devices, and it can be manipulated by the planner on-thefly respecting tasks precedence constraints and machine availability. A case study from the mould-making industry is used for validating the proposed method and application.

show abstract

A survey of the state-of-the-art of common due date assignment and scheduling research

Cited by 399 publications

References 117 publications

Uric acid as a risk factor for progression of non-diabetic chronic kidney disease? The Mild to Moderate Kidney Disease (MMKD) Study

Uric acid as a risk factor for progression of non-diabetic chronic kidney disease? The Mild to Moderate Kidney Disease (MMKD) Study

A Particle Swarm Optimization Algorithm for Scheduling Against Restrictive Common Due Dates

A mobile application for knowledge-enriched short-term scheduling of complex products

Contact Info

Product

Resources

About