Scheduling problems with a learning effect

Mosheiov, Gur

doi:10.1016/s0377-2217(00)00175-2

Cited by 336 publications

(125 citation statements)

References 7 publications

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“…2 Similar to Mosheiov [13], for the next three problems, we show that the optimal schedule of the classical version is not optimal for the problems 1|LE t | w j C j , 1|LE t |L max and 1|LE t | U j , respectively.…”

Section: Several Single Machine Scheduling Problemssupporting

confidence: 66%

“…They also proposed two heuristics and analysed their worstcase performance. Mosheiov [13,14] investigated several other single machine problems and the minimum total flow time problem on identical parallel machines. Liu et al [11] proved that the weighted shortest processing time (WSPT) rule, the earliest due date (EDD) rule and the modified Moore-Hodgson algorithm can construct optimal sequences under certain conditions for the following three objectives: the total weighted completion time, the maximum lateness and the number of tardy jobs, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Single-machine scheduling with a time-dependent learning effect

Wang

Cheng

et al. 2008

International Journal of Production Economics

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In this paper we consider the single machine scheduling problem with a time-dependent learning effect. The time-dependent learning effect of a job is assumed to be a function of the total normal processing time of the jobs scheduled in front of the job. For the following three objective functions: the weighted sum of completion times, the maximum lateness and the number of tardy jobs, we show by examples that the optimal schedule of the classical version is not optimal in the presence of a time-dependent learning effect. But for some special cases, we prove that the weighted shortest processing time (WSPT) rule, the earliest due date (EDD) rule and Moore's algorithm can construct an optimal sequence for these objective functions, respectively. We also give the worst-case error bound of these three rules for the general cases.

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Section: Several Single Machine Scheduling Problemssupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Single-machine scheduling with a time-dependent learning effect

Wang

Cheng

et al. 2008

International Journal of Production Economics

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“…In earlier papers, the focus has been on particular functions that define the positional factors g(r), e.g., polynomial [23,24] or exponential [25]. In what follows, it is assumed that the values g(r), 1 ≤ r ≤ n, are either given as an array of numbers or g(r) can be computed for each r in constant time.…”

Section: Model Descriptionmentioning

confidence: 99%

Combining time and position dependent effects on a single machine subject to rate-modifying activities

Rustogi

Strusevich

2014

Omega

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Citation for this version held on GALA:Rustogi, Kabir and Strusevich, Vitaly (2014) Combining time and position dependent effects on a single machine subject to rate-modifying activities. London: Greenwich Academic Literature Archive. We introduce a general model for single machine scheduling problems, in which the actual processing times of jobs are subject to a combination of positional and timedependent effects, that are job-independent but additionally depend on certain activities that modify the processing rate of the machine, such as, e.g., maintenance. We focus on minimizing two classical objectives: the makespan and the sum of the completion times. The traditional classification accepted in this area of scheduling is based on the distinction between the learning and deterioration effects, on one hand, and between the positional effects and the start-time dependent effects, on the other hand. Our results show that in the framework of the introduced model such a classification is not necessary, as long as the effects are job-independent. The model introduced in this paper covers most previously known models. The solution algorithms are developed within the same general framework and their running times are no worse than those available earlier for problems with less general effects.

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“…Simulation [9], [20] is the most usual tool to produce such an aggregation, as it will be pointed out in section 4.2. Some articles go even further by formalising the mechanisms of performance evolution: individual learning mechanisms have for instance an impact on medium and long term temporal performances [21], [22].…”

Section: Causal Models: Impact Of Competencies On the Performance Of mentioning

confidence: 99%

Formalisation and use of competencies for industrial performance optimisation: A survey

Boucher

Bonjour

Grabot³

2007

Computers in Industry

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. Formalisation and use of competencies for industrial performance optimisation : a survey.. Computers in Industry, Elsevier, 2007, 58 (2) Abstract. For many years, industrial performance has been implicitly considered as deriving from the optimisation of technological and material resources (machines, inventories,...), made possible by centralized organisations. The topical requirements for reactive and flexible industrial systems have progressively reintroduced the human workforce as the main source of industrial performance. Making this paradigm operational requires the identification and careful formalisation of the link between human resource and industrial performance, through concepts like skills, competencies or know-how. This paper provides a general survey of the formalisation and integration of competence-oriented concepts within enterprise information systems and decision systems, aiming at providing new methods and tools for performance management.

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Scheduling problems with a learning effect

Cited by 336 publications

References 7 publications

Single-machine scheduling with a time-dependent learning effect

Single-machine scheduling with a time-dependent learning effect

Combining time and position dependent effects on a single machine subject to rate-modifying activities

Formalisation and use of competencies for industrial performance optimisation: A survey

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