Formulation of workforce skill constraints in assembly line balancing models

Koltai, Tamás; Tatay, Viola

doi:10.1007/s11081-013-9230-x

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…The new lower bounds allowed the authors to improve results for a benchmark set of instances. Koltai and Tatay [35] propose a general framework to model skill requirements and skill conditions for assembly line balancing models.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal workforce assignment to operations of a paced assembly line

Dolgui

Kovalev

Kovalyov

et al. 2018

European Journal of Operational Research

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To cite this version:Alexandre Dolgui, Sergey Kovalev, Mikhail Kovalyov, Sergey Malyutin, Ameur Soukhal. Optimal workforce assignment to operations of a paced assembly line. European Journal of Operational Research, Elsevier, 2018, 264 (1), pp.200-211. 10.1016/j.ejor.2017 Optimal workforce assignment to operations of a paced assembly line AbstractWe study a paced assembly line intended for manufacturing different products. Workers with identical skills perform non-preemptable operations whose assignment to stations is known. Operations assigned to the same station are executed sequentially, and they should follow the given precedence relations. Operations assigned to different stations can be performed in parallel.The operation's processing time depends on the number of workers performing this operation.The problem consists in assigning workers to operations such that the maximal number of workers employed simultaneously in the assembly line is minimized, the line cycle time is not exceeded and the box constraints specifying the possible number of workers for each operation are not violated. We show that the general problem is NP-hard in the strong sense, develop conventional and randomized heuristics, propose a reduction to a series of feasibility problems, present a MILP model for the feasibility problem, show relation of the feasibility problem to multi-mode project scheduling and multiprocessor scheduling, establish computational complexity of several special cases based on this relation and provide computer experiments with real and simulated data.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal workforce assignment to operations of a paced assembly line

Dolgui

Kovalev

Kovalyov

et al. 2018

European Journal of Operational Research

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“…Shehory and Kraus [23] present several algorithms to optimise allocation by forming coalitions of agents to perform tasks. In physical industries, such as manufacturing, more emphasis is placed on resource scheduling, due to the inherent constraints of physical resources and their location [24,25]. Havur et al [26] consider how dependencies defined during design-time affect resource scheduling.…”

Section: Resource Allocation Mechanismsmentioning

confidence: 99%

A Method to Enable Ability-Based Human Resource Allocation in Business Process Management Systems

Erasmus

Vanderfeesten

Traganos

et al. 2018

Lecture Notes in Business Information Processing

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Business process management systems are used to orchestrate the activities in an organization. These information systems allocate resources to perform activities based on information that describes those resources and activities. It is widely recognized that resource allocation can be enhanced by considering resource characteristics during selection. However, little guidance is available that shows how such characteristics should be specified. Human ability is one such characteristic, with the advantage that it is well-defined in the Fleishman Taxonomy of Human Abilities. This paper presents a method that leverages the Fleishman taxonomy to specify activities and human resources. Those specifications are then used to allocate resources to activities during process run-time. We show how ability-based resource allocation can be implemented in a business process management system and evaluate the method in a real-world scenario.

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“…Second, workers -who were classified into four types based on their skill levels -were assigned to the workstations in which they are qualified to work. A general framework to model skill requirements and skill conditions for assembly line balancing configurations was provided by Koltai and Tatay [41]. They defined three types of skill constraints, i.e.…”

Section: The International Journal Of Advanced Manufacturing Technologymentioning

confidence: 99%

Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

Küçükkoç

Zhang

2015

Int J Adv Manuf Technol

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Usage guidelinesThis version is made available online in accordance with publisher policies. To see the final version of this paper, please visit the publisher's website (a subscription may be required to access the full text).Before reusing this item please check the rights under which it has been made available. Some items are restricted to non-commercial use. Please cite the published version where applicable. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent based ant colony optimization -genetic algorithm approach is developed for the solution of mixed-model parallel two-sided assembly line balancing and sequencing problem. The existing agent based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely Paired-Sample tTest. In accordance with the test results, it is statistically proven that the integrated genetic algorithm based model sequencing engine helps agent based ant colony optimization algorithm robustly find significantly better quality solutions.

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Formulation of workforce skill constraints in assembly line balancing models

Cited by 13 publications

References 17 publications

Optimal workforce assignment to operations of a paced assembly line

Optimal workforce assignment to operations of a paced assembly line

A Method to Enable Ability-Based Human Resource Allocation in Business Process Management Systems

Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

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