Bucket brigades with worker learning

Armbruster, Dieter; Gel, Esma S.; Murakami, Junko

doi:10.1016/j.ejor.2005.06.052

Cited by 42 publications

(29 citation statements)

References 10 publications

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“…(See Muñoz and Villalobos [18] and Armbruster et al [3] for discussions of bucket brigades under models of learning.) Indeed, in our chaotic bucket brigade, no portion of the workcontent remains the specialty of either worker.…”

Section: Discussionmentioning

confidence: 99%

Deterministic chaos in a model of discrete manufacturing

Bartholdi

Eisenstein

Lim

2009

Naval Research Logistics

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A natural extension of the bucket brigade model of manufacturing is capable of chaotic behavior in which the product intercompletion times are, in effect, random, even though the model is completely deterministic. This is, we believe, the first proven instance of chaos in discrete manufacturing. Chaotic behavior represents a new challenge to the traditional tools of engineering management to reduce variability in production lines. Fortunately, if configured correctly, a bucket brigade assembly line can avoid such pathologies. ChaosSome simple deterministic systems can generate surprisingly complicated behavior that has been termed "chaotic". A system that is chaotic has long-term behavior that can be hard to describe, hard to predict, and hard even to simulate. Indeed, long-term behavior of chaotic systems seems to be deeply connected to randomness. discrete-event models of discrete production systems that show chaotic behavior, as defined in the theory of chaos in dynamic systems, were not found in this study".To our knowledge, only one model of a production system has heretofore been formally shown to exhibit chaotic dynamics and that is the switched arrival system studied in Chase et al. [10]. This model treats manufacturing as a process and the product as a fluid. In this model there are no discrete or intermediate products and therefore it did not satisfy the criteria of Schmitz et al. [21]. Armbruster [2] agrees, saying "One major drawback of the switched arrival system is the fact that the chaotic dynamics are strictly internal to the production-the total throughput through the set of machines is always constant and does not reflect the chaotic dynamics". In short, it is not the strong example one would prefer to display if chaos can indeed be found in manufacturing systems.In the model of Chase et al.[10] a single switching server distributes work over n parallel machines. The amount of work in the buffer in front of each machine is assumed to be a continuous variable and the processing rate of each machine is assumed to be constant. The server continues to fill the current buffer until some other buffer empties. The rate at which the server fills a buffer is equal to the sum of the processing rates of all machines. When the system is sampled at the instants when any buffer empties, the dynamics of the system can be represented by a function that, for n = 3, maps the unit interval into itself. Chase et al.[10] showed that this function, which describes the amount of work in the buffers, can be chaotic. Others have extended the model in various ways, as may be found in Ushio et al. We offer a model of bucket brigade assembly lines that meets the criteria of Schmitz et al.[21]. The model is realistic: Indeed, bucket brigades are currently used in a variety of manufacturing environments, examples of which are documented at Bartholdi and Eisenstein [4]. Furthermore, the model explicitly represents each product as a discrete entity with start and completion times. As we show, the product intercompletion times...

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Section: Discussionmentioning

confidence: 99%

Deterministic chaos in a model of discrete manufacturing

Bartholdi

Eisenstein

Lim

2009

Naval Research Logistics

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“…We omit the details since our interest is here rather in the learning model. Armbruster et al (2007) present a learning model which is used within the context of the organization of a bucket brigade production system. As an effect of training and growing experience, workers improve their velocity in performing assigned jobs.…”

Section: Learning Curves In Management Sciencementioning

confidence: 99%

Project Portfolio Selection Under Skill Development

Gutjahr

2014

Handbook on Project Management and Scheduling Vol. 2

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This chapter surveys models for project portfolio selection that incorporate the development of skills by learning and/or forgetting. Basic learning models (starting with Wright's learning curve) are recapitulated and their relations are discussed. Attention is given to simple exponential as well as to S-shaped learning curves or laws derived from inventory-like considerations. Moreover, it is shown how these models have been used by diverse authors as components of approaches to support staffing and scheduling decisions. Then, the integration of learning and forgetting within models for project selection is described in more detail by providing mathematical programming formulations, discussing approximations, and outlining numerical solution techniques. Also analytical results on optimal project portfolio selection over time are recalled. The survey discusses both, models where skill development goals are formulated as objectives, and models where they are used as constraints. Multi-objective formulations and corresponding solution techniques are outlined as well. Skill-based project selection under uncertainty is identified as a major open issue for future research. Keywords IntroductionIt is well-recognized that project selection is one of the most crucial tasks in project management, especially in a multi-project context. The job of conducting a set of projects in a best-possible way concerns the "how" of project management, but it does not yet address the "what" question. As it is rarely the case that a company or institution does not have any influence on which projects to carry out, a suitable choice of a set of projects, a so-called project portfolio, constitutes a substantial managerial decision (cf. also Chap. 33 in this book).

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“…For treating the staffing aspect, it is often necessary to take account of heterogeneous competencies or skills of employees. Examples of approaches to project scheduling and staffing considering the skills of the available human resources are provided in [1,7,11,32]. In combination with the project portfolio selection decision, scheduling and/or staffing is studied in [4,17,18,31,33].…”

Section: Introductionmentioning

confidence: 99%

Project portfolio selection under uncertainty with outsourcing opportunities

Gutjahr

Froeschl

2011

Flex Serv Manuf J

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The paper presents a stochastic optimization model for project portfolio selection under uncertainty about the real efforts required for the execution of the work packages contained in the projects. As a subproblem, the assignment of the work to human resources and the distribution of work over time is addressed. The available workforce is assumed as multi-skilled. Required efforts are modeled as random variables. The recourse action for the case where the available capacities of the internal human resources do not suffice to cover the actual work times consists in delegating parts of the work to external human resources. The staffingand-scheduling subproblem is solved by means of a Frank-Wolfe type algorithm. To solve the upper-level problem of project portfolio determination, a modification of the Variable Neighborhood Search (VNS) algorithm is applied. Experimental results for a benchmark of synthetically generated test instances and for an illustrative example from the E-Commerce Competence Center Austria are provided.

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Bucket brigades with worker learning

Cited by 42 publications

References 10 publications

Deterministic chaos in a model of discrete manufacturing

Deterministic chaos in a model of discrete manufacturing

Project Portfolio Selection Under Skill Development

Project portfolio selection under uncertainty with outsourcing opportunities

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