Optimal material release times in stochastic production environments

Hasan, Cengis; Spearman, M. L.

doi:10.1080/002075499191210

Cited by 15 publications

(8 citation statements)

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Order By: Relevance

“…Once the aggregate order release plan is determined, the selection of the orders to be released within this`budget' can be based on short-term information, such as urgency of the orders. (Models for non-hierarchical order release planning are presented in Hasan andHomem-de-Mello et al 1999. ) .…”

Section: H Missbauermentioning

confidence: 98%

Aggregate order release planning for time-varying demand

Missbauer¹

2002

International Journal of Production Research

103

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Order release is the interface between the control of the material¯ow through the entire logistic chain and the control of the production units. Order release mechanisms based on the workload control concept usually are based on functional relationships of time averages of work-in-process, output,¯ow time, etc. This causes severe limitations especially in the case of time-varying demand. The paper shows that order release can be designed as a hierarchical system with aggregate order release planning as the upper level; aggregate order release planning determines the amount of released work in the planning periods and thus decides on the time-dependent state of the production units and on the balance between inventory variation and production level variation. A decision model for the optimization of aggregate order release is presented that takes into account the dependence of the work centre output on the work-in-process and therefore can be considered as a workload control method. Simulation results show the performance of the model. It is concluded that the optimization model leads to improvements over order release models currently available. Further improvements of the model, based on better integration of research on the transient behaviour of queuing networks, are a topic of future research.

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Section: H Missbauermentioning

confidence: 98%

Aggregate order release planning for time-varying demand

Missbauer¹

2002

International Journal of Production Research

103

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“…However, for problems with many time periods and products, this results in a very large decision space. Hasan and Spearman [39] consider the problem of determining the optimal release times for a fixed sequence of work using a simulation-optimization approach based on infinitesimal perturbation analysis. This approach is further developed by Homem de Mello et al [40].…”

Section: Literature Reviewmentioning

confidence: 99%

A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems

2016

Journal of Manufacturing Systems

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Production planning is concerned with finding a release plan of jobs into a manufacturing system so that its actual outputs over time match the customer demand with the least cost. For a given release plan, the system outputs, work in process inventory (WIP) levels and job completions, are non-stationary bivariate time series that interact with time series representing customer demand, resulting in the fulfillment/non-fulfillment of demand and the holding cost of both WIP and finished-goods inventory. The relationship between a release plan and its resulting performance metrics (typically, mean/variance of the total cost and the fill rate) has proven difficult to quantify. This work develops a metamodelbased Monte Carlo simulation (MCS) method to accurately capture the dynamic, stochastic behavior of a manufacturing system, and to allow real-time evaluation of a release plan's performance metrics. This evaluation capability is then embedded in a multi-objective optimization framework to search for near-optimal release plans. The proposed method has been applied to a scaled-down semiconductor fabrication system to demonstrate the quality of the metamodel-based MCS evaluation and the results of plan optimization.

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“…Setting planned lead-times or activity start times in the situations with stochastic processing times was reported in Yano (1987a,b), Gong et al (1994), Matsuura et al (1996), Molinder (1997), Hasan and Spearman (1999), Song et al (2001) and Elmaghraby et al (2000). However, the majority of the above research is limited to relative simple environments such as one or two stage assembly systems (Yano, 1987a), serial production line (Yano, 1987b, Gong et al, 1994, non-assembly job shop (Matsuura and Tsubone, 1993, Matsuura et al, 1996, Hasan and Spearman, 1999, Emaghraby et al, 2000. This paper considers general multistage assembly systems operating on a make to order basis with stochastic processing times.…”

Section: Introductionmentioning

confidence: 99%