Learning curves and lot sizing for independent and dependent demand

Kopcso, David P.; Nemitz, William C.

doi:10.1016/0272-6963(83)90026-8

Cited by 26 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Demands of three final products P 1 , P 2 , and P 3 follow Weibull distribution. We assume that the demand is both product and period independent [30,31]. The details of statistical properties are shown in Table 2.…”

Section: Data Sourcesmentioning

confidence: 99%

See 1 more Smart Citation

A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty

2018

Computers & Industrial Engineering

View full text Add to dashboard Cite

A stochastic lot-sizing and scheduling problem with demand uncertainty is studied in this paper. Lot-sizing determines the batch size for each product and scheduling decides the sequence of production. A multi-stage stochastic programming model is developed to minimize overall system costs including production cost, setup cost, inventory cost and backlog cost.We aim to find the optimal production sequence and resource allocation decisions. Demand uncertainty is represented by scenario trees using moment matching technique. Scenario reduction is used to select scenarios with the best representation of original set. A case study based on a manufacturing company has been conducted to illustrate and verify the model.We compared the two-stage stochastic programming model to the multi-stage stochastic programming model. The major motivation to adopt multi-stage stochastic programming models is that it extends the two-stage stochastic programming models by allowing revised decision at each period based on the previous realizations of uncertainty as well as decisions.Stability test and weak out-of-sample test are applied to find an appropriate scenario sample size. By using the multi-stage stochastic programming model, we improved the quality of solution by 10% -13%.

show abstract

Section: Data Sourcesmentioning

confidence: 99%

“…A good solution is obtained. We assume that the demand of final material are both period and product independent [3,30,31]. Multi-product and multi-period scenario trees are generated in this paper.…”

Section: Scenario Generation Techniquementioning

confidence: 99%

A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty

2018

Computers & Industrial Engineering

View full text Add to dashboard Cite

show abstract

“…Among the major assumptions of the classical EOQ and EPQ models are that the setup and unit variable manufacturing costs are constant and independent of order quantities (Silver and Peterson 1985). These assumptions are generally valid for items produced by machines exhibiting near-identical operational behavior (Kopsco and Nemitz 1983). Even for manual operations, these assumptions may be appropriate under conditions of stable production, loose standards, and short production run with limited product life (Keachie and Fontana 1966).…”

Section: Introductionmentioning

confidence: 99%

An Economic Production Quantity Model With Learning and Forgetting Considerations

Cheng

1994

Production and Operations Management

View full text Add to dashboard Cite

The focus of this work is on the effects of learning on economic production quantity in batch production systems. We assumed that both unit variable manufacturing time and setup time follow a learning curve. We modified the classical Economic Production Quantity model to incorporate these two types of learning phenomena. We also incorporated the forgetting effect in our model so that a fraction of the learning is lost between consecutive lots. We developed a dynamic program to obtain the optimal solution to the problem. We investigated the nonincreasing lot size property and used it to improve the efficiency of our dynamic program. We consider a special case of the model in which all lot sizes are assumed equal. After theoretical treatment, we carried out a computational study of the effect of assuming equal lot sizes on the optimal solutions. The results of our examples strongly indicate that the assumption of equal lot sizes not only simplifies the determination of the optimal solutions, but also provides close approximations to the optimal solutions.

show abstract

Multifactor learning and forgetting models for productivity and performance analysis

Badiru

1994

Int. J. Hum. Factors Manuf.

View full text Add to dashboard Cite

This article presents an approach to modeling multifactor learning curve models for productivity and performance analysis in manufacturing. The models account for alternate periods of learning and forgetting and the resulting performance of an operator. Multi‐factor learning curves facilitate the inclusion of more than one important factor in performance and productivity analysis. The inclusion of a component that accounts for the rate of forgetting establishes a realistic representation of the effects of learning and forgetting on performance. Analyses based on the proposed approach can facilitate more reliable management decisions in productivity analysis. Two‐factor models are used to illustrate cases where forgetting occurs continuously or intermittently over a given time period.

show abstract

Learning curves and lot sizing for independent and dependent demand

Cited by 26 publications

References 4 publications

A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty

A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty

An Economic Production Quantity Model With Learning and Forgetting Considerations

Multifactor learning and forgetting models for productivity and performance analysis

Contact Info

Product

Resources

About