Note—Learning Effects in Economic Lot Sizing

Muth, Eginhard J.; Spremann, Klaus

doi:10.1287/mnsc.29.2.264

Cited by 44 publications

(9 citation statements)

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“…As discussed in Section 4, when learning occurs only in setups, the resulting lot-sizing problem can be modeled as (6). The three components of the objective function of (6) represent the setup, production, and holding costs, respectively.…”

Section: Appendixmentioning

confidence: 99%

“…Models for optimal production lot sizing in the presence of learning have been proposed in the literature; see, for example, Keachie and Fontana [S], Spradlin and Pierce [13], Fisk and Ballou [2], Muth and Spremann [6], and Smunt and Morton [12]. The common underlying assumption in these studies is that learning affects unit production costs which decrease with experience in a log-linear function; i.e.,…”

Section: Introductionmentioning

confidence: 99%

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Production lot sizing under setup and worker learning

Karwan

Mazzola

Morey

1988

Naval Research Logistics

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Previous lot‐sizing models incorporating learning effects focus exclusively on worker learning. We extend these models to include the presence of setup learning, which occurs when setup costs exhibit a learning curve effect as a function of the number of lots produced. The joint worker/setup learning problem can be solved to optimality by dynamic programming. Computational experience indicates, however, that solution times are sensitive to certain problem parameters, such as the planning horizon and/or the presence of a lower bound on worker learning. We define a two‐phase EOQ‐based heuristic for the problem when total transmission of worker learning occurs. Numerical results show that the heuristic consistently generates solutions well within 1% of optimality.

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Section: Appendixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Production lot sizing under setup and worker learning

Karwan

Mazzola

Morey

1988

Naval Research Logistics

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“…Alder and Nanda (1974) developed a general equation for the common production time per unit where some proportion of learning isn't maintained between tons. Muth and Spremann (1983) introduced a transcendental price perform to work out the best heap size beneath learning effects. Elmaghraby (1990) reviewed some antecedently projected models and distended one in all them to accommodate a finite horizon.…”

Section: Introductionmentioning

confidence: 99%

Effect of learning and salvage worth on an inventory model for deteriorating items with inventory-dependent demand rate and partial backlogging with capability constraints

Kumar¹,

Kumar²

2016

10.5267/j.uscm

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In this paper, an inventory model is built with inventory-dependent demand rate and twowarehouses in which demand rate is a polynomial of current inventory level. Moreover, it is typically the case in which the value of system engaged in repetitive operations decreases. Thus, the impact of learning from repetitive method cannot be unnoticed while developing the inventory model with two-level storage. Here, we tend to assume that the capability of the own warehouse, holding value of own and rented warehouse is partly constant and partly decreasing in every cycle, merit to learning impact. Impact of salvage worth is additionally thought of for the deteriorating things. Additionally, we tend to give shortages and assume that the backlogging demand rate depends on the period of the stock-out. The solution is found with the help of some numerical examples. Sensitivity analysis in relation to numerous parameters is also shown.

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“…Wortham and Mayyasi (1972) demonstrated the impact of the learning effect on economic order quantity (EOQ) and total relevant cost. Muth and Spremann (1983) studied the learning effect on the production cost for an economic lot size problem and provided a numerical solution as well as an approximation 904…”

Section: Introductionmentioning

confidence: 99%

An optimal production and shipment policy for a single-vendor single-buyer integrated system with both learning effect and deteriorating items

Tsai

2011

International Journal of Production Research

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The collaboration of vendor and buyer is one of the key factors for successful supply chain management. The most common strategy for a collaborative system is to propose an integrated replenishment plan aimed at maintaining a win-win partnership for both vendor and buyer. The objective of this study is to develop a production and shipment model for a system that incorporates learning effect and deteriorating items and to derive an optimal joint total cost from the integrated perspective of both vendor and buyer. A simple solution procedure is presented to determine the optimal production time and number of deliveries. A numerical example is provided to illustrate the proposed model. A sensitivity analysis is conducted to study the effect of changes in the related parameters on the optimal solution. This paper shows that the proposed integrated model can result in a significant cost reduction as compared with the independent decisions made by either vendor or buyer.

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Note—Learning Effects in Economic Lot Sizing

Cited by 44 publications

References 0 publications

Production lot sizing under setup and worker learning

Production lot sizing under setup and worker learning

Effect of learning and salvage worth on an inventory model for deteriorating items with inventory-dependent demand rate and partial backlogging with capability constraints

An optimal production and shipment policy for a single-vendor single-buyer integrated system with both learning effect and deteriorating items

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