The Economic Lot Sizes in Multistage Production Systems

Taha, Hamdy A.; Skeith, Ronald W.

doi:10.1080/05695557008974746

Cited by 60 publications

(16 citation statements)

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“…Two classes of these models, both based on an infinite time horizon, can be distinguished in the literature. One class, which we call"variable lot-size models" [2,3,5,9), allows different and non-increasing lot-sizes across stages. Only complete lots are transported to the next stage and the lot-size of a stage is an integer multiple of the lot-size that follows it.…”

Section: Introductionmentioning

confidence: 99%

Multi-stage production with variable lot sizes and transportation of partial lots

Drezner

Szendrovits

Wesolowsky

1984

European Journal of Operational Research

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II G. O. WesolowskyThis paper describes a model for a multi-stage production/inventory system where lots may be of different sizes. In addition, either completed lots or partial lots, called batches, may be transported to succeeding stages. The model incorporates constraints on lot and batch-sizes and thus provides a rather comprehensive set of possibilities for organizing a production/inventory system. A heuristic solution procedure is developed and is shown to be "close to optimal" by bounding.

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Section: Introductionmentioning

confidence: 99%

Multi-stage production with variable lot sizes and transportation of partial lots

Drezner

Szendrovits

Wesolowsky

1984

European Journal of Operational Research

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“…A successful application of this approach to a real-world problem is reported in El-Najdawi (1997). On the other hand, several simpli® ed versions of this problem have been addressed (Taha and Skeith 1970, Jensen and Khan 1972, Crowston et al 1973, Szendrovits 1975, Goyal 1976, and Moily 1986). This quick review shows that most contributions to the economic lot sizing and scheduling problem do not consider sequencing decisions nor do they guarantee the feasibility of the resulting schedule.…”

Section: Introductionmentioning

confidence: 96%

The G-group heuristic to solve the multi-product, sequencing, lot sizing and scheduling problem in flow shops

Ouenniche¹,

Boctor²

2001

International Journal of Production Research

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This paper presents a new heuristic to solve the problem of making sequencing, lot sizing and scheduling decisions for a number of products manufactured in ā ow shop environment, so as to minimize the sum of setup and inventory holding costs while a given demand is ful® lled without backlogging. The proposed solution method ® rst determines sequencing decisions then lot sizing and scheduling decisions are simultaneously determined. This heuristic, called the G-group method, divides the set of products into G groups and requires that products belonging to the same group have the same cycle time. Also, the cycle time of each group is restricted to be an integer multiple of a basic period. For each basic period of the global cycle, the products to be produced during this period and the production sequence to be used are chosen. Then, a non-linear program is solved to determine lot sizes and to construct a feasible production schedule. To evaluate its performance, the G-group method was compared to four other methods. Numerical results show that the proposed heuristic outperforms all these methods.

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“…We can find only three articles that address the case: two that address two stage systems 7,8 and one that considers the serial multistage case. 9 The basic assumptions involved in extending the single-stage economic order quantity (EOQ) style model to the general multistage case requires that the process be stationary (periodic), nested (coordinated), and follow a cycle time that is an integer multiple cycle time (power-of-two). Under these assumptions, an approximate, near optimal solution can be developed that is within 2% of optimality.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Generalized Batch‐Storage Network Considering Shortage Costs

Reklaitis²

2013

AIChE Journal

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Product shortages, which cause backlogging and/or lost sales costs commonly occur in the chemical industries, especially in the commodity polymer business. Shortage costs in a supply chain optimization model are studied under the framework of a generalized batch-storage network. A classical economic order quantity model with backlogging costs suggested an optimal time delay and final product delivery lot size. A product shortage can be mitigated by advancing production/transportation or by purchasing a substitute product from a third party as well as by a product delivery delay in the supply chain network. Optimal solutions that consider all means for recovering shortage are more complicated than the classical case. Four solutions are identified depending on the parametric range and variable bounds. The optimal capacity for production/transportation processes associated with a product in shortage can differ from that of a product not in shortage. An illustrative example is presented in support of the analytical results.

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The Economic Lot Sizes in Multistage Production Systems

Cited by 60 publications

References 1 publication

Multi-stage production with variable lot sizes and transportation of partial lots

Multi-stage production with variable lot sizes and transportation of partial lots

The G-group heuristic to solve the multi-product, sequencing, lot sizing and scheduling problem in flow shops

Optimal Design of Generalized Batch‐Storage Network Considering Shortage Costs

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