Lot-sizing in a class of multistage production inventory systems

Gunasekaran, Angappa; Babu, A. Subash; Ramaswamy, N.

doi:10.1080/00207729008910549

Cited by 8 publications

(9 citation statements)

References 15 publications

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“…The results obtained for the cases with and without machine failures (see Gunasekaran 1987) are presented in Table 2.…”

Section: Comparison Of the Results For The Cases With And Without Macmentioning

confidence: 97%

“…The model presented here is an extension of the model developed by Gunasekaran (1987). Its main objective is to determine the economic batch size at each facility for each product.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…However, they did not take into account the effect of in-process inventory while optimizing the batch sizes. Gunasekaran (1987) Production researchers have also considered the effect of machine reliability on the efficiency of production systems. Most of the models developed in the past consider the efficiency of unreliable production lines with and without buffer storage.…”

Section: Ovemew Of Earlier Researchmentioning

confidence: 99%

“…The actual resultant output rate is represented by The average waiting time for the production run quantity BSR, is estimated using the M/M/I (finite source) queuing formula (see Gunasekaran 1987 andPanico 1969) and it is represented as PR; is the probability that there are zero batches in a production-inventory-queuing system. The time-weighted in-process inventory value for the production run quantity BSR, can be estimated using (3), (I I) and (24) as WT,BSR,CAij…”

Section: The Actual Operation Time For a Batch Is Given In Time Units Bymentioning

confidence: 99%

“…The production system (see $3) considered in this mathematical model is simulated and the details of the simulation model are reported by Gunasekaran (1987). The listings of the coding for this simulation can be obtained from authors on request.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Effect of machine failures in a multi-stage multi-facility and multi-product production-inventory system

Gunasekaran

Goyal

Babu

et al. 1991

International Journal of Systems Science

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A mathematical model for a multi-stage, multi-facility and multi-product productioninventory system is presented to study the effect of machine unreliability on the economic production quantity and total system cost. The production system considered here has multiple spindle and batch blocking facilities. A search method has been used for optimizing the batch sizes. The basic criterion considered for optimizing the batch sizes is the minimization of the total system cost. An example is presented lo explain the behaviour and application of the model. Notation

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“…The results obtained for the cases with and without machine failures (see Gunasekaran 1987) are presented in Table 2.…”

Section: Comparison Of the Results For The Cases With And Without Macmentioning

confidence: 97%

“…The model presented here is an extension of the model developed by Gunasekaran (1987). Its main objective is to determine the economic batch size at each facility for each product.…”

Section: Mathematical Modelmentioning

confidence: 99%

Section: Ovemew Of Earlier Researchmentioning

confidence: 99%

Section: The Actual Operation Time For a Batch Is Given In Time Units Bymentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Effect of machine failures in a multi-stage multi-facility and multi-product production-inventory system

Gunasekaran

Goyal

Babu

et al. 1991

International Journal of Systems Science

Self Cite

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Analysis of Optimum Batch Size in Multistage, Multifacility and Multiproduct Manufacturing Systems

Nagaraj

Selladurai²

2002

The International Journal of Advanced Manufacturing Technology

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In any type of manufacturing environment the most common configuration will be a multistage, multiproduct, manufacturing inventory system. An in-process inventory is inherent in any multistage manufacturing system, when the product is manufactured through several operations and stages. To satisfy demand requirements in multistage, multiproduct manufacturing systems, it is important to specify feasible production quantities, as batch sizes for each end product, over a finite time period at every stage. This paper deals with different methods for determining batch sizes in a multiproduct, multistage situation in the presence of decision support systems for maximum capacity. The mathematical model suggested by Gunasekaren is considered as the basis for comparison. A fuzzy logic model of the above has been developed to calculate the economic batch sizes such that machine hour and labour productivity constraints are not violated. Using Promodel, a simulation technique, the model was subjected to variable analysis and cost analysis and the results were compared. Case studies were conducted for validating the models for their ability to produce reliable output. MATLAB software package is used for developing the program to solve the problem by fuzzy approximation.

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