Managing disruption in an imperfect production–inventory system

Paul, Sanjoy Kumar; Sarker, Ruhul A.; Essam, Daryl

doi:10.1016/j.cie.2014.09.013

Cited by 75 publications

(45 citation statements)

References 39 publications

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“…Khedlekar et al (2014) decided the production stage before and after disruption by solving the disruption analytically in production scheduling system. Paul et al (2015) presented the rescheduling model and dynamic solution method to handle the disruption for production-inventory system.…”

Section: Introductionmentioning

confidence: 99%

Combining prospect theory with fuzzy theory to handle disruption in production scheduling

Jiang

Ding

Ruan

et al. 2018

Filomat

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This paper focuses on revising a production scheduling that an unpredictable disruption happens after a subset of jobs has been processed. Under these circumstances, continuing with the original schedule will not be optimal. This paper combines prospect theory and fuzzy theory to present a recovery model to handle the disruption. The proposed model is different from most rescheduling approaches in that the difference between the original schedule and the recovery schedule is contained by taking human behavior into consideration. The computational result demonstrates that due to the tradeoff between all participators involved in production scheduling, our model is more effective than existing rescheduling approaches.

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Section: Introductionmentioning

confidence: 99%

Combining prospect theory with fuzzy theory to handle disruption in production scheduling

Jiang

Ding

Ruan

et al. 2018

Filomat

View full text Add to dashboard Cite

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“…In most of the previous production inventory models, the researchers considered that all the produced items are of perfect quality. But, in real life, due to complex design of mechinaries items, it is not possible to produce all the items of perfect quality and is directly affected by the reliability of the production process.Recently some research works have been done in an imperfect production process like as Bazan [5] , Paul [43], Dey [17], Sarkar[[51], [52]], Mohammadia [40], Haidar[22] etc. In the literature there are few research publications in the two ware-house inventory model with defective items like as Rad [45], Pal[44] etc .…”

Section: Introductionmentioning

confidence: 99%

A multi-item EPQ model with imperfect production process for time varying demand with shortages

Majumder¹,

Bera²,

Maiti³

2015

HJMS

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In this paper, economic production quantity(EPQ)models for breakable or deteriorating items are developed with time dependent linear variable demands. Here rate of production and holding cost are time dependent and unit production cost is a function of both production reliability indicator and production rate. Set-up cost is also partially production rate dependent.The production process produces some imperfect quantities which are instantly reworked at a cost to bring back those units to perfect ones. Ultimately, the production process depends on both time and reliability indicator. Here two models are developed in optimal control framework considering the effect of time value of money and inflation. Shortages are allowed for both the models. The first model consists of two items and the second model is of single item. For the first model, a budget constraint is considered. For both the models, total profit function are calculated with the effect of inflation and time value of money in a finite integral over a finite planning horizon.The problems are solved using Eulers-Lagrangian function based on variational calculus and applying generalized reduced gradient method using LINGO 13.0 software to determine the optimal reliability indicator (r) and then corresponding production rates and total profits. From the above two breakable models, other ten models are obtained as particular cases, out of which two are with stock dependent breakability/deterioration and another two are simple EPQ models without breakability.Out of these particular cases two models are of constant demand,two models are of constant holding cost and two models are of constant setup cost. Numerical experiments are performed for both the models to illustrate the models both numerically and graphically.

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“…Numerous researchers have carried out research in this field and a good number of researchers is still working on this field. Interested readers are referred to the work of Hishamuddin et al (2012Hishamuddin et al ( , 2014Hishamuddin et al ( , 2015, Paul (2015) and Paul et al (2014Paul et al ( , 2015aPaul et al ( , 2015b to have the idea of the some contemporary researches. This field became popular to the eyes of researchers as Parlar and Perry (1996) pointed out in most inventory models existed in the literature, one of the implicit assumptions is that the product to be ordered is always available; that is, when an order is placed it is either received immediately (the case of zero lead time) or after a deterministic or perhaps random lead time.…”

Section: Introductionmentioning

confidence: 99%

Development of an inventory model for two suppliers with random capacity considering supply disruption

Ahmed

Sultana

Azeem

2017

IJLSM

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Supply disruption occurs for diverse reasons including transportation problem, equipment failure, raw material shortages, natural calamities etc. As a result supplier may become unavailable at random times for random time length. In addition, suppliers may also have random capacities leading to uncertain yield in orders. Again under random supplier capacities, the retailer should order from a number of suppliers in order to diversify the risk associated with shortages. The objective of this paper is to develop an inventory model for two suppliers with random capacities considering supply disruption. A modified (Q, r) model is developed to tackle the problem of future supply uncertainty in response to the demand generated by Poisson process. Two suitable optimisation algorithms are applied to search for the optimal values of the decision variables which are state dependent order quantities and reorder point to minimise the cost per unit time. A hypothetical example and its solution are then provided to have a better understanding about the demonstration of the proposed model. Finally, sensitivity analysis is also carried out to have better insights about the model developed.

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Managing disruption in an imperfect production–inventory system

Cited by 75 publications

References 39 publications

Combining prospect theory with fuzzy theory to handle disruption in production scheduling

Combining prospect theory with fuzzy theory to handle disruption in production scheduling

A multi-item EPQ model with imperfect production process for time varying demand with shortages

Development of an inventory model for two suppliers with random capacity considering supply disruption

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