Optimal batch sizing in a multi-stage production system with rework consideration

Sarker, Bhaba R.; Jamal, A. M. M.; Mondal, Sanjay

doi:10.1016/j.ejor.2006.12.005

Cited by 119 publications

(58 citation statements)

References 25 publications

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“…Especially the latter deals with quite some fargoing issues and hence provides some useful insights. Nonetheless, the paper stresses the need for flawless production, since rework will always be more expensive than first-time right production [3]. Wadhwa and Ravindran (2010) introduced a multipeobjective multiple-supplier selection model for low risk and cost products.…”

Section: Introductionmentioning

confidence: 99%

“…There were three objectives to achieve, minimizing purchasing cost, rejected units and late delivered units [2]. Sarker and et al (2008) consider EOQ-like batch sizing models that account for the possibility of rework being done during cycles, as well as after a certain number of cycles. Especially the latter deals with quite some fargoing issues and hence provides some useful insights.…”

Section: Introductionmentioning

confidence: 99%

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Decision Making to Select the Best Order Quantity (BOQ) from Supplier using Genetic Algorithm

Laith¹,

Ali²,

Mahmoud³

2016

International Advanced Research Journal in Science, Engineering

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Manufacturers need to have a good supply chain management system in order to achieve low inventory levels, short lead times and adjustability to meet customer demands at minimal total operation cost. The most important drawback of existing methods used to minimize inventory costs as Just-in-time (JIT) methodology or to minimize transportation and order costs as Economic Order Model (EOQ). This minimization strategy may not be able to give the best order quantity because of the relationship between inventory cost and transportation cost, In this paper, we used genetic algorithm (GA) to reduce the inventory and transportation costs together to determine the Best Order Quantity (BOQ). The main advantage of this new method, it is covers pull system, push systems, short planning horizon, and long planning horizon.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision Making to Select the Best Order Quantity (BOQ) from Supplier using Genetic Algorithm

Laith¹,

Ali²,

Mahmoud³

2016

International Advanced Research Journal in Science, Engineering

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“…Technical note by [3] was made to do corrections of the examples in [2]. [4] extended their research from a single-stage to a multi-stage production system with rework. The other researches related to rework were addressed by [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16] and [17].…”

Section: Introductionmentioning

confidence: 99%

The optimal manufacturing batch size with rework under time-varying demand process for a finite time horizon

Musa¹,

Supadi²,

Omar³

2014

AIP Conference Proceedings

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Abstract. Rework is one of the solutions to some of the main issues in reverse logistic and green supply chain as it reduces production cost and environmental problem. Many researchers focus on developing rework model, but to the knowledge of the author, none of them has developed a model for time-varying demand rate. In this paper, we extend previous works and develop multiple batch production system for time-varying demand rate with rework. In this model, the rework is done within the same production cycle.

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“…Chiu et al (2007) discussed a lot size problem with random scrap rate and backlogging by alternative approach instead of calculated method. Sarker et al (2008) addressed the issue relating to reworking of defective items in a multi-stage production system by considering two operational policies: reworking of defective items within the same cycle and after N cycles. Biswas and Sarker (2008) studied an optimal batch quantity model with rework and scrap and detected the scraps after production in three ways with the assumption that once the scrap is detected, it is discarded immediately.…”

Section: Introductionmentioning

confidence: 99%

Coordination policy for a three echelon supply chain considering imperfect quality items

Singh¹,

Vaish²,

Singh³

2014

10.5267/j.ijiec

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In this study, we develop a three echelon supply chain model for items to determine the optimal reliability and production rate, which achieves the biggest total integrated profit for an imperfect manufacturing process. Here, we have taken a supplier, a manufacturer and a retailer in which supplier supplies raw materials to manufacturer, manufacturer produces perfect and imperfect quality items because practically it happens and manufacturer supplies perfect quality items to the retailers. In production system, production facility may shift from an in-control state to an out-ofcontrol state at any random time. The basic assumption of classical economic manufacturing quantity model is that all manufacturing items are of perfect quality but the assumption is not true in practice. The proposed study is formulated assuming that a certain percent of total product is defective. This percentage also varies with production rate and production run time. The defective items are restored in original quality by reworked at some costs to maintain the quality of products in a competitive market. Finally, numerical example and its graphical representation are given to illustrate the proposed model. Sensitivity analysis is also provided to test feasibility of the model.

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Optimal batch sizing in a multi-stage production system with rework consideration

Cited by 119 publications

References 25 publications

Decision Making to Select the Best Order Quantity (BOQ) from Supplier using Genetic Algorithm

Decision Making to Select the Best Order Quantity (BOQ) from Supplier using Genetic Algorithm

The optimal manufacturing batch size with rework under time-varying demand process for a finite time horizon

Coordination policy for a three echelon supply chain considering imperfect quality items

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