A sustainable economic production model: effects of quality and emissions tax from transportation

Yassine, Noura

doi:10.1007/s10479-018-3069-7

Cited by 18 publications

(29 citation statements)

References 46 publications

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“…A great deal of research works have been conducted by incorporating into the classical EPQ model factors encountered in real-life situations. Example of such factors include time value of money and credit facilities (Salameh et al, 2003), quality of the finished product (Salameh and Jaber, 2000;Khan et al, 2011a), cost and quality of the raw materials used in production (Salameh and El-Kassar, 2007;Yassine, 2016), deterioration (Bandaly and Hassan, 2019), supply chain considerations (Bandaly et al 2014(Bandaly et al , 2016Khan and Jaber, 2011), inspection errors and learning (Khan et al, 2014), as well as green and sustainable practices (Fornes et al, 2019;Lamba et al, 2019;Rani et al, 2018;Yassine, 2018).…”

Section: Introductionmentioning

confidence: 99%

Sustainable supply chains based on supplier selection and HRM practices

Yassine

Singh

2020

JEIM

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PurposeThe purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered.Design/methodology/approachA mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy.FindingsThis study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided.Practical implicationsThis study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors.Originality/valueThis study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.

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Section: Introductionmentioning

confidence: 99%

Sustainable supply chains based on supplier selection and HRM practices

Yassine

Singh

2020

JEIM

Self Cite

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“…Note that the classical production model is very simplistic and does not take into account real life factors. For instance, the model ignores the costs of components/raw materials required for the production of the finished product (Salameh and El-Kassar, 2007), the quality of components (Yassine, 2018; El-Kassar et al , 2012) and finished items (Khan et al ., 2011; El-Kassar, 2009), as well as lead time and reorder point (Yassine and El-Rabih, 2019). Nevertheless, practitioners are still using this classical model (Andriolo et al , 2014) and researchers are extending or modifying it so that the modeling approach is more realistic.…”

Section: The Classical Production Model Notations and Assumptionsmentioning

confidence: 99%

“…Although the 2nd assumption seems as restrictive, it is a common practice to impose such assumption (Yassine, 2018; Khan and Jaber, 2011) as it does not lead to a loss of generality. To see this, consider the case where m components of a certain kind are required to produce a single item of the finished product.…”

Section: The Classical Production Model Notations and Assumptionsmentioning

confidence: 99%

“…Other recent studies considered decision-making at the supply chain level (Pereira et al , 2019; Khan and Jaber, 2011; Bandaly et al , 2014), learning and imperfect inspection processes (Khan et al , 2014) and sustainable practices (Lamba et al , 2019; Yassine, 2018). Decision-making in logistics management has been incorporated in the classical production/inventory models (Bandaly et al , 2016; Khan and Jaber, 2011; Yassine, 2018; Yassine and El-Rabih, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In the following, the production process described above is mathematically modelled and the optimal production/ordering policy is derived. Moreover, recent results (Yassine, 2016, 2018; Yassine and El-Rabih, 2019) are used to show that the optimal policy of the proposed model depends on the maximum of the lead times and the minimum of the percentages of perfect components. Recent works based on the modeling approach of Yassine (2016) for the treatment of excess perfect components (Yassine, 2018; Yassine et al , 2018, 2020) did not consider the effect of the lead times.…”

Section: Introductionmentioning

confidence: 99%

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An economic production model with imperfect quality components and probabilistic lead times

El-Kassar¹,

Ishizaka²,

Temouri

et al. 2020

IJLM

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PurposeThis study investigates a production process that requires N kinds of components for the production of a finished product. The producer orders the various kinds of components from different suppliers and receives the orders in lots at the beginning of each production cycle. Similar to situations often encountered in real life, the lead times are random variables with known probability distributions so that a production cycle starts whenever all N kinds of components become available. Each of the lots received at the start of a production run contains both perfect and imperfect quality components. Once all N kinds of components become available, the producer initiates a screening process to detect the imperfect components. The production of the finished product uses only perfect quality components. The imperfect components are removed from inventory whenever the screening process is completed. The percentage of components of perfect quality present in each lot is a random variable with a known probability distribution.Design/methodology/approachThis production process is described and modeled mathematically and the optimal production/ordering policy is derived based on the mathematical model.FindingsThe formulated mathematical model resulted in the determination of the optimal policy consisting of the optimal number of finished items ordered to be produce during each production run, the number of components ordered from each supplier, and the reorder point. The derived closed form expression for the optimal lot size depends on the minimum of the number of perfect quality components in a lot, whereas the reorder point is determined based on the maximum lead time.Practical implicationsThe modeling approach and results of this study provide practical implications that may be beneficial to both production and supply chain managers as well as researchers.Originality/valueThis modeling approach that incorporates decision-making related to the logistics of acquiring the components and accounts for the probabilistic nature of the lead times and quality of components addresses a gap in the logistics/production literature.

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Innovative maintenance problem in a two-stage production-inventory system with imperfect processes

Weng

Huang

2019

Ann Oper Res

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A sustainable economic production model: effects of quality and emissions tax from transportation

Cited by 18 publications

References 46 publications

Sustainable supply chains based on supplier selection and HRM practices

Sustainable supply chains based on supplier selection and HRM practices

An economic production model with imperfect quality components and probabilistic lead times

Innovative maintenance problem in a two-stage production-inventory system with imperfect processes

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