Optimizing a Multi-Product Continuous-Review Inventory Model With Uncertain Demand, Quality Improvement, Setup Cost Reduction, and Variation Control in Lead Time

Malik, Asif Iqbal; Sarkar, Biswajit

doi:10.1109/access.2018.2849694

Cited by 29 publications

(12 citation statements)

References 43 publications

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“…The main limitation of the model is that the setup cost in the model is considered to be constant, which can be reduced by an initial investment. In this direction, the model can be extended (refer to the references Malik and Sarkar, [56]; Sarkar and Moon, [57]; Sarkar et al [58]; Sarkar and Majumdar, [59]; Sarkar et al [60]; and Majumdar et al [61]). This model may also be extended by incorporating inspection errors, as the inspection may be done by human beings.…”

Section: Discussionmentioning

confidence: 99%

Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

2019

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The advertising of any smart product is crucial in generating customer demand, along with reducing sale prices. Naturally, a decrease in price always increases the demand for any smart product. This study introduces a multi-product production process, taking into consideration the advertising- and price-dependent demands of products, where the failure rate of the production system is reduced under the optimum energy consumption. For long-run production systems, unusual energy consumption and machine failures occur frequently, which are reduced in this study. All costs related with the production system are included in the optimum energy costs. The unit production cost is dependent on the production rate of the machine and its failure rate. The aim of this study is to obtain the optimum profit with a reduced failure rate, under the optimum advertising costs and the optimum sale price. The total profit of the model becomes a complex, non-linear function, with respect to the decision variables. For this reason, the model is solved numerically by an iterative method. However, the global optimality is proved numerically, by using the Hessian matrix. The numerical results obtained show that for smart production, the maximum profit always occurs at the optimum values of the decision variables.

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Section: Discussionmentioning

confidence: 99%

Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

2019

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“…They adopted normal distribution and distribution-free approaches to solve the stochastic lead-time demand problem. Furthermore, Malik and Sarkar [8] studied a continuous-review policy for multiple products with uncertain demand, investments for quality improvements and setup cost reduction, and lead-time control with unknown lead-time demand distribution. Malik and Sarkar [9] recently presented a backorder price discount model with controllable lead time and unknown distribution for lead-time demands.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Therefore, researchers adopted a distribution-free approach to solve these types of problems, which is a more realistic approach [3][4][5]. It is proved from the literature that additional investments can reduce the setup cost for the production system [6][7][8][9]. Mostly, researchers consider continuous investments for controlling the setup cost; however, the discrete investment can be more realistic as the industry may not prefer continuous investment; thus, this is another research gap in the literature.…”

Section: Introductionmentioning

confidence: 99%

Coordinating Supply-Chain Management under Stochastic Fuzzy Environment and Lead-Time Reduction

Malik

Sarkar

2019

Mathematics

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In this paper, a supply-chain (SC) coordination method based on the lead-time crashing is proposed for a seller–buyer system. By considering different transportation modes, we control the lead-time (LT) variability. For the first time, we have attempted to determine the impact of the reliable and unreliable seller in a continuous-review supply-chain model under the stochastic environment. The authors discussed two reliability cases for the seller. First, we consider the seller is unreliable and in the second case, the seller is reliable. In addition, the demand during the lead time is stochastic with the known mean and variance. The proposed approach tries to find an optimal solution that performs well without a specific probability distribution. Besides, a discrete investment is made to reduce the setup cost, which will indirectly help supply-chain members to increase the total profit of the system. In the proposed model, the seller motivates the buyer by reducing lead time to take part in coordinating decision-making for the system’s profit optimization. We derive the coordination conditions for both members, the seller and the buyer, under which they are convinced to take part in the cooperative decision-making plan. Therefore, lead-time crashing is the proposed incentive mechanism for collaborative supply-chain management. We use a fixed-charge step function to calculate the lead-time crashing cost for slow and fast shipping mode. We give two numerical examples to validate the proposed models and demonstrate the service-level enhancement under the collaborative supply-chain management in case of an unreliable seller. Concluding remarks and future extensions are discussed at the end.

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“…They considered the normally distributed probabilistic demand during the lead time. Malik and Sarkar [29] studied the inventory problem under uncertain demands and with service level constraints. They considered the setup cost reductions and quality improvements with continuous investments.…”

Section: Introductionmentioning

confidence: 99%

“…Further, this study incorporates the budget constraints for the vendor and the space constraint for the buyer while considering the variable production rate and the customers' service level. [4] Production NA Tajbakhsh [32] Inventory NA Glock [6] Production NA Pasandideh et al [37] SCM NA Jamshidi et al [38] SCM NA Sarkar et al [33] Production Continuous Bazan et al [22] SCM NA Gholami-Qadikolaei et al [28] Inventory NA Shin et al [30] SCM NA Sarkar et al [39] SCM Discrete Wangsa [16] SCM NA AlDurgam et al [7] SCM NA Malik and Sarkar [29] Inventory Continuous Aljazzar et al [25] SCM NA Sarkar et al [8] SCM NA Malik and Sarkar [40] SCM Discrete Marchi et al [26] SCM NA Sarkar and Chung [3] SCM Discrete This Model SCM Discrete SCM: Supply chain management; NA: Not applicable to the study.…”

Section: Introductionmentioning

confidence: 99%

A Constrained Production System Involving Production Flexibility and Carbon Emissions

Malik

Kim

2020

Mathematics

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The proposed study presents an economic lot size and production rate model for a single vendor and a single buyer setup. This model involves greenhouse gas (GHG) emissions from industrial sources. The carbon emissions in this model are considered as two types: direct emissions and indirect emissions. The production rate affects carbon emissions generation in production, i.e., generally, higher production rates result in more emissions, which is governable in many real-life cases. The production rate also impacts the process reliability and quality. Faster production deteriorates the production system quickly, leading to machine failure and defective items. Such reliability and quality problems increase energy consumptions and supply chain (SC) costs. This paper formulates a vendor-buyer SC model that tackles these issues. It considers two decision-making policies: integrated or centralized as well as decentralized, where the aim is to obtain the optimal values of the decision variables that give the minimum total SC cost. It includes the costs of setup, holding inventory, carbon emissions, order processing, production, reworking, and inspection processes. The decision variables are the production rate, lead time, order quantity, the number of shipments, and the investments for setup cost reduction. In the later sections, this paper compares the numerical outcomes of the two centralized and decentralized policies. It also provides sensitivity analysis and useful insights on the economic and environmental execution of the SC.

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Optimizing a Multi-Product Continuous-Review Inventory Model With Uncertain Demand, Quality Improvement, Setup Cost Reduction, and Variation Control in Lead Time

Cited by 29 publications

References 43 publications

Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

Coordinating Supply-Chain Management under Stochastic Fuzzy Environment and Lead-Time Reduction

A Constrained Production System Involving Production Flexibility and Carbon Emissions

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