An Advanced Optimization Technique for Smart Production Using α-Cut Based Quadrilateral Fuzzy Number

Kumar, Rajesh; Chandrawat, Rajesh Kumar; Sarkar, Biswajit; Joshi, Varun; Majumder, Arunava

doi:10.1007/s40815-020-01002-9

Cited by 39 publications

(19 citation statements)

References 50 publications

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“…Uncertain market condition can be solve by using the novel concept of fuzzy set theory. In this direction a modified optimization technique for quadrilateral fuzzy number was invented by Kumar et al [26]. In the similar direction a production model with variable lead time under fuzzy demand was proposed by [27].…”

Section: Selling Price Dependent Demandmentioning

confidence: 99%

Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time

et al. 2022

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With the advancement of technologies, industries tries to adopt the advantages of the technology. Customers are busy in their daily life, and the online platform is the best option for retail, whereas traditional customers still prefer to visit the retail shop. Few customers choose the product online but buy it online or vice-versa. Owing to all those circumstances, current study focuses on an intelligent dual channel (online-to-offine) strategy in industry to arrange the optimal services for customers. The selling price of the product vary with different channel, which helps to determine the demand of product for entire supply chain. Two important factors, backorder and lead-time are examined precisely through marginal value which helps to arrange optimal service and calculate the exact profit. The profit for a centralized and decentralized case are computed for both the players. Some propositions are developed to prove the global optimality. Numerical results prove that a centralized case provides 7.77% better pro t than a decentralized case due to bonding between the players.

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Section: Selling Price Dependent Demandmentioning

confidence: 99%

Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time

et al. 2022

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“…The transportation of biofuel for a smart manufacturing system played an essential role in a bioenergy supply chain network [4]. By utilizing a smart manufacturing [27], the defective products could be reduced. A partial backorder was studied by Park [38] while backordering was purchasing-price depended.…”

Section: Closed-loop Supply Chain Managementmentioning

confidence: 99%

Technology license sharing strategy for remanufacturing industries under a closed-loop supply chain management bonding

et al. 2022

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Remanufacturing is getting attention nowadays due to increasing waste and corresponding emissions. One of the important factors of remanufacturing is the quality of the remanufactured products. The collection and distribution of used products require proper management. Based on this situation, this study discusses a hybrid closed-loop supply chain management in cooperation with a hybrid production system. The vendor comes up with the policy of sharing remanufacturing responsibility by sharing the technology license with other supply chain players. The carbon cap restricts emissions from the entire hybrid production system of the vendor. Other factors of this proposed study are service by the retailer and quality, gift policy, and customer awareness by the vendor. This study examines the scenario under random market demand. Classical optimization provides the solution under the Stackelberg game policy where the vendor acts as leader and the retailer & third party act as followers. This paper considers two scenarios: Scenario A for a continuous distribution and Scenario B for no specific distribution. A comparison is drawn between various motivating factors-based policies to control supply chain management.

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“…Liu et al [38] Probabilistic Robust optimization method Inventory-routing Kumar et al [35] Quadrilateral fuzzy number Fuzzy programming SCN Present study IFN Fuzzy bi-level multi-objective SCN…”

Section: Logistic Transportationmentioning

confidence: 99%

Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment

Gupta

Haq

Ali

et al. 2021

Complex Intell. Syst.

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Determining the methods for fulfilling the continuously increasing customer expectations and maintaining competitiveness in the market while limiting controllable expenses is challenging. Our study thus identifies inefficiencies in the supply chain network (SCN). The initial goal is to obtain the best allocation order for products from various sources with different destinations in an optimal manner. This study considers two types of decision-makers (DMs) operating at two separate groups of SCN, that is, a bi-level decision-making process. The first-level DM moves first and determines the amounts of the quantity transported to distributors, and the second-level DM then rationally chooses their amounts. First-level decision-makers (FLDMs) aimed at minimizing the total costs of transportation, while second-level decision-makers (SLDM) attempt to simultaneously minimize the total delivery time of the SCN and balance the allocation order between various sources and destinations. This investigation implements fuzzy goal programming (FGP) to solve the multi-objective of SCN in an intuitionistic fuzzy environment. The FGP concept was used to define the fuzzy goals, build linear and nonlinear membership functions, and achieve the compromise solution. A real-life case study was used to illustrate the proposed work. The obtained result shows the optimal quantities transported from the various sources to the various destinations that could enable managers to detect the optimum quantity of the product when hierarchical decision-making involving two levels. A case study then illustrates the application of the proposed work.

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An Advanced Optimization Technique for Smart Production Using α-Cut Based Quadrilateral Fuzzy Number

Cited by 39 publications

References 50 publications

Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time

Intelligent servicing strategy for an online-to-offline (O2O) supply chain under demand variability and controllable lead time

Technology license sharing strategy for remanufacturing industries under a closed-loop supply chain management bonding

Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment

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