An application of interval differential equation on a production inventory model with interval‐valued demand via center‐radius optimization technique and particle swarm optimization

Rahman, Sadikur; Manna, Amalesh Kumar; Shaikh, Ali Akbar; Bhunia, Asoke Kumar

doi:10.1002/int.22254

Cited by 56 publications

(20 citation statements)

References 45 publications

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“…An application of interval differential equation on a production-inventory model with interval-valued demand via center-radius optimization technique, and particle swarm optimization has also been studied recently. 40 Gül et al 41 invented a novel operator-based cumulative belief degree to quantify credit rating. A spherical aggregation operator and spherical fuzzy sets have been used in multi-attribute group decision making.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Indeed, Xu and Cai 39 analyzed a nonlinear optimization model for multiple attribute group decision making under an intuitionistic fuzzy environment. An application of interval differential equation on a production‐inventory model with interval‐valued demand via center–radius optimization technique, and particle swarm optimization has also been studied recently 40 . Gül et al 41 invented a novel operator‐based cumulative belief degree to quantify credit rating.…”

Section: Introductionmentioning

confidence: 99%

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Solution of an imperfect‐quality EOQ model with backorder under fuzzy lock leadership game approach

Mahata

2020

Int J Intell Syst

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Section: Review Of Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solution of an imperfect‐quality EOQ model with backorder under fuzzy lock leadership game approach

Mahata

2020

Int J Intell Syst

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“…In the same year, Taleizadeh et al [13] developed an imperfect production inventory model with return and warranty policies. Recently, Rahman et al [14] developed a production inventory model where the demand and inventory cost parameters are interval valued. Taleizadeh et al [15] proposed manufacturing and selling tactics under cost sharing and refund agreement in a green supply chain system.…”

Section: Literature Reviewmentioning

confidence: 99%

Product Replacement Policy in a Production Inventory Model with Replacement Period-, Stock-, and Price-Dependent Demand

Das

Manna

Mahmoud

et al. 2020

Journal of Mathematics

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In the competitive market situation, several companies confer various types of incentives and facilities during product sell to their customers with certain terms and conditions. For the products such as mobile, TV, water purifiers, marshal products, and many more, its corresponding companies offer replacement facility during the guarantee period to enhance the customers’ demand. In this study, we have formulated a production inventory model with considering product’s replacement facility of the failure product within guarantee periods to their customers. This work also leads two vital assumptions: (i) customers’ demand is depending on the replacement period, stock level, and selling price of the product and (ii) the rate of replacement loss of manufacturer’s capital is dependent on the replacement period, and it is a nonlinear function. Since the corresponding optimization problem is highly nonlinear, we have solved it by MATHEMATICA software. The concavity of the centre of interval-valued average profit of the proposed model is shown graphically. In order to justify the validity of the proposed model, a numerical example is considered and solved. Finally, the sensitivity analyses are carried out with respect to the different model parameters.

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“…24 The paper addresses this issue by proposing a mechanism based on two fuzzy logic controllers (FLCs) for energy-efficient communication. Fuzzy logic is commonly exploited in decision making [25][26][27] and optimization techniques, 28,29 as it can deal with fuzzy information. In the proposed mechanism, fuzzy rules are established to optimize the power consumption without detriment to quality of service (QoS).…”

Section: Introductionmentioning

confidence: 99%

A fuzzy control system for energy‐efficient wireless devices in the Internet of vehicles

et al. 2021

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Embedded systems are common in the Internet of Things domain: their integration in vehicles and mobile devices is being fostered in the Internet of vehicles (IoV). IoV has direct applications on intelligent transportation systems and smart cities. Besides basic requirements, such as ease of installation, cost-effectiveness, scalability, and flexibility, IOV applications need to guarantee energyefficient and good-quality communication. In fact, IoV implementations are commonly based on wireless nodes, which rely on a limited energy source; therefore, an efficient communication among the nodes is desirable to prolong the lifetime of the devices. In particular, the alternation of active and sleep states and the regulation of the transmission power represent two common approaches to save energy. Based on this strategy, an effective fuzzy control system is presented in the paper to manage power consumption and quality of services of IoV applications. Two fuzzy controllers increase the battery life while keeping a good throughput to workload ratio. This technique has been simulated with two leading technologies in IoV: IEEE 802.11b/g/n and IEEE 802.11p. Experimental results show a network lifetime improvement ranging from 30% to 40%, according to the adopted medium access control protocol.

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An application of interval differential equation on a production inventory model with interval‐valued demand via center‐radius optimization technique and particle swarm optimization

Cited by 56 publications

References 45 publications

Solution of an imperfect‐quality EOQ model with backorder under fuzzy lock leadership game approach

Solution of an imperfect‐quality EOQ model with backorder under fuzzy lock leadership game approach

Product Replacement Policy in a Production Inventory Model with Replacement Period-, Stock-, and Price-Dependent Demand

A fuzzy control system for energy‐efficient wireless devices in the Internet of vehicles

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