Mijanur Rahaman Seikh
@knu.ac.in
Professor(Assistant), Department of Mathematics
Kazi Nazrul University
RESEARCH INTERESTS
Fuzzy Set Theory, Multi-criteria Decision Making, Game Theory, Inventory Control and SupplyChain Management
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mijanur Rahaman Seikh and Prayosi Chatterjee
Elsevier BV
Mijanur Rahaman Seikh and Shibaji Dutta
Elsevier BV
Santanu Kumar Ghosh, Mijanur Rahaman Seikh, and Milan Chakrabortty
Springer Science and Business Media LLC
Mijanur Rahaman Seikh and Utpal Mandal
Elsevier BV
Mijanur Rahaman Seikh and Utpal Mandal
MDPI AG
The q-rung orthopair fuzzy (q-ROF) set is an efficient tool for dealing with uncertain and inaccurate data in real-world multi-attribute decision-making (MADM). In MADM, aggregation operators play a significant role. The majority of well-known aggregation operators are formed using algebraic, Einstein, Hamacher, Frank, and Yager t-conorms and t-norms. These existing t-conorms and t-norms are some special cases of Archimedean t-conorms (ATCNs) and Archimedean t-norms (ATNs). Therefore, this article aims to extend the ATCN and ATN operations under the q-ROF environment. In this paper, firstly, we present some new operations for q-ROF sets based on ATCN and ATN. After that, we explore a few desirable characteristics of the suggested operational laws. Then, using these operational laws, we develop q-ROF Archimedean weighted averaging (geometric) operators, q-ROF Archimedean order weighted averaging (geometric) operators, and q-ROF Archimedean hybrid averaging (geometric) operators. Next, we develop a model based on the proposed aggregation operators to handle MADM issues. Finally, we elaborate on a numerical problem about site selection for software operating units to highlight the adaptability and dependability of the developed model.
Utpal Mandal and Mijanur Rahaman Seikh
Elsevier BV
Subhendu Ruidas, , Mijanur Rahaman Seikh, Prasun Kumar Nayak, , and
American Institute of Mathematical Sciences (AIMS)
Subhendu Ruidas, Mijanur Rahaman Seikh, Prasun Kumar Nayak, and Ming-Lang Tseng
Springer Science and Business Media LLC
Shuvasree Karmakar and Mijanur Rahaman Seikh
Springer Science and Business Media LLC
Mijanur Rahaman Seikh and Shibaji Dutta
Springer Science and Business Media LLC
Shuvasree Karmakar and Mijanur Rahaman Seikh
Springer International Publishing
Subhendu Ruidas, M. R. Seikh and P. Nayak
American Institute of Mathematical Sciences (AIMS)
This paper explores a production inventory model considering two high-tech products of the same kind. One is the primary product and the other is the updated version of that primary product. Due to continuous development in technology, the life-cycle of some high-tech products, like, smartphone, tablet, laptop, etc. have become shorter. We witness the launching of new products more frequently in this field. This prompts the manufacturers to release an updated or pro version of their existing products after a certain time to compete in the market. The reputation of the primary product (in terms of quality and performance) plays an important role in generating the demand for the updated product. Due to the short life-cycle of the products, the proposed model considers only two consecutive production runs. One for the primary product and one for the updated product. Here the demands of both the products depend on the respective selling prices. Moreover, the demand of the updated product is also dependent on the quality of the primary product. Shortages for the primary product are allowed. Those shortages are backlogged partially with the updated product. Also, the possibility of imperfect production during regular production runs is considered. The selling prices, production rates, and the production run times for both the products are considered here as decision variables. Due to the complexity in the resulting optimization problem, the quantum-behaved particle swarm optimization technique is applied to derive the optimal profit. The concavity natures of the profit function are shown graphically. A numerical illustration is presented for the economic validation of the model. Finally, sensitivity analysis of the optimal solutions concerning the key inventory parameters is conducted for identifying several managerial implications.
Subhendu Ruidas, Mijanur Rahaman Seikh, and Prasun Kumar Nayak
Springer Science and Business Media LLC
Mijanur Rahaman Seikh and Utpal Mandal
Elsevier BV
Mijanur Rahaman Seikh and Utpal Mandal
Springer Science and Business Media LLC
Mijanur Rahaman Seikh and Utpal Mandal
Springer Science and Business Media LLC
Subhendu Ruidas, Mijanur Rahaman Seikh, and Prasun Kumar Nayak
Springer Science and Business Media LLC
This paper explores a production inventory model in an imperfect production system under a rough interval environment. The occurrences of shortages are allowed and are fully backlogged. In this paper, two models are presented. In the first model, the demand of the product is considered as a rough interval. In the second model, both the demand and the defective rate are considered as rough intervals. In reality, these two factors cannot be assessed exactly due to a lack of available data. Moreover, there are few scenarios where the demand of the product is assessed in two-layer information. The inner layer demand is obvious, and it may extend up to the outer layer depending on the situation. Also, the same thing may happen for the defective rate of the product. In those cases, it is more reasonable to express the demand and the defective rate as rough intervals instead of fuzzy or interval numbers. Based on the expected value of rough intervals, the necessary and sufficient conditions are determined analytically to obtain the optimal inventory policies. The main advantage of this method is that the optimal profit and the optimal lot size are determined as crisp numbers rather than rough intervals. Obtaining a precise value for these two quantities is desirable for any production manager. Numerical examples for both models are provided to illustrate the solution methodology. Sensitivity analysis of the optimal solutions concerning the key parameters is conducted for identifying several managerial implications.
Prasun Kumar Nayak and Mijanur Rahaman Seikh
CRC Press
Mijanur Rahaman Seikh and Shibaji Dutta
Springer Science and Business Media LLC
Utpal Mandal and Mijanur Rahaman Seikh
Springer Nature Singapore
Mijanur R. Seikh, Shibaji Dutta, and Deng‐Feng Li
Wiley
Mijanur Rahaman Seikh and Shuvasree Karmakar
Springer Science and Business Media LLC