Bijoy Krishna Debnath
@tezu.ernet.in/appsc
Assistant Professor, Department of Applied Sciences
Tezpur University
EDUCATION
Ph.D. (Mathematics), 2019
National Institute of Technology Agartala
Master of Science (Mathematics), 2015
Gauhati University
Bachelor of Science (Mathematics), 2013
Tripura University
RESEARCH INTERESTS
Deterministic and Fuzzy Inventory Modelling, Supply Chain Models, Imperfect Manufacturing Systems
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Abhijit Saha, Arunodaya Raj Mishra, Pratibha Rani, Muhammet Deveci, Bijoy Krishna Debnath, Norziana Jamil, and Moamin A. Mahmoud
Elsevier BV
Brajamohan Sahoo and Bijoy Krishna Debnath
Elsevier BV
Puspendu Giri, Somnath Paul, and Bijoy Krishna Debnath
Elsevier BV
Brajamohan Sahoo and Bijoy Krishna Debnath
IGI Global
Selecting the ideal location for regenerative tourism is vital for environmental preservation and sustainable progress. Destination choice significantly impacts regenerative initiatives' effectiveness, affecting ecological benefits and socio-economic outcomes. A well-selected site fosters ecosystem restoration and positive engagement with indigenous communities, leveraging tourism as a force for biodiversity preservation, carbon capture, and local empowerment. In this chapter, the fuzzy multi-attributive border approximation area comparison (MABAC) approach is utilized to select the optimal site for regenerative tourism initiatives, considering six criteria each with five alternatives and input from three decision-makers. Normalization occurs after forming the initial decision matrix, followed by weight normalization. Performance index and rank are determined using the fuzzy multi-attributive border approximation area comparison (MABAC) procedure. Ultimately, after careful evaluation and consideration, it becomes evident that the fifth alternative stands out as the most suitable location for implementing regenerative practices in the field of tourism.
Abhijit Saha, Bijoy Krishna Debnath, Prasenjit Chatterjee, Annapurani K. Panaiyappan, Surajit Das, and Gogineni Anusha
Elsevier BV
Puspendu Giri, Somnath Paul, and Bijoy Krishna Debnath
Elsevier BV
Bijoy Krishna Debnath, Pinki Majumder, and Uttam Kumar Bera
Inderscience Publishers
Sourav Mahata and Bijoy Krishna Debnath
Elsevier BV
Sourav Mahata and Bijoy Krishna Debnath
EDP Sciences
This paper addresses a single item two-level supply chain inventory model considering deterioration during carrying of deteriorating item from a supplier’s warehouse to a retailer’s warehouse as well as deterioration in the retailer’s warehouse. The model assumes preservation technology in the retailer’s warehouse to prevent the rate of deterioration. An upper limit for the preservation technology investment has been set as a constraint to the model. The model maximizes the retailer’s profit per unit time, simultaneously calculated optimal order quantity. A price dependent demand and storage-time dependent holding cost is considered to develop the model. Some theorems are proven to get optimal values of the total cost. A numerical problem is workout as per the developed algorithm and with the help of MATLAB software to study the applicability of our theoretical results.
Abhijit Saha, Priyanka Majumder, Debjit Dutta, and Bijoy Krishna Debnath
Springer Science and Business Media LLC
Bijoy Krishna Debnath, Pinki Majumder, and Uttam Kumar Bera
Inderscience Publishers
B.K. Debnath, P. Majumder, and U.K. Bera
Hacettepe University
A sustainable fuzzy economic production quantity (SFEPQ) inventory model is formulated by introducing the concept of fuzzy differential equation (FDE) due to dynamic behavior of the production-demand system. Generalized Hukuhara (gH) differentiability proceedure is applied to solve FDE. Since the demand parameter is taken as trapezoidal type-2 fuzzy number, to get corresponding defuzzified values, first critical value (CV)-based reduction method is applied on demand function to transfer into type-1 fuzzy variable which turns to hexagonal fuzzy number in form. After that $\\alpha$-cut of a hexagonal fuzzy number is used to find the upper and lower value of demand. To apply the $\\alpha$-cut operation on FDE, we divided the interval [0,1] into two sub-intervals [0,0.5] and [0.5,1] and gH-differentiation is applied on this sub-intervals. The objective of this paper is to maximize the profit and simultaneously minimize the carbon emission cost occurring due to the process of inventory management. Finally, the non-linear objective functions are solved by using of multi-objective genetic algorithm and sensitivity analyses on various parameters are also performed in numerically and graphically.
Bijoy Krishna Debnath, Pinki Majumder, and Uttam Kumar Bera
Inderscience Publishers
B. Debnath, P. Majumder, U. K. Bera and M. Maiti
An inventory model is formulated with type-2 fuzzy parameters under trade credit policy and solved by using Generalized Hukuhara derivative approach. Representing demand parameter of each expert's opinion is a membership function of type-1 and thus, this membership function again becomes fuzzy. The final opinion of all experts is expressed by a type-2 fuzzy variable. For this present problem, to get corresponding defuzzified values of the triangular type-2 fuzzy demand parameters, first critical value (CV)-based reduction methods are applied to reduce corresponding type-1 fuzzy variables which becomes pentagonal in form. After that $alpha$- cut of a pentagonal fuzzy number is used to construct the upper $alpha$- cut and lower $alpha$- cut of the fuzzy differential equation. Different cases are considered for fuzzy differential equation: gH-(i) differentiable and gH-(ii) differentiable systems. The objective of this paper is to find out the optimal time so as to minimize the total inventory cost. The considered problem ultimately reduces to a multi-objective problem which is solved by weighted sum method and global criteria method. Finally the model is solved by generalised reduced gradient method using LINGO (13.0) software. The proposed model and technique are lastly illustrated by providing numerical examples. Results from two methods are compared and some sensitivity analyses both in tabular and graphical forms are presented and discussed. The effects of total cost with respect to the change of demand related parameter ($beta$), holding cost parameter ($r$), unit purchasing cost parameter ($p$), interest earned $(i_e)$ and interest payable $(i_p)$ are discussed. We also find the solutions for type-1 and crisp demand as particular cases of type-2 fuzzy variable. This present study can be applicable in many aspects in many real life situations where type-1 fuzzy set is not sufficient to formulate the mathematical model. From the numerical studies, it is observed that under both gH-(i) and gH-(ii) cases, total cost of the system gradually reduces for the sub-cases - 1.1, 1.2 and 1.3 depending upon the positions of N(trade credit for customer) and M (trade credit for retailer) with respect to T (time period).