Tarun Biswas | Jessore University of Science & Technology (original) (raw)
Papers by Tarun Biswas
International Journal of Research, 2019
In this paper, an optimization model for aggregate planning of multi-product and multi-period pro... more In this paper, an optimization model for aggregate planning of multi-product and multi-period production system has been formulated. Due to the involvement of too many stakeholders as well as uncertainties, the aggregate production planning sometimes becomes extremely complex in dealing with all relevant cost criteria. Most of the existing approaches have focused on minimizing only production related costs, consequently ignored other cost factors, for instance, supply chain related costs. However, these types of other cost factors are greatly affected by aggregate production planning and its mismanagement often results in increased overall costs of the business enterprises. Therefore, the proposed model has attempted to incorporate all the relevant cost factors into the optimization model which are directly or indirectly affected by the aggregate production planning. In addition, the considered supply chain related costs have been segregated into two major categories. While the raw ...
Expert Systems With Applications, Apr 1, 2022
2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Nov 10, 2022
Now-a-day's many leading manufacturing industry have started to practice Six Sigma and Lean m... more Now-a-day's many leading manufacturing industry have started to practice Six Sigma and Lean manufacturing concepts to boost up their productivity as well as quality of products. In this paper, the Six Sigma approach has been used to reduce process variability of a food processing industry in Bangladesh. DMAIC (Define, Measure, Analyze, Improve, & Control) model has been used to implement the Six Sigma Philosophy. Five phases of the model have been structured step by step respectively. Different tools of Total Quality Management, Statistical Quality Control and Lean Manufacturing concepts likely Quality function deployment, P Control chart, Fish-bone diagram, Analytical Hierarchy Process, Pareto analysis have been used in different phases of the DMAIC model. The process variability have been tried to reduce by identify the root cause of defects and reducing it. The ultimate goal of this study is to make the process lean and increase the level of sigma.
International Journal of Fuzzy Systems, 2019
In this thesis, a methodology for construction projects risk assessment under epistemic uncertain... more In this thesis, a methodology for construction projects risk assessment under epistemic uncertainty (i.e., uncertainty arising from lack of data/knowledge) has been proposed. In practice, as the sufficient data from historical sources for probabilistic analysis is quite difficult to obtain, qualitative risk assessment methodologies based on expert's judgments (i.e., using linguistic terms) are commonly used in construction industry. However, these insufficient probabilistic data combining with experts' judgments can be used in the risks evaluation process to reduce uncertainties and biasness. Since the assessment of risk is basically a measure of uncertainties, fuzzy reasoning technique can be an effective tool to deal with these uncertainties and capture the vagueness in the linguistic variables. Most of the existing risk analysis models have evaluated risks based on two factors: risk likelihood and risk severity. In all these methodologies developed so far, it has been assumed that the degrees of uncertainties (level of uncertainties) involved in individual risk event are equal. However, in practice, the degree of uncertainties that involved in each risk event may vary due to the variation in the availability or quality of data obtained from multiple sources (e.g., from experts' opinions and past data from similar projects). Therefore, evaluation of risks considering the degree of uncertainty involved in individual risk events may assist project manager in setting-up response strategies to mitigate threat to the project objectives. This thesis proposes a risk assessment methodology using triangular fuzzy numbering system to compute risk value by combining expert's opinion and insufficient historical data. A modified form of general ramp type fuzzy membership function for quantification of uncertainty range of each risk event and an extended VIKOR method for risks ranking with these uncertainty ranges have been proposed. The most notable difference with other fuzzy risk assessment methods is the use of algorithm to handle the uncertainties involved in individual risk event. An illustrative example on risk assessment of a building construction project is used to demonstrate the proposed methodology. v ACKNOWLEDGEMENT First, the author would like to express his deepest gratefulness to the most benevolent and Almighty God, because without His grace and mercy it was quite impossible to complete this thesis. Then also would like to extend thanks to his family for their continuous inspiration, sacrifice and support to complete the thesis successfully.
ACM Transactions on Knowledge Discovery from Data
Uncertainty is ubiquitous in almost every real-life optimization problem, which must be effective... more Uncertainty is ubiquitous in almost every real-life optimization problem, which must be effectively managed to get a robust outcome. This is also true for the Influence Maximization (IM) problem, which entails locating a set of influential users within a social network. However, most of the existing IM approaches have overlooked the uncertain factors in finding the optimal solution, which often leads to subpar performance in reality. A few recent studies have considered only the epistemic uncertainty (i.e., arises from the imprecise data), while ignoring completely the aleatory uncertainty (i.e., arises from natural or physical variability). In this article, we propose a formulation and a novel algorithm for the Robust Influence Maximization (RIM) problem under both types of uncertainties. First, we develop a robust influence spread function under aleatory uncertainty that, in contrast to the existing IM theory, is no longer monotone and submodular. Thereafter, we expand our RIM for...
The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adopti... more The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adoption of products among users in a social network by identifying and activating a set of initial users. In real-life applications, it is not unrealistic to have a higher activation cost for a user with higher influence. However, the existing works on IM consider finding the most influential users as the seed set, ignoring either the activation costs of such individual nodes and the total budget or the size of the seed set, which may not be always an optimal solution, particularly from the financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation termed as multi-constraint influence maximization (MCIM) aiming to achieve a cost-effective solution under both budgetary and cardinality constraints. Unlike the existing IM formulations, the proposed MCIM is no longer a monotone but a submodular function. As it is also prov...
2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020
Influence maximization (IM) in social networks aims to figure out the best subset of seed nodes w... more Influence maximization (IM) in social networks aims to figure out the best subset of seed nodes which have maximum cascading influence under a diffusion model. This paper proposes a hybrid Community-based Simulated Annealing (ComSA) approach for the IM problem. A community detection algorithm is employed to segregate the entire social network structure into some more deeply clustered communities. Thereafter, a degree-based metric has been used to select the candidate pool from each community by excluding less influential nodes at the preliminary data preprocessing phase. A community-based seed initialization and neighborhood search technique have been proposed. To speed up the convergence of stable solutions in Simulated Annealing approach, a greedy hill climbing strategy is also implemented instead of using probabilistic based solution acceptance processes. Experimental results on four real-world datasets show that our proposed algorithm has comparable solution with greedy and outperforms the other existing meta-heuristic approaches.
Information Sciences, 2021
Abstract The Influence Maximization (IM) problem aims to identify a small subset of nodes that ha... more Abstract The Influence Maximization (IM) problem aims to identify a small subset of nodes that have the most influence spread in a network. Although it is an NP-hard problem, the continuous increasing size of the social networks leads to a substantially higher computational complexity, and therefore, has motivated numerous researchers to explore better approaches. This paper proposes a multi-criteria decision making (MCDM) based meta-heuristic approach to solve the IM problem in social networks. An MCDM approach is utilized to select candidate nodes by eliminating less influential ones at the preliminary phase which decreases the computation cost. Thereafter, to find the optimal solution, a modified version of Simulated Annealing (SA) with an enhanced search strategy is proposed. The performance of this proposed approach is tested and verified by solving the IM problem on eight real-life social networks of different sizes and types and comparing the results with six benchmark algorithms. The experimental results indicate that the proposed algorithm has a better trade-off between the solution quality and computational run time than the other algorithms.
International Journal of Research, 2019
In this paper, an optimization model for aggregate planning of multi-product and multi-period pro... more In this paper, an optimization model for aggregate planning of multi-product and multi-period production system has been formulated. Due to the involvement of too many stakeholders as well as uncertainties, the aggregate production planning sometimes becomes extremely complex in dealing with all relevant cost criteria. Most of the existing approaches have focused on minimizing only production related costs, consequently ignored other cost factors, for instance, supply chain related costs. However, these types of other cost factors are greatly affected by aggregate production planning and its mismanagement often results in increased overall costs of the business enterprises. Therefore, the proposed model has attempted to incorporate all the relevant cost factors into the optimization model which are directly or indirectly affected by the aggregate production planning. In addition, the considered supply chain related costs have been segregated into two major categories. While the raw ...
Expert Systems With Applications, Apr 1, 2022
2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Nov 10, 2022
Now-a-day's many leading manufacturing industry have started to practice Six Sigma and Lean m... more Now-a-day's many leading manufacturing industry have started to practice Six Sigma and Lean manufacturing concepts to boost up their productivity as well as quality of products. In this paper, the Six Sigma approach has been used to reduce process variability of a food processing industry in Bangladesh. DMAIC (Define, Measure, Analyze, Improve, & Control) model has been used to implement the Six Sigma Philosophy. Five phases of the model have been structured step by step respectively. Different tools of Total Quality Management, Statistical Quality Control and Lean Manufacturing concepts likely Quality function deployment, P Control chart, Fish-bone diagram, Analytical Hierarchy Process, Pareto analysis have been used in different phases of the DMAIC model. The process variability have been tried to reduce by identify the root cause of defects and reducing it. The ultimate goal of this study is to make the process lean and increase the level of sigma.
International Journal of Fuzzy Systems, 2019
In this thesis, a methodology for construction projects risk assessment under epistemic uncertain... more In this thesis, a methodology for construction projects risk assessment under epistemic uncertainty (i.e., uncertainty arising from lack of data/knowledge) has been proposed. In practice, as the sufficient data from historical sources for probabilistic analysis is quite difficult to obtain, qualitative risk assessment methodologies based on expert's judgments (i.e., using linguistic terms) are commonly used in construction industry. However, these insufficient probabilistic data combining with experts' judgments can be used in the risks evaluation process to reduce uncertainties and biasness. Since the assessment of risk is basically a measure of uncertainties, fuzzy reasoning technique can be an effective tool to deal with these uncertainties and capture the vagueness in the linguistic variables. Most of the existing risk analysis models have evaluated risks based on two factors: risk likelihood and risk severity. In all these methodologies developed so far, it has been assumed that the degrees of uncertainties (level of uncertainties) involved in individual risk event are equal. However, in practice, the degree of uncertainties that involved in each risk event may vary due to the variation in the availability or quality of data obtained from multiple sources (e.g., from experts' opinions and past data from similar projects). Therefore, evaluation of risks considering the degree of uncertainty involved in individual risk events may assist project manager in setting-up response strategies to mitigate threat to the project objectives. This thesis proposes a risk assessment methodology using triangular fuzzy numbering system to compute risk value by combining expert's opinion and insufficient historical data. A modified form of general ramp type fuzzy membership function for quantification of uncertainty range of each risk event and an extended VIKOR method for risks ranking with these uncertainty ranges have been proposed. The most notable difference with other fuzzy risk assessment methods is the use of algorithm to handle the uncertainties involved in individual risk event. An illustrative example on risk assessment of a building construction project is used to demonstrate the proposed methodology. v ACKNOWLEDGEMENT First, the author would like to express his deepest gratefulness to the most benevolent and Almighty God, because without His grace and mercy it was quite impossible to complete this thesis. Then also would like to extend thanks to his family for their continuous inspiration, sacrifice and support to complete the thesis successfully.
ACM Transactions on Knowledge Discovery from Data
Uncertainty is ubiquitous in almost every real-life optimization problem, which must be effective... more Uncertainty is ubiquitous in almost every real-life optimization problem, which must be effectively managed to get a robust outcome. This is also true for the Influence Maximization (IM) problem, which entails locating a set of influential users within a social network. However, most of the existing IM approaches have overlooked the uncertain factors in finding the optimal solution, which often leads to subpar performance in reality. A few recent studies have considered only the epistemic uncertainty (i.e., arises from the imprecise data), while ignoring completely the aleatory uncertainty (i.e., arises from natural or physical variability). In this article, we propose a formulation and a novel algorithm for the Robust Influence Maximization (RIM) problem under both types of uncertainties. First, we develop a robust influence spread function under aleatory uncertainty that, in contrast to the existing IM theory, is no longer monotone and submodular. Thereafter, we expand our RIM for...
The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adopti... more The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adoption of products among users in a social network by identifying and activating a set of initial users. In real-life applications, it is not unrealistic to have a higher activation cost for a user with higher influence. However, the existing works on IM consider finding the most influential users as the seed set, ignoring either the activation costs of such individual nodes and the total budget or the size of the seed set, which may not be always an optimal solution, particularly from the financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation termed as multi-constraint influence maximization (MCIM) aiming to achieve a cost-effective solution under both budgetary and cardinality constraints. Unlike the existing IM formulations, the proposed MCIM is no longer a monotone but a submodular function. As it is also prov...
2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020
Influence maximization (IM) in social networks aims to figure out the best subset of seed nodes w... more Influence maximization (IM) in social networks aims to figure out the best subset of seed nodes which have maximum cascading influence under a diffusion model. This paper proposes a hybrid Community-based Simulated Annealing (ComSA) approach for the IM problem. A community detection algorithm is employed to segregate the entire social network structure into some more deeply clustered communities. Thereafter, a degree-based metric has been used to select the candidate pool from each community by excluding less influential nodes at the preliminary data preprocessing phase. A community-based seed initialization and neighborhood search technique have been proposed. To speed up the convergence of stable solutions in Simulated Annealing approach, a greedy hill climbing strategy is also implemented instead of using probabilistic based solution acceptance processes. Experimental results on four real-world datasets show that our proposed algorithm has comparable solution with greedy and outperforms the other existing meta-heuristic approaches.
Information Sciences, 2021
Abstract The Influence Maximization (IM) problem aims to identify a small subset of nodes that ha... more Abstract The Influence Maximization (IM) problem aims to identify a small subset of nodes that have the most influence spread in a network. Although it is an NP-hard problem, the continuous increasing size of the social networks leads to a substantially higher computational complexity, and therefore, has motivated numerous researchers to explore better approaches. This paper proposes a multi-criteria decision making (MCDM) based meta-heuristic approach to solve the IM problem in social networks. An MCDM approach is utilized to select candidate nodes by eliminating less influential ones at the preliminary phase which decreases the computation cost. Thereafter, to find the optimal solution, a modified version of Simulated Annealing (SA) with an enhanced search strategy is proposed. The performance of this proposed approach is tested and verified by solving the IM problem on eight real-life social networks of different sizes and types and comparing the results with six benchmark algorithms. The experimental results indicate that the proposed algorithm has a better trade-off between the solution quality and computational run time than the other algorithms.