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Multi-criteria decision-making (MCDM) methods are smart tools to deal with numerous criteria in decision-making. These methods have been widely applied in the area of sustainable supply chain management (SSCM) because of their computational capabilities. This paper conducts a systematic literature review on MCDM methods applied in different areas of SSCM. From the literature search, a total of 106 published journal articles have been selected and analyzed. Both individual and integrated MCDM methods applied in SSCM are reviewed and summarized. In addition, contributions, methodological focuses, and findings of the reviewed articles are discussed. It is observed that MCDM methods are widely used for analyzing barriers, challenges, drivers, enablers, criteria, performances, and practices of SSCM. In recent years, studies have focused on integrating more than one MCDM method to highlight methodological contributions in SSCM; however, in the literature, limited research papers integrate multiple MCDM methods in the area of SSCM. Most of the published articles integrate only two MCDM methods, and integration with other methods, such as optimization and simulation techniques, is missing in the literature. This review paper contributes to the literature by analyzing existing research, identifying research gaps, and proposing new future research opportunities in the area of sustainable supply chain management applying MCDM methods.
Ananna Paul; Nagesh Shukla; Sanjoy Paul; Andrea Trianni. Sustainable Supply Chain Management and Multi-Criteria Decision-Making Methods: A Systematic Review. Sustainability 2021, 13, 7104 .
AMA StyleAnanna Paul, Nagesh Shukla, Sanjoy Paul, Andrea Trianni. Sustainable Supply Chain Management and Multi-Criteria Decision-Making Methods: A Systematic Review. Sustainability. 2021; 13 (13):7104.
Chicago/Turabian StyleAnanna Paul; Nagesh Shukla; Sanjoy Paul; Andrea Trianni. 2021. "Sustainable Supply Chain Management and Multi-Criteria Decision-Making Methods: A Systematic Review." Sustainability 13, no. 13: 7104.
The current COVID-19 pandemic has hugely disrupted supply chains (SCs) in different sectors globally. The global demand for many essential items (e.g., facemasks, food products) has been phenomenal, resulting in supply failure. SCs could not keep up with the shortage of raw materials, and manufacturing firms could not ramp up their production capacity to meet these unparalleled demand levels. This study aimed to examine a set of congruent strategies and recovery plans to minimize the cost and maximize the availability of essential items to respond to global SC disruptions. We used facemask SCs as an example and simulated the current state of its supply and demand using the agent-based modeling method. We proposed two main recovery strategies relevant to building emergency supply and extra manufacturing capacity to mitigate SC disruptions. Our findings revealed that minimizing the risk response time and maximizing the production capacity helped essential item manufacturers meet consumers’ skyrocketing demands and timely supply to consumers, reducing financial shocks to firms. Our study suggested that delayed implementation of the proposed recovery strategies could lead to supply, demand, and financial shocks for essential item manufacturers. This study scrutinized strategies to mitigate the demand–supply crisis of essential items. It further proposed congruent strategies and recovery plans to alleviate the problem in the exceptional disruptive event caused by COVID-19.
Towfique Rahman; Firouzeh Taghikhah; Sanjoy Kumar Paul; Nagesh Shukla; Renu Agarwal. An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic. Computers & Industrial Engineering 2021, 158, 107401 .
AMA StyleTowfique Rahman, Firouzeh Taghikhah, Sanjoy Kumar Paul, Nagesh Shukla, Renu Agarwal. An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic. Computers & Industrial Engineering. 2021; 158 ():107401.
Chicago/Turabian StyleTowfique Rahman; Firouzeh Taghikhah; Sanjoy Kumar Paul; Nagesh Shukla; Renu Agarwal. 2021. "An agent-based model for supply chain recovery in the wake of the COVID-19 pandemic." Computers & Industrial Engineering 158, no. : 107401.
Consumer behavior is key in shifts towards organic products. A diversity of factors influences consumer preferences, driving planned, impulsive, and unplanned purchasing decisions. We study choices among organic and conventional wine using an extensive survey among Australian consumers (N = 1003). We integrate five behavioral theories in the survey design, and use supervised and unsupervised machine learning algorithms for analysis. We quantify a gap between intention and behavior, and emphasize the importance of cognitive factors. Findings go beyond correlation to the causation of behavior when combining predictive prowess with explanatory power. Results reveal that affective factors and normative cues may prompt unplanned and spontaneous purchasing behavior, causing consumers to act against their beliefs.
Firouzeh Taghikhah; Alexey Voinov; Nagesh Shukla; Tatiana Filatova. Shifts in consumer behavior towards organic products: Theory-driven data analytics. Journal of Retailing and Consumer Services 2021, 61, 102516 .
AMA StyleFirouzeh Taghikhah, Alexey Voinov, Nagesh Shukla, Tatiana Filatova. Shifts in consumer behavior towards organic products: Theory-driven data analytics. Journal of Retailing and Consumer Services. 2021; 61 ():102516.
Chicago/Turabian StyleFirouzeh Taghikhah; Alexey Voinov; Nagesh Shukla; Tatiana Filatova. 2021. "Shifts in consumer behavior towards organic products: Theory-driven data analytics." Journal of Retailing and Consumer Services 61, no. : 102516.
The current intense food production-consumption is one of the main sources of environmental pollution and contributes to anthropogenic greenhouse gas emissions. Organic farming is a potential way to reduce environmental impacts by excluding synthetic pesticides and fertilizers from the process. Despite ecological benefits, it is unlikely that conversion to organic can be financially viable for farmers, without additional support and incentives from consumers. This study models the interplay between consumer preferences and socio-environmental issues related to agriculture and food production. We operationalize the novel concept of extended agro-food supply chain and simulate adaptive behavior of farmers, food processors, retailers, and customers. Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. We propose an integrated approach combining agent-based, discrete-event, and system dynamics modeling for a case of wine supply chain. Findings demonstrate the feasibility and superiority of the proposed model over the traditional sustainable supply chain models in incorporating the feedback between consumers and producers and analyzing management scenarios that can urge farmers to expand organic agriculture. Results further indicate that demand-side participation in transition pathways towards sustainable agriculture can become a time-consuming effort if not accompanied by the middle actors between consumers and farmers. In practice, our proposed model may serve as a decision-support tool to guide evidence-based policymaking in the food and agriculture sector.
Firouzeh Taghikhah; Alexey Voinov; Nagesh Shukla; Tatiana Filatova; Mikhail Anufriev. Integrated modeling of extended agro-food supply chains: A systems approach. European Journal of Operational Research 2020, 288, 852 -868.
AMA StyleFirouzeh Taghikhah, Alexey Voinov, Nagesh Shukla, Tatiana Filatova, Mikhail Anufriev. Integrated modeling of extended agro-food supply chains: A systems approach. European Journal of Operational Research. 2020; 288 (3):852-868.
Chicago/Turabian StyleFirouzeh Taghikhah; Alexey Voinov; Nagesh Shukla; Tatiana Filatova; Mikhail Anufriev. 2020. "Integrated modeling of extended agro-food supply chains: A systems approach." European Journal of Operational Research 288, no. 3: 852-868.
Understanding barriers to healthcare access is a multifaceted challenge, which is often highly diverse depending on location and the prevalent surroundings. The barriers can range from transport accessibility to socio-economic conditions, ethnicity and various patient characteristics. Australia has one of the best healthcare systems in the world; however, there are several concerns surrounding its accessibility, primarily due to the vast geographical area it encompasses. This review study is an attempt to understand the various modeling approaches used by researchers to analyze diverse barriers related to specific disease types and the various areal distributions in the country. In terms of barriers, the most affected people are those living in rural and remote parts, and the situation is even worse for indigenous people. These models have mostly focused on the use of statistical models and spatial modeling. The review reveals that most of the focus has been on cancer-related studies and understanding accessibility among the rural and urban population. Future work should focus on further categorizing the population based on indigeneity, migration status and the use of advanced computational models. This article should not be considered an exhaustive review of every aspect as each section deserves a separate review of its own. However, it highlights all the key points, covered under several facets which can be used by researchers and policymakers to understand the current limitations and the steps that need to be taken to improve health accessibility.
Nagesh Shukla; Biswajeet Pradhan; Abhirup Dikshit; Subrata Chakraborty; Abdullah M. Alamri. A Review of Models Used for Investigating Barriers to Healthcare Access in Australia. International Journal of Environmental Research and Public Health 2020, 17, 4087 .
AMA StyleNagesh Shukla, Biswajeet Pradhan, Abhirup Dikshit, Subrata Chakraborty, Abdullah M. Alamri. A Review of Models Used for Investigating Barriers to Healthcare Access in Australia. International Journal of Environmental Research and Public Health. 2020; 17 (11):4087.
Chicago/Turabian StyleNagesh Shukla; Biswajeet Pradhan; Abhirup Dikshit; Subrata Chakraborty; Abdullah M. Alamri. 2020. "A Review of Models Used for Investigating Barriers to Healthcare Access in Australia." International Journal of Environmental Research and Public Health 17, no. 11: 4087.
Organic food has important environmental and health benefits, decreasing the toxicity of agricultural production, improving soil quality, and overall resilience of farming. Increasing consumers’ demand for organic food reinforces the rate of organic farming adoption and the level of farmers' risk acceptance. Despite the recorded 20% growth in organically managed farmland, its global land area is still far less than expected, only 1.4%. Increasing demand for organic food is an important pathway towards sustainable food systems. We explore this consumer-centric approach by developing a theoretically- and empirically-grounded agent-based model. Three behavioral theories – theory of planned behavior, alphabet theory, and goal-framing – describe individual food purchasing decisions in response to policies. We take wine sector as an example to calibrate and validate the model for the case study of Sydney, Australia. The discrepancy between consumer intention and purchasing behavior for organic wine can be explained by a locked-in vicious cycle. We assess the effectiveness of different policies such as wine taxation, and informational-education campaigns to influence consumer choices. The model shows that these interventions are non-additive: raising consumer awareness and increasing tax on less environmentally friendly wines simultaneously is more successful in promoting organic wine than the sum of the two policies introduced separately. The phenomenon of undercover altruism amplifies the preference for organic wine, and the tipping point occurs at around 35% diffusion rate in the population. This research suggests policy implications to help decision-makers in the food sector make informed decisions about organic markets.
Firouzeh Taghikhah; Alexey Voinov; Nagesh Shukla; Tatiana Filatova. Exploring consumer behavior and policy options in organic food adoption: Insights from the Australian wine sector. Environmental Science & Policy 2020, 109, 116 -124.
AMA StyleFirouzeh Taghikhah, Alexey Voinov, Nagesh Shukla, Tatiana Filatova. Exploring consumer behavior and policy options in organic food adoption: Insights from the Australian wine sector. Environmental Science & Policy. 2020; 109 ():116-124.
Chicago/Turabian StyleFirouzeh Taghikhah; Alexey Voinov; Nagesh Shukla; Tatiana Filatova. 2020. "Exploring consumer behavior and policy options in organic food adoption: Insights from the Australian wine sector." Environmental Science & Policy 109, no. : 116-124.
One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.
Abolfazl Abdollahi; Biswajeet Pradhan; Nagesh Shukla; Subrata Chakraborty; Abdullah Alamri. Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review. Remote Sensing 2020, 12, 1444 .
AMA StyleAbolfazl Abdollahi, Biswajeet Pradhan, Nagesh Shukla, Subrata Chakraborty, Abdullah Alamri. Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review. Remote Sensing. 2020; 12 (9):1444.
Chicago/Turabian StyleAbolfazl Abdollahi; Biswajeet Pradhan; Nagesh Shukla; Subrata Chakraborty; Abdullah Alamri. 2020. "Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review." Remote Sensing 12, no. 9: 1444.
This paper provides a methodological overview of service supply chain research through a comprehensive review of published literature, enabling us to describe the service supply chain from a knowledge perspective. The nature of the service supply chain is substantially different from the characteristics of the traditional supply chain. Consequently, the robustness of ideas underpinning this area of research has not been fully analyzed by the academic community and a more cross-disciplinary approach is needed. Following a comprehensive review, all the selected papers can be divided into nine generic groups in terms of problem focus in the service supply chain. These were production processes, human resources, logistics, information technology, theory and model generation, productivity and profitability, environmentally friendly practices, customer satisfaction and other cross-disciplinary studies. Four key aspects of the service supply chain are recommended for future research, namely: environment-friendly practices, market relationships, information technology integration and adoption of industry-specific case studies. In future extensions, additional work can include and correlate knowledge from other disciplines, theoretical perspectives, intellectual trends, and traditional practices associated with service industries. Lastly, this study could be used as a starting point for establishing a future research agenda in the area of the service supply chain.
Tonmoy Toufic Choudhury; Sanjoy Kumar Paul; Humyun Fuad Rahman; Zhenguo Jia; Nagesh Shukla. A systematic literature review on the service supply chain: research agenda and future research directions. Production Planning & Control 2020, 31, 1363 -1384.
AMA StyleTonmoy Toufic Choudhury, Sanjoy Kumar Paul, Humyun Fuad Rahman, Zhenguo Jia, Nagesh Shukla. A systematic literature review on the service supply chain: research agenda and future research directions. Production Planning & Control. 2020; 31 (16):1363-1384.
Chicago/Turabian StyleTonmoy Toufic Choudhury; Sanjoy Kumar Paul; Humyun Fuad Rahman; Zhenguo Jia; Nagesh Shukla. 2020. "A systematic literature review on the service supply chain: research agenda and future research directions." Production Planning & Control 31, no. 16: 1363-1384.
Computer Methods and Programs in Biomedicine (CMPB) is a leading international journal that presents developments about computing methods and their application in biomedical research. The journal published its first issue in 1970. In 2020, the journal celebrates the 50th anniversary. Motivated by this event, this article presents a bibliometric analysis of the publications of the journal during this period (1970–2017). The objective is to identify the leading trends occurring in the journal by analysing the most cited papers, keywords, authors, institutions and countries. For doing so, the study uses the Web of Science Core Collection database. Additionally, the work presents a graphical mapping of the bibliographic information by using the visualization of similarities (VOS) viewer software. This is done to analyze bibliographic coupling, co-citation and co-occurrence of keywords. CMPB is identified as a leading and core journal for biomedical researchers. The journal is strongly connected to IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging. Paper from Wang, Jacques, Zheng (published in 1995) is its most cited document. The top author in this journal is James Geoffrey Chase and the top contributing institution is Uppsala U (Sweden). Most of the papers in CMPB are from the USA followed by the UK and Italy. China and Taiwan are the only Asian countries to appear in the top 10 publishing in CMPB. A keyword co-occurrences analysis revealed strong co-occurrences for classification, picture archiving and communication system (PACS), heart rate variability, survival analysis and simulation. Keywords analysis for the last decade revealed that machine learning for a variety of healthcare problems (including image processing and analysis) dominated other research fields in CMPB. It can be concluded that CMPB is a world-renowned publication outlet for biomedical researchers which has been growing in a number of publications since 1970. The analysis also conclude that the journal is very international with publications from all over the world although today European countries are the most productive ones.
Nagesh Shukla; José M. Merigó; Thorsten Lammers; Luis Miranda. Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017. Computer Methods and Programs in Biomedicine 2019, 183, 105075 .
AMA StyleNagesh Shukla, José M. Merigó, Thorsten Lammers, Luis Miranda. Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017. Computer Methods and Programs in Biomedicine. 2019; 183 ():105075.
Chicago/Turabian StyleNagesh Shukla; José M. Merigó; Thorsten Lammers; Luis Miranda. 2019. "Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017." Computer Methods and Programs in Biomedicine 183, no. : 105075.
In today's growing economy, overconsumption and overproduction have accelerated environmental deterioration worldwide. Consumers, through unsustainable consumption patterns, and producers, through production based on traditional resource depleting practices, have contributed significantly to the socio-environmental problems. Consumers and producers are linked by supply chains, and as sustainability became seen as a way to reverse socio-environmental degradation, it has also started to be introduced in research on supply chains. We look at the evolution of research on sustainable supply chains and show that it is still largely focused on the processes and networks that take place between the producer and the consumer, hardly taking into account consumer behavior and its influence on the performance of the producer and the supply chain itself. We conclude that we cannot be talking about sustainability, without extending the supply chains to account for consumers' behavior and their influence on the overall system performance. A conceptual framework is proposed to explain how supply chains can become sustainable and improve their economic and socio-environmental performance by motivating consumer behavior toward green consumption patterns, which, in turn, motivate producers and suppliers to change their operations.
Firouzeh Taghikhah; Alexey Voinov; Nagesh Shukla. Extending the supply chain to address sustainability. Journal of Cleaner Production 2019, 229, 652 -666.
AMA StyleFirouzeh Taghikhah, Alexey Voinov, Nagesh Shukla. Extending the supply chain to address sustainability. Journal of Cleaner Production. 2019; 229 ():652-666.
Chicago/Turabian StyleFirouzeh Taghikhah; Alexey Voinov; Nagesh Shukla. 2019. "Extending the supply chain to address sustainability." Journal of Cleaner Production 229, no. : 652-666.
Most of landlocked developing countries (LLDCs) such as Mongolia suffer economically due to their geographical location, lack of access to seaports and underdeveloped infrastructure. Political influences and cross-border delays add to the challenges in which Mongolian firms involved in trade operate. However, recent changes in the political atmosphere of the Northeast Asian region have encouraged firms to conduct trade through advanced logistics designs. This chapter discusses a multi-method simulation approach using Anylogic software as one of the few approaches which can be used to model end-to-end cross-border trade logistics in Mongolia with a view to optimise/improve trade opportunities/operations. Successful implementation of this method could significantly impact the effectiveness of supply chain networks and trade logistics of LLDCs with similar geographical and political attributes.
Nagesh Shukla; Arjun Radhakrishnan. Modelling Trade Logistics Based on Multi-Method Simulation Approach: Case-in-Point: Mongolia. Trade Logistics in Landlocked and Resource Cursed Asian Countries 2019, 125 -154.
AMA StyleNagesh Shukla, Arjun Radhakrishnan. Modelling Trade Logistics Based on Multi-Method Simulation Approach: Case-in-Point: Mongolia. Trade Logistics in Landlocked and Resource Cursed Asian Countries. 2019; ():125-154.
Chicago/Turabian StyleNagesh Shukla; Arjun Radhakrishnan. 2019. "Modelling Trade Logistics Based on Multi-Method Simulation Approach: Case-in-Point: Mongolia." Trade Logistics in Landlocked and Resource Cursed Asian Countries , no. : 125-154.
Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.
Omer Saud Azeez; Biswajeet Pradhan; Helmi Z. M. Shafri; Nagesh Shukla; Chang-Wook Lee; Hossein Mojaddadi Rizeei. Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks. Applied Sciences 2019, 9, 313 .
AMA StyleOmer Saud Azeez, Biswajeet Pradhan, Helmi Z. M. Shafri, Nagesh Shukla, Chang-Wook Lee, Hossein Mojaddadi Rizeei. Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks. Applied Sciences. 2019; 9 (2):313.
Chicago/Turabian StyleOmer Saud Azeez; Biswajeet Pradhan; Helmi Z. M. Shafri; Nagesh Shukla; Chang-Wook Lee; Hossein Mojaddadi Rizeei. 2019. "Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks." Applied Sciences 9, no. 2: 313.
This special issue explores advancements in the next generation manufacturing and service systems by examining the novel methods, practical challenges and opportunities in the use of big data analytics. The selected articles analyse a range of scenarios where big data analytics and its applications were used for improving decision making in manufacturing and services sector such as online data analytics, sourcing decisions with considerations for big data analytics, barriers in the adoption of big data analytics, maintenance planning, and multi-sensor data for fault pattern extraction. The paper summarises the discussions on the use of big data analytics in manufacturing and service sectors.
Nagesh Shukla; Manoj Kumar Tiwari; Ghassan Beydoun. Next generation smart manufacturing and service systems using big data analytics. Computers & Industrial Engineering 2018, 128, 905 -910.
AMA StyleNagesh Shukla, Manoj Kumar Tiwari, Ghassan Beydoun. Next generation smart manufacturing and service systems using big data analytics. Computers & Industrial Engineering. 2018; 128 ():905-910.
Chicago/Turabian StyleNagesh Shukla; Manoj Kumar Tiwari; Ghassan Beydoun. 2018. "Next generation smart manufacturing and service systems using big data analytics." Computers & Industrial Engineering 128, no. : 905-910.
Modern-day logistics companies require increasingly shorter lead-times in order to cater for the increasing popularity of on-demand services. There is consequently an urgent need for fast scheduling algorithms to provide high quality, real-time implementable solutions. In this work we model a spare part delivery problem for an on-demand logistics company, as a variant of vehicle routing problem. For large delivery networks, the optimisation solution technique of column generation has been employed successfully in a variety of vehicle routing settings and is often used in combination with exact methods for solving problems with a large number of variables. Challenges may arise when the pricing subproblem is difficult to solve in a realistic period due to complex constraints or a large number of variables. The problem may become intractable when the network structure varies daily or is known with less certainty over longer period. In such instances, a high quality heuristic solution may be more preferable than an exact solution with excessive run time. We propose an improved version of column generation approach integrating an efficient genetic algorithm to obtain fast and high-quality solutions for a sustainable spare parts delivery problem. More specifically, we propose to retain the traditional column generation iterative framework, with master problem solved exactly, but with pricing subproblem solved using a metaheuristic. Computational results on a real dataset indicate that this approach yields improved solutions compared to the current best-case business-as-usual costs. It also substantially decreases the computational time; allowing for high-quality, tractable solutions to be obtained in few minutes. We propose to strike a balance between the practical and efficient solution aspects of metaheuristic algorithms, and the exact decomposition and iterative aspect of the column generation solution technique.
Michelle Dunbar; Simon Belieres; Nagesh Shukla; Mehrdad Amirghasemi; Pascal Perez; Nishikant Mishra. A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network. Annals of Operations Research 2018, 290, 923 -941.
AMA StyleMichelle Dunbar, Simon Belieres, Nagesh Shukla, Mehrdad Amirghasemi, Pascal Perez, Nishikant Mishra. A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network. Annals of Operations Research. 2018; 290 (1-2):923-941.
Chicago/Turabian StyleMichelle Dunbar; Simon Belieres; Nagesh Shukla; Mehrdad Amirghasemi; Pascal Perez; Nishikant Mishra. 2018. "A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network." Annals of Operations Research 290, no. 1-2: 923-941.
Kannan Govindan; T.C.E. Cheng; Nishikant Mishra; Nagesh Shukla. Big data analytics and application for logistics and supply chain management. Transportation Research Part E: Logistics and Transportation Review 2018, 114, 343 -349.
AMA StyleKannan Govindan, T.C.E. Cheng, Nishikant Mishra, Nagesh Shukla. Big data analytics and application for logistics and supply chain management. Transportation Research Part E: Logistics and Transportation Review. 2018; 114 ():343-349.
Chicago/Turabian StyleKannan Govindan; T.C.E. Cheng; Nishikant Mishra; Nagesh Shukla. 2018. "Big data analytics and application for logistics and supply chain management." Transportation Research Part E: Logistics and Transportation Review 114, no. : 343-349.
A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and survivability. The survivability prediction accuracy of a MLP is improved by using identified patient cohorts as opposed to using raw historical data. Analysis of variable values in each cohort provide better insights into survivability of a particular subgroup of breast cancer patients.
Nagesh Shukla; Markus Hagenbuchner; Khin Than Win; Jack Yang. Breast cancer data analysis for survivability studies and prediction. Computer Methods and Programs in Biomedicine 2018, 155, 199 -208.
AMA StyleNagesh Shukla, Markus Hagenbuchner, Khin Than Win, Jack Yang. Breast cancer data analysis for survivability studies and prediction. Computer Methods and Programs in Biomedicine. 2018; 155 ():199-208.
Chicago/Turabian StyleNagesh Shukla; Markus Hagenbuchner; Khin Than Win; Jack Yang. 2018. "Breast cancer data analysis for survivability studies and prediction." Computer Methods and Programs in Biomedicine 155, no. : 199-208.
In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.
Kalpit Patne; Nagesh Shukla; Senevi Kiridena; Manoj Kumar Tiwari. Solving closed-loop supply chain problems using game theoretic particle swarm optimisation. International Journal of Production Research 2017, 56, 5836 -5853.
AMA StyleKalpit Patne, Nagesh Shukla, Senevi Kiridena, Manoj Kumar Tiwari. Solving closed-loop supply chain problems using game theoretic particle swarm optimisation. International Journal of Production Research. 2017; 56 (17):5836-5853.
Chicago/Turabian StyleKalpit Patne; Nagesh Shukla; Senevi Kiridena; Manoj Kumar Tiwari. 2017. "Solving closed-loop supply chain problems using game theoretic particle swarm optimisation." International Journal of Production Research 56, no. 17: 5836-5853.
Akshit Singh; Nishikant Mishra; Syed Imran Ali; Nagesh Shukla; Ravi Shankar. Cloud computing technology: Reducing carbon footprint in beef supply chain. International Journal of Production Economics 2015, 164, 462 -471.
AMA StyleAkshit Singh, Nishikant Mishra, Syed Imran Ali, Nagesh Shukla, Ravi Shankar. Cloud computing technology: Reducing carbon footprint in beef supply chain. International Journal of Production Economics. 2015; 164 ():462-471.
Chicago/Turabian StyleAkshit Singh; Nishikant Mishra; Syed Imran Ali; Nagesh Shukla; Ravi Shankar. 2015. "Cloud computing technology: Reducing carbon footprint in beef supply chain." International Journal of Production Economics 164, no. : 462-471.
In this paper, the problem of capacity planning under risk from demand and price/cost uncertainty of the finished products is addressed. The deterministic model is extended into a two-stage stochastic model with fixed recourse by means of various expected levels of demand as random. A recourse penalty is also included in the objective for both shortage and surplus in the finished products. The model is analysed to quantify the risk using Markowitz mean-variance model.
Anoop Verma; Nagesh Shukla; Satish Tyagi; Nishikant Mishra. Stochastic modelling and optimisation of multi-plant capacity planning problem. International Journal of Intelligent Engineering Informatics 2014, 2, 139 .
AMA StyleAnoop Verma, Nagesh Shukla, Satish Tyagi, Nishikant Mishra. Stochastic modelling and optimisation of multi-plant capacity planning problem. International Journal of Intelligent Engineering Informatics. 2014; 2 (2/3):139.
Chicago/Turabian StyleAnoop Verma; Nagesh Shukla; Satish Tyagi; Nishikant Mishra. 2014. "Stochastic modelling and optimisation of multi-plant capacity planning problem." International Journal of Intelligent Engineering Informatics 2, no. 2/3: 139.