Article
Operations Research & Management Science
Rasul Esmaeilbeigi, Richard Middleton, Rodolfo Garcia-Flores, Mojtaba Heydar
Summary: Designing a value-creating whey recovery network is crucial in the dairy industry, where raw whey is processed into commercial products rather than disposed of. This research presents a hierarchical facility location problem with two levels of facilities and utilizes two-stage stochastic programming to address uncertainty. The proposed methodology outperformed a commercial solver by an order of magnitude, providing optimal solutions for real case studies with 51 cheese makers and emphasizing the importance of considering uncertainties in the dairy industry.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Management
Mohammad Jeihoonian, Masoumeh Kazemi Zanjani, Michel Gendreau
Summary: Motivated by the recovery of modular-structured products, this study proposes a flexible design model for a reverse supply chain (RSC) that considers the uncertain behavior of product returns. The model is decomposed into smaller scenario cluster submodels and coordinated using a Lagrangian-progressive hedging-based method. Computational results based on a realistic case demonstrate the superiority of the proposed model and the efficiency of the solution approach.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Soumya Ranjan Pathy, Hamed Rahimian
Summary: Motivated by the disruptions in the global supply chain due to COVID-19, this study investigates the optimal procurement and inventory decisions in a pharmaceutical supply chain with uncertain and spatiotemporal demand. A two-stage optimization framework is proposed to address demand uncertainty, where the first stage minimizes the cost and risk associated with pre-positioning drugs, and the second stage minimizes the cost of recourse decisions. The study considers different risk preferences and proposes two models, stochastic programming and robust optimization, to capture the risk of demand uncertainty. Efficient algorithms are also proposed for solving these models.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Engineering, Chemical
Congqin Ge, Lifeng Zhang, Zhihong Yuan
Summary: This paper proposes a hybrid stochastic and distributionally robust optimization approach to tackle uncertainty and disruptions in the closed-loop supply chain network. By customizing an algorithm, large-scale mixed integer linear programming problems can be solved efficiently. Computational experiments demonstrate the advantages of this approach in terms of costs and variances.
Article
Engineering, Chemical
Ariel A. Boucheikhchoukh, Christopher L. E. Swartz, Eric Bouveresse, Pierre Lutran, Anna Robert
Summary: Uncertainty in refinery planning poses challenges to the day-to-day operations of an oil refinery. Stochastic programming framework can incorporate parameter uncertainty and provide robust solutions, which is more effective than deterministic modeling techniques.
Article
Green & Sustainable Science & Technology
Lily Poursoltan, Seyed-Mohammad Seyed-Hosseini, Armin Jabbarzadeh
Summary: This study introduces a green closed-loop supply chain framework for ventilators, addressing environmental sustainability and carbon emissions constraints, using a stochastic optimization model with strategic and tactical decision making. The proposed model is applied to a case study of Iranian medical ventilator production to analyze the impact of carbon emissions and demand variations on the optimal solution in the COVID-19 pandemic context. Sensitivity analyses show the managerial dimensions of the proposed model under the COVID-19 pandemic.
Article
Management
Penghui Guo, Jianjun Zhu
Summary: This study develops two-stage stochastic models that incorporate prepositioning, physical capacity reservation, and production capacity reservation for reactive procurement. By minimizing the supply-side monetary costs and the demand-side social impacts, the models aim to reduce costs and improve efficiency. The logic-based Benders decomposition method is used, and a new type of logic-based subgradient cut is introduced. Extensive numerical results and a case study validate the efficiency of the solution method, the value of incorporating stochasticity, and the superiority of the capacity reservation.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Operations Research & Management Science
Jyotirmoy Dalal, Halit Uster
Summary: This study addresses the uncertainties in relief logistics caused by evacuation activities in response to natural disasters. By developing a robust optimization model, the research proposes a threshold time window for relief distribution, taking into account the interaction between evacuation and supply activities. The model provides decision makers with flexibility and can assist in efficient relief distribution.
TRANSPORTATION SCIENCE
(2021)
Article
Management
Jesus A. Rodriguez, Miguel F. Anjos, Pascal Cote, Guy Desaulniers
Summary: The maintenance scheduling problem for hydroelectric generators involves uncertainty in water flows and nonlinearity in hydroelectric production, solved using a two-stage stochastic program and parallelized Benders decomposition algorithm. By approximating hydroelectric production with linear inequalities and indicator variables, tailoring and testing various acceleration techniques successfully sped up the algorithm fourfold. Industrial results confirm high scalability of the parallelized Benders implementation in various scenarios.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Hongtao Hu, Shuyuan Guo, Yichen Qin, Wenjin Lin
Summary: This paper investigates the supply chain network design problem and risk mitigation strategy in the context of uncertain supply disruption risk in the aircraft manufacturing industry. A two-stage stochastic programming model is formulated to optimize decisions for supplier selection, strategic partnership establishment, and quantities of production and transportation under uncertain supply disruptions. An improved Benders decomposition algorithm incorporating heuristic searching mechanism is designed to enhance computational efficiency. The computational results demonstrate the effectiveness of the proposed algorithm in terms of accuracy and convergence efficiency, providing practical values to the aircraft manufacturing industry in managing supply risk.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Management
Niels van der Laan, Ward Romeijnders
Summary: We propose a new solution method for two-stage mixed-integer recourse models that can handle general mixed-integer variables in both stages. Our method is based on Benders' decomposition, where we iteratively construct tighter approximations of the expected second stage cost function using a new family of optimality cuts derived from extended formulations of the second stage problems. We show convergence of our method by proving that the optimality cuts recover the convex envelope of the expected second stage cost function. Finally, we demonstrate the potential of our approach through numerical experiments on investment planning and capacity expansion problems.
OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Engineering, Industrial
Junhyeok Lee, Changseong Ko, Ilkyeong Moon
Summary: This paper presents a study on designing e-commerce supply chain networks considering on-demand warehousing and decisions for commitment periods. The proposed two-stage stochastic programming model captures inherent uncertainties and formulates the problem effectively. The developed method, which generates effective initial cuts for improving the convergence speed of the Benders decomposition algorithm, shows significant cost-saving effects when an on-demand warehousing system is used.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Management
Xavier Blanchot, Francois Clautiaux, Boris Detienne, Aurelien Froger, Manuel Ruiz
Summary: This paper presents a new exact algorithm for solving two-stage stochastic linear programs. The algorithm, based on the multicut Benders reformulation, divides the subproblems into batches and solves only a small proportion of them in each iteration. A general framework is proposed to stabilize the algorithm, and its finite convergence and exact behavior are demonstrated. Computational experiments on large-scale stochastic optimization instances show the efficiency of the proposed algorithm compared to nine alternative algorithms in the literature. Additional computational time savings are obtained using primal stabilization schemes.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Mahboobeh Peymankar, Morteza Davari, Mohammad Ranjbar
Summary: This paper discusses the maximization of expected net present value of a project under uncertain cash flows using discrete scenarios. It proposes two ILP formulations and two-stage stochastic programming approaches, utilizing Benders decomposition, to address the problem efficiently. The computational results demonstrate that the developed Benders-based methods outperform the ILP formulations.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Industrial
Sanjoy Kumar Paul, Md. Abdul Moktadir, Karam Sallam, Tsan-Ming Choi, Ripon Kumar Chakrabortty
Summary: This study develops a new recovery planning optimisation model for managing the impacts of the recent COVID-19 outbreak on online business operations. The model considers dynamic and uncertain situations and uses mathematical models and differential evolution technique to generate recovery plans.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Sanjoy Kumar Paul, Priyabrata Chowdhury, Md. Tarek Chowdhury, Ripon Kumar Chakrabortty, Md. Abdul Moktadir
Summary: This study systematically investigates and ranks the operational challenges caused by COVID-19. The findings provide practical implications for practitioners in understanding these challenges and formulating long-term strategies.
INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT
(2023)
Article
Business
Udukumburage Shalinda Kusal De Silva, Ananna Paul, Kazi Wahadul Hasan, Sanjoy Kumar Paul, Syed Mithun Ali, Ripon Kumar Chakrabortty
Summary: Managing supply chain risks is essential for the long-term sustainability of any organization or industry. This study focuses on the spice industry in Sri Lanka and uses two popular multi-criteria decision-making techniques (AHP and TOPSIS) to assess the risks and derive mitigation strategies.
INTERNATIONAL JOURNAL OF EMERGING MARKETS
(2023)
Article
Operations Research & Management Science
Ashish Dwivedi, Dindayal Agrawal, Sanjoy Kumar Paul, Saurabh Pratap
Summary: This study analyzes the Blockchain Readiness Challenges (BRCs) for the implementation of the Product Recovery System (PRS) in the manufacturing industry. By observing 20 readiness challenges and using a Multi-Criteria Decision-Making approach, the study prioritizes these challenges and identifies inadequate financing, lack of governance and standards, and security challenges as the most influential barriers to adopting blockchain in PRS.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Management
Milad Darzi Ramandi, Morteza Khakzar Bafruei, Amir H. Ansaripoor, Sanjoy Kumar Paul, Md Maruf Hossan Chowdhury
Summary: This study examines the impact of government intervention on replenishment decisions, transportation, and safety factor in the supply chain. The findings show that government constraints on greenhouse gas emissions result in increased shortage costs for the buyer. To increase profitability, the study proposes two-part tariff and cost-sharing contracts.
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
(2023)
Article
Engineering, Industrial
Humyun Fuad Rahman, Ripon K. Chakrabortty, Sanjoy Kumar Paul, Sondoss Elsawah
Summary: This study investigates the vaccine distribution problem in Canberra, Australia by using bi-objective mathematical models that consider uncertainties and disruptions. The findings will aid decision-makers in streamlining the COVID-19 vaccine supply chains.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS
(2023)
Article
Computer Science, Artificial Intelligence
Kazi Wahadul Hasan, Syed Mithun Ali, Sanjoy Kumar Paul, Golam Kabir
Summary: The benefits of the circular economy are driving industries to form closed-loop supply chains (CLSCs) that minimize cost and environmental impact. However, disruptions in the production process hinder the attainment of these objectives. This study develops a complex mathematical model to minimize total cost, energy consumption, CO2 emissions, and waste generation by considering disruption risks. Three existing heuristics and an updated hyper-heuristic algorithm are employed to compare their efficiency and effectiveness. The results show that CLSCs can mitigate production shortages and reduce costs, energy consumption, CO2 emissions, and waste generation.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Tazim Ahmed, Chitra Lekha Karmaker, Sumaiya Benta Nasir, Md. Abdul Moktadir, Sanjoy Kumar Paul
Summary: The COVID-19 pandemic has disrupted manufacturing activities in emerging economies, impacting their global supply chains. To survive and thrive in the post-COVID era, adopting artificial intelligence (AI) technologies to revamp traditional manufacturing activities is crucial. Industry 5.0 and AI offer the potential to build a resilient and sustainable digital future. This research aims to identify and evaluate the AI-based imperatives of Industry 5.0 to improve supply chain resilience using an integrated and intelligent approach. Real-time tracking of supply chain activities using IoT is found to be the most crucial AI-based imperative for manufacturing supply chain survivability. The research findings can assist industry leaders and practitioners in dealing with the impacts of large-scale supply chain disruptions in the post-COVID era.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Geosciences, Multidisciplinary
Anchal Patil, Vipulesh Shardeo, Ashish Dwivedi, Sanjoy Kumar Paul
Summary: Digital Supply Chains (DSCs) are reshaping industries across domains by improving coordination, data collection, funding mechanisms, and operational performance. However, constraints like funding and infrastructure hinder DSC adoption. This study proposes a framework to digitally transform the Humanitarian Supply Chain (HSC) in the post COVID-19 era, identifying drivers for digitalization and prioritizing them through analysis.
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION
(2023)
Article
Management
Ashish Dwivedi, Priyabrata Chowdhury, Sanjoy Kumar Paul, Dindayal Agrawal
Summary: This study develops a mix-method approach to analyze factors for sustaining Circular Economy (CE) practices during a global disruption and identifies continued stakeholder pressure, retention of CE and sustainability culture, continued implementation of cleaner technology, feedback system, and ongoing CE training for resilience issues as the top five factors for sustaining CE practices during a global disruption.
INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Ashish Dwivedi, Priyabrata Chowdhury, Dindayal Agrawal, Sanjoy Kumar Paul, Yangyan Shi
Summary: This study aims to identify and analyze the factors and conditions that lead to the implementation of digital supply chain (DSC) for transitioning toward circular economy (CE). The study found that advanced information sharing arrangement, effective government policies, and digitalizing the supply chains are the top three potential antecedents of DSC for CE.
INDUSTRIAL MANAGEMENT & DATA SYSTEMS
(2023)
Article
Engineering, Industrial
Towfique Rahman, Sanjoy Kumar Paul, Renu Agarwal, Nagesh Shukla, Firouzeh Taghikhah
Summary: The COVID-19 pandemic has revealed the vulnerability of global supply chains and the need for more resilient and viable strategies. Panic-buying poses a major challenge for supply chains as it leads to unpredictable surges in demand. This study aims to identify and model recovery strategies to increase supply chain agility, resilience, and survivability, and to reduce the impact of panic-buying during disruptions.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Naveen Virmani, Vaishali Agarwal, Koppiahraj Karuppiah, Satakshi Agarwal, Rakesh D. Raut, Sanjoy Kumar Paul
Summary: The world is tackling significant environmental challenges, particularly in the transportation sector. Electric vehicles (EVs) have emerged as a critical solution, but there are numerous barriers to their adoption. This research assesses the critical barriers and effective mitigation strategies for adopting EVs using a quantitative approach. The study identifies the major barriers to be the high purchase price and scarce charging stations for EVs, while government policies, support, and strategic planning are deemed the most effective mitigation strategies. This study provides crucial insights for professionals, executives, and researchers seeking EVs adoption.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Engineering, Environmental
Majid Alipanah, Hongyue Jin, Qiang Zhou, Caitlin Barboza, David Gazzo, Vicki Thompson, Yoshiko Fujita, Jiangping Liu, Andre Anderko, David Reed
Summary: Recycling spent lithium-ion batteries is crucial for mitigating supply risks and protecting the environment. This study successfully optimized bioleaching conditions through thermodynamic modeling and experimental design, demonstrating its feasibility and economic competitiveness.
RESOURCES CONSERVATION AND RECYCLING
(2023)
Article
Engineering, Industrial
Meenu Singh, Sunil Kumar Jauhar, Millie Pant, Sanjoy Kumar Paul
Summary: The demand for ventilators has increased during the COVID-19 pandemic, and Indian nonmedical equipment companies have risen to meet this demand. 3PRLPs play a crucial role in the reverse logistics activities of the healthcare industry. This study proposes a two-phase hybrid decision-making problem for selecting and allocating orders to 3PRLPs. The results show that the proposed framework can be successfully implemented in the current scenario of the healthcare industry.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
