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, Artificial Intelligence
Heris Golpira, Erfan Babaee Tirkolaee, Reza Maihami, Kajal Karimi
Summary: This paper proposes a novel tri-objective robust MILP model for a two-echelon supply chain configuration considering Vendor-Managed Inventory (VMI) policy and Supply Chain Visibility (SCV) under uncertain demand. The objectives are to maximize total visibility, minimize deficient products, and minimize total cost. The LP-metric is used to handle the tri-objectiveness of the model. The results show that the proposed approach successfully establishes robustness by deteriorating the optimal values of the objective functions as the cost of robustness. A sensitivity analysis reveals that uncertainty has the greatest impact on network cost and deficiency of objective functions. The change in the lower visibility threshold parameter has the greatest effect on the SCV function.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
H. Abdullah Ali Ahmadini, Umar Muhammad Modibbo, Ali Akbar Shaikh, Irfan Ali
Summary: In this study, a multi-objective inventory model with green investment is proposed to address the increasing pressure to conserve the environment from global warming. The model is formulated as a multi-objective fractional programming problem with various objectives and constraints, aiming to provide useful suggestions to decision-makers in the manufacturing sectors.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Palash Sahoo, Dipak Kumar Jana, Sutapa Pramanik, Goutam Panigrahi
Summary: In this paper, an investigation is conducted on an uncertain interval supply chain network model with risk and visibility. The model considers various parameters such as supply chain visibility budget, production capacity, cost of reducing supply risk, cost of enhancing supply chain visibility, demand for each product, minimum order quantity, purchase price, and the impact of supply risk. Two different models, namely the expected value model and the chance-constrained model, are developed using uncertain interval programming techniques. The models are solved using goal programming method and linear weighted method, and a real-life example is included to illustrate the effectiveness of the study.
Article
Engineering, Industrial
Tadeusz Sawik, Bartosz Sawik
Summary: This paper applies stochastic optimisation of CVaR to maintain risk-averse viability and improve resilience of a supply chain under propagated disruptions. Two stochastic optimisation models are developed with conflicting objectives, and a stochastic mixed integer quadratic programming model is used to select a risk-averse viable production trajectory. The proposed approach is applied to smartphone manufacturing, and the findings show that more risk-aversive decision-making leads to a larger viability space and higher resilience of the supply chain. Single-objective decision-making may reduce supply chain viability.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Management
Fernando Lejarza, Michael Baldea
Summary: Improved supply chain optimization strategies are crucial for addressing global food security and safety in the future. Integrated supply chain decision-making frameworks that explicitly consider product quality control are needed, but large-scale optimization problems pose a challenge.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Operations Research & Management Science
Belleh Fontem, Megan Price
Summary: This study focuses on the expected payoff maximization problem for a risk-sensitive broker designing and underwriting option contracts on GBM spot price trajectories, highlighting the importance of trigger price function selection and client firm selection. The findings reveal that the optimal value and trigger price function of the contract increase strictly monotonically with the cost parameter and volatility coefficient in the model, while they decrease strictly monotonically with the GBM's drift coefficient.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Chemistry, Physical
Ahmet Erdogan, Ebru Gecici, Mehmet Guray Guler
Summary: Hydrogen fuel cell vehicle (HFCV) technology is an important alternative to conventional fossil fuel vehicles in the transportation sector. However, the hydrogen supply chain (HSC) infrastructure poses a significant obstacle to their widespread use. This study proposes an HSC design for Turkey that minimizes cost, carbon emissions, and security risks. The problem is modeled using mixed integer linear programming (MILP), and five different optimization cases are studied.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Engineering, Industrial
Vineeth Dharmapalan, William J. O'Brien, Douglas J. Morrice
Summary: This study investigates the effectiveness of using information technologies (IT) for improving visibility in the supply chain through the identification and definition of enabling factors. The survey results indicate that there are structural limits to implementing these enabling factors, despite investments in visibility.
JOURNAL OF MANAGEMENT IN ENGINEERING
(2022)
Article
Management
Tadeusz Sawik
Summary: A multi-portfolio approach and a scenario-based stochastic mixed integer program are developed to enhance the resilience of the supply chain, with a focus on the impact of unit penalty for unfulfilled demand on risk-averse supply portfolios. The findings show that the developed approach leads to a computationally efficient stochastic mixed integer program with a strong LP relaxation.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2021)
Article
Business
Joao Henrique Lopes Guerra, Fernando Bernardi de Souza, Silvio Roberto Ignacio Pires, Anderson Luiz Ribeiro de Sa
Summary: Supply chains are important and complex systems that require effective risk management. This study provides a maturity model to guide companies in improving their supply chain risk management process. The proposed model addresses critical issues and offers practical implications for strengthening and implementing risk management practices.
SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL
(2023)
Article
Green & Sustainable Science & Technology
Lida Safari, Seyed Jafar Sadjadi, Farzad Movahedi Sobhani
Summary: This paper addresses the issue of resilient sustainable supply chain design and planning under supply disruption risk. A multi-objective robust model is developed to solve the problem, considering various decisions related to supply chain design and planning. The proposed resilience strategies are found to be efficient in mitigating supply disruptions and maintaining supply chain sustainability.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Computer Science, Interdisciplinary Applications
Brendan Ruskey, Eric Rosenberg
Summary: In this article, we propose an approach to minimize expected unmet demand in a supply chain using a Bayesian network. We consider node upgrades to reduce the probability of node failure and formulate the problem as a linear binary integer program. Unlike previous formulations, our model allows for flexible conditional probability tables and multiple types of upgrades. We present computational results and discuss the application to a larger food supply chain. We also introduce a preprocessing method to reduce the number of constraints and evaluate the runtime savings achieved.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Industrial
Jafar Namdar, S. Ali Torabi, Navid Sahebjamnia, Ninad Nilkanth Pradhan
Summary: This paper proposes a novel framework for designing a resilient supply chain network to address operational and disruption risks. The framework includes quantifying the resilience score of facilities, identifying critical processes and business continuity metrics, and designing a multi-echelon, multi-product supply chain network model. The model aims to incorporate risk attitudes into the design process and provides useful managerial insights through sensitivity analyses on hypothetical disruptions and risk attitudes.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Economics
Saeid Jafarzadeh-Ghoushchi, Mohammad Asghari, Abbas Mardani, Vladimir Simic, Erfan Babaee Tirkolaee
Summary: Efficient humanitarian supply chain management is crucial in disasters. This study proposes a sustainable location-allocation-inventory problem to optimize fairness, timeliness, economic productivity, and social justice. A scenario-based multi-objective mixed-integer linear programming model is developed to address the problem under uncertainty.
SOCIO-ECONOMIC PLANNING SCIENCES
(2023)
Article
Computer Science, Cybernetics
Yushi Xie, Lina He, Wei Xiang, Zhenxing Peng, Xinguo Ming, Mark Goh
Summary: This paper develops a hybrid method to prioritize risk factors of sustainable supply chain considering sustainable customer requirements and uncertain evaluation. The proposed method integrates fuzzy Kano model and interval-valued intuitionistic fuzzy set theory to translate sustainable customer requirements into risk factors of supply chain. Objective analysis is conducted using IVIF cross-entropy to prioritize risk factors. The proposed method enables the integration of sustainable customer requirements into risk factors management and provides objective importance of risk factors.
Article
Computer Science, Artificial Intelligence
Jing Wang, Congjun Rao, Mark Goh, Xinping Xiao
Summary: This paper proposes a cloud-random forest (C-RF) model combining cloud model and random forest to assess the risk of coronary heart disease (CHD). The empirical analysis shows that the C-RF model performs better than other comparison models in terms of classification accuracy, error rates, and AUC value.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Business
Navid Zarbakhshnia, Kannan Govindan, Devika Kannan, Mark Goh
Summary: This study proposes an innovative method for evaluating sustainable third-party logistics service providers in order to achieve the goals of circular economy and environmental protection.
BUSINESS STRATEGY AND THE ENVIRONMENT
(2023)
Article
Environmental Studies
Wen-Ze Wu, Chong Liu, Wanli Xie, Mark Goh, Tao Zhang
Summary: This study proposes a new method to estimate the dynamic trend of the industrial water-waste-energy system and validates the stability of the model and the rationality of the predicted results through simulation studies.
ENERGY & ENVIRONMENT
(2023)
Article
Environmental Sciences
Qiaoyu Peng, Chuanxu Wang, Mark Goh
Summary: Green financing is an effective means to encourage SMEs to improve environmental efficiency. This paper studies two financing strategies in the e-commerce supply chain, finding that financing can increase profits and environmental benefits. The study also shows that manufacturers' choice of financing strategies is influenced by the commission provided by the platform, and explores the impact of government supervision policies on corporate decision-making.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Education & Educational Research
Wei Wang, Yongyong Zhao, Yenchun Jim Wu, Mark Goh
Summary: This paper examines the effects of text interaction strategies in online learning, specifically focusing on the decision to respond to questions, the identity of respondents, and the topics of textual interaction. The study analyzed a large dataset from the online learning platform iMOOC and found that responding to questions online enhances learning and reduces dropout rates. Peer learning was found to be more beneficial for online learners compared to learning from instructors. Moreover, providing solutions was more effective in reducing dropout rates than encouragement and evaluation. Additionally, code writing was found to be more effective than providing references, encouragement, and normative interpretation. This study contributes to our understanding of the interaction strategies between learners and instructors in online learning platforms and provides insights for improving the online learning experience and retaining learners.
EDUCATION AND INFORMATION TECHNOLOGIES
(2023)
Review
Information Science & Library Science
Wei Wang, Yongyong Zhao, Yenchun Jim Wu, Mark Goh
Summary: This study analyzes the reasons for the high dropout rate in MOOCs from the perspective of learners and platforms, and emphasizes the importance of motivation and interaction in the dropout rate of MOOCs.
Article
Engineering, Multidisciplinary
Jianghua Zhang, Son Duy Dao, Wei Zhang, Mark Goh, Guodong Yu, Yan Jin, Weibo Liu
Summary: This article introduces the self-attention mechanism into scheduling, characterizes job similarities based on the dot-product of processing time matrices, and proposes a new priority rule and heuristic algorithm for makespan minimization. Computational results demonstrate that the new approaches outperform existing ones with a nominal cost of computation time.
ENGINEERING OPTIMIZATION
(2023)
Article
Management
Yongyi Zhou, Yulin Zhang, Mark Goh
Summary: This paper explores how an incumbent platform should respond to the advertisement of a new entrant platform in the local market of mobile service providers. The study's game theoretic results suggest that the incumbent should lower prices in response to a new entrant's intense advertising, despite the potential negative impact on profitability. Additionally, the research reveals that while the mobile rate benefits the incumbent, it does not necessarily harm the entrant. By extending the current model to incorporate substitutability on both sides, different initial utilities, and the incumbent's partial market capture, the study provides managerial insights.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Green & Sustainable Science & Technology
Zhongwei Yu, Yang Lin, Yingming Wang, Mark Goh
Summary: This paper presents a systematic two-stage analysis to improve coordination in a three-echelon closed-loop supply chain under differentiated carbon tax regulation. The study applies non-cooperative and cooperative game theory to explore optimal operations and achieve profit coordination. The results show that adjusting carbon tax rates on different products can effectively reduce emissions and a grand coalition can achieve optimal environmental and economic performance. The proposed Shapley value algorithm incorporating differentiated carbon tax costs improves the utilities of carbon taxpayers and benefits policy formulation and industrial practices.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Environmental Sciences
Yang Lin, Zhongwei Yu, Yingming Wang, Mark Goh
Summary: With the rapid development of electric vehicles (EVs), the scrap tide of EV batteries poses a significant challenge to ecological protection. This article investigates a dual-recycle channel closed-loop supply chain and proposes regulatory solutions for retired EV batteries' recycling. Various scenarios are evaluated based on the Stackelberg game and empirical data, and the results provide insights into the recycling performance and its impact on society, economy, and environment. The findings contribute to policy-making and managerial practices for EV battery recycling.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Computer Science, Cybernetics
Xu Zhang, Mark Goh, Sijun Bai, Zonghan Wang
Summary: This study develops an integrated approach to select risk response strategies for single projects in project portfolios, considering objective adjustments and project interdependencies. The study finds that project portfolio objective adjustments and project interdependencies significantly affect risk response decisions. The results provide robust methodological guidance for SPPP managers to control risks.
Article
Operations Research & Management Science
Ri-Peng Huang, Ze-Shui Xu, Shao-Jian Qu, Xiao-Guang Yang, Mark Goh
Summary: This paper addresses the problem of robust portfolio selection with distributional ambiguity and integer constraint. Unlike previous studies that assume known expected returns of risky assets, the authors define an ambiguity set containing the true probability distribution based on Kullback-Leibler divergence. They also consider the realistic scenario of integer investment amounts for risky assets. By using Fenchel duality, they transform the resulting semi-infinite programming into a convex mixed-integer nonlinear programming problem. The proposed modified generalized Benders decomposition method effectively solves the problem. Back-tests using real market data demonstrate the robustness of the proposed model, making it highly valuable for individual and institutional investors.
JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA
(2023)
Article
Green & Sustainable Science & Technology
Mingyun Gao, Lixin Xia, Qinzi Xiao, Mark Goh
Summary: With the increasing climate disasters, consumers have a growing interest in low-carbon products. This paper analyzes incentive strategies for low-carbon supply chains considering the updating of low-carbon preferences. It describes information updating in low-carbon supply chains and analyzes the response decisions of the supply chain under the given incentive strategies. An optimal model of carbon reduction is designed for the government based on these decisions, and the best incentive strategies are optimized using a heuristic algorithm. The results show that cooperation among profit-driven supply chain members improves both their profits and carbon reduction efficiency.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Environmental Sciences
Congjun Rao, Qifan Huang, Lin Chen, Mark Goh, Zhuo Hu
Summary: The impact of global greenhouse gas emissions is increasingly serious, and the development of green low-carbon circular economy has become an inevitable trend for the development of all countries in the world. To achieve emission peak and carbon neutrality is the primary goal of energy conservation and emission reduction. This paper analyzes the future development trend of carbon emissions in Hubei Province, predicts the emission peak value and carbon peak time, and provides corresponding suggestions on carbon emission reduction.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
