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
Computer Science, Interdisciplinary Applications
Seyed Mohammad Gholami-Zanjani, Mohammad Saeed Jabalameli, Mir Saman Pishvaee
Summary: A novel bi-objective stochastic model is developed in this research for meat inventory planning, with two resiliency strategies embedded. The results indicate that resilient solutions can improve network performance and are sensitive to throughput capacity and lead-time. Trade-off interactions between the objectives provide managerial insights.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Meimei Zheng, Ningxin Du, Hui Zhao, Edward Huang, Kan Wu
Summary: In response to the increasing competition in the pharmaceutical industry, this study focuses on improving the efficiency of clinical trial supply chains to reduce drug supplying costs. The research investigates inventory levels and allocation problems, proposing a stochastic mixed-integer model and algorithm for optimization. Several structural results and strategies are derived to enhance the effectiveness of the process.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Computer Science, Artificial Intelligence
Amirhossein Moadab, Ghazale Kordi, Mohammad Mahdi Paydar, Ali Divsalar, Mostafa Hajiaghaei-Keshteli
Summary: Effective supply chain management is crucial for economic growth and sustainability is a key consideration for large companies. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact, and environmental impact using a scenario-based approach. A real-life case study is conducted to validate the model, and sensitivity analyses are performed to analyze the behavior of the developed Mixed-Integer Linear Programming.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Management
Tadeusz Sawik
Summary: This paper presents a stochastic mixed integer programming model for optimizing supply chain reshoring under the ripple effect from a foreign disruption source region. The proposed approach integrates strategic reshoring and operational scheduling, allowing for evaluation of the operational impact. Results from computational experiments in the smartphone industry indicate the reliance on government subsidies and the potential benefits of reshoring for meeting domestic market demand and mitigating the ripple effect. However, for risk-averse decision-making, reshoring is not selected if it cannot reduce worst-case costs and lost sales.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2023)
Article
Green & Sustainable Science & Technology
Florencia Lujan Garcia-Castro, Ruben Ruiz-Femenia, Raquel Salcedo-Diaz, Jose A. Caballero
Summary: This paper aims to improve the modeling of supply chain designs by considering correlated uncertainty among multiple parameters. A new methodology is presented to generate forecasts for historically correlated time series and applied to energy and carbon prices. These scenarios are then used to obtain a three-echelon supply chain design in Europe.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Jaber Valizadeh, Shadi Boloukifar, Sepehr Soltani, Ehsan Jabalbarezi Hookerd, Farzaneh Fouladi, Anastasia Andreevna Rushchtc, Bo Du, Jun Shen
Summary: The study presents a solution for the challenges in the vaccine supply chain through a robust optimization model, considering various costs and risks in the public vaccination program. Numerical experiments based on the vaccine supply chain in Kermanshah, Iran, show that the proposed model significantly reduces mortality risk, inequality in vaccine distribution, and total cost.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
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
Computer Science, Interdisciplinary Applications
Vanessa Simard, Mikael Ronnqvist, Luc Lebel, Nadia Lehoux
Summary: The objective of this study is to evaluate the benefits of including coordination mechanisms in a forest supply chain to better face yield uncertainty. A stochastic program is developed to simulate a sawmill production planning decision process, and six coordination mechanisms are proposed to reduce the impact of uncertain wood supply. The results show that the Free Supply with Free Demand mechanism generates more stable plans with reduced transportation cost and the volume of extra resources needed.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Razieh Mousavi, Amirhossein Salehi-Amiri, Ali Zahedi, Mostafa Hajiaghaei-Keshteli
Summary: The study introduces a bi-objective and sustainable blood supply chain network considering the social and environmental factors of blood decomposition, with uncertainties also taken into account. The findings suggest that an increase in social factors typically leads to higher costs, thus blood supply chain managers must adjust their goals and scope to achieve optimal societal outcomes.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Chemical
Congqin Ge, Lifeng Zhang, Wenhui Yang, Zhihong Yuan
Summary: Traditional supply chains are becoming more volatile, leading to an increasing adoption of mobile modularization. This allows for flexible capacity adjustments and facility relocations to tackle market volatility. A mixed-integer linear programming model is proposed for closed-loop supply chain network planning with modular distribution and collection facilities. The model is further extended to incorporate uncertain customer demands and recovery rates, and is efficiently solved using a tailored stochastic dynamic dual integer programming approach. Computational experiments demonstrate the effectiveness of the proposed algorithm and the benefits of mobile modules in high temporal and spatial variability of customer demand.
Article
Engineering, Industrial
Amir Azaron, Uday Venkatadri, Alireza Farhang Doost
Summary: In this study, a multi-objective two-stage stochastic programming model is developed to address various decision-making aspects within a supply chain network. By utilizing the epsilon-constraint method to generate a set of Pareto optimal solutions, treating uncertain parameters as continuous random variables, and employing the SAA scheme for near optimal solutions, the efficiency of the proposed solution methodology is demonstrated through computational studies involving hypothetical and real supply chain networks of different sizes.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Engineering, Industrial
Mohammad Fattahi, Kannan Govindan, Mehdi Farhadkhani
Summary: This paper explores the design and planning of a supply chain system for power generation from biomass. By developing a two-stage stochastic programming model, the study analyzes various factors affecting the system, including social and environmental aspects. Through social life cycle assessment (S-LCA), the social impact is quantified and sustainability is improved by reducing environmental risks. The research findings demonstrate the effectiveness of the stochastic model in assessing economic potential and guiding the planning of the supply chain system.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Maria Aranguren, Krystel K. Castillo-Villar, Mario Aboytes-Ojeda
Summary: Biomass is a sustainable alternative to fossil fuels and efficient design of biomass networks is crucial. Hub-and-spoke networks are proposed to design large scale biomass supply chains. Variations in weather affect biomass yield, causing supply fluctuations and creating complex large-scale problems.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Engineering, Industrial
Tadeusz Sawik
Summary: “This paper presents a novel quantitative approach and stochastic quadratic optimisation model to maintain supply chain viability under the ripple effect. Instead of viability kernel commonly used in the viability theory, this paper establishes the boundaries on acceptable production states for which the production can be continued under the ripple effect, with no severe losses. The findings indicate that for the extreme values of the weight factor, the viable production trajectory is inclined toward the corresponding boundary trajectory and remains in-between the two boundaries, when both objectives are equally important. Keeping production trajectory in-between the two boundaries makes the supply chain more resilient to disruption risks, while the supply chain resilience diminishes as the production trajectory approaches a boundary trajectory. Then a more severe disruption may push the production outside the viability region and cause greater losses.”
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Rafael Praxedes, Teobaldo Bulhoes, Anand Subramanian, Eduardo Uchoa
Summary: The Vehicle Routing Problem with Simultaneous Pickup and Delivery is a classical optimization problem that aims to determine the least-cost routes while meeting pickup and delivery demands and vehicle capacity constraints. In this study, a unified algorithm is proposed to solve multiple variants of the problem, and extensive computational experiments are conducted to evaluate the algorithm's performance.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ragheb Rahmaniani, Teodor Gabriel Crainic, Michel Gendreau, Walter Rei
Summary: Benders decomposition (BD) is a popular solution algorithm for stochastic integer programs. However, existing parallelization methods often suffer from inefficiencies. This paper proposes an asynchronous parallel BD method and demonstrates its effectiveness through numerical studies and performance enhancement strategies.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaowen Yao, Chao Tang, Hao Zhang, Songhuan Wu, Lijun Wei, Qiang Liu
Summary: This paper examines the problem of two-dimensional irregular multiple-size bin packing and proposes a solution that utilizes an iteratively doubling binary search algorithm to find the optimal bin combination, and further optimizes the result through an overlap minimization approach.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Decheng Wang, Ruiyou Zhang, Bin Qiu, Wenpeng Chen, Xiaolan Xie
Summary: Consideration of driver-related constraints, such as mandatory work break, in vehicle scheduling and routing is crucial for safety driving and protecting the interests of drivers. This paper addresses the drop-and-pull container drayage problem with flexible assignment of work break, proposing a mixed-integer programming model and an algorithm for solving realistic-sized instances. Experimental results show the effectiveness of the proposed algorithm in handling vehicle scheduling and routing with work break assignment.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Summary: This research provides a novel probabilistic perspective on the manipulation of hidden Markov model inferences through corrupted data, highlighting the weaknesses of such models under adversarial activity and emphasizing the need for robustification techniques to ensure their security.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Davood Zaman Farsa, Shahryar Rahnamayan, Azam Asilian Bidgoli, H. R. Tizhoosh
Summary: This paper proposes a multi-objective evolutionary framework for compressing feature vectors using deep autoencoders. The framework achieves high classification accuracy and efficient image representation through a bi-level optimization scheme. Experimental results demonstrate the effectiveness and efficiency of the proposed framework in image processing tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Matthew E. Scherer, Raymond R. Hill, Brian J. Lunday, Bruce A. Cox, Edward D. White
Summary: This paper discusses instance generation methods for the multidemand multidimensional knapsack problem and introduces a primal problem instance generator (PPIG) to address feasibility issues in current instance generation methods.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Yin Yuan, Shukai Li, Lixing Yang, Ziyou Gao
Summary: This paper investigates the design of real-time train regulation strategies for urban rail networks to reduce train deviations and passenger waiting times. A mixed-integer nonlinear programming (MINLP) model is used and an efficient iterative optimization (IO) approach is proposed to address the complexity. The generalized Benders decomposition (GBD) technique is also incorporated. Numerical experiments show the effectiveness and computational efficiency of the proposed method.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xinghai Guo, Netirith Narthsirinth, Weidan Zhang, Yuzhen Hu
Summary: This study proposes a bi-level scheduling method that utilizes unmanned surface vehicles for container transportation. By formulating mission decision and path control models, efficient container transshipment and path planning are achieved. Experimental results demonstrate the effectiveness of the proposed approach in guiding unmanned surface vehicles to complete container transshipment tasks.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Review
Computer Science, Interdisciplinary Applications
Jose-Fernando Camacho-Vallejo, Carlos Corpus, Juan G. Villegas
Summary: This study aims to review the published papers on implementing metaheuristics for solving bilevel problems and performs a bibliometric analysis to track the evolution of this topic. The study provides a detailed description of the components of the proposed metaheuristics and analyzes the common combinations of these components. Additionally, the study provides a detailed classification of how crucial bilevel aspects of the problem are handled in the metaheuristics, along with a discussion of interesting findings.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Xudong Diao, Meng Qiu, Gangyan Xu
Summary: In this study, an optimization model for the design of an electric vehicle-based express service network is proposed, considering limited recharging resources and power management. The proposed method is validated through computational experiments on realistic instances.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Ramon Piedra-de-la-Cuadra, Francisco A. Ortega
Summary: This study proposes a procedure to select candidate sites optimally for ensuring energy autonomy and reinforced service coverage for electric vehicles, while considering demand and budget restrictions.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Danny Blom, Christopher Hojny, Bart Smeulders
Summary: This paper focuses on a robust variant of the kidney exchange program problem with recourse, and proposes a cutting plane method for solving the attacker-defender subproblem. The results show a significant improvement in running time compared to the state-of-the-art, and the method can solve previously unsolved instances. Additionally, a new practical policy for recourse is proposed and its tractability for small to mid-size kidney exchange programs is demonstrated.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Anqi Li, Congying Han, Tiande Guo, Bonan Li
Summary: This study proposes a general framework for designing linear programming instances based on the preset optimal solution, and validates the effectiveness of the framework through experiments.
COMPUTERS & OPERATIONS RESEARCH
(2024)