Article
Mathematics
Zaher Abusaq, Muhammad Salman Habib, Adeel Shehzad, Mohammad Kanan, Ramiz Assaf
Summary: This study proposes a decision support system for optimizing the biomass-based wood pellet production supply chain network design. The system aims to minimize the total supply chain cost and carbon emissions while considering the fuzzy nature of objective parameters. The fuzzy flexible robust possibilistic programming technique is developed to solve the uncertain model and achieve robust decisions within the wood pellet supply chain.
Article
Green & Sustainable Science & Technology
Aixia Chen, Yankui Liu
Summary: This study addresses the optimization challenge of multi-period, multi-feedstock, and multi-technology biomass-based power generation supply chain planning problem with uncertain parameters and conflicting objectives. A novel globalized robust goal programming model is proposed to balance economic, environmental, and social goals, and the tractable counterpart of the model is obtained as mixed-integer linear programming. A case study on the design of a sustainable biomass-based power generation supply chain in Hubei Province, China, demonstrates the effectiveness of the proposed model.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Interdisciplinary Applications
Xinghao Yan
Summary: This study examines the role of order quantity in supply chains in the presence of demand and/or supply uncertainty. It is found that order quantity serves as a tool for the buyer to manage potential supply shortage or surplus caused by uncertainties. The decision makers utilize order quantity differently to hedge against various uncertainties. The analysis also reveals the impact of order quantity on coordinating contract design and identifies the challenges in designing quantity-based coordinating contracts under supply uncertainty.
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
Management
Qi Lin, Qiuhong Zhao, Benjamin Lev
Summary: This paper examines production and procurement decisions in influenza vaccine supply chains, highlighting the inefficiency that exists in both centralized and decentralized systems. A procurement strategy is proposed to improve coordination in the supply chain, with the aim of enhancing efficiency.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Seyyed Jalaladdin Hosseini Dehshiri, Maghsoud Amiri
Summary: The integration of Circular Economy (CE) principles in Supply Chain (SC) is crucial for sustainable competitive advantage, but faces challenges of uncertainty and long-term decision-making in Closed-Loop Supply Chain Network Design (CLSCND). This study proposes a scenario-based possibilistic-stochastic programming approach to simultaneously consider cognitive and random uncertainties while achieving CE goals.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Huili Pei, Hongliang Li, Yankui Liu
Summary: This paper addresses the issue of demand uncertainty in dual-channel supply chain, proposing a novel uncertainty distribution set to model ambiguous demand distribution and developing a distributionally robust bilevel optimization framework for capital-constrained scenarios. Different financing strategies and their impact on manufacturers are investigated, revealing that demand ambiguity and equity ratio can influence the manufacturer's equilibrium financing strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Environmental Sciences
Sourena Rahmani, Alireza Goli
Summary: The excessive consumption of fossil fuels has led to environmental damage, prompting the global community to search for a suitable alternative. Biodiesel, a clean and eco-friendly fuel, has emerged as one viable option. To promote mass-level production of biodiesel, a sustainable supply chain network is necessary. This study proposes a mathematical model and scenario-based robust optimization approach to design such a network, resulting in achievable and efficient production and distribution of biodiesel fuel.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Social Sciences, Interdisciplinary
Dasheng Wu, Feng Chen
Summary: This paper develops a distributionally robust optimization model to factor in the retailer's overconfidence when dealing with the inventory problem with supply uncertainty. The analysis shows that overconfidence leads to higher or lower order quantities depending on the profit conditions. The research also highlights the asymmetry of the pull-to-center effect and characterizes the performance of overconfidence in terms of expected profits.
Article
Operations Research & Management Science
Runyan Cui, Min Zhang, Shaoyun Zhang, Songtao Zhang
Summary: This study investigates a cost optimization strategy and a robust control strategy to maintain low cost and stability in a dynamic supply chain system. The findings suggest that by reducing the interference of uncertainties and lead time on the system operation, it is possible to achieve stable operation at low cost.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Environmental Sciences
Zeinab Asadi, Mohammad Valipour Khatir, Mojtaba Rahimi
Summary: This study designs a green-responsive closed-loop supply chain network and employs a multi-objective robust possibilistic programming model to handle uncertainty. By developing a hybrid solution approach, the complex research problem is effectively solved. The results demonstrate the efficiency of the proposed model and method, and the impact of critical parameters is explored through sensitivity analysis.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Hashem Omrani, Meisam Shamsi, Ali Emrouznejad, Tamara Teplova
Summary: Conventional Data Envelopment Analysis (DEA) lacks the ability to evaluate Decision-Making Units (DMUs) in vast industries like banks with only one type of efficiency and uncertain data. In this paper, a multi-objective DEA model is proposed to calculate three types of efficiencies for bank branches under uncertain data. The model employs a modified DEA model, a robust approach to handle uncertainty, and a fuzzy programming method to convert the multi-objective model into a single-objective one. The results from a real case study of 45 Agriculture bank branches in Iran validate the accuracy of the proposed model and enable a comparative analysis to identify benchmark and inefficient branches.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Management
Xianpei Hong, Yimeng He, Pin Zhou, Jiguang Chen
Summary: Contract farming is crucial for protecting small farmers from market volatility and has experienced rapid growth. However, previous studies have overlooked the sharing of demand information by for-profit companies engaged in contract farming.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Ramesh Krishnan, K. Arshinder, Renu Agarwal
Summary: This study presents an integrated robust multi-objective optimization model for designing a sustainable food supply chain (FSC). The model considers economic, social, and environmental sustainability dimensions, as well as perishability, food waste valorization, and supply uncertainty. Applied to a real-time case of the Indian mango pulp supply chain, the model provides insights for transforming the FSC towards sustainability.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
M. S. Al-Ashhab
Summary: This research aimed to develop a multi-objective MILP mathematical model for the design and planning of closed-loop supply chain networks (CLSCN) in order to tackle the challenges posed by global crises such as COVID-19 pandemic and the Russian-Ukrainian war. The model considered the uncertainty in both the supplying capacity of raw materials and the return rate of used products, with the goal of maximizing total profit, minimizing total cost, and maximizing overall customer service level (OCSL) using the e-lexicographic procedure.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Operations Research & Management Science
Marc Goerigkl, Adam Kasperski, Pawel Zielinski
Summary: This paper discusses a class of combinatorial optimization problems where a feasible solution can be constructed in two stages, using the minmax regret criterion. The general properties of the problem are established, with specific results shown for the shortest path and selection problems.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Theory & Methods
Adam Kasperski, Pawel Zielinski
Summary: This paper discusses a class of uncertain optimization problems, where unknown parameters are modeled by fuzzy intervals. Known concepts of robustness and light robustness for traditional interval uncertainty representation can be generalized to optimize solutions against plausible parameter realizations under this possibilistic setting. Solutions can be efficiently computed for linear programming problems with fuzzy parameters, making them not much computationally harder than their deterministic counterparts.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Marc Goerigk, Adam Kasperski, Pawel Zielinski
Summary: This paper discusses a class of robust two-stage combinatorial optimization problems, showing that the robust two-stage versions of basic network optimization and selection problems are NP-hard even in very restrictive cases. The paper constructs some exact and approximation algorithms for the general problem, as well as polynomial and approximation algorithms for robust two-stage versions of basic problems such as selection and shortest path problems.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2022)
Article
Engineering, Industrial
Damien Lovato, Romain Guillaume, Caroline Thierry, Olga Battaia
Summary: In this study, a rescheduling problem in paced aircraft assembly lines was addressed using constraint programming. The newly proposed optimization criteria outperformed classic criteria in terms of solution time and quality, as demonstrated through experiments and managerial insights. This approach provided rapid solutions to managers in efficiently rescheduling tasks with specific constraints.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Adam Kasperski, Pawel Zielinski
Summary: This paper discusses a production planning problem with inventory and backordering levels. It models the uncertainty in cumulative demands using an interval uncertainty representation with continuous budget and applies the robust minmax criterion to compute an optimal production plan. A row and column generation algorithm is constructed to solve the problem. Computational tests demonstrate the efficiency of the algorithm for instances with up to 100 periods and its ability to generate solutions that are robust against demand uncertainty.
VIETNAM JOURNAL OF COMPUTER SCIENCE
(2022)
Article
Computer Science, Theory & Methods
Romain Guillaume, Adam Kasperski, Pawel Zielinski
Summary: This paper discusses optimization problems with uncertain linear constraints. The constraint coefficients are assumed to be random vectors with partially known probability distributions. Imprecise probabilities are modeled using possibility theory. The distributionally robust approach is used to transform the imprecise constraints into deterministic counterparts, making the resulting problem computationally tractable for a wide class of optimization models, particularly for linear programming.
FUZZY SETS AND SYSTEMS
(2023)
Article
Operations Research & Management Science
Romain Guillaume, Adam Kasperski, Pawel Zielinski
Summary: This paper examines a robust inventory problem with uncertain cumulative demands, where interval-budgeted uncertainty sets are used to model possible demand scenarios. The study demonstrates that for discrete budgeted uncertainty, the robust min-max problem can be solved in polynomial time. Conversely, for continuous budgeted uncertainty, the problem is weakly NP-hard but can still be solved in pseudopolynomial time, particularly for nonoverlapping cumulative demand intervals, using an FPTAS.
OPTIMIZATION LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Marc Goerigk, Romain Guillaume, Adam Kasperski, Pawel Zielinski
Summary: This paper investigates an optimization problem with uncertain objective function coefficients. The uncertainty is described by a discrete scenario set. The concept of belief function is used to define admissible probability distributions over the scenario set. The generalized Hurwicz criterion is applied to compute a solution. The complexity of the problem is explored and exact and approximation methods are proposed.
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
(2023)
Proceedings Paper
Automation & Control Systems
D. Lovato, R. Guillaume, C. Thierry, O. Battaia
Summary: The importance of ergonomics in assembly lines is increasing, as its study helps improve the working environment and quality of life for operators. Research on ergonomics in aircraft assembly lines has identified tasks that increase the risk of musculoskeletal disorders. To assist industrial managers in reducing these risks, a novel ergonomic index for task scheduling has been developed and tested on real-life instances of aircraft assembly lines.
Proceedings Paper
Management
Romain Guillaume, Adam Kasperski, Pawel Zielinski
Summary: This paper examines a class of optimization problems with uncertain objective function coefficients, using a scenario set and mass function to specify additional knowledge, and utilizing the generalized Hurwicz criterion to calculate solutions. Various computational properties of the resulting optimization problem are presented.
OPERATIONS RESEARCH PROCEEDINGS 2021
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Florence Dupin de Saint-Cyr, Romain Guillaume
Summary: BLF, a bipolar structure, expresses knowledge about decisions through ranked decision principles based on utility of consequences, allowing comparison of decisions under incomplete knowledge. BLF returns a vector of utility/disutility for a decision in terms of achieving positive/negative goals, enabling comparison of decisions. The uncertain knowledge aggregation by BLF is linked to classical aggregation functions used in decision under uncertainty and multi-criteria approaches. The bipolar scale of BLF allows independent handling of positive and negative goals from both optimistic and pessimistic viewpoints.
IEEE CIS INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS 2021 (FUZZ-IEEE)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Romain Guillaume, Adam Kasperski, Pawel Zielinski
Summary: This paper discusses a class of optimization problems with uncertain constraint coefficients using possibility distributions to encode a family of probability distributions. The distributionally robust approach is applied to transform imprecise constraints into crisp counterparts, with an extension of the model taking into account individual risk aversion of decision makers.
IEEE CIS INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS 2021 (FUZZ-IEEE)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Romain Guillaume, Adam Kasperski, Pawel Zielinski
Summary: This paper discusses a linear optimization problem with uncertain objective function coefficients modeled by possibility distributions and applies a fuzzy robust optimization framework to compute a solution. By considering the dependencies between objective coefficients using a family of copula functions, it is shown that this new approach limits the conservatism of fuzzy robust optimization, evaluates possibility distributions for the objective function values more accurately, and does not increase the complexity of the problem.
IEEE CIS INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS 2021 (FUZZ-IEEE)
(2021)
