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Resilient design of hyperconnected multiactor Physical Internet supply chain networks

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第0期

作者： Tordecilla, Rafael D. Montoya-Torres, Jairo R. Guerrero, William J. Univ Sabana Sch Engn Campus Puente Comun Chia Cundinamarca Colombia

The Physical Internet (PI) is a relatively new logistics paradigm defined as a supply chain framework whose physical components are standardized and optimized with the main objective of increasing the supply chain's overall efficiency, resilience, and sustainability. Given the novelty of the PI concept, there is a lack of scientific literature addressing it from a quantitative point of view, although formulating and solving mathematical models representing resilient PI problems are relevant and innovative issues for academics, practitioners, and governments. In this work, we present a multiperiod mixed-integer programming model to design PI-enabled supply chain networks, in which both cost and resilience are optimized. Hyperconnection and multiple actors are considered in the proposed models. A lexicographic method is proposed to solve these models with multiple objectives, which includes a modified version of the hypervolume measure. Both newly designed and adapted benchmark instances are employed to assess our models' performance. We compare this model against a traditional proprietary logistics model and a horizontal collaboration model between two companies. Results show that hyperconnectivity increases resilience by 5.5%$5.5\%$ and reduces the supply chain network design costs by 26.4%$26.4\%$. The risk of not satisfying the destination's demands is reduced as well. Furthermore, in the PI context, we propose a minimax model that has been proved to increase cost equity between the considered actors. This model reduces the average difference between the costs of these actors from 77%$77\%$ to 4%$4\%$.

关键词： physical Internet supply chain network design resilience mixed-integer programming

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Inventory placement with carbon cap-and-trade in guaranteed service supply chains

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 193卷

作者： Wang, Yuan Shu, Jia Su, Jingjing Zhen, Lu Univ Elect Sci & Technol China Sch Management & Econ Chengdu Peoples R China Southeast Univ Sch Econ & Management Nanjing Peoples R China

In this paper, we explore the impact of carbon trading on the safety stock placement optimization in multi-echelon supply chains. The carbon emission in each stage of the supply chain is measured through a function of the service time quoted by the stage as a key variable. Adopting the guaranteed service time modelling framework, we develop a safety stock placement model under the carbon cap-and-trade policy to study the complex trade-off among the carbon cap, the carbon price, and the service time. Based on the different relationship between the unit purchasing and selling carbon prices, we derive the tractable formulations of the proposed model using successive mixed-integer programming approximations. A series of observations through the model implementation on a real-world chain data from Willems (2008) are summarized to understand how the carbon cap and price can affect a firm's safety stock placement and carbon emissions.

关键词： Inventory placement Cap-and-trade mixed-integer programming Carbon emission

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Automated deep learning model development based on weight sensitivity and model selection statistics

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CHEMICAL ENGINEERING SCIENCE 2025年 306卷

作者： Yalcin, Damla Deliismail, Ozgun Tuncer, Basak Boy, Onur Can Bayar, Ibrahim Kayar, Gizem Ozpinar, Muratcan Sildir, Hasan Izmir Inst Technol Dept Chem Engn TR-35430 Izmir Turkiye SOCAR Turkey R&D& Innovat Co TR-35800 Izmir Turkiye SOCAR Turkey Refinery & Petrochem Business Unit Refinery & Petrochem Business Unit TR-35800 Izmir Turkiye

Current sustainable production and consumption processes call for technological integration with the realm of computational modeling especially in the form of sophisticated data-driven architectures. Advanced mathematical formulations are essential for deep learning approach to account for revealing patterns under nonlinear and complex interactions to enable better prediction capabilities for subsequent optimization and control tasks. Bayesian Information Criterion and Akaike Information Criterion are introduced as additional constraints to a mixed-integer training problem which employs a parameter sensitivity related objective function, unlike traditional methods which minimize the training error under fixed architecture. The resulting comprehensive optimization formulation is flexible as a simultaneous approach is introduced through algorithmic differentiation to benefit from advanced solvers to handle computational challenges and theoretical issues. Proposed formulation delivers 40% reduction, in architecture with high accuracy. The performance of the approach is compared to fully connected traditional methods on two different case studies from large scale chemical plants.

关键词： mixed-integer programming Deep learning Input selection Weight sensitivity Bayesian Information Criterion Akaike Information Criterion

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A constraint programming approach for resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery

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COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Zeng, Chuangfeng Liu, Jianjun Li, Qinsong Guangdong Univ Technol Sch Electromech Engn State Key Lab Precis Elect Mfg Technol & Equipment Guangdong CIM Prov Key Lab Guangzhou Peoples R China

Production and distribution are both crucial components of supply chains. Integrated production and distribution scheduling (IPDS) in the context of flexible assembly flow shop scheduling and batch delivery problems is often overlooked. A realistic problem inspired by the production and distribution processes of a dishwasher factory can be modeled as a resource-constrained flexible assembly flow shop scheduling problem with batch direct delivery (RCFAFSP-BDD). In this problem, order requirements are decomposed into several production tasks processed at different stages of the workshop, and are then delivered in batches via a third-party logistics provider to regional distributors at various locations. Auxiliary resource restrictions, hierarchical coupling constraints, machine eligibility restrictions and sequence-dependent setup times, are incorporated into the problem as operational constraints. To the best of our knowledge, this is the first attempt to solve this problem. This work formulates a mixed-integer linear programming (MIP) model to minimize total costs, including tardiness, inventory, and delivery costs. Given the problem's strong NP-hard nature, the focus is on developing an efficient solution approach using constraint programming (CP). A CP model is proposed and enhanced with multiple redundant constraints. To reduce runtime, two branching strategies are designed for the CP model. Numerical experiments with varying instance scales reveal that the proposed CP model outperforms the MIP model in accuracy and efficiency within the given time limit. The redundant constraints and search strategy can reduce CP model runtime by up to 263.83%. Compared to manual scheduling at the studied factory, the CP model can cut costs by up to 26.59% for real data, offering viable alternatives for factory planners.

关键词： Integrated production and distribution scheduling Flexible assembly flow shop Batch delivery Auxiliary resource Constraint programming mixed-integer programming

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Optimizing research in Antarctica: a novel approach to project selection and scheduling across multiple stations

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年

作者： Vega-Hidalgo, Mauricio Pradenas, Lorena Parada, Victor Univ Concepcion Dept Ingn Ind Casilla 160-C Correo 3 Concepcion 4070043 Chile Inst Sistemas Complejos Ingn Santiago Chile Univ Santiago Chile Dept Ingn Informat Santiago Chile

Antarctica's unique research environment necessitates innovative project selection and scheduling strategies to maximize scientific output while addressing logistical, environmental, and resource constraints. This study introduces a novel framework for the Research Project Selection and Scheduling in Multiple Antarctic Stations Problem (RPSAP), formulated as a mixed-integer programming model. The model incorporates resource-sharing constraints, station-specific capacities, transportation delays, and sustainability considerations. Given the problem's NP-hard complexity, three metaheuristic methods-Iterated Local Search (ILS), Variable Neighborhood Search (VNS), and Simulated Annealing (SA)-were developed to efficiently solve large-scale instances. Metaheuristics demonstrated robust performance through extensive computational experiments involving 480 test instances across 60 classes. The ILS consistently outperformed others in solution quality and scalability, while SA offered competitive results in execution time. Results reveal that the metaheuristics are superior to exact methods in handling large problem sizes, with optimality achieved only for small instances using the proposed exact model. This research bridges the gap between theoretical optimization models and practical applications, offering decision-making tools for research managers to enhance resource utilization, minimize ecological impacts, and prioritize high-impact projects.

关键词： Project scheduling Project selection Multisite context mixed-integer programming Metaheuristic Antarctic science

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A slope scaling heuristic for the multi-period strategic planning of carbon capture and storage

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COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Homsi, Gabriel Ayotte-Sauve, Etienne Jena, Sanjay Dominik Nat Resources Canada CanmetENERGY 1615 Lionel Boulet Blvd Varennes PQ J3X 1P7 Canada Univ Montreal Dept Comp Sci & Operat Res Montreal PQ Canada Univ Quebec Montreal Sch Management Montreal PQ Canada Ctr Interuniv Rech Reseaux Entreprise Logist & Tra Montreal PQ Canada

Around the world, efforts are currently underway to implement various decarbonization strategies to meet net-zero emissions objectives. This includes carbon capture and storage (CCS), which involves capturing CO(2 )at emitter sites, and transporting it to geological reservoirs, where it is to be injected underground for long-term storage. In this work, we focus on the multi-period strategic planning of a CCS value chain involving pipeline CO2 transportation. From an Operations Research standpoint, this problem exhibits the characteristics of combined facility location and network design. To account for multiple scenarios of input parameters ( e.g. market and geological variability), this problem has to be solved hundreds or thousands of times. Thus, reaching high-quality solutions quickly is crucial. As commercial solvers struggle to provide high quality solutions under these time constraints, we propose a slope scaling heuristic based on previous work on single-period CCS planning and network design. This new heuristic approximates the cost of design variables, generates upper bounds via dynamic programming, uses a long-term memory search strategy, and includes a final improvement phase where a restricted model is solved. Computational experiments show that the proposed heuristic generates better solutions than CPLEX for most instances considered, at a fraction of the computational time.

关键词： Carbon capture and storage Network design Facility location mixed-integer programming Heuristics Slope scaling

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A hybrid constraint programming and cross-entropy approach for balancing U-Shaped disassembly line with flexible workstations and spatial constraints

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2025年 45卷

作者： Zhang, Yu Zhang, Zeqiang Chu, Feng Mammar, Said Southwest Jiaotong Univ Sch Mech Engn Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Southwest Jiaotong Univ Tangshan Inst Tangshan 063000 Peoples R China Univ Paris Saclay Univ Evry Blvd Francois Mitterrand F-91000 Evry France

Disassembly lines are an effective means for the large-scale, industrialized recycling of end-of-life products. Among these, U-shaped disassembly lines are particularly noted for their combination of flexibility and production efficiency. This study addresses the U-shaped disassembly line balancing problem, considering the coexistence of separate stations and spatial limitations within workstations. A mixed-integer nonlinear programming model and a constraint programming model are developed to accurately capture this complex problem. Additionally, a novel hybrid constraint programming with a goal-driven cross-entropy optimization algorithm (CP-GDCE) is introduced. This algorithm combines a multi-objective cross-entropy grouping framework, a constraint programming-based heuristic initialization, a multi-point crossover recombination mechanism, and large neighborhood search techniques, significantly enhancing solution efficiency and accuracy. Extensive benchmarking and experimental validation indicate that the CP-GDCE not only excels in addressing the specific problem of this study but also demonstrates superiority in classic disassembly line balancing issues. In 21 test cases, the CP-GDCE achieved superior hypervolume and inverted generational distance values compared to 11 benchmark algorithms. A practical application using a printer disassembly example shows that the proposed U-shaped configuration is highly flexible and efficient, compatible with both traditional U-shaped and straight disassembly lines. This configuration significantly reduces the total length of the disassembly line, improving space utilization and highlighting its practical potential and advantages.

关键词： U-shaped disassembly line Flexible workstation Spatial limitation mixed-integer programming Constraint programming Cross-entropy method

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Improved linear programming relaxations for flow shop problems with makespan minimization

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COMPUTERS & OPERATIONS RESEARCH 2025年 177卷

作者： Wallrath, Roderich Franke, Meik Walter, Matthias Univ Twente Fac Sci & Technol Sustainable Proc Technol Proc Design & Optimizat Drienerlolaan 5 NL-7522 NB Enschede Netherlands Univ Twente Fac Elect Engn Math & Comp Sci Appl Math Math Operat Res Drienerlolaan 5 NL-7522 NB Enschede Netherlands

Machine scheduling problems with makespan minimization have been addressed in various academic and industrial fields using mixed-integer programming (MIP). Inmost MIP models, however, the makespan variable is poorly linked to the natural date variables of jobs. To address this, we propose novel, strengthening inequalities, derived from the single-machine scheduling polyhedron augmented by a makespan variable. While the associated optimization problem fora single machine is trivial, these inequalities can be applied as cutting planes to more complicated scheduling problems. In this work, we demonstrate their use for non- permutation flow shops. Using the Taillard benchmark set, we analyze the effect of the inequalities on the linear programming relaxations and mixed-integer programs of three commonly used MIP models. The experiments show that the inequalities significantly improve the ability of linear-ordering and time-indexed models to bound the optimum. The positive effect also extends to linear-ordering models with changeover times, demonstrating the potential of these inequalities to improve more general, application-oriented flow shop problems.

关键词： Flow shops Makespan minimization mixed-integer programming Scheduling polytope

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Planning method of droplet fusion scheduling based on mixedinteger programming

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Science China(Technological Sciences) 2024年第1期67卷 157-171页

作者： XIONG JiaCong CHEN JianKui WANG YiXin YIN ZhouPing LI YiQun State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

The emergence of new display devices,such as organic light-emitting diodes,has brought about numerous advantages,including high material utilization,low cost,and high *** devices are manufactured using inkjet printing and possess the potential to become a key technology for display ***,a challenge in achieving this is the display effect that reveals uneven brightness and darkness,which can be avoided by controlling the volume of ink solution in a pixel to within 5%.Currently,the volume difference among the nozzles of commercial printheads does not meet the requirements for volume uniformity,thus challenging the printing ***,designing a suitable printing method that allows for the fusion of different volumes of ink droplets,ultimately reducing the error of the post fusion process,is *** this study,we propose a print display droplet fusion scheduling method comprising two main ***,we use a dichotomous trust domain algorithm to obtain a feasible range of printhead docking point spacings for different nozzle and pixel panel ***,we model the printing process as a droplet fusion scheduling model based on mixed integer programming,with the optimization objective of achieving intra pixel volume uniformity via ensuring the volume uniformity of ink droplets within all *** verified this method through numerical simulations and printing experiments using 394 pixels per inch(ppi)pixel panels and successfully reduced the volume uniformity error among pixels to within 5%.

关键词： printing display droplet fusion mixed-integer programming dichotomous trust domain

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Sequential dynamic resource allocation in multi-beam satellite systems:A learning-based optimization method

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Chinese Journal of Aeronautics 2023年第6期36卷 288-301页

作者： Yixin HUANG Shufan WU Zhankui ZENG Zeyu KANG Zhongcheng MU Hai HUANG School of Aeronautics and Astronautics Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Academy of Spaceflight Technology Shanghai 200240China School of Astronautics Beihang UniversityBeijing 100191China

Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency *** of the primary challenges accompanying with Multi-Beam Satellites(MBS)is an efficient Dynamic Resource Allocation(DRA)*** paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN)algorithm to address the sequential decision-making optimization problem in *** using a parameterized hybrid action space,HADQN makes it possible to schedule the beam pattern and allocate transmitter power more *** pursue multiple long-term QoS requirements,HADQN adopts a multi-objective optimization method to decrease system transmission delay,loss ratio of data packets and power consumption load *** results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.

关键词： Beam hopping Deep reinforcement learning Dynamic resource allocation mixed-integer programming Multi-beam satellite systems Multi-objective optimization

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