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Optimal reactive operation of general topology supply chain and manufacturing networks under disruptions

AICHE JOURNAL 2025年第0期71卷

作者： Ovalle, Daniel Pulsipher, Joshua L. Ye, Yixin Harshbarger, Kyle Bury, Scott Laird, Carl D. Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Univ Waterloo Dept Chem Engn Waterloo ON Canada Dow Chem Co USA Core R&D Lake Jackson TX USA Dow Chem Co USA Supply Chain Innovat Midland MI USA Dow Chem Co USA Core R&D Midland MI USA

Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation, among other factors. In such context, we propose a multiperiod mixed-integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of such disruptions. The model accommodates arbitrary supply chain topologies and incorporates various disruption scenarios, offering adaptability to real-world complexities. A case study from the chemical industry demonstrates the scalability of the model under finer time discretization and explores the influence of disruption types and order management costs on optimal schedules. This approach provides a tractable, adaptable framework for developing responsive operational plans in supply chain and manufacturing networks under uncertainty.

关键词： supply chain optimization disruptions mixed-integer linear programming operation scheduling

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MILP models and effective heuristic for energy-aware parallel machine scheduling with shared manufacturing

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Cheng, Junheng Cheng, Jingya Lin, Yanhong Lu, Shu Wu, Peng Fujian Normal Univ Sch Econ Fuzhou 350117 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350116 Peoples R China

Driven by advancements in information technology and the modernization of the manufacturing industry, shared manufacturing has emerged as a novel industrial organization mode that enables manufacturing enterprises to mutually utilize external idle resources. Concurrently, to meet the requirements of sustainable development, manufacturing enterprises need to balance economic efficiency with energy consumption in their production practices. This study investigates a new energy-aware parallel machine scheduling problem shared manufacturing environments, where production time, cost, and energy consumption vary across different jobs and machines. The objective is to minimize the weighted sum of makespan, total energy consumption, and overall sharing costs. We first formulate the problem as a mixed-integer linear programming (MILP) model. Then the problem is further refined into an improved MILP model with fewer integer variables. Given the strong NP-hard nature of the problem, an effective tailored heuristic (ETH) is developed based on analyzed problem properties to solve large-scale instances. It includes three main phases: pre-processing, job assignment, and local search. Extensive experimental results demonstrate that the improved MILP model significantly outperforms the original one, and the ETH can solve large-sized instances with up to 400 machines and 20000 jobs within one second, with gaps of less than 1%. The proposed MILP models and ETH can assist manufacturing enterprises in making more informed decisions and scheduling resource allocation while considering economic and environmental factors.

关键词： Parallel machine scheduling Shared manufacturing Energy consumption mixed-integer linear programming Heuristic

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An exact method and a heuristic for last-mile delivery drones routing with centralized graph-based airspace control

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COMPUTERS & OPERATIONS RESEARCH 2025年 178卷

作者： Franco, Jorge Luiz Curtis, Vitor Venceslau Senne, Edson Luiz Franca Verri, Filipe Alves Neto Inst Tecnol Aeronaut ITA Comp Sci Div BR-12228900 Sao Jose Dos Campos SP Brazil Univ Sao Paulo Inst Ciencias Matemat & Computacao BR-13566590 Sao Carlos SP Brazil Univ Fed Sao Paulo UNIFESP Inst Ciencia & Tecnol BR-12247014 Sao Jose Dos Campos SP Brazil

The increasing demand for efficient last-mile delivery, driven by the rise of e-commerce, has intensified the need for innovative solutions to manage the complexities of urban logistics. Among the most pressing challenges are the Multi-Agent Pathfinding (MAPF) problem and collision avoidance, both of which are NP- hard and critical for the safe and efficient operation of drones. Collision avoidance is particularly challenging due to the expected high density of drones in future urban environments, making it a problem that remains largely unsolved. Traditional approaches often rely on heuristic and metaheuristic methods to manage these complexities, as large instances are beyond the reach of exact methods. Additionally, distributed relaxations to these problems can lead to suboptimal outcomes and highlights the need for a more centralized and controlled solution. This research adopts a graph-based representation of the delivery area, transforming the centralized Last-Mile Delivery Drones (LMDD) problem into a network flow optimization problem. We propose two graph-based novelty methods in LMDD, a purely exact, NP-hard mixed integer linear programming (MILP) solution that is evaluated against a heuristic. The complexity of the heuristic is bounded by O(1.5), where represents the number of permits and is the number of drones. In contrast, the complexity of the MILP model root is approximated by O(75.2522 ), making it intractable for larger instances. The findings from simulations indicate that the graph-based heuristic effectively balances computational efficiency and operational reliability, making it a viable solution for real-world LMDD applications, where large instances and practical execution times are required. This research significantly contributes to the fields of drone logistics and transportation by providing a scalable method for optimizing LMDD paths.

关键词： Optimization Heuristic Logistics Multi-agent systems Graphs Network flow Drones mixed-integer linear programming

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Resilient pharmaceutical supply chains: Assessment of stochastic optimization strategies for process uncertainty integration in network design problems

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COMPUTERS & CHEMICAL ENGINEERING 2025年 195卷

作者： Sarkis, Miriam Shah, Nilay Papathanasiou, Maria M. Imperial Coll London Sargent Ctr Proc Syst Engn London SW72BB England Imperial Coll London Dept Surg & Canc London SW72BB England

In recent years, the market boom of next-generation therapies and vaccines has pressured the pharmaceutical industry to rapidly scaleup capacity to meet societal needs. Manufacturers catering for these markets reported shortages due to unforeseen demand trends and a crucial uncertainty in capabilities of platforms still under development. In this work, we present an optimization-simulation framework for the design of resilient supply chains to manufacturing uncertainty. Given previously quantified probability distributions of process parameters, we formulate stochastic optimization problems integrating process uncertainty via a sampling-based methodology. Stochastic programming results in networks of higher optimal costs compared to deterministic approaches. Furthermore, stochastic designs ensure product supply meets target demands under simulated uncertainty and result in a larger probability of achieving lower costs per dose. The optimization- simulation framework is used to test solution stability for a varying number of optimization scenarios, highlighting that the minimum number of samples to guarantee stability is problem-specific, thus motivating the investigation of scenario reduction techniques to ensure stability of scenario sets a priori. Overall, the cost-supply benefits of integrating manufacturing uncertainty are quantified, demonstrating the scope for its consideration in strategic planning problems in the sector.

关键词： Supply chain optimization mixed-integer linear programming Stochastic programming Manufacturing uncertainty Monte Carlo Sampling

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Generalized Optimal Scheduling of Cluster Tools With Reentrance and Residency Time Constraints

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第1期55卷 307-318页

作者： Li, Xin Educ Univ Hong Kong Dept Math & Informat Technol Hong Kong Peoples R China

Cluster tools have been widely used in the semiconductor industry. In previous work, singe-arm and dual-arm cluster tools are dealt with separately. When considering residency time constraints, algorithms for scheduling robot moving sequences are mainly developed based on backward and swap strategies, respectively. How about the performance of other robot moving sequences is still an open problem. The present work addresses general conditions by considering all feasible robot moving sequences-beyond backward- and swap-based strategies. In addition, both dual-arm and single-arm cluster tools with reentrance are handled simultaneously. To solve this, the problem is first divided into two parts-the basic one without reentrance and reentrant operations. Based on detailed analysis of all available robot operations in a cluster tool, mixed-integer programming formulations are developed for both parts. Subsequently, both illustrative examples and randomly generated instances are tested to validate the efficiency of the proposed approach. Results of one example show improvement comparing to previous work. Another two examples-not scheduled previously-are scheduled well by the proposed approach. Meanwhile, the flexibility of operations obtained by the proposed model is demonstrated by an example with six processing modules and 12 stages in a dual-arm cluster tool. Additionally, more randomly generated instances are tested more to validate the proposed approach and analyze computation time.

关键词： Generalized formulation mixed-integer linear programming reentrance residency time single-arm and dual-arm cluster tools

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Strategic optimization and design of cost-effective and sustainable hydrogen supply chain networks - Qatar case study

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ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Amjath, Mohamed Eljack, Fadwa Haouari, Mohamed Qatar Univ Chem Engn Dept Coll Engn POB 2713 Doha Qatar Qatar Univ Coll Engn Dept Mech & Ind Engn POB 2713 Doha Qatar

This study introduces a multi-period integrated optimization model for designing a strategic hydrogen supply chain (HSC) network, concentrating on the post-production stages of conditioning, storage, transportation, and post-conditioning. Qatar serves as the case study for evaluating three HSC pathways-ammonia (as a hydrogen carrier), liquefied hydrogen, and compressed hydrogen-across pre-conditioning, storage, shipping, and post- conditioning stages. The optimization framework spans a 20-year plan, supporting strategic, long-term hydrogen export infrastructure planning. Economic and environmental factors are incorporated to analyze HSC performance under various scenarios, accounting for realistic constraints, such as investment limits and emission caps. Key findings reveal trade-offs between pathways and design strategies that must account for balancing costs with environmental impacts. Results indicate that the ammonia pathway is preferred in scenarios without emission penalties but becomes less favorable with increased penalties, shifting preference toward the liquified hydrogen pathway. With stringent emission limits, short- and mid-range markets are prioritized, underscoring the importance of emissions-conscious strategies. This study demonstrates the utility of optimization tools in balancing economic and environmental objectives, offering policymakers and industry stakeholders a robust framework for developing sustainable and efficient HSC networks.

关键词： Hydrogen Supply chain management Optimization mixed-integer linear programming Hydrogen economy

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mixed-integer linear programming model for solving reconfiguration problems in large-scale distribution systems

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ELECTRIC POWER SYSTEMS RESEARCH 2012年 88卷 137-145页

作者： Llorens-Iborra, Francisco Riquelme-Santos, Jesus Romero-Ramos, Esther Univ Cadiz Dept Elect Engn Cadiz Spain Univ Seville Dept Elect Engn Seville Spain

An improved mixed-integer linear program for determining the tree of minimum active power losses in balanced large medium voltage systems is presented and assessed throughout this paper. The proposed approach depends on a number of parameters, which can be tuned for each specific system, ensuring a desired level of accuracy in relation to the final obtained solution. Eight systems were tested with the new algorithm to highlight the efficiency and accuracy of the proposed model. The model is mainly designed for large systems with a high number of switches whose statuses need to be determined. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Distribution networks Loss minimisation Network reconfiguration mixed-integer linear programming

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More Efficient Energy Management for Hybrid AC/DC Microgrids With Hard Constrained Neural Network-Based Conversion Loss Surrogate Models

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IEEE TRANSACTIONS ON SMART GRID 2024年第3期15卷 2534-2552页

作者： Liu, Xuan Wang, Yuanze Liu, Xiong Zhao, Tianyang Wang, Peng Jinan Univ Energy & Elect Res Ctr Zhuhai 510630 Guangdong Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Conversion loss modeling plays a crucial role in hybrid AC/DC microgrid (MG) energy management (EM). However, accurate calculation of the conversion losses is often very costly. Additionally, existing surrogate models typically rely on fixed-voltage DC buses, leading to excessive voltage magnitudes. To overcome these limitations, we propose surrogate models based on piecewise linear neural networks (NNs) that estimate conversion losses using converter power and variable-voltage DC buses. These NNs employs the Rectified linear Unit (ReLU) activation function, allowing reformulation into linear constraints. During NN training, additional hard constraints are enforced via the augmented Lagrangian method to enhance feasibility. To limit the computational cost of the reformulated NN constraints, weights sparsity and ReLU stability regularizations are applied to the NN training process. The efficient energy management is implemented as a two-stage stochastic unit commitment (UC) problem with integer recourse. To solve this UC problem, we propose a finite iteration convergent Benders decomposition algorithm with several acceleration techniques, which reduce the computational burden significantly. Our case study demonstrates that: 1) the proposed surrogate model accurately approximates conversion losses while satisfying hard constraints, 2) optimizing the DC bus voltage leads to more efficient system operation, energy savings, and better overall performance, and 3) combining the NN weight optimization with Benders decomposition acceleration techniques significantly reduces the computational associated with the NN surrogates.

关键词： Hybrid AC/DC microgrids power conversion losses linear surrogate models variable DC bus voltage control neural networks mixed-integer linear programming

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Multi-objective optimization of a hydrogen supply chain network: Wind and solid biomass as primary energy sources for the public transport in Sicily

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ENERGY CONVERSION AND MANAGEMENT 2024年 314卷

作者： Cutore, E. Volpe, R. Alriols, M. Gonzalez Antxustegi, M. M. Fichera, A. Univ Catania Dept Elect Elect & Comp Engn Catania Italy Univ Basque Country Chem & Environm Engn Dept San Sebastian Spain

Europe's heavy reliance on diesel power for nearly half of its railway lines, for both goods and passengers, has significant implications for carbon emissions. To address this challenge, the European Union advocates for a shift towards hydrogen-based mobility, necessitating the development of robust and cost-effective hydrogen supply chains at regional and national levels. Leveraging renewable energy sources such as wind farms and solid biomass could foster the transition to sustainable hydrogen-based transportation. In this study, a mixed-integer linear programming approach integrated with an external heavy-duty refueling station analysis model is employed to address the optimal design of a new hydrogen supply chain. Through multi-objective optimization, this study aimed to minimize the overall daily costs and emissions of the supply chain. By applying the model to a case study in Sicily, different scenarios with varying supply chain configurations and wind curtailment factors were explored. The findings revealed that increasing the wind curtailment factor from 1% to 2% led to reductions of 12% and 15% in the total daily emission costs and network costs, respectively. Additionally, centralized biomass-based plants dominated hydrogen production, accounting for 96% and 94% of the total production under 1% and 2% wind curtailment factors, respectively. Furthermore, transporting gaseous hydrogen via tube trailers proved more cost effective than using tanker trucks for liquid hydrogen when compressed gaseous hydrogen is required at the dispenser of forecourt refueling stations. Finally, the breakdown of the levelized cost for the hydrogen refuelling station strongly depends on the form of hydrogen received at the gate, namely, liquid or gaseous. Specifically, for the former, the dispenser accounts for 60% of the total cost, whereas for the latter, the compressor is responsible for 58% of the total cost. This study highlights the importance of preliminary and quantitativ

关键词： Renewable hydrogen Multi-objective optimization Design mixed-integer linear programming Public transport Wind Biomass

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A robust LP-based approach for a dynamic surgical case scheduling problem with sterilisation constraints

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第16期62卷 5925-5944页

作者： Al Hasan, H. Gueret, C. Lemoine, D. Rivreau, D. UCO LARIS Angers France Univ Angers LARIS SFR MATHST Angers France IMT Atlantique LS2N Nantes France UCO LARIS 44 Rue Rabelais F-49000 Angers France

The purpose of this article is to investigate a practical scheduling problem in which a group of elective surgical cases are scheduled over time, while considering their unpredictable durations and potential delays in the sterilisation of surgical instruments. The primary objectives were to schedule the maximum number of surgeries and decrease overtime for the surgical staff, as well as limit the number of instruments requiring emergency sterilisation. The study was conducted in collaboration with the University Hospital of Angers in France, which also contributed historical data for the experiments. We propose two robust mixed integer linear programming models, which are then solved iteratively through a rolling horizon approach, in which the objective functions are taken into account in lexicographic order. Experiments on randomly generated instances indicated which of the two approaches had better performance. Comparison of the results for a real-world scenario involving actual planning at the hospital indicated a greater than 69% decrease in overtime, and a minimum of 92% fewer stressful situations in the sterilising unit.

关键词： Operations research elective surgeries scheduling mixed-integer linear programming rolling horizon approach robust scheduling relax-and-fix heuristic

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