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Quantifying performance indicators in perishable food supply chain networks: Assessing dynamic performance under ripple effects

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 201卷

作者： Karanam, Malleswari Krishnanand, Lanka Manupati, Vijaya Kumar NIT Warangal Dept Mech Engn Warangal Telangana India Indian Inst Management Mumbai Operat & Supply Chain Div Mumbai India

Developing an efficient and resilient perishable food supply chain network (PFSCN) is crucial for cost-effective and timely delivery from suppliers to deficit and surplus regions under disturbed situations. In this paper, we formulated a multi-objective mixed-integer linear programming (MILP) model for a perishable food supply chain (PFSC) under static and dynamic conditions, with the objective of minimizing total cost (TC) and total time (TT) while considering several realistic constraints. As the problem is computationally complex and non-polynomial deterministic (NP-hard), an evolutionary algorithm-based approach is considered. To tackle the problem, first, we considered a static condition and proposed a modified non-dominated sorting genetic algorithm (MNSGA-II) and then compared it to a non-dominated sorting genetic algorithm (NSGA-II). Later, for dynamic conditions, a heuristic method based on the degree of disruption (DOD) and node threshold (NT) has been proposed for estimating the impact of the ripple effect and its impact on PFSCN. Furthermore, the performance characteristics such as TC, TT, Service Level (SL), robustness, resilience, adaptability, efficiency, and centralization were quantified by varying the DOD and NT across various scenarios. To validate the method, extensive computer experiments are conducted, including a case study and sensitivity analysis. Finally, the results reveal that the proposed method outperforms almost all instances.

关键词： Perishable food supply chain network Meta-heuristic algorithms Ripple effect mixed integer linear programming Disruption

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Simultaneous planning of standpoints and routing for laser scanning of buildings with network redundancy

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2025年 224卷 59-74页

作者： Knechtel, Julius Dehbi, Youness Klingbeil, Lasse Haunert, Jan-Henrik Univ Bonn Inst Geodesy & Geoinformat Meckenheimer Allee 172 D-53115 Bonn Germany HafenCity Univ Computat Methods Lab Henning Voscherau Pl 1 D-20457 Hamburg Germany

Stop-and-go laser scanning is becoming increasingly prevalent in a variety of applications, e.g., the survey of the built environment. For this, a surveyor needs to select a set of standpoints as well as the route between them. This choice, however, has a high impact on both the economic efficiency of the respective survey as well as the completeness, accuracy, and subsequent registrability of the resulting point cloud. Assuming a set of building footprints as input, this article proposes a one-step optimization method to find the minimal number of selected standpoints based on scanner-related constraints. At the same time, we incorporate the length of the shortest route connecting the standpoints in the objective function. A local search method to speed up the time for solving the corresponding mixed-integer linear Program (MILP) is additionally presented. The results for different scenarios show constantly shorter routes in comparison to existing approaches while still maintaining the minimal number of standpoints. Moreover, in our formulation we aim to minimize the effects of inaccuracies in the software-based registration. Inspired by the ideas of network survivability, we to this end propose a novel definition of connectivity tailored for laser scanning networks. On this basis, we enforce redundancy for the registration network of the survey. To prove the applicability of our formulation, we applied it to a large real-world scenario. This paves the way for the future use of fully automatic autonomous systems to provide a complete and high-quality model of the underlying building scenery.

关键词： Laser scanning Network survivability Optimization Redundancy mixed integer linear programming Route planning

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Techno-economic pathway for green hydrogen adoption in thermal applications across US small and medium manufacturing sectors

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 98卷 295-307页

作者： Paudel, Anup Choi, Jun-Ki Univ Dayton Dept Mech Engn Dayton OH 45409 USA Univ Dayton Renewable & Clean Energy Program Dayton OH 45409 USA

This study presents an optimization framework for designing a renewable hydrogen production system to meet the annual thermal energy demand of US small and medium-sized manufacturers (SMMs). The proposed system integrates solar photovoltaics, battery storage, and an electrolyser and the use of a mixed-integer linear programming model to determine the optimal sizing of components to minimize system cost while satisfying hydrogen demand under varying natural gas replacement scenarios. The optimization results indicate that increased PV and battery capacities are required for higher shares of hydrogen, while the size of the electrolyser remains constant within each industry. The economic analysis reveals significant cost escalation at higher hydrogen shares, with photovoltaics contributing more than half of the net present cost. Although a carbon pricing mechanism could help make the proposed system economically feasible, a cost analysis indicates that very high emission prices would be required to render hydrogen economically competitive with natural gas. The study thus recommends the use of targeted incentives and renewable grid integration to meet the challenges of transitioning SMMs to hydrogen and accelerate decarbonization.

关键词： Handling Editor: J. W. Sheffield Green hydrogen Natural gas replacement mixed integer linear programming Carbon price

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Maximizing the active power flow controllability in a distribution system with optimum SOP placement

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 244卷

作者： Sezgin, Mustafa Erdem Uzunalioglu, Aysenur Deveci, Umur Gol, Murat METU Elect & Elect Eng Dept Ankara Turkiye R&D Bogaz Elekt Dagitim AS BEDAS Istanbul Turkiye Novator Energy Ankara Turkiye

The classical distribution system has recently begun evolving with the introduction of new assets, such as distributed energy resources and electric vehicles. Although these technologies help nations reduce their greenhouse gas emissions, their adoption at the distribution level presents challenges for system operators. For example, distributed generation can raise network voltage, and uneven electric vehicle use across neighborhoods can lead to feeder imbalance. Soft open points (SOPs) offer a potential solution to these issues without compromising radial operation. However, given their high investment costs, determining their optimal placement in the network is crucial. This paper proposes a linearized approach for optimal SOP placement, designed to maximize network controllability by analyzing the distribution system's behavior and solving the problem using mixed-integer linear programming. The proposed approach can be applied to ring-designed but radial-operated systems by considering various open branches. The strategy is tested on both the IEEE 33-bus test system and the METU distribution system. Using this approach, optimal SOP locations can be determined for all possible radial operation scenarios in a ring-designed system.

关键词： Soft open points Distribution network Power flow control Optimum placement mixed integer linear programming

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Human-centric optimisation in manual picker-to-parts systems: a holistic energy expenditure approach

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第7期63卷 2507-2525页

作者： Lucchese, Andrea Digiesi, Salvatore Sgarbossa, Fabio Polytech Univ Bari Dept Mech Math & Management ViaEdoardo Orabona 4 I-70125 Bari Italy Norwegian Univ Sci & Technol Dept Mech & Ind Engn Trondheim Norway

Despite the increasing automation trends in current Industry 5.0 scenarios, human operators remain a key resource in manual warehouse operations thanks to their flexibility and cost-effectiveness. Ensuring the well-being of workers in manual picker-to-parts systems remains a crucial challenge. Existing methodologies tend to overlook or inadequately consider the energy expenditure that can be provided by workers during manual picking activities, emphasising the need for a more comprehensive approach. In this work, a novel methodology to quantify the working allowable energy expenditure rate (WAEER) of workers is proposed. The approach considers individual (age, sex, body weight) as well as more holistic features such as lifestyle and non-working daily activities to tailor workloads in executing manual picking tasks. By adopting mixed integer linear programming (MILP), the proposed approach optimises the assignment of picking items by minimising workers' fatigue or by avoiding it through rest allowances. Results obtained highlight the effectiveness of the approach as a planning tool, ensuring the workers' well-being. Future research directions include extending the methodology to hybrid warehouse scenarios or assembly lines. The present article contributes to a forward-thinking framework for human-centric energy-based optimisation within Industry 5.0.

关键词： Manual order picking Rest Allowance ageing workforce lifestyle Total Energy Expenditure mixed integer linear programming

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Pickup and delivery problem with electric vehicles and time windows considering queues

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 167卷

作者： Zhou, Saiqi Zhang, Dezhi Yuan, Wen Wang, Zhenjie Zhou, Likun Bell, Michael G. H. Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Province Peoples R China Shanghai Steel Union Logist Co Ltd Shanghai 200441 Peoples R China Univ Sydney Inst Transport & Logist Studies Sydney NSW 2006 Australia

The electric vehicle, as a green and sustainable technology, has gained tremendous development and application recently in the logistics distribution system. However, the increasing workload and limited infrastructure capacity pose challenges for electric vehicles in the pickup and delivery operating system, including task allocation, electric vehicle routing, and queue scheduling. To address these issues, this paper introduces a pickup and delivery problem with electric vehicles and time windows considering queues, which considers queue scheduling for multiple electric vehicles when operating at the same site. A novel mixed integer linear programming model is proposed to minimize the cost of travel distance and queue time. An adaptive hybrid neighborhood search algorithm is developed to solve the moderately large-scale problem. Experimental results demonstrate the effectiveness of the model and adaptive hybrid neighborhood search algorithm. The competitive performance of the developed algorithm further confirmed by finding 9 new best solutions for the pickup and delivery problem with electric vehicles and time windows benchmark instances. Moreover, the results and sensitivity analysis of objective weight costs highlight the impact and importance of considering queues in the studied problem and obtain some management insights.

关键词： Pickup and delivery problem Electric vehicle Queue scheduling mixed integer linear programming Adaptive hybrid neighborhood search

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Optimization of the future hydrogen supply chain for China: A new design of hydrogen storage and transportation

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 105卷 701-718页

作者： Yu, Zijian Wu, Xuantao Wei, Baohuan Sun, Yuhan Wang, Hui ShanghaiTech Univ Sch Phys Sci & Technol Shanghai 201210 Peoples R China ShanghaiTech Univ Inst Carbon Neutral Shanghai 201210 Peoples R China Shanghai Inst Cleantech Innovat Shanghai 201616 Peoples R China

Hydrogen as a clean energy can be stored, delivered, and distributed on a large scale in the form of high-pressure gaseous hydrogen, liquid hydrogen, organic liquids, etc., has emerged as the most promising energy option to address energy and environmental problems. As the hydrogen technology matures, the biggest problem facing the development of the hydrogen energy industry is the constraints caused by the inadequate hydrogen energy infrastructure. Improving the inadequacy of the existing hydrogen infrastructure and design a new and complete hydrogen supply chain (HSC) has become an urgent problem. In this paper, we propose a new model for designing a HSC in China using a mixed integer linear programming approach. This model introduces organic liquids as an alternative option for hydrogen transportation and storage, and incorporates the dehydrogenation process into the HSC. Based on the energy endowment of different regions in China, we optimize the model with three single-objective functions (total daily costs, daily CO2 emissions, and total daily costs with carbon tax) and two multi-objective functions (epsilon-constraint method and weighted summation method), taking into account the cost and carbon emissions. The possible states of the hydrogen supply chain network in China are predicted, as well as the impact of carbon tax changes on the HSC design is explored. The results indicate that the hydrogen cost is between 3.854 and 8.121 $/kg. When considering the carbon tax and CO2 emissions, hydrogen is transported and stored in the form of organic liquid, suggesting that incorporating organic liquid can effectively reduce the total carbon emissions generated by HSC. The HSC is significantly impacted by the carbon tax, therefore, a reasonable carbon tax policy is crucial to the design of HSC.

关键词： Hydrogen supply chain Organic liquid Dehydrogenation process Carbon tax mixed integer linear programming Multi-objective optimization

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Multi objective optimization of human-robot collaboration: A case study in aerospace assembly line

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COMPUTERS & OPERATIONS RESEARCH 2025年 174卷

作者： Hemono, Pierre Chabane, Ahmed Nait Sahnoun, M'hammed eXcent 101 Rue Henri Gautier F-44550 Montoir de Bretagne Pays de la Loir France CESI LINEACT 24 PaquebotCS 60133 F-44600 St Nazaire Pays de la Loir France CESI LINEACT 80 Ave Edmund Halley Rouen Madrillet Innovat F-76800 St Etienne Du Rouvray France

Collaborative robotics is becoming increasingly prevalent in industry 5.0, leading to a growing need to improve interactions and collaborations between humans and robots. However, the current approach to defining the sharing of responsibilities between humans and robots is empirical and uses the robot as an active fixture of parts, which is a sub-optimal method for establishing efficient collaboration. This article focuses on optimizing human-robot collaboration on an assembly line within the aerospace industry based on a real-world use case. The methodology adopted in this research entails employing the multi-objective optimization (MOO) method to effectively tackle both the reduction of makespan and the mitigation of working difficulty. Two techniques have been compared for implementation: the weighted sum and the epsilon-constraint methods, which allow the generation of solutions addressing multiple objectives simultaneously. The results offer chief robotics officers a new tool to design collaboration patterns between humans and robots, with practical implications for real industrial applications. This solution produces several results, including improving company competitiveness and productivity, while maintaining the central role of humans within the company and improving its well-being.

关键词： Multi-objective optimization Human-robot collaboration Working difficulty level Flexible manufacturing systems Flexible job-shop problem mixed integer linear programming

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Multi-Mobile-Robot Transport and Production Integrated System Optimization

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 7480-7491页

作者： Zhao, Ziyan Li, Xingyang Liu, Shixin Zhou, Mengchu Yang, Xiaochun Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110169 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Northeastern Univ Software Coll Shenyang 110169 Peoples R China

A production workshop with mobile robots can be considered as a hybrid system consisting of a production system and a transportation one. Mobile robots are responsible for transferring production tasks among the machines of a production system and constitute a multi-robot transport system. It is highly coupled with a production system because of the interdependency that exists between production scheduling and mobile robot assignment. In this work, we study their integrated optimization problem for a mobile robot-based job shop with blocking properties. Its aim is to minimize total completion time as an objective function to improve overall operational efficiency. We consider the speed of a robot that varies according to whether it is loaded or not. We formulate this new problem into a mixed integer linear program to provide an algebraic description. Then, we propose a constraint programming method to solve it with high efficiency. The superiority of constraint programming over mixed integer linear programming in terms of the number of variables and constraints is analyzed. Numerous experiments on benchmark examples show that constraint programming can well handle the concerned problem. Under a one-hour time limit, it can exactly solve its instances while mixed integer linear programming cannot. Under a one-minute time limit, it obtains much better solutions than mixed integer linear programming and heuristic strategies, thus implying its high potential to be put into industrial applications.

关键词： Robots Mobile robots Job shop scheduling Constraint handling Task analysis Production systems Optimization Blocking job shop scheduling transfer robot multi-agent system mixed integer linear programming constraint programming

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Sizing of energy storage for spinning reserve and efficiency increase in isolated power systems within a data-driven stochastic unit commitment framework

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JOURNAL OF ENERGY STORAGE 2025年 111卷

作者： Adeyemo, Ayotunde A. Marra, Francesco Tedeschi, Elisabetta Norwegian Univ Sci & Technol NTNU Dept Elect Energy N-7034 Trondheim Norway Equinor Res Ctr Arkitekt Ebbells Veg 10 N-7053 Ranheim Norway Univ Trento Dept Ind Engn Via Sommar 9 I-38123 Trento Italy

This paper proposes a data-driven stochastic unit commitment (SUC) framework for sizing battery energy storage system (BESS) for spinning reserve and efficiency increase in isolated power system. BESS is used to provide spinning reserve to manage the uncertainties in the power system. Additionally, load-shifting demand response (DR) is incorporated in the data-driven SUC model to reduce the size of the BESS required. Also, battery degradation is modelled in the data-driven SUC formulation to make the results as accurate as possible. Furthermore, to reduce the computational burden a novel scenario reduction method based on the Kantorovich distance metric is proposed for selecting scenarios (from the high number of generated scenarios for load and renewable sources) that describe the problem with limited loss of information. The case study is based on an offshore oil and gas platform in the North Sea which has four identical gas turbine generators and is assumed to integrate offshore wind turbines and an offshore solar farm, thus having three sources of uncertainty (load, wind and solar). Five test cases were analyzed: a baseline case without BESS and flexible load (DR), and other cases including degrading BESS and flexible load, either separately or jointly. First, the selected scenarios were analyzed and seen to retain most of the information in the original historical dataset, which validates the accuracy of the proposed scenario reduction method. The results showed that the methodology proposed in this work can be used to get an accurate optimal BESS capacity and that the inclusion of load-shifting DR can help reduce the optimal BESS capacity. Cases where an optimally sized BESS was considered provided cost reductions in the range 2.54 % to 3.68 % and carbon emissions reductions in the range 3.88 % to 4.5 % compared to the baseline case. Finally, the result also showed that battery degradation modelling is important as the cost reduction increased from 2.54 % to 3

关键词： Stochastic unit commitment mixed integer linear programming Lithium Iron Phosphate Battery Battery energy storage system Scenario reduction Multiple uncertainties

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