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Scheduling truck arrivals for efficient container flow management in port logistics

CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2025年 1-28页

作者： Baldouski, Daniil Kresz, Miklos David, Balazs Univ Primorska Titov Trg 4 Koper 6000 Slovenia InnoRenew CoE Livade 6a Izola 6310 Slovenia Univ Szeged 13 Dugon Sq H-6720 Szeged Hungary

The management of truck arrivals at container terminals is crucial for efficient port operations. Congestions developing both outside and inside the gates can cause logistical problems, while also having a significant impact on the environment and the surroundings of the port. Therefore, optimizing truck queues outside the gates of the port, as well as routing of trucks inside the terminals can lead to an improvement in the overall efficiency of the port processes. This paper presents a mixed-integer linear programming formulation to determine these optimal truck routes and schedules. The model considers a port with an external parking lot, multiple gates, internal roadways, and docks. A rolling horizon heuristic is also developed for the solution of instances where the model is otherwise intractable. The developed methods are evaluated on instances simulated based on real-world data.

关键词： Scheduling Port logistics Container flow optimization Simulation mixed-integer linear programming

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Coordinating paths and schedules of groups visiting multiple capacitated locations

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年第5期76卷 904-921页

作者： Guastaroba, Gianfranco Morandi, Valentina Speranza, M. Grazia Univ Brescia Brescia Italy

We study a situation where a large number of people visit a popular venue (e.g., an art gallery, a mall, a theme park, or an exhibition) where points of interest are located (e.g., paintings, shops, attractions, or pavilions). Visitors have a maximum amount of time available for the overall experience. The points of interest have a limiting capacity and, whenever the turnout of visitors exceeds such capacity, queues and service disruptions occur. Given the maximum time available along with the time spent queuing, a selection of the points of interest may become necessary. Visitors usually act as autonomous decision-makers and do not take into account their interaction with other visitors. This leads to remarkable inefficiencies that could be, to a certain extent, overcome through a coordination of the paths and schedules of the different visitors. The resulting optimization problem is modeled as a mixed-integer linear Program (MILP), where the goal is to minimize a weighted combination of the points of interest not selected and the time spent queuing. Computational results show the benefits that can be achieved by using the model proposed as a tool to support decision-making.

关键词： Coordinated approaches optimal paths time-space networks combinatorial optimization mixed-integer linear programming

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MILP modeling and optimization of flexible job shop scheduling problem with preventive maintenance

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 201卷

作者： Zhao, Lixin Cheng, Weiyao Meng, Leilei Zhang, Chaoyong Ren, Yaping Zhang, Biao Duan, Peng Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Dept Ind Engn Zhuhai 519070 Peoples R China

This study investigated the flexible job shop scheduling problem considering preventive maintenance (FJSP-PM), which can be categorised into two types: FJSP-PM with a fixed maintenance strategy (FJSP-FPM) and FJSP-PM with a periodic maintenance strategy (FJSP-PPM). The objective is to minimize the makespan. To prove the optimality of small-sized instances, for the first time, two mixed-integer linear programming (MILP) models for each problem are established based on different modeling approaches. A Q-learning-based collaborative variable neighbourhood search algorithm (CVNS-Q) is proposed to efficiently obtain approximate optimal solutions for large-sized instances. In the CVNS-Q, eight neighbourhoods are utilized. Two VNS modules are used to guide the evolution of the two individuals, and a Q-learning algorithm guide the selection of neighbourhoods. In addition, a restart strategy is designed to reduce the possibility of sinking into local optima. The effectiveness of the MILP model and CVNS-Q is evaluated using 20 benchmark instances.

关键词： Flexible job shop scheduling Preventative maintenance mixed-integer linear programming Collaborative variable neighbourhood search algorithm Q -learning

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Event-based models for the electric autonomous dial-a-ride problem

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2025年 171卷

作者： Stallhofer, Verena Parragh, Sophie N. Johannes Kepler Univ Linz Inst Prod & Logist Management JKU Business Sch Linz Austria

On-demand transportation systems can serve to complement standard scheduled public transport in areas with low population density or to address the mobility needs of handicapped and elderly people. In this paper, we address the electric autonomous dial-a-ride problem (e-ADARP). In the e-ADARP, vehicle routes for serving user requests consisting of pickup and drop-off locations are determined. The objective is to minimize a weighted combination of travel distances and excess user ride time. Since it is assumed that an electric and autonomous vehicle fleet is used for the ride-sharing service, in addition to vehicle capacity, time windows, and maximum user ride times, also battery capacity constraints have to respected. We develop a mixed-integer linear programming (MILP) model for the e-ADARP that relies on an event-based graph. By using an event-based graph, capacity, pairing, and precedence constraints are implicitly applied. Several valid inequalities from the literature as well as newly developed ones are used to strengthen the model. In comparison to existing exact methods for the e-ADARP, we obtain competitive results on a set of available benchmark instances: we provide several improved upper and lower bounds and provide optimal solutions to previously unsolved instances. Furthermore, we analyze the impact of the capacity setting as well as different weight combinations on solution time and demonstrate the effect of battery start and end levels over several periods.

关键词： Electric autonomous vehicles Vehicle routing Event-based graph mixed-integer linear programming Valid inequalities Dial-a-ride problem

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Inbound logistics optimization for fresh oranges with waste management

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JOURNAL OF FOOD ENGINEERING 2025年 391卷

作者： Anwar, Kiran Turkay, Metin Koc Univ Rumelifeneri Yolu TR-34450 Istanbul Turkiye

Typical fruit supply chains include several echelons such as farms, primary fruits-processing-facilities to produce main products, secondary fruits-processing-facilities to produce by-products, distribution centers, retailers, and consumers. This paper focuses on the inbound logistics operations that cover the logistics planning for transporting the fruits collected at the farms to the primary fruit processing facilities. Fruit supply chains are considered difficult to manage due the perishable nature of fruits and time constraints to avoid contamination and wastage. The objective of this research is to optimize inbound logistics in fruit supply chains by developing an optimization model that minimizes fruit losses during transportation and identifies optimal waste management practices. This approach not only reduces waste but also lowers greenhouse gas (GHG) emissions associated with fruit wastage. A bi-objective optimization model for the inbound logistics of fresh fruits is presented;the first objective is to minimize the total cost considering the environmental sustainability factors and the second objective is to minimize the carbon footprint and GHG emissions. Fresh fruit losses and wastage caused by changes in temperature, shelf life, and total time spent on transportation during inbound logistics is quantified. The model is applied to original data of fresh Valencia oranges harvested from farms in Egypt and computational results indicate that the fruit loss per month is decreased from 29% to less than 2% during the inbound logistics operations. Three methods are used to solve the bi-objective optimization problem, and the results are compared.

关键词： Bi-objective optimization model mixed-integer linear programming Fresh fruit waste management Pareto optimal solutions Logistics planning

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Long term turnaround planning for an oil refinery using a MILP model

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COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Lima, Ricardo A. de O. Guirardello, Reginaldo Univ Campinas UNICAMP Sch Chem Engn Ave Albert Einstein 500 BR-13083852 Campinas Brazil

This study presents a discrete-time mixed-integer linear programming (MILP) model to optimize long-term maintenance turnaround scheduling in an oil refinery focused on fuel production. Refineries are complex networks of integrated process units, and maintenance turnarounds, involving temporary shutdowns for inspection and repair, can significantly disrupt production and reduce revenues. The MILP model aims to minimize these disruptions by optimizing turnaround schedules while maintaining product supply and maximizing economic performance. The model incorporates flow, labor, resource, and planning constraints, allowing for different unit groupings and scenario simulations. Key outputs include the maintenance schedule, unit utilization rates, intermediate stock levels, production, manpower, and maintenance costs. The model serves as a decision-support tool for refining managers, enabling them to plan maintenance interventions that maximize operating profit while adhering to operational constraints.

关键词： Oil refinery Long-term maintenance Turnaround scheduling mixed-integer linear programming Operating profit

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Integrated optimisation of operations and financing for large-scale multi-period multi-product capital-constrained supply chains

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年

作者： Cheng, Junheng Lin, Yanhong Cheng, Jingya Liao, Lingtong Chu, Feng Fujian Normal Univ Sch Econ Fuzhou Peoples R China Univ Paris Saclay Univ Evry IBISC F-91020 Evry France

In a typical supply chain, the operations of procurement, production, inventory, distribution, transportation, and financing decisions are interdependent, collectively impacting the overall performance of the system. However, traditional supply chain management often prioritises minimising operating costs, rarely considering financial strategies simultaneously to achieve enhanced operational benefits. This study addresses a novel operations and financing integrated optimisation problem in multi-period, multi-product, capital-constrained supply chain systems, with the objective of maximising profit. The studied NP-hard problem is formulated as a mixed-integer linear programming (MILP) model. To efficiently and effectively tackle large-sized problems, two decomposition-based effective heuristics are developed. The first decomposes the original problem into two sub-problems: an integrated optimisation problem involving inventory, production, transportation, distribution, and financing (sub-problem 1) and a procurement problem (sub-problem 2), both of which are solved using MILP models. The second method further develops a polynomial-time heuristic for sub-problem 1. To evaluate the performance of the developed methods, 180 instances involving up to 600 products and 1200 raw materials are conducted. Experimental results show that the proposed algorithms can achieve high quality solutions with an average gap of less than 2.24% within 50 seconds. Based on the numerical analysis, managerial insights are discussed.

关键词： Multi-period supply chain management operations and financing integrated optimisation production-inventory-distribution mixed-integer linear programming matheuristic

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Smarter facility layout design: leveraging worker localisation data to minimise travel time and alleviate congestion

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第4期63卷 1326-1353页

作者： Aslan, Ayse Vasantha, Gokula El-Raoui, Hanane Quigley, John Hanson, Jack Corney, Jonathan Sherlock, Andrew Edinburgh Napier Univ Sch Comp Engn & Built Environm Merchiston CampusRoom C6710 Colinton Rd Edinburgh EH10 5DT Scotland Univ Strathclyde Dept Management Sci Glasgow City Scotland Univ Edinburgh Sch Engn Edinburgh Scotland Univ Strathclyde Natl Mfg Inst Scotland Glasgow City Scotland

This paper introduces a novel methodology leveraging worker localisation data from ultrawide-band sensors to formulate alternative facility layouts aimed at minimising travel time and congestion in labour-intensive manufacturing systems. The system preprocesses sensor data to discern flow patterns between existing stations within the production facility, such as machine tools, workbenches, and stores. This information about the movement of people and materials informs the generation of optimised layouts through scenario-based optimisation. We explored two methods to devise these new layouts: a mixed-integer linear programming method and a simulated annealing metaheuristic, the latter being specifically developed to find high-quality solutions to the quadratic layout design formulation. Both methods employ biobjective formulations, focussing on the minimisation of travel time and the reduction of congestion risk on the manufacturing floor, an aspect often neglected in prior studies. Our methodology, applied to a real-world manual assembly line case study, demonstrated the potential to reduce travel time by a minimum of 32% and alleviate congestion while maintaining significant safety distances between facilities. This was achieved by automatically identifying design features that position high-traffic facilities closely and align them to eliminate movement overlaps.

关键词： SDG 9: industry facility layout optimisation smart manufacturing mixed-integer linear programming process mining indoor localisation sensors

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Multi-objective optimization of integrated energy systems in natural gas industrial zones: Enhancing performance through variable hydrogen blending ratios

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APPLIED THERMAL ENGINEERING 2025年 268卷

作者： Mao, Yudong Li, Xinwei Liu, Jiying Yu, Mingzhi Kim, Moon Keun Yang, Kaimin Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Oslo Metropolitan Univ Dept Built Environm N-0130 Oslo Norway

In order to solve the problem of new energy consumption and reduce the use of fossil fuels, a multi-objective optimization and evaluation system is constructed for Integrated energy system (IES). The system takes into account factors including economy, environmental conservation, and hydrogen blending ratio. It establishes a mixed-integer linear programming model for multi-objective optimal scheduling. The conversion of renewable energy sources into electricity, followed by its conversion into hydrogen. By changing the hydrogen blending ratio of natural gas pipeline network, each objective function can reach the best. The optimal operation of each equipment of the heat and cold networks is obtained by changing the proportion of hydrogen blending into the natural gas pipeline network. Based on the multi-objective comprehensive analysis under conditions of constant heat consumption, the optimal solution is a hydrogen blending ratio of 10.67 %. This solution results in a cost of $305,426.29, carbon emissions of 19,709,221.40 kg, and a total natural gas consumption of 19,128,759.35 m3. Compared with the conventional energy system, this scheme not only offers better economy but also has characteristics of energy saving and emission reduction, making it superior in comprehensive evaluation compared to other schemes. In conclusion, this system enhances energy utilization efficiency, maximizes the integration of renewable energy sources, and reduces carbon emissions.

关键词： Integrated energy system Multi-objective optimization algorithm mixed-integer linear programming Hydrogen blending of natural gas Renewable energy

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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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