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MILP Modeling and Optimization of Multi-Objective Three-Stage Flexible Job Shop Scheduling Problem With Assembly and AGV Transportation

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IEEE ACCESS 2025年 13卷 25369-25386页

作者： Yang, Shiming Meng, Leilei Ullah, Saif Zhang, Biao Sang, Hongyan Duan, Peng Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Univ Engn & Technol Dept Ind Engn Taxila 47080 Pakistan

In the real manufacturing environment, the machining stage of the jobs and the assembly stage of the products are often completed in different workshops. In addition, automatic guided vehicle (AGV) plays an indispensable role in the transportation of jobs from machining workshop to assembly workshop. This paper studies multi-objective three-stage flexible job shop scheduling problem (FJSP-T-A) with minimizing both the makespan and the total energy consumption. In FJSP-T-A, jobs are first machined in flexible job shop, then are transported to assembly workshop by AGVs, and finally are assembled in assembly workshop. To solve this problem, a mixed-integer linear programming model (MILP) is developed and the optimal Pareto front for small-scale instances are solved by using the $\varepsilon $ -method. FJSP-T-A is NP-hard, and an efficient multi-population co-evolutionary algorithm (MPCEA) is proposed to efficiently solve large-scale instances. In the MPCEA, we design a strategy to select relatively high-quality individuals to enhance the algorithm's convergence speed, and design a multi-objective variable-neighborhood search (MOVNS) method to improve the local search ability. Experiments are conducted to prove the effectiveness of the MILP model and the MPCEA.

关键词： Assembly Transportation Energy consumption Conferences Machining Job shop scheduling Sequential analysis Systematic literature review Genetic algorithms Optimization Three-stage flexible job shop scheduling problem mixed-integer linear programming co-evolutionary algorithm variable-neighborhood search

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Optimal operation of hydrogen-based multi-energy microgrid integrating water network and transportation sector

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 97卷 501-515页

作者： Khan, Muhammad Ahsan Rehman, Talha Kim, Hak-Man Incheon Natl Univ Dept Elect Engn 119 Acad Ro Incheon 22012 South Korea Incheon Natl Univ Res Inst Northeast Asian Super Grid 119 Acad Ro Incheon 22012 South Korea

To address the rising demand for hydrogen energy and its reliance with the water sector, this study presents an optimal scheduling framework fora multi-energy microgrid (MEMG) that integrates electric, thermal, water, and hydrogen energy networks. To this end, a mixed-integer linear programming (MILP) model is formulated to minimize both operational costs and emissions. A bi-variate piecewise McCormick envelope technique is utilized to manage the non-linear constraints associated with the water network. The model also incorporates the transportation sector, including electric and hydrogen vehicles (EVs, HVs), with vehicle-to-grid (V2G) technology, and models their associated uncertainties using Monte Carlo simulation (MCS). Additionally, the sale of oxygen as a by-product of the hydrogenation process is also considered. The case study shows significant economic and environmental benefits, with a 29.66% cost reduction and 22.26% emissions decrease from water network integration. Oxygen sales further reduce costs by 14.19%, and V2G technology contributes an additional 2.35% cost and 6.01% emissions reduction. The proposed linear approximation method achieved superior performance, with a root mean square error (RMSE) of 0.72 and a relative error of 2.132%.

关键词： Electric vehicles Hydrogen energy Hydrogen vehicles mixed-integer linear programming Multi-energy microgrid Water-energy nexus

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Data-driven robust optimization for pipeline scheduling under flow rate uncertainty

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COMPUTERS & CHEMICAL ENGINEERING 2025年 193卷

作者： Baghban, Amir Castro, Pedro M. Oliveira, Fabricio Azarbaijan Shahid Madani Univ Dept Math Tabriz *** Iran Univ Lisbon CERENA Dept Chem Engn Inst Super Tecn P-1049001 Lisbon Portugal Aalto Univ Sch Sci Dept Math & Syst Anal POB 11100 Aalto 00076 Finland

Frequently, parameters in optimization models are subject to a high level of uncertainty coming from several sources and, as such, assuming them to be deterministic can lead to solutions that are infeasible in practice. Robust optimization is a computationally efficient approach that generates solutions that are feasible for realizations of uncertain parameters near the nominal value. This paper develops a data-driven robust optimization approach for the scheduling of a straight pipeline connecting a single refinery with multiple distribution centers, considering uncertainty in the injection rate. For that, we apply support vector clustering to learn an uncertainty set for the robust version of the deterministic model. We compare the performance of our proposed robust model against one utilizing a standard robust optimization approach and conclude that data-driven robust solutions are less conservative.

关键词： Straight liquid pipelines Continuous-time formulation mixed-integer linear programming Support vector clustering Robust optimization

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A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system

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ENERGY REPORTS 2025年 13卷 117-139页

作者： Bornemann, Luka Lange, Jelto Kaltschmitt, Martin Hamburg Univ Technol Inst Environm Technol & Energy Econ Eissendorfer Str 40 D-21073 Hamburg Germany

Thoroughly assessing future energy systems requires examining both their end states and the paths leading to them. Employing dynamic investment or multi-stage optimization models is crucial for this analysis. However, solving these optimization problems becomes increasingly challenging due to their long time horizons - often spanning several decades - and their dynamic nature. While simplifications like aggregations are often used to expedite solving procedures, they introduce higher uncertainty into the results and might lead to suboptimal solutions compared to non-simplified models. Against this background, this paper presents a rigorous optimization method tailored for multi-stage optimization problems in long-term energy system planning. By dividing the solution algorithm into a design and operational optimization step, the proposed method efficiently finds feasible solutions for the non-simplified optimization problem with simultaneous quality proof. Applied to a real-life energy system of a waste treatment plant in Germany, the method significantly outperforms a benchmark solver by reducing the computational time to find the first feasible solution from more than two weeks to less than one hour. Furthermore, it exhibits greater robustness compared to a conventional long-term optimization approach and yields solutions closer to the optimum. Overall, this method offers decision-makers computationally efficient and reliable information for planning investment decisions in energy systems.

关键词： Green hydrogen Multi-energy system Multi-stage optimization Rigorous optimization Long-term planning mixed-integer linear programming

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Destination-to-gate assignment to mitigate congestion-related risks in oversaturated metro lines: A new passenger flow control strategy

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 196卷

作者： Zhong, Linhuan Xu, Guangming Liu, Wei Liu, Yang Liu, Xinyi Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore Singapore Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore

The accumulation of large passenger flows at metro stations often poses congestion risks to urban rail systems, including an increased likelihood of accidents (e.g., slips, trips, and falls) and train departure delays. However, during peak hours, the priority boarding rights of passengers at upstream stations often lead to congestion and overcrowding at downstream popular stations. We propose a new passenger flow control strategy to address this issue, namely destination- to-gate assignment. This approach assigns specific gates to destinations served by the station, enabling passengers to board the appropriate train carriages. This strategic allocation facilitates a more even distribution of passengers, reducing congestion and enhancing spatial equity in passenger travel, thereby mitigating operational risks associated with overcrowding. For the problem of interest, we propose a nonlinear integer programming model to optimize the destination-to-gate assignment, aiming to simultaneously minimize risks related to passenger crowding and waiting times. The model adopts a first-come, first-served (FCFS) boarding rule to accurately capture the dynamic nature of passenger flow while considering the capacity limitations of train carriages. Leveraging the model's characteristics, we employ a set of linearization methods to equivalently transform it into a mixed-integer linear programming (MILP) model. To address the computational challenges posed by real-world scale, we develop a customized heuristic algorithm that uses Variable Neighborhood Search (VNS) combined with passenger flow simulation to efficiently generate high-quality solutions. Finally, we conduct a series of numerical experiments using data from Guangzhou Metro Line 9 to demonstrate the effectiveness of our proposed approach. The results show that the proposed destination- to-gate assignment strategy effectively alleviates congestion-related risks across all stations and promotes spatial equity in passenger tra

关键词： Oversaturated metro line Passenger flow control Destination-to-gate assignment Congestion-related risk mixed-integer linear programming

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An improved compact formulation for the assortment optimization problem with small consideration sets

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2025年

作者： Roberti, Roberto Salvagnin, Domenico Fischetti, Matteo Univ Padua Dept Informat Engn Padua Italy

We investigate the assortment optimization problem with small consideration sets, where customers belong to classes and choose according to the k-product non-parametric ranking-based choice model - i.e., each customer's preference list contains at most k products, and customers purchase the most preferred product among the ones offered in the assortment. This problem is known to be NP-hard even when k is equal to 2. The best approximation method from the literature has a performance guarantee of 2(1-1k)k-1(1k) and can find, empirically, assortments that are 0.3-0.5% within optimality when k equals 4 and there are 100 products and 10 000 customer classes. By building upon a compact mixed-integer linear programming model proposed, in the literature, for the full non-parametric ranking-based choice model, we propose an improved compact model that features a very tight continuous relaxation and can be easily solved with a general-purpose solver. An extensive set of computational experiments shows that our improved formulation can find provably optimal assortments of instances with up to 200 products, 100 000 customers classes, and k equal to 5, in a few minutes of runtime.

关键词： Assortment optimization choice models mixed-integer linear programming exact methods

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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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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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Optimal investment planning for production networks with fixed production profiles

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COMPUTERS & OPERATIONS RESEARCH 2025年 176卷

作者： Liu, Donghao Leibowicz, Benjamin D. Bard, Jonathan F. Zhu, Yuzixuan Guo, Yuanyuan Shao, Yufen Univ Texas Austin Cockrell Sch Engn Operat Res & Ind Engn Austin TX 78712 USA ExxonMobil Technol & Engn Co 22777 Springwoods Village Pkwy Spring TX 77389 USA

In this paper, we consider an oilfield planning problem with decisions about where and when to invest in wells and facilities to maximize profit. The model, in the form of a mixed-integer linear program, includes an option to expand capacity for existing facilities, annual budget constraints, well closing decisions, and fixed production profiles once wells are opened. While fixed profiles area novel and important feature, they add another set of time-indexed binary variables that makes the problem difficult to solve. To find solutions, we develop a three-phase sequential algorithm that includes (1) ranking, (2) branching, and (3) refinement. Phases 1 and 2 determine which facilities and wells to open, along with well-facility assignments. Phase 3 ensures feasibility with respect to budget constraints and adjusts construction times and facility capacities to increase profit. We first demonstrate how our algorithm navigates the problem's complex features by applying it to a case study parameterized with realistic production profiles. Then, we perform computational experiments on small instances and show that our algorithm generally achieves the same objective function values as CPLEX but in much less time. Lastly, we solve larger instances using our three-phase algorithm and several variations to demonstrate its scalability and to highlight the roles of specific algorithmic components.

关键词： Oilfield planning Oil and gas mixed-integer linear programming Investment planning Project scheduling Network optimization

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Optimizing Multi-Row Layouts With the Through-Aisle Structure: A Hybrid Approach of Teaching-Learning-Based Optimization and linear programming

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4972-4987页

作者： Zhang, Zeqiang Zhang, Yu Chen, Feng Liu, Junqi Wang, Can Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Chengdu SIWI High Tech Ind Co Ltd Chengdu 610031 Peoples R China

The multi-row facility layout problem is a prevalent and significant planning challenge in manufacturing workshops. This problem requires distributing facilities with pairwise transport weights among several rows to attain a layout with minimal logistics costs. However, the significance of aisles in multi-row facility layout has frequently been overlooked. An efficient aisle structure can result in a smooth transportation path and reduced material-handling costs. This paper contributes to the existing literature by introducing a new multi-row facility layout problem that considers long-straight aisles. First, mathematical formulas for the actual transportation distance between facilities through aisles are defined, and a mixed-integer programming model is constructed. Second, a hybrid algorithm based on an intelligent algorithm and a mathematical model is proposed. This method utilizes an improved teaching-learning-based optimization algorithm as a framework for optimizing the discrete facility sequence, and two decoding methods based on linear programming are designed to obtain the facility locations and transportation paths. Experimental results demonstrate that the two decoding strategies have their own advantages in terms of solution quality, efficiency, and area utilization. Moreover, improvement strategies for teaching-learning-based optimization algorithms are observed to be effective. Finally, we present two actual workshop examples of multi-row layout designs. The comparison of different algorithms reveals that the proposed algorithm has significant advantages in terms of solution quality and stability.

关键词： Layout Transportation Logistics Costs Mathematical models Conferences Optimization Multi-row facility layout through-aisle structure mixed-integer linear programming teaching-learning-based optimization

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