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Maintenance scheduling at high-speed train depots: An optimization approach

RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 243卷

作者： Wang, Jiaxi Zhejiang Lab Res Ctr Intelligent Transportat Hangzhou Zhejiang Peoples R China

Under the preventive maintenance policy, high-speed trains need to conduct periodical inspection/overhaul at dedicated depots, where maintenance facilities and maintenance crews are available. Due to the complicated structure of trains, the maintenance work is rich in a variety of types, characterized by mechanical components repaired, maintenance levels and etc. In practice, these types of maintenance work are integrated into maintenance packets, and different packets may require the specific types of crews. In this paper, a mixed integer linear programming model is proposed for the depot maintenance packet assignment and crew scheduling problem. The objective of the problem is to minimize the overall crew worktime, while the main constraints include the compatibility between maintenance packets and crew types, maintenance packet duration time and execution order, maintenance time window, and crew worktime limit. Besides, two families of valid inequalities are proposed to improve the baseline model. Computational experiments on a set of randomly generated instances show the effectiveness and efficiency of the improved model compared to the baseline one. Finally, a real-world case study from Shanghai South Depot is carried out to further validate the proposed approach. Improvements on both solution time and quality are achieved in contrast with the manual schedule.

关键词： High-speed train depot Maintenance packet Crew scheduling mixed integer linear programming Valid inequality

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A new mathematical formulation and a hybrid evolutionary algorithm for re-entrant flow-shop problem with release date

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SCIENTIA IRANICA 2024年第6期31卷 500-517页

作者： Behmanesh, R. Kianfar, K. Naghshejahan Higher Educ Inst Dept Ind Engn Esfahan *** Iran Univ Isfahan Fac Engn Esfahan *** Iran

Tn this paper, we address the weighted multi-objective re-entrant flowshop scheduling problem considering release dates in order to minimize makespan, total completion time, total tardiness, maximum idle time, and number of tardy jobs. Each job is taken into account with deterministic processing times, and release dates. The flow-shop comprised of two workshops in whose jobs are entered to the main workshop and after the first part of the processing, they are transferred to the second workshop and after this stage, the jobs are returned to the main workshop for the last part of the processing. We model the problem by a new mixed integer programming based on formulating sum of idle time as a new concept. Moreover, a hybrid evolutionary algorithm is proposed based on some dispatching rules, ant colony optimization, and genetic algorithm. The performance of the proposed algorithm on some test instances is compared to the mixed integer linear programming model as well as the state-of-the-art algorithms called genetic algorithm, tabu search, bio-geography based optimization, and artificial bee colony. The computational experiments show that our proposed approach outperforms other algorithms and the results indicate efficiency and capability of the proposed algorithm in comparison with the traditional algorithms. (c) 2024 Sharif University of Technology. All rights reserved.

关键词： Re-entrant flow-shop scheduling Tdle time Release date Makespan Tardiness Hybrid evolutionary algorithm mixed integer linear programming

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A Fast Predictive Control Method for Vehicle Path Tracking Based on a Recurrent Neural Network

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IEEE ACCESS 2024年 12卷 141104-141115页

作者： Wu, Xialai Lin, Ling Chen, Junghui Du, Shuxin Huzhou Univ Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan

The paper introduces a Fast Model Predictive Control (FMPC) approach for vehicle path tracking, addressing the challenge of real-time performance in highly nonlinear systems. Utilizing a recurrent neural network with symmetric saturating linear transfer functions (SSL-RNN), our method efficiently constructs an SSL-RNN model for the vehicle. By transforming the MPC optimal control problem into a mixed integer linear programming problem, a swift online solution is achieved. Through simulations on a CarSim/Simulink platform, our FMPC outperforms RNN-based nonlinear MPC and long-short-term memory network-based MPC, demonstrating superior accuracy in vehicle path tracking and enhanced controller solution efficiency.

关键词： Vehicle dynamics Adaptation models Accuracy Heuristic algorithms Data models Computational modeling mixed integer linear programming Predictive control Recurrent neural networks Path planning model predictive control recurrent neural network symmetric saturating linear transfer functions vehicle path tracking

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A Novel GA-CP Method for Fixed-Type Multi-Robot Collaborative Scheduling in Flexible Job Shop

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13531-13543页

作者： Huang, Jin Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

With the rapid development of intelligent manufacturing, multi-robot collaborative systems are increasingly integrated into various production processes. In the flexible job shop environment of automotive stamping, achieving smooth operation and efficient manufacturing of production lines hinges on solving the critical issues of multi-robot task allocation and scheduling. However, for such fixed-type multi-robot collaboration problems, robots are constrained by specific areas or predetermined trajectories, and processing times can only be adjusted by varying the number of available robots. Therefore, the scheduling problem in multi-robot collaborative flexible job shop problems (MCFJSP) is divided into two sub-problems: FJSP with controllable processing times and multi-robot collaborative task balancing. To address these, we propose three distinct methods: mixed integer linear programming (MILP), constraint programming (CP), and a hybrid genetic algorithm-constraint programming (GA-CP). Finally, a set of 48 benchmark cases and two real-world cases are developed to test these methods. Comparative experiments demonstrate that the MILP model is superior in small-scale cases, while the GA-CP model exhibits the best overall performance in medium to large-scale cases. Furthermore, through comparisons with two advanced algorithms, the effectiveness and superiority of the GA-CP method in addressing real-world cases are confirmed.[8pt]Note to Practitioners-In modern manufacturing environments, particularly in industries like automotive manufacturing, multiple robots working together on complex tasks are increasingly common. This paper addresses the practical challenge of effectively scheduling these robots to maximize efficiency while reducing the number of robots assigned to each task. This study introduces and compares different methods, including MILP, CP, and GA-CP methods, that can help practitioners determine the best way to allocate tasks among robots and schedule the

关键词： Robots Robot kinematics Collaboration Job shop scheduling Manufacturing Service robots Resource management Optimal scheduling Assembly Process control Fixed-type multi-robot collaborative problem mixed integer linear programming constraint programming genetic algorithm-constraint programming

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Optimization models for the installation planning of offshore wind farms

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期315卷 1182-1196页

作者： Amorosi, Lavinia Fischetti, Martina Paradiso, Rosario Roberti, Roberto Sapienza Univ Rome Dept Stat Sci Rome Italy Univ Seville Inst Matemat Seville Spain Vrije Univ Amsterdam Dept Operat Analyt Amsterdam Netherlands Univ Padua Dept Informat Engn Padua Italy

Challenges posed by global warming motivate the increasing interest in green energy sources, such as wind energy. To make wind energy attractive from an economic viewpoint, the decision -making problems faced in designing, installing, and maintaining wind farms have to be optimized. In this paper, we focus on the problem of optimally planning the installation process of offshore wind farms, as faced by Vattenfall, a leading European energy company. We first formulate the deterministic version of this Installation Planning of Offshore Wind Farms (IPOWF) problem as a mixed integer linear programming (MILP) model. From this model, we then derive other MILPs that can provide lower and upper bounds to the optimal installation cost. To cope with weather uncertainty, we also provide a light -robust MILP. These models are tested on real -life instances corresponding to two wind farms that Vattenfall has recently built in Denmark and Germany. Computational results show that tight primal and dual solutions can be computed for the deterministic case with the proposed models and that the light -robust model yields solutions that are robust to weather uncertainty.

关键词： Wind farms Wind energy Installation mixed integer linear programming

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Residential consumer enrollment in demand response: An agent based approach

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APPLIED ENERGY 2024年 374卷

作者： Sridhar, Araavind Honkapuro, Samuli Ruiz, Fredy Stoklasa, Jan Annala, Salla Wolff, Annika LUT Univ Lappeenranta Finland Politecn Milan Milan Italy

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a +/- 10% variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector.

关键词： Demand response Home energy management system Agent-based simulation Monte Carlo analysis mixed integer linear programming

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Iterative MILP algorithm to find alternate solutions in linear programming models

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OPTIMIZATION AND ENGINEERING 2024年第4期25卷 2401-2424页

作者： Kakkad, Dev A. Grossmann, Ignacio E. Springub, Bianca Galanopoulos, Christos de Assis, Leonardo Salsano Tran, Nga Wassick, John M. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Aurubis AG Hamburg 20539 Germany

We address in this paper linear programming (LP) models in which it is desired to find a finite set of alternate optima. An LP may have multiple alternate solutions with the same objective value or with increasing objective values. For many real life applications, it can be interesting to have a pool of solutions to compare what operations should be executed and what is the cost/benefit of doing it. To obtain a specified number of these alternate solutions in the increasing order of objective values, we propose an iterative MILP algorithm in which we successively add integer cuts on inactive constraints. We demonstrate the application and effectiveness of this algorithm on a 2 dimensional LP and on small and large supply chain problems. The proposed iterative MILP algorithm provides an effective approach for finding a specified number of alternate optima in LP models, which provides a useful tool in a variety of applications as for instance in supply chain optimization problems.

关键词： Supply chain network linear programming mixed integer linear programming Alternate optima

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Modular vehicle-based transit system for passenger and freight co-modal transportation

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 160卷

作者： Lin, Jie Zhang, Fangni Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China

The emergence of vehicle modularity technologies provides an opportunity to develop flexible public transport services that allow en -route vehicle capacity adjustments to meet fluctuating demands. Given the largely imbalanced demands for passenger and freight transport in urban areas, the use of modular vehicles in collaborative passenger and freight transport (comodality) is expected to increase overall transport efficiency. This study explores the potential of integrating modular vehicles into a co -modal transit system that serves both passengers and freight. Specifically, we consider a bi-directional transit corridor where modular vehicles are scheduled to meet time -dependent passenger and freight demands. Each modular vehicle comprises identical modules that can be assigned to either passengers or freight individually. Moreover, a modular vehicle can adjust (i) the number of composed modules by docking and undocking modules, and (ii) module allocation between passengers and freight at intermediate stations. To minimize passenger waiting costs and vehicle operating costs, a mixed integer programming formulation is established that optimizes vehicle dispatch times, (un)docking operations, module allocation, and freight allocation. Given the solution complexity, we propose a two -stage algorithm to solve this problem efficiently. In the first stage, we address vehicle dispatch times, and in the second stage, vehicle formation and freight allocation are optimized. Numerical experiments verify that the proposed two -stage algorithm outperforms state-of-the-art solvers and can obtain near -optimal solutions in an acceptable time for large-scale problems. A case study based on a transit line in Hong Kong validates the effectiveness of co -modal modular transit systems, where station -wise (un)docking and co -modality lead to a cost reduction of 8.2% and 11.2%, respectively. Finally, sensitivity analysis examines the impact of key parameters on the performance of co

关键词： Modular vehicle Urban co-modality Bi-directional transit corridor system mixed integer linear programming Two-stage heuristics

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Appraisal of viable optimization techniques for efficient design and control planning of renewable energy-based microgrid systems

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Adewuyi, Oludamilare Bode Krishnamurthy, Senthil Cape Peninsula Univ Technol Ctr Substn Automat & Energy Management Syst Dept Elect Elect & Comp Engn ZA-7535 Bellville South Africa

Due to the rapid increase in energy demand and technological improvements, a noteworthy shift has been witnessed in the energy sector through the implementation of the distributed energy systems concept. At the hub of these changes are renewable energy sources, which have risen to be the substitute solution for meeting the proliferating energy demand while mitigating the effects of climate change and contributing to socioeconomic growth. Contrary to the conventional approach, these distributed energy systems are integrated using microgrid systems, and this sophisticated technological solution necessitates the management of information exchange and power scheduling between the dispersed generating sources, customer loads, and the primary grid infrastructure. Thus, there is a need for improvement in the optimal planning and control of microgrid operation regarding computational efficiency, reliability of the obtained solution, and algorithm robustness. In addition to comparing various MG optimization problem formulations and solution approaches using multiple optimization algorithms, this paper reviews various optimization techniques used in renewable energy-based microgrid systems design and control planning. The paper evaluates particle swam optimization and mixed integer linear programming capacity for microgrid planning, considering computational efficiency, solution dependability, and algorithm resilience while providing credible information on the capabilities of parallel computing for robust optimal of high renewable microgrid design considering uncertainties and energy market dynamics.

关键词： Microgrid system planning Capacity optimization Operational scheduling and control Particle swam optimization mixed integer linear programming Parallel computing

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Exact and heuristic methods for a university course scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Xiang, Kewei Hu, Xinyao Yu, Mingzheng Wang, Xingyu Nanjing Univ Sch Management & Engn Management Sci & Engn Nanjing 210008 Peoples R China Tsinghua Univ Sch Econ & Management Management Sci & Engn Beijing 100084 Peoples R China Huazhong Univ Sci & Technol Sch Management Management Sci & Engn Wuhan 430074 Peoples R China Chongqing Univ Sch Econ & Business Adm Mkt Chongqing 400044 Peoples R China

The University Course Scheduling Problem (UCSP) is a complex combinatorial optimization problem. Most universities still use hand-operated scheduling, which usually takes a long time and results in numerous irrationalities. The imperative of conceiving a algorithm to navigate this predicament is undeniable. This paper introduces an advanced mixed integer linear programming paradigm, incorporating constraints encompassing both the temporal coherence of courses and the equitable dispersion of these courses throughout the academic week. To tackle this issue, we design a novel two-stage meta-heuristic algorithm;the first stage harnesses the genetic algorithm to cluster courses into sets by exploiting graph coloring techniques, while the subsequent stage leverages an enhanced tabu search heuristic to assign these clusters into distinct temporal intervals. The effectiveness and efficiency of the proposed heuristic has been validated by extensive computational studies based on authentic data gleaned from Huazhong University of Science and Technology's School of Management and the results are compared with CPLEX and common heuristic. Moreover, our research examines the algorithm's convergence properties and the effects of fast neighborhood evaluation strategy. Sensitivity analysis is performed to probe the algorithm's responsiveness to the new constraints.

关键词： Course scheduling mixed integer linear programming Genetic algorithm Tabu search Graph coloring

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