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Timeliness-oriented rush repair optimization of workforce scheduling and routing for logically complex systems under uncertainty

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 253卷

作者： Cui, Xinhao Li, Bo Wang, Shitao Zhang, Siyue Yang, Xue Ren, Yi Xiao, Yiyong Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

The optimization of workforce scheduling and routing problems (WSRPs) with a focus on timeliness-oriented rush repair in logically complex systems has received extensive attention. As there are many characteristics of complex system rush-repair, such as limited repair time and complex correlations, conventional approaches exhibit shortcomings in terms of timeliness (i.e., a comprehensive indicator that integrates both timely and mission reliability). To improve the overall emergency response efficiency under uncertain disaster scenarios, we develop a general decision-making framework for rush-repair WSRP. An optimization model is then formulated based on integer linear programming to maximize the overall timeliness while satisfying the skill-matching requirements and system-level recovery constraints. Furthermore, a novel matheuristic algorithm is proposed as a general solution approach for rush-repair decision-making. Finally, diverse benchmark groups, including different scales and sparsity levels, are simulated to conduct computational experiments. Compared to several representative algorithms, the results show that the proposed method has strong robustness and high efficiency.

关键词： Rush repair scheduling and routing Timeliness Mixed-integer linear programming Matheuristics

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A modified memetic algorithm for the home health care scheduling and routing problem considering caregiver skill deviation and synchronous service

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 203卷

作者： Li, Yan Wang, Qiangqiang Wang, Heping Wang, Fuyu Zhao, Dan Anhui Univ Technol Sch Management Sci & Engn Maanshan 243000 Peoples R China Multiple Sci Management & Control Complex Syst Key Maanshan 243000 Peoples R China

Based on the real scenario that two caregivers are needed to serve an elderly patient simultaneously, the paper studies the home health care synchronous scheduling and routing problem. The value function of prospect theory is used to describe their perceived satisfaction from the perspective of caregivers' bounded rationality towards skill deviations. A mixed-integer programming model is proposed to maximize the caregivers' satisfaction and minimize the total operating cost. An improved multi-objective memetic algorithm (IMOMA) is designed to solve the problem. In the IMOMA, an improved push-forward insertion heuristic (IPFIH) algorithm is proposed to generate initial solutions. Two types of crossover operators, three types of mutation operators and four types of neighborhood search operators with the properties of the problem are designed to improve the performance of the IMOMA. Taguchi experiment is constructed to set the optimal parameters of the algorithm. Simulation experiments are conducted in cases of various scales. The results indicate that the IMOMA can efficiently solve the scheduling problem by comparing with the three algorithms. Finally, the sensitivity analysis is conducted on the key parameters of the scheduling model to explore their impact on the optimization objectives of the scheduling scheme.

关键词： Home health care scheduling and routing Skill deviation Synchronous service An improved multi-objective memetic algorithm

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Resilience-based joint optimization of heterogeneous multi-crew scheduling and routing in urban road network recovery

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2025年

作者： Tan, Xiaoyan Mao, Xinhua Xu, Ting Xu, Huijia Wang, Yixuan Bao, Enze Changan Univ Sch Transportat Engn Xian Peoples R China Engn Res Ctr Digital Construct & Management Transp Xian Peoples R China Changan Univ Changan Dublin Int Coll Transportat Xian Peoples R China

Effective recovery strategies are necessary for road network resilience enhancement after disruptive events. Since urban road networks affected by small-scale disruptive events have a loss of capacity but still serve users, traffic flow evolves during the road network recovery process. This study addresses the joint optimization of multi-crew scheduling and routing considering the evolution of traffic flow from the perspective of road network resilience. We incorporate the heterogeneity of crews into the recovery process and introduce a bi-level programming model to simulate the joint optimization problem. Furthermore, an intertwined double-layer heuristic algorithm integrating the enhanced genetic algorithm, the Frank-Wolfe algorithm, and Dijkstra's algorithm is designed to solve the bi-level programming model. Finally, numerical experiments are utilized to validate the effectiveness of our proposed method. Additionally, the discussions regarding the impact of traffic flow evolution and heterogeneity on the recovery effect, as well as the sensitivity analysis of the number of crews, offer significant insights for decision-makers.

关键词： Bi-level programming heterogeneity homogeneity resilience road network recovery scheduling and routing user equilibrium

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scheduling and routing of AMOs in an Intelligent Transport System

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2009年第3期10卷 547-552页

作者： Chiew, Kevin Qin, Shaowen Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore Flinders Univ S Australia Sch Comp Sci Engn & Math Adelaide SA 5001 Australia

Autonomous moving objects (AMOs), such as automated guided vehicles (AGVs) and autonomous robots, have widely been used in the industry for decades. In an intelligent transport system with a great number of AMOs involved, it is important to eliminate potential congestion and deadlocks among AMOs to maintain a well-organized traffic flow. In this paper, we propose an algorithm that adapts bitonic merge sort algorithm for concurrent scheduling and routing of a great number (i.e., 4n(2)) of AMOs on an n x n mesh topology of path network without congestion or deadlocks among AMOs during their moves. The results are tested by experiments with randomly generated data and the comparison of a related model.

关键词： Autonomous moving object (AMO) mesh topology scheduling and routing

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scheduling and routing of autonomous moving objects on a mesh topology

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OPERATIONAL RESEARCH 2012年第3期12卷 385-397页

作者： Chiew, Kevin Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore

Autonomous moving objects (AMOs), such as automated guided vehicles (AGVs) and autonomous robots, have been widely used in industry for decades. In an intelligent transport system with a great number of AMOs involved, it is desirable to maintain a smooth traffic flow of AMOs without congestion or deadlocks. Continuing our previous study, in this paper we propose a new algorithm for concurrent scheduling and routing of a great number (i.e., 4n(2)) of AMOs on an n x n mesh topology of path network. As a significant improvement compared with the previous algorithm, this new algorithm achieves 3n concurrent rectilinear steps of routing for all 4n(2) AMOs, while no congestion or deadlocks arise from them when all AMOs are moving around on the mesh topology. The theoretical analysis and calculations of the algorithm are verified by randomly generated data in our experiments.

关键词： scheduling and routing Autonomous moving object Mesh topology

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Simulation Budget Allocation for Improving scheduling and routing of Automated Guided Vehicles in Warehouse Management

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JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA 2024年 1-35页

作者： Zhang, Gong-Bo Li, Hao-Bin Liu, Xiao-Tian Peng, Yi-Jie Peking Univ PKU Wuhan Inst Artificial Intelligence Guanghua Sch Management Beijing 100871 Peoples R China Xiangjiang Lab Changsha 410205 Hunan Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore

Simulation budget allocation is a widely used technique for evaluating and optimizing dynamic discrete event stochastic system via efficient sampling. In warehouse management, the scheduling and routing algorithms of automated guided vehicles aim to optimize order assignments and travel paths under certain constraints to achieve specific objectives. However, these algorithms often rely on deterministic optimization methods, neglecting the dynamic and stochastic nature of warehouse management systems. In this work, we propose an efficient method that integrates simulation budget allocation methods with deterministic scheduling and routing algorithms to enhance overall system performance. The proposed method leverages the benefits of both simulation optimization and deterministic optimization techniques, accounting for the inherent uncertainty of the system. We adopt a discrete event simulation model used in the Case Study Competition of the 2022 Winter Simulation Conference, where the objective is to minimize the adjusted average order cycle time (ACT) in a given warehouse simulation scenario. Numerical examples demonstrate that the use of simulation budget allocation methods can significantly further reduce the ACT, thereby highlighting the effectiveness of our proposed method.

关键词： Simulation optimization scheduling and routing Automated guided vehicles Warehouse management

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Mathematical Model for the Home Health Care scheduling and routing Problem with Flexible Lunch Break Requirements

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IFAC-PapersOnLine 2018年第11期51卷 334-339页

作者： Xiao, Liyang Dridi, Mahjoub El Hassani, Amir Hajjam Nanomedicine Lab Univ. Bourgogne Franche-Comté UTBM BelfortF-90010 France

This paper formulates a mixed integer linear programming model of a home health care scheduling and routing problem with daily planning horizon. The entire procedure involves dispatching caregivers to serve visits demanded by patients at their homes with the objective function aims at minimizing the total operating cost. In some cases, based on the needs of patients, several caregivers should be synchronized and allocated to one patient under a given time window. The proposed model also accounts for multiple constraints such as the restricted working duration, the qualification and preference of patients, and innovative flexible lunch break requirements. Finally, the problem is solved to optimality by Gurobi, a commercial program solver. Numerical results shown that the solution performs quite well compared to the previous artificial planning collected in the Institute Montéclair, shows the the problems can be solved efficiently for optimal solutions. © 2018

关键词： Home health care Integer programming scheduling Synchronization Lunch breaks Mixed integer linear programming model Multiple constraint Numerical results Objective functions Optimal solutions routing problems scheduling and routing

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Non-preemptive Coflow scheduling and routing 59

Non-preemptive Coflow Scheduling and Routing

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59th Annual IEEE Global Communications Conference (IEEE GLOBECOM) / Conference on Network Coding and Applications (NetCod)

作者： Yu, Ruozhou Xue, Guoliang Zhang, Xiang Tang, Jian Arizona State Univ Tempe AZ 85287 USA Syracuse Univ Syracuse NY 13244 USA

ISBN: (纸本)9781509013289

As more and more data-intensive applications have been moved to the cloud, the cloud network has become the new performance bottleneck for cloud applications. To boost application performance, the concept of coflow has been proposed to bring application-awareness into the cloud network. A coflow consists of many individual data flows, and a coflow is completed only when all its component flows are transmitted. The network performance of a cloud application is dependent on the completion time of coflows, rather than the completion time of each individual flow. Existing coflow-aware optimization solutions employ flow preemption to reduce the completion time, which brings difficulty in practical implementation and non-negligible overhead. In this paper, we study the non-preemptive coflow scheduling and routing problem in the cloud network. We propose an offline optimization framework for coflow scheduling, as well as two subroutines for coflow routing using single-path routing and multi-path routing respectively. We also show that our proposed framework is easily extensible to the online scenario. Extensive evaluations show that the proposed solutions can greatly reduce coflow completion time compared to coflow-agnostic solutions, and are also computationally efficient.

关键词： Coflow scheduling and routing delay-awareness

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Modified ACO for Home Health Care scheduling and routing Problem in Chinese Communities 15

Modified ACO for Home Health Care Scheduling and Routing Pro...

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15th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Zhang, Ting Yang, Xintong Chen, Qingxin Bai, Liping Chen, Wenge Guangdong Univ Technol Guangdong CIMS Prov Key Lab Guangzhou Guangdong Peoples R China Guangdong Univ Technol Sch Electrochem Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538650530

This paper studies the Home Health Care scheduling and routing Problem (HHCSRP) in Chinese communities, in which patients are located intensively. Significantly, HHCSRP in Chinese communities need to consider the intensive distributions of patients and their vertical locations, which are different from the same problem in Europe. In the mix integer programming model, three main factors are considered, including uncertain service time, time windows and match qualities. Therefore a modified Ant-colony optimization is utilized, four instances are analyzed and management science significances are provided.

关键词： Home Health Care scheduling and routing ACO

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SmartHART: A Priority-aware scheduling and routing Scheme for IIoT Networks using Deep Reinforcement Learning 15

SmartHART: A Priority-aware Scheduling and Routing Scheme fo...

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15th International Conference on Communication Systems and Networks (COMSNETS)

作者： Chilukuri, Shanti Gupta, Aditya Pulamolu, Hemanth Sri Sai GITAM GITAM Sch Technol Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781665477062

The aims of the Industrial Internet of Things (IIoT) are improved efficiency of production facilities and operations in factories and better logistics and supply chain management. Communication of data in the IIoT can be challenging due to diverse and critical demands on the Quality of Service (QoS). Medium Access Control (MAC) in such networks is typically using Time Division Multiple Access as recommended by standards such as the WirelessHART. In this paper, we propose and evaluate a Deep Reinforcement Learning (DRL)-based scheduling and routing scheme (called SmartHART) for WirelessHART networks. SmartHART leverages on redundancy in the network and chooses schedules and routes that minimize the end-toend delay of data, taking priority of data into consideration. The reward design of the SmartHART agent also minimizes the maximum packet queue length in a network, making it suitable for memory-constrained field devices. Simulation results show that SmartHART can reduce the end-to-end delay by as much as 18% and the maximum packet queue size by up to 60%.

关键词： Deep Reinforcement Learning Industrial IoT scheduling and routing

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