Computers & Industrial Engineering最新文献_第2页

Exact solution of workload consistent vehicle routing problem with priority distribution and demand uncertainty

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-07 DOI: 10.1016/j.cie.2025.110940

Shiping Wu, Chun Jin, Hongguang Bo

This study attempts to solve a workload consistent vehicle routing problem with priority distribution and demand uncertainty. Workload consistency requires the difference in working time allocated to drivers each day within a planning horizon to be limited to a fixed range. Partial split delivery, multi-trips, and uncertain demand are also considered. To address both transportation costs and priority-based distribution concerns, hierarchical objectives are adopted with the primary objective of minimizing travel costs and the secondary objective of maximizing distribution rewards. An exact algorithm based on set-partitioning formulation and robust column-and-cut generation is proposed to solve the problem, where a lower bound and an upper bound are used to derive some feasible columns, and these candidate columns are used in solving the set-partitioning formulation to obtain the optimal solution. Simultaneous decisions on visit sequence and distribution amount under conditions of demand uncertainty exacerbate the difficulty of solving the pricing subproblem. Therefore, we design a robust labelling algorithm involving a robust feasible extension check and an optimal distribution pattern computation to address this difficulty. The upper bound is obtained by a clustering-routing-assignment heuristics. Numerical experiments indicate that the proposed exact method can effectively solve medium-and partially large-scale instances, and the results have good robustness.

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引用次数: 0

A framework of risk response strategy selection considering the loss caused by risk propagation in the project portfolio

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-07 DOI: 10.1016/j.cie.2025.110935

Zhong Shen , Xingmei Li , Dongqing Jia , Xiaoyan Lv

The successful implementation of project portfolio calls for effective risk management, in which how to select appropriate strategies to respond to upcoming risks, is called risk response strategy (RRS) selection. However, two deficiencies are discovered in current RRS selection research. The one is that researchers ignore the phenomenon of risk propagation and the risk losses it brings, which makes some risks with propagation potential underestimated in the process of risk evaluation. The other is that lacking approach to quantify the loss caused by risk propagation, which is not conducive to assessing risks and selecting RRSs from a quantitative perspective. Under this circumstance, this paper firstly proposes a framework of RRS selection considering the loss caused by risk propagation in the project portfolio. In the proposed framework, Bayesian network and fuzzy theory are used to measuring the probability of risk propagation between projects. Subsequently, the propagation process of risks among projects is generated, based on which the probability and time point of loss caused by each risk are calculated. Finally, the losses caused by risk propagation before and after applying risk response strategies are quantified, and an RRS selection model to maximize the recovered risk loss is constructed. The analysis of a case study demonstrates that 1) with the increase of available funds used to respond to risks, the recovered risk loss by each unit of fund declines; 2) before the implementation of projects, the decision-maker should select the RRSs that can recover more direct risk losses; 3) when projects have been implemented, decision-maker needs to focus on the indirect losses caused by risk propagation and select RRSs that can block the propagation of risks in the project portfolio.

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引用次数: 0

Calendar design for assignment of ongoing appointments: Modeling, analysis and application

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-06 DOI: 10.1016/j.cie.2025.110931

Yossi Luzon , Yariv N. Marmor

Appointment booking (AB) is a widely used technique for managing elective services like hospital surgical units, law courtrooms, and other services with high demand and limited resources. AB typically assigns services to specific time slots using an appointment calendar. However, devising an effective AB policy is challenging due to varying service types, durations, and demand variability. In this study, we present a preplanned appointment calendar (PAC), designed using a two-stage stochastic programming model to tackle service scheduling challenges based on demand and historical data. The design of the PAC is generated offline, prior to the arrival of any customers requesting an appointment, and aims to minimize the time from the initial appointment request to service completion, namely, the patient’s sojourn time. Despite the substantial public expenses incurred when appointments are scheduled far in the future, encompassing both indirect costs (e.g., those related to the development of chronic diseases) and direct costs (e.g., those arising from employee absence), the sojourn-time measure has not received sufficient attention in the existing literature. Our method minimizes patients’ sojourn time while considering operational constraints and quality of service (QoS) considerations, resulting in a practical and user-friendly appointment booking system. Our approach is adjustable and easy to apply in real time. We introduce the chained-PAC (CPAC) mechanism, in which multiple, smaller PACs are joined together, and demonstrate the applicability of this approach by implementing it in a cardiac surgical operating room at a major hospital in Toronto, Canada. Results show the PAC approach reduces wait times and improves resource utilization in the surgical unit compared to the existing AB system. Our approach benefits healthcare providers and patients and can extend to other similar service systems.

{"title":"Calendar design for assignment of ongoing appointments: Modeling, analysis and application","authors":"Yossi Luzon , Yariv N. Marmor","doi":"10.1016/j.cie.2025.110931","DOIUrl":"10.1016/j.cie.2025.110931","url":null,"abstract":"<div><div>Appointment booking (AB) is a widely used technique for managing elective services like hospital surgical units, law courtrooms, and other services with high demand and limited resources. AB typically assigns services to specific time slots using an appointment calendar. However, devising an effective AB policy is challenging due to varying service types, durations, and demand variability. In this study, we present a preplanned appointment calendar (PAC), designed using a two-stage stochastic programming model to tackle service scheduling challenges based on demand and historical data. The design of the PAC is generated offline, prior to the arrival of any customers requesting an appointment, and aims to minimize the time from the initial appointment request to service completion, namely, the patient’s sojourn time. Despite the substantial public expenses incurred when appointments are scheduled far in the future, encompassing both indirect costs (e.g., those related to the development of chronic diseases) and direct costs (e.g., those arising from employee absence), the sojourn-time measure has not received sufficient attention in the existing literature. Our method minimizes patients’ sojourn time while considering operational constraints and quality of service (QoS) considerations, resulting in a practical and user-friendly appointment booking system. Our approach is adjustable and easy to apply in real time. We introduce the chained-PAC (CPAC) mechanism, in which multiple, smaller PACs are joined together, and demonstrate the applicability of this approach by implementing it in a cardiac surgical operating room at a major hospital in Toronto, Canada. Results show the PAC approach reduces wait times and improves resource utilization in the surgical unit compared to the existing AB system. Our approach benefits healthcare providers and patients and can extend to other similar service systems.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"201 ","pages":"Article 110931"},"PeriodicalIF":6.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143334169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated optimization of maintenance, spare parts management and operation for a multi-component system: A case study

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-06 DOI: 10.1016/j.cie.2025.110942

Jinjin Tang , Qianwang Deng , Changwen Wang , Mengqi Liao , Weifeng Han

Efficient maintenance activities are essential for the safe operation of industrial systems, and rational spare parts management, as an integral support to maintenance activities, is also closely linked to operation planning. In this paper, an integrated optimization model of maintenance, spare parts management, and operation for a single-machine multi-component system is proposed, shortened to MSO-SMPS. The goal of MSO-SMPS is the rational design of maintenance strategy, supported by an excellent collaborative management mechanism for new and used spare parts, achieving simultaneous optimization of the total cost and the completion time. Specifically, an adaptive opportunistic maintenance (OM) strategy and a reuse mechanism of retired components are designed to cope with dynamic changes in the system state and operating environment. Combining new and used spare parts can significantly improve the utilization of spare parts while ensuring that maintenance activities are carried out efficiently. In addition, to better address MSO-SMPS, an improved memetic algorithm (IMA) is proposed, in which an initialization method and four local search operators are designed to improve the solve efficiency. Finally, taking the tunnel boring machine (TBM) cutterhead system as a case, extensive experiments verify the effectiveness of the proposed designs.

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引用次数: 0

A feature based neural network model for distributed flexible flow shop scheduling considering worker and transportation factors

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-06 DOI: 10.1016/j.cie.2025.110917

Tianpeng Xu, Fuqing Zhao, Jianlin Zhang, Jianxin Tang, Hao Zhou

In the context of distributed flexible flow shop scheduling (DFFSP), the factors of worker and transportation have a significant impact on the production efficiency within a manufacturing environment. However, previous research rarely considers both worker and transportation simultaneously. Therefore, this paper investigates a DFFSP with worker and transportation factors (DFFSP-WT). Considering the characteristic of DFFSP-WT, a neural network-based monarch butterfly optimization (NNMBO) is designed to minimize the objectives of makespan, total cost, and worker fatigue. In the NNMBO, the monarch butterfly optimization (MBO) is employed as the primary optimization operator to determine the job sequence. Furthermore, a feature-based search strategy (FSS), which encompasses six distinct local search operators, is developed to enhance the search capability. Additionally, a feature-based neural network model (FNN) is designed to adaptively select the best FSS. To verify the effectiveness of NNMBO, the simulation experiments are conducted with other state-of-the-art algorithms on test instances, the experimental results demonstrate that the NNMBO is a promising algorithm to solve DFFSP-WT.

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引用次数: 0

Eco-friendly long-haul perishable product transportation with multi-compartment vehicles

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-05 DOI: 10.1016/j.cie.2025.110934

Pisit Jarumaneeroj , Supisara Krairiksh , Puwadol Oak Dusadeerungsikul , Dong Li , Çağatay Iris

Multi-compartment refrigerated vehicles (MCVs) have been recently utilized in long-haul perishable product transportation, thanks to their flexibility in storage capacity with different temperature settings. To better understand trade-offs between economic and environmental aspects of long-haul transportation of perishable products with refrigerated vehicles, a Multi-Compartment Vehicle Loading and Scheduling Problem (MCVLSP) that minimizes three objectives—transportation cost, carbon emissions, and total food loss—is herein solved by mathematical modeling and genetic algorithm (GA) approaches. Our computational results indicate that larger MCVLSP instances cannot be solved to optimality using the mathematical model with off-the-shelf optimization software packages. The proposed GA delivers strong computational performance for MCVLSP with respect to solution quality and computational time. We find that, among three objectives, the environmental objective is the most sensitive one as slight difference in either vehicle loading or scheduling decisions could result in solutions with significantly varying carbon emissions. Moreover, solutions with fewer MCVs are not necessarily environmentally sustainable. Rather, deploying larger MCV fleets could potentially result in lower carbon emissions and food weight loss for perishable products—albeit a slight increase in total transportation cost—due to the changes in vehicle loading and scheduling decisions.

{"title":"Eco-friendly long-haul perishable product transportation with multi-compartment vehicles","authors":"Pisit Jarumaneeroj , Supisara Krairiksh , Puwadol Oak Dusadeerungsikul , Dong Li , Çağatay Iris","doi":"10.1016/j.cie.2025.110934","DOIUrl":"10.1016/j.cie.2025.110934","url":null,"abstract":"<div><div>Multi-compartment refrigerated vehicles (MCVs) have been recently utilized in long-haul perishable product transportation, thanks to their flexibility in storage capacity with different temperature settings. To better understand trade-offs between economic and environmental aspects of long-haul transportation of perishable products with refrigerated vehicles, a Multi-Compartment Vehicle Loading and Scheduling Problem (MCVLSP) that minimizes three objectives—transportation cost, carbon emissions, and total food loss—is herein solved by mathematical modeling and genetic algorithm (GA) approaches. Our computational results indicate that larger MCVLSP instances cannot be solved to optimality using the mathematical model with off-the-shelf optimization software packages. The proposed GA delivers strong computational performance for MCVLSP with respect to solution quality and computational time. We find that, among three objectives, the environmental objective is the most sensitive one as slight difference in either vehicle loading or scheduling decisions could result in solutions with significantly varying carbon emissions. Moreover, solutions with fewer MCVs are not necessarily environmentally sustainable. Rather, deploying larger MCV fleets could potentially result in lower carbon emissions and food weight loss for perishable products—albeit a slight increase in total transportation cost—due to the changes in vehicle loading and scheduling decisions.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110934"},"PeriodicalIF":6.7,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-objective integrated harvest and distribution scheduling for fresh agricultural products with farm-to-door requirements using Q-learning and problem knowledge-based cooperative evolutionary algorithms

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110755

Xiaomeng Ma , Xujin Pu , Yaping Fu

Due to growing concerns about food safety and dietary health, the freshness and quality of fresh agricultural products (FAPs) are becoming increasingly important. The farm-to-door supply mode is a promising method for delivering FAPs with high freshness and quality by shortening supply chains. This supply mode is characterized by scheduling harvest and distribution together, which is more complex than scheduling the two activities independently. It is challenging to simultaneously make multiple decisions related to the harvest and distribution stages and handle their relationships to achieve overall optimization. These challenges create the need for particular modelling and optimization approaches to improve operation efficiency. To this end, this study proposes a multi-objective integrated FAP harvest and distribution scheduling problem. First, a mathematical model is formulated to minimize the total operation cost and maximize customer satisfaction. Second, a Q-learning-based cooperative evolutionary algorithm with problem-specific knowledge (Q-CEA-K) is developed, in which the population and Pareto archive execute global and local searches, respectively. Two heuristic rules based on problem-specific knowledge are designed to produce initial solutions, and five properties are derived and used to develop knowledge-based local search methods. Cooperative strategies are proposed to realize collaborative search between the population and Pareto archive. Furthermore, the Q-learning method is used to select a search framework for the population. Finally, Q-CEA-K is compared with three state-of-the-art algorithms and a mathematical programming solver CPLEX through extensive experiments. The comparison and statistical analysis results confirm the superiority of Q-CEA-K in solving the problem under consideration.

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引用次数: 0

A human digital twin approach for fatigue-aware task planning in human-robot collaborative assembly

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110774

Yingchao You , Boliang Cai , Duc Truong Pham , Ying Liu , Ze Ji

Human-robot collaboration (HRC) has emerged as a pivotal paradigm in manufacturing, integrating the strengths of both human and robot capabilities. Neglecting human physical fatigue may adversely affect worker health and, in extreme cases, may lead to musculoskeletal disorders. However, human fatigue has rarely been considered for decision-making in HRC manufacturing systems. Integrating adaptive decision-making to optimise human fatigue in HRC manufacturing systems is crucial. Nonetheless, real-time perception and estimation of human fatigue and decision-making informed by human fatigue face considerable challenges. To address these challenges, this paper introduces a human digital twin method, a bidirectional communication system for physical fatigue assessment and reduction in human-robot collaborative assembly tasks. The methodology encompasses an IK-BiLSTM-AM-based surrogate model, which consists of inverse kinematics analysis (IK), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), for real-time muscle force estimation integrated with a muscle force-fatigue model for muscle fatigue assessment. An And-Or graph and optimisation model-based HRC task planner is also developed to alleviate physical fatigue via task allocation. The efficacy of this approach has been validated through proof-of-concept assembly experiments involving multiple subjects. The results show that the IK-BiLSTM-AM model achieves a minimum of 8 % greater accuracy in muscle force estimation than the baseline methods. The 12-subject assessment results indicate that the task planner effectively reduces the physical fatigue of workers while performing collaborative assembly tasks.

{"title":"A human digital twin approach for fatigue-aware task planning in human-robot collaborative assembly","authors":"Yingchao You , Boliang Cai , Duc Truong Pham , Ying Liu , Ze Ji","doi":"10.1016/j.cie.2024.110774","DOIUrl":"10.1016/j.cie.2024.110774","url":null,"abstract":"<div><div>Human-robot collaboration (HRC) has emerged as a pivotal paradigm in manufacturing, integrating the strengths of both human and robot capabilities. Neglecting human physical fatigue may adversely affect worker health and, in extreme cases, may lead to musculoskeletal disorders. However, human fatigue has rarely been considered for decision-making in HRC manufacturing systems. Integrating adaptive decision-making to optimise human fatigue in HRC manufacturing systems is crucial. Nonetheless, real-time perception and estimation of human fatigue and decision-making informed by human fatigue face considerable challenges. To address these challenges, this paper introduces a human digital twin method, a bidirectional communication system for physical fatigue assessment and reduction in human-robot collaborative assembly tasks. The methodology encompasses an IK-BiLSTM-AM-based surrogate model, which consists of inverse kinematics analysis (IK), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), for real-time muscle force estimation integrated with a muscle force-fatigue model for muscle fatigue assessment. An And-Or graph and optimisation model-based HRC task planner is also developed to alleviate physical fatigue via task allocation. The efficacy of this approach has been validated through proof-of-concept assembly experiments involving multiple subjects. The results show that the IK-BiLSTM-AM model achieves a minimum of 8 % greater accuracy in muscle force estimation than the baseline methods. The 12-subject assessment results indicate that the task planner effectively reduces the physical fatigue of workers while performing collaborative assembly tasks.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110774"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Memetic algorithm based on non-dominated levels for flexible job shop scheduling problem with learn-forgetting effect and worker cooperation 基于非支配水平的记忆算法，用于具有学习遗忘效应和工人合作的灵活作业车间调度问题

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110845

KaiXing Han, Wenyin Gong

Traditional flexible job shop scheduling problems (FJSP) often focus on the flexibility of machines, neglecting the effectiveness and flexibility of workers. In real production environments, workers’ processing proficiency is influenced by the learn-forgetting effect, and they tend to cooperate when handling complex tasks to reduce difficulties. The impact and interests of workers are increasingly becoming indispensable factors in modern manufacturing systems. Therefore, this paper investigates a FJSP with learn-forgetting effect and worker cooperation (FJSP-LFWC) to simultaneously optimize makespan and maximum worker workload. A mathematical model is established for this problem, and a memetic algorithm based on non-dominated levels (MANL) is proposed to efficiently solve it. MANL addresses the problem in several key ways. Firstly, it generates a high-quality initial population through a meticulously designed hybrid initialization strategy. Secondly, it applies a novel decoding method to improve solution quality. Thirdly, it adjusts the selection strategy based on the convergence of the population. Additionally, a tailored local search strategy incorporating five local search operators is utilized for three types of candidate solutions to accelerate convergence and fully utilize the solution space. Extensive experiments are conducted based on 28 newly formulated instances. The experimental results demonstrate that MANL significantly outperforms five well-known comparison algorithms, showcasing its efficiency in solving FJSP-LFWC.

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引用次数: 0

A Multi-Type data driven framework for solving flexible job shop scheduling problem considering multiple production resource states

IF 6.7 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Industrial Engineering

Pub Date : 2025-02-01 DOI: 10.1016/j.cie.2024.110835

Siyang Ji, Zipeng Wang, Jihong Yan

The development of flexible manufacturing models has been propelled by Industry 4.0, making it a cornerstone of intelligent manufacturing. To address the challenges posed by frequent order changes and multiple production state disruptions in highly customized manufacturing processes. In this paper, a new framework for solving dynamic flexible job shop scheduling problem is proposed for the first time. A state constraint representation method is proposed, which can decouple the relationship between the scheduling optimization algorithm and various constraint conditions. The feasibility of the method is validated under six dynamic production states, including the shift calendar for equipment, equipment availability, equipment failures, equipment maintenance, job rework, and the insertion of jobs. Moreover, an improved Genetic Algorithm is deployed within the framework to address scheduling optimization. Compared to multiple algorithms, the proposed method is competitive in terms of optimization effectiveness and efficiency. Furthermore, the framework is deployed in a certain aerospace engine machining workshop, and the results demonstrate that the proposed framework is competitive in performing complex tasks.

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引用次数: 0