Computers & Industrial Engineering最新文献_第6页

Unlocking the Synergy: Increasing productivity through Human-AI collaboration in the industry 5.0 Era 释放协同效应：在工业 5.0 时代通过人机协作提高生产力

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.2024.110657

Xue Sun , Yu Song

The prevailing trajectory of technological evolution emphasizes the sustainable development of human-AI collaboration. In this study, we employ the coupling coordination degree model to evaluate the dynamics of human-AI collaboration in China and match it with listed companies. Through panel models, the study not only quantifies the contribution of such collaboration to enhancing company input–output efficiency but also explores how it serves as a catalyst for technological catch-up. Our findings indicate that the integration of human capital with AI emerges as a potent driver of company efficiency, with the extent of the impact also tied to organizational characteristics. Furthermore, the scale of investment and organizational size play a crucial role in the effectiveness of HIC, underscoring the adaptability of human-AI collaboration strategies to various organizational contexts and the importance of tailored implementation. Our research highlights the inherent collaborative potential of AI within the Industry 5.0 framework, advocating for the fusion of human creativity with AI precision to foster a development paradigm that is resource-efficient, cost-effective, and human-centric.

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引用次数: 0

Electric vehicle supply chain investment under demand uncertainty: A jointly held real options perspective 需求不确定情况下的电动汽车供应链投资：共同持有实物期权的视角

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.2024.110840

Feng Liu , Carman K.M. Lee , Min Xu

An increasing number of electric vehicle (EV) companies are facing supply chain investment decisions, which are essential for the effective operation and management of their businesses. This study proposes a real options approach to explore the investment timing threshold of the EV supply chain under demand uncertainty. It addresses the limitations of previous studies that primarily focused on the perspective of a single investor under a deterministic demand. To achieve the objective, an analytical real options game model is first presented for investment in the EV supply chain. Then, the investment timing threshold and option value of the EV supply chain are derived under three different scenarios: the integrated case, the revenue-sharing contract case, and the revenue-sharing contract through bargaining. The findings reveal that the investment timing threshold is lower when bargaining occurs between the two parties in the EV supply chain compared to the revenue-sharing contract case. Furthermore, the investment timing threshold exhibits a negative correlation with the drift and learning rates. It also increases with the volatility of the bargaining parameter, risk-free interest rate, and market demand volatility. The option value, on the other hand, shows a positive correlation with the demand-shift and volatility parameters. A bargaining-based revenue-sharing contract is proposed as a means to coordinate the supply chain. These results provide theoretical guidance for investments in the EV supply chain.

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引用次数: 0

A Production-Logistics prediction method integrating Spatial-Temporal features in flexible production workshop for buffer allocation problem 针对缓冲区分配问题的灵活生产车间空间-时间特征集成生产-物流预测方法

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.2024.110761

Qi Zhang , Anmin Wang , Jie Li , Longhui Zheng , Jinsong Bao , Dan Zhang

To meet the demands of personalized manufacturing, characterized by customized production with varying batch sizes, logistics equipment such as Automated Guided Vehicles (AGVs) play a critical role in the manufacturing process. However, the distribution of multiple batches is influenced by various factors, with buffer zone capacity allocation emerging as a key challenge. Optimizing buffer zone allocation necessitates a thorough consideration of both spatial characteristics (e.g., shop floor layout and workpiece pathways) and temporal characteristics (e.g., the sequence of material distribution) to enhance resource allocation, reduce bottlenecks, and improve efficiency. This research proposes a novel logistics prediction method for flexible production plants, utilizing a graph attention network that integrates spatial–temporal features. The method first applies a multi-head attention mechanism to capture significant temporal information. Then, a graph convolutional network is employed to model the workshop layout topology and workpiece processing paths, thereby extracting the spatial features of logistics. This spatial information is sequentially processed through a gated recurrent unit and the multi-head attention mechanism to capture the dynamic temporal features of logistics. The proposed model is ultimately employed to predict production logistics in a flexible manufacturing workshop. The experimental results of the MA-T-GCN (Multi-head Attention Temporal Graph Convolution Network) model on production logistics prediction demonstrate an improvement over the best-performing baseline methods on standard benchmark metrics under varying experimental conditions.

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引用次数: 0

Cooperation strategies for competing ports considering risk Linkages 考虑风险的竞争港口合作战略联系

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.2024.110796

Jie Wu, Jiaguo Liu

Port cooperation significantly enhances the competitiveness of the region where a port operates, thereby attracting increased cargo demand. However, close operational collaboration also heightens the risk of inter-port transmission during disasters, escalating their vulnerability. In this paper, we develop a risk transmission game model to investigate the impact of regional port co-operation (competition and cooperation) on disaster prevention investments and identify the optimal cooperation strategy that holds paramount practical significance. We uncover a counter-intuitive finding suggesting that, in a competitive environment, close cooperation between ports may adversely affect the resilience and profit of ports. Through a synthesis of consumer surplus and social welfare analyses, we observe that when the positive effect of cooperation between the ports is large, public integration is a four-win strategy, optimizing disaster prevention investment, port profits, consumer surplus, and social welfare. Moreover, we show that intense competition between ports is always detrimental to port resilience, port profits, consumer interests, and societal interests.

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引用次数: 0

A horizontal collaboration approach for grape transportation in a pisco cooperative 皮斯科合作社葡萄运输的横向协作方法

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.2024.110797

Franco Basso , Carlos A. Monardes-Concha , Francisco Lorca , Raúl Pezoa , Mauricio Varas

This paper proposes a novel horizontal collaborative framework for grape transportation within a cooperative that supplies grapes for pisco production. Nowadays, each cooperative member collects and delivers grapes to previously assigned processing plants without coordinating with the other members. In order to evaluate the potential impact of shared transport resources, we model collaboration among farmers as a cooperative game with transferable cost, where a mixed integer programming formulation that models transport activities is repeatedly solved for computing the characteristic function. Although the grand coalition is theoretically better than any other coalition structure, our approach limits the number of farmers per coalition to assess incremental collaboration’s benefits. By conducting a case study involving sixty farmers, we show that a collaborative approach leads to a 13.4% reduction in the cooperative’s transportation costs. However, we observe that the benefits of collaboration show diminishing returns. Particularly, we show that a coalition structure with a maximum number of three players per coalition captures almost 94% of the maximum potential savings.

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引用次数: 0

A dynamic analysis of a green closed-loop supply chain with different on-line platform smart recycling and selling models 采用不同在线平台智能回收和销售模式的绿色闭环供应链动态分析

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.2024.110748

Xiaohong Chen , Cheng Zhang

Global warming and waste product recycling have become increasingly serious. Green innovation of a closed-loop supply chain (GCLSC) and selection of on-line platform recycling and sales models need to be solved as a matter of urgency. To achieve this purpose, dynamic influences of green innovation, platform smart marketing, and recycling investments on goodwill are considered. Dynamic game models are used to solve the equilibrium solutions and optimal trajectories under different models, and influences of commission rates on the profits and key variables are compared via numerical simulations. The results show that the evolution paths of goodwill under the four decision models all descend at first, then stabilize; the optimal strategy can be selected based on the intervals of sales and recycling commission rates. Under the dual-agency model (agency selling and platform agency recycling (Model AP)), the higher the sales commission rate (SCR) is, the more likely the manufacturer chooses the reselling and platform agency recycling (Model RP), which, however, may lead to insufficient green innovation investment. The higher the RCR, the greater the platform profit when selecting agency selling. The system profit first increases, then decreases with growing sales commission, and Model AP represents the optimal strategy. The research offers reference for the selection of operation models and investment decision, is conducive to improving the enterprise goodwill, and promotes the sustainable development of the GCLSC.

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引用次数: 0

Random replacement strategies modeling through back and front warranties with preventive maintenance 通过前后保修和预防性维护建立随机更换策略模型

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.2024.110819

Lijun Shang , Yongzheng Tian , Yongjun Du , Jiangbin Zhao , Zhiqiang Cai

In the context of advanced digital technology enabling real-time monitoring of data elements throughout the product lifecycle, numerous researchers have proposed multi-dimensional random warranties integrating monitored missions. However, some key aspects have been commonly overlooked. For example, when the principle of ’whichever occurs first’ or ’whichever occurs last’ is adopted to handle warranty-expiry limits, the heterogeneous durations of warranty time will either reduce the warranty time for the consumers or increase the warranty cost for the manufacturers. To address these issues, this paper proposes two random warranties, each of which is combined with mission elements. The proposed warranties include a random renewable repair back warranty and a random renewable repair front warranty with preventive maintenance (PM) designed to control warranty time and cost by dynamic means. Additionally, extending the design concepts derived from these warranties to the post-warranty scopes, this paper defines and models a random renewable-periodic back replacement strategy and a random renewable-periodic front replacement strategy with PM, which can flexibly sustain post-warranty reliabilities. Finally, a numerical analysis is carried out to explore the insights and effectiveness of the proposed method, demonstrating that the proposed warranties are superior and the proposed replacement strategies are effective.

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引用次数: 0

Battery swapping station location routing problem: A Cooperative Business Model 电池交换站位置路由问题：一种合作商业模式

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.2024.110775

Ying Li, Feifan Li, Qiuyi Li, Pengwei Zhang

In the context of green logistics, the promotion of electric logistics vehicles is gaining momentum, but the process is constrained by insufficient infrastructure, such as battery swapping stations (BSSs). The high costs of BSS make it challenging for companies to expand their construction, while the low penetration rate of EVs further diminishes the investment value and motivation, creating a circular dependency problem. To address this challenge, cooperative models can be employed to lower investment barriers through cost sharing and encourage broader enterprise participation in constructing BSSs. This study focuses on the battery swapping station location-routing problem (BSS-LRP) by introducing a ”Cooperative Business Model” in which logistics companies can complete or participate in BSSs construction based on their operational needs. The model considers battery electric vehicles (BEVs) load, range, etc., and aims to minimize the investment, usage, and BEVs transport costs of BSSs. A heuristic algorithm, Simulated Annealing-K-Means-Ant Colony Optimization (SA-K-Means-ACO), is designed to solve this problem. The effectiveness and accuracy of the proposed algorithm have been validated by comparing it with the TS-ACO algorithm, especially when dealing with clustered customer instances. Furthermore, research conducted a comprehensive experiment to discuss the impact of battery capacity, Investment, and usage costs, offering insights for logistics companies.

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引用次数: 0

Energy-constrained orienteering problem for green tourist trip design: Mathematical formulation and heuristic solution approaches

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.2024.110853

Tolga Karabaş, Mustafa Kemal Tural

Planning a touristic itinerary is a challenging task that requires personalized tour generation for tourists interested in visiting available points of interest (POIs). The negative externalities of using vehicles (e.g., emissions) can be reduced by considering such aspects in itinerary planning. To address this need, we propose the Energy-Constrained Tourist Trip Design Problem to plan environmentally friendly touristic itineraries. We model this problem as an Energy-Constrained Orienteering Problem (ECOP), where we consider continuous vehicle speed, speed-dependent travel time, and fuel consumption

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accordingly emissions

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which have not been considered in the scope of the OP before. First, the ECOP is formulated as a mixed-integer second-order cone programming (MISOCP) model which is able to solve only small-size instances to optimality within a reasonable time. Second, for large-size instances, we develop two heuristic methods, namely Greedy Insertion (GI) and Iterated Local Search (ILS) algorithms. In the latter, several improvement heuristics are coupled with an SOCP-based speed optimization procedure. Based on our computational experiments, the GI heuristic is the fastest among the proposed methods, while generally yielding suboptimal solutions compared to the ILS algorithm, but for larger instances, these solutions are better than the ones obtained via the exact method. The ILS algorithm outperforms the exact method by reaching good-quality solutions in a relatively short computing time. The ECOP has prospective applications on selective routing problems involving other transportation modes (e.g., cruise, aerial) and has a significant potential on reducing transportation-related GHG emissions.

{"title":"Energy-constrained orienteering problem for green tourist trip design: Mathematical formulation and heuristic solution approaches","authors":"Tolga Karabaş, Mustafa Kemal Tural","doi":"10.1016/j.cie.2024.110853","DOIUrl":"10.1016/j.cie.2024.110853","url":null,"abstract":"<div><div>Planning a touristic itinerary is a challenging task that requires personalized tour generation for tourists interested in visiting available points of interest (POIs). The negative externalities of using vehicles (e.g., emissions) can be reduced by considering such aspects in itinerary planning. To address this need, we propose the Energy-Constrained Tourist Trip Design Problem to plan environmentally friendly touristic itineraries. We model this problem as an Energy-Constrained Orienteering Problem (ECOP), where we consider continuous vehicle speed, speed-dependent travel time, and fuel consumption <span><math><mo>−</mo></math></span>accordingly emissions<span><math><mo>−</mo></math></span> which have not been considered in the scope of the OP before. First, the ECOP is formulated as a mixed-integer second-order cone programming (MISOCP) model which is able to solve only small-size instances to optimality within a reasonable time. Second, for large-size instances, we develop two heuristic methods, namely Greedy Insertion (GI) and Iterated Local Search (ILS) algorithms. In the latter, several improvement heuristics are coupled with an SOCP-based speed optimization procedure. Based on our computational experiments, the GI heuristic is the fastest among the proposed methods, while generally yielding suboptimal solutions compared to the ILS algorithm, but for larger instances, these solutions are better than the ones obtained via the exact method. The ILS algorithm outperforms the exact method by reaching good-quality solutions in a relatively short computing time. The ECOP has prospective applications on selective routing problems involving other transportation modes (e.g., cruise, aerial) and has a significant potential on reducing transportation-related GHG emissions.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110853"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource allocation models and heuristics for the multi-project scheduling with global resource transfers and local resource constraints

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.2024.110843

Wanjun Liu , Jingwen Zhang , Mario Vanhoucke , Weikang Guo

The transfer times and costs of global resources between different projects and the choice of transfer modes significantly affect the multi-project scheduling. This paper investigates four versions of the resource-constrained multi-project scheduling problem with global resource transfers and local resource constraints based on four realistic transfer scenarios, in which the global resource transfer times and costs are considered with a single transfer mode or multiple transfer modes. Three classes of heuristics with huge amount of priority rules are adapted and tested for the new problems. The schedule generation schemes of each class of heuristics are improved from two aspects. On the one hand, resource availability checks are divided into global and local phases due to their different characteristics. On the other hand, resource transfer rules and transfer mode rules are introduced to deal with resource transfer and transfer mode issues, respectively. The three class of heuristics are tested on well-known datasets of the multi-project problem, which are extended with transfer data using a transfer time/cost generation procedure. The numerical experiments first evaluate the performance of a set of priority rules, then effectively apply the priority rule heuristics in the genetic algorithm, and finally compare the performance of the priority rule heuristics with CPLEX on small-scale instances. Additionally, a multi-project case study verifies the applicability and good performance of priority rules that perform well in numerical experiments. Furthermore, the best performing rules are used by two machine learning methods in literature to automatically select the most promising ones.

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引用次数: 0