Computers & Industrial Engineering最新文献

英文中文

Adaptive manufacturing control with Deep Reinforcement Learning for dynamic WIP management in industry 4.0

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

Computers & Industrial Engineering

Pub Date : 2025-02-17 DOI: 10.1016/j.cie.2025.110966

Silvestro Vespoli, Giulio Mattera, Maria Grazia Marchesano, Luigi Nele, Guido Guizzi

In the context of Industry 4.0, manufacturing systems face increased complexity and uncertainty due to elevated product customisation and demand variability. This paper presents a novel framework for adaptive Work-In-Progress (WIP) control in semi-heterarchical architectures, addressing the limitations of traditional analytical methods that rely on exponential processing time distributions. Integrating Deep Reinforcement Learning (DRL) with Discrete Event Simulation (DES) enables model-free control of flow-shop production systems under non-exponential, stochastic processing times. A Deep Q-Network (DQN) agent dynamically manages WIP levels in a CONstant Work In Progress (CONWIP) environment, learning optimal control policies directly from system interactions. The framework’s effectiveness is demonstrated through extensive experiments with varying machine numbers, processing times, and system variability. The results show robust performance in tracking the target throughput and adapting the processing time variability, achieving Mean Absolute Percentual Errors (MAPE) in the throughput – calculated as the percentage difference between the actual and the target throughput – ranging from 0.3% to 2.3% with standard deviations of 5. 5% to 8. 4%. Key contributions include the development of a data-driven WIP control approach to overcome analytical methods’ limitations in stochastic environments, validating DQN agent adaptability across varying production scenarios, and demonstrating framework scalability in realistic manufacturing settings. This research bridges the gap between conventional WIP control methods and Industry 4.0 requirements, offering manufacturers an adaptive solution for enhanced production efficiency.

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引用次数: 0

Dynamic reliability evaluation of multi-performance sharing and multi-state systems with interdependence

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

Computers & Industrial Engineering

Pub Date : 2025-02-16 DOI: 10.1016/j.cie.2025.110965

Heping Jia , He Lu , Rui Peng , Kaiye Gao

Numerous engineering systems are designed to be performance sharing systems with multiple interdependent performances. Motivated by practical engineering systems, in this study, a multi-performance sharing and multi-state system (MPSMS) with performance interdependence was modeled. The model includes a feasible operating region (FOR) considering the interdependent operation constraints of the components. Moreover, a multi-performance sharing mechanism including performance sharing limits and performance conversion efficiency was developed. Furthermore, an approach combining Markov process model and extended Lz-transform technique was adopted to develop unified representations of the dynamic reliability models of the considered MPSMSs. Dynamic reliability indices including the time-varying reliability and the expected instantaneous performance deficiency (EIPD) were evaluated through four case studies to validate the developed model and approach.

引用次数: 0

AS-IS representation and strategic framework for the design and implementation of a disassembly system

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

Computers & Industrial Engineering

Pub Date : 2025-02-15 DOI: 10.1016/j.cie.2025.110975

Thibault Hervouet, Sotirios Panagou, Fabio Sgarbossa

Disassembly is a critical process in circular manufacturing models for end-of-life (EOL) products, enabling the recovery of components for reuse or repurposing, and reducing the reliance on raw material extraction. However, disassembly systems (DS) face significant barriers and uncertainties. Key challenges include the uncertainty of volume of returned EOL products, the variability of product variants due to mass customization, and the uncertain condition of these products. These factors complicate decision-making in DS design and implementation, impacting resource allocation and sustainability efforts. Moreover, the lack of reliable industrial case studies adds up to these challenges and highlights a gap in the literature. Apart from the low number of available resources and solutions, there is a fundamental need for a deeper understanding of the systematic barriers and strategic challenges facing DS. This work addresses these issues by providing both practitioners and researchers with a methodology for the AS-IS representation of disassembly system design and implementation (DSDI). Additionally, it offers a strategic level framework for DSDI, tested through a case study of a Norwegian electronics company.

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引用次数: 0

Personalized product design and user review and experience analysis: A data-driven hybrid novel approach

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

Computers & Industrial Engineering

Pub Date : 2025-02-13 DOI: 10.1016/j.cie.2025.110939

Shulin Lan , Yinfei Jiang , Tao Guo , Shaochun Li , Chen Yang , T.C. Edwin Cheng , Kanchana Sethanan , Ming-Lang Tseng

This study contributes to mass customization by addressing the lack of effective methods for extracting and analyzing personalized demand indicators from user feedback. Prior studies often neglect the mapping relationship between user feedback and production characteristics, the practical integration of user experience data with product design constraints, limiting their ability to meet diverse consumer needs. To overcome these challenges, this study proposes a data-driven approach that combines k-means clustering, sentiment analysis, and deep learning to identify key comment factors impacting the user experience of customized products. This study offers substantial scientific value by proposing a systematic and scalable method for understanding consumer preferences in mass customization. It provides manufacturers with actionable insights for improving product competitiveness and customer satisfaction. The results demonstrate that product thinness and performance are the most critical factors for personalized information technology product design, significantly influencing user satisfaction. Regression analysis confirms that while these factors, along with price, heavily affect user ratings, battery life and heat dissipation are of secondary importance.

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引用次数: 0

Scenario simulation and regulation policy optimization of industrial enterprise production water considering scale heterogeneity: A case study in the chemical industry

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

Computers & Industrial Engineering

Pub Date : 2025-02-12 DOI: 10.1016/j.cie.2025.110961

Dongying Sun , Jialin Dong , Xiaoya Gu , Zhisong Chen

To build a water-saving society, it is crucial to integrate the water-saving priority concept into the production and operation of enterprises and optimize the regulation policy. Hence, this study constructs a scenario simulation model for production water decision-making. Taking the chemical enterprises in Zhenjiang as an example, it investigates the response mechanism of water use decision-making of enterprises of different scales under designed scenarios comprising three policies (i.e., water price, subsidy, and penalty). Moreover, considering the government’s different preferences for environmental and economic benefits, the particle swarm optimization (PSO) algorithm is used to find the optimal combination of water-saving policies for enterprises of different scales. The results show that the response pattern of enterprises to changes in government water-saving regulation policies is heterogeneous by scale. Additionally, multiple policy combinations can better achieve the tradeoff between water-saving and economic benefits. The PSO can optimize the objective function composed of water-saving and economic profit under different weights. Considering the scale heterogeneity, it is more reasonable to formulate policies by scale than uniform policies. Finally, based on our findings, this study proposes targeted policy recommendations to promote water conservation in industrial enterprises.

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引用次数: 0

A systematic review of mathematical programming models and solution approaches for the textile supply chain

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

Computers & Industrial Engineering

Pub Date : 2025-02-11 DOI: 10.1016/j.cie.2025.110937

Giovanna Abreu Alves , Roberto Tavares , Pedro Amorim , Victor Claudio Bento Camargo

The textile industry is a complex and dynamic system where structured decision-making processes are essential for efficient supply chain management. In this context, mathematical programming models offer a powerful tool for modeling and optimizing the textile supply chain. This systematic review explores the application of mathematical programming models, including linear programming, nonlinear programming, stochastic programming, robust optimization, fuzzy programming, and multi-objective programming, in optimizing the textile supply chain. The review categorizes and analyzes 163 studies across the textile manufacturing stages, from fiber production to integrated supply chains. Key results reveal the utility of these models in solving a wide range of decision-making problems, such as blending fibers, production planning, scheduling orders, cutting patterns, transportation optimization, network design, and supplier selection, considering the challenges found in the textile sector. Analyzing those models, we point out that sustainability considerations, such as environmental and social aspects, remain underexplored and present significant opportunities for future research. In addition, this study emphasizes the importance of incorporating multi-objective approaches and addressing uncertainties in decision-making to advance sustainable and efficient textile supply chain management.

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引用次数: 0

An optimal effectiveness-driven target segment selection modeling approach for marketing campaign management

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

Computers & Industrial Engineering

Pub Date : 2025-02-10 DOI: 10.1016/j.cie.2025.110945

Cesar Salazar-Santander, Alejandro F. Mac Cawley, Carolina Martinez-Troncoso

Defining a target group for a mass marketing campaign is a non-trivial goal, which depends on the correct definition of the commercial stimuli and the selection of a customer target segment that will maximize the campaign’s effectiveness. This process requires the analysis of multiple customer variables and interactions. The problem becomes even more complex if we consider a limited budget for the campaign. This research proposes a methodology based on a mixed multi-objective optimization formulation that allows us to determine a minimum continuous customer target segment for massive campaigns to maximize its effectiveness with a maximum budget constraint. The multi-objective function of the model maximizes the effectiveness of the campaign while minimizing the “broadness” of the targeted segments, allowing the detection of the most effective and homogeneous target group possible for a commercial action within a set of

N

continuous variables. The methodology performance was benchmarked against traditional customer clustering and greedy segmentation algorithms. The experiments were carried out in (1) simulated data environments and (2) based on real campaign information. The compared scenarios show that the proposed methodology outperforms the baseline model, the complexity of the problem scales non-linearly, increasing the number of variables, and the model increases 54% the effectiveness of a campaign without an increment in the segment range.

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引用次数: 0

Small and medium-sized enterprise dedicated knowledge exploitation mechanism: A recommender system based on knowledge relatedness

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

Computers & Industrial Engineering

Pub Date : 2025-02-08 DOI: 10.1016/j.cie.2025.110941

Xingyu Sima , Thierry Coudert , Laurent Geneste , Aymeric de Valroger

Knowledge is a vital asset for organizations, especially in today’s Industry 4.0 context with the ever-increasing amount of information being produced. Organizations must consider knowledge management (KM) to create a sustainable competitive advantage. Currently, KM is applied relatively well in large organizations; however, small and medium-sized enterprises (SMEs) encounter various constraints. Knowledge exploitation is a key phase in KM for the retrieval of relevant knowledge. Therefore, a recommender system (RS), which is a promising and widely used information technology (IT) tool, is proposed in this study, for SMEs to enable effective knowledge exploitation. The RS can be adapted to SME KM specificities and a dedicated RS based on knowledge relatedness derived from different information sources is proposed herein. The proposed RS enables the recommendation of knowledge item balancing: i) historical application data, that is, information regarding how items were related during past projects, and ii) initial relatedness knowledge, which represents the relationships between knowledge items defined by knowledge experts. The proposed RS was developed in collaboration with the Axsens-bte SME, who specialize in consultancy and training in the supply chain, Industry 4.0, and quality requirements management. The proposed RS improved SME KM processes and increased efficiency in terms of exploiting knowledge assets. This demonstrated the ability of the proposed RS to assist SMEs in efficiently and effectively navigating complex information environments.

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引用次数: 0

A novel hybrid approach for designing green robust manufacturing cells

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.2025.110946

Habib Heydari , Mohammad Mahdi Paydar , Iraj Mahdavi

People in today’s world pay more attention to environmental issues. This positive sensitivity which is accompanied by governmental regulations forces manufacturers to take account of cleaner design attributes in production system configurations. Companies face challenges in increasing flexibility and efficiency as preliminaries of production evolution while considering green components. An integrated framework in the context of green production is a practical solution. This paper introduces a comprehensive bi-objective model for designing green robust cellular manufacturing. Besides the minimization of cell formation, layout design, and production planning costs, an environmental objective is formulated in terms of the minimization of production wastes. Simple augmented ε-constraint (SAUGMECON) to obtain trade-off solutions in small scales of the model is applied. An illustrative example is provided to detail the SAUGMECON and the model characteristics. Analyses of the objective functions’ weights in a fuzzy decision-making framework are also performed. For larger examples, a hybrid optimizer based on branch-and-bound, genetic, and SAUGMECON algorithms is developed. It is concluded that our hybrid multi-objective strategy yields very outstanding results.

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引用次数: 0

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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