Computers & Industrial Engineering最新文献

{"title":"Toward energy-efficient blockchain system: A game theoretic analysis","authors":"Xu Wang ,&nbsp;Ling-Yun Wu","doi":"10.1016/j.cie.2024.110821","DOIUrl":"10.1016/j.cie.2024.110821","url":null,"abstract":"<div><div>The waste of energy is one of the major disadvantages when applying the permissionless blockchain technology such as the proof-of-work (PoW) consensus mechanism. In order to improve the energy efficiency, some new public blockchain systems without mining have been developed. In the blockchain with mining, the block creation rate is automatically adjusted by the system via the difficulty of the hash puzzle. In the blockchain without mining, the participating node can actively adjust its block creation rate instead of passively complying with the rule of system. In this new circumstance, how to maintain a desired block creation rate of the whole system through a proper mechanism design is an important task in the development of energy-efficient blockchain systems. In this study, we have established a two-party game model for the blockchain system without mining. Game participants (participating nodes) can obtain revenue by charging commission from the transactions recorded in the blocks, and at the same time, they need to bear block-building cost. Each node can adjust its own obtained income by controlling its block creation frequency. We considered the game in two situations based on the time delay of the same transaction arriving at two nodes. In the case without time delay, we gave a closed-form solution of Nash equilibrium, in which the block creation interval is proportional to the block-building cost, and inversely proportional to the transaction fee and to the revenue allocation proportion. In the case with time delay, we gave the theoretical analysis and the numerical solution of Nash equilibrium, and found that the conclusion is the same as the case without time delay. In a word, in the blockchain system without mining, the block-building cost and transaction fee can be utilized to control the block creation rate of the whole system, which makes the system remain steady even without mining. More important, how the transaction fees are allocated among the nodes will affect the final equilibrium. Therefore, in order to have positive incentives for participating nodes, it is necessary to reasonably design the allocation mechanism of transaction fees. This study provides theoretical support for the development of energy-efficient blockchain technology and brings about the hope to the future development of blockchain technology without mining.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110821"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181809","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}

{"title":"Accelerating the stabilized column generation using machine learning","authors":"Puja Sarkar ,&nbsp;Vivekanand B. Khanapuri ,&nbsp;Manoj Kumar Tiwari","doi":"10.1016/j.cie.2024.110837","DOIUrl":"10.1016/j.cie.2024.110837","url":null,"abstract":"<div><div>Column Generation (CG) is a well-established methodology for tackling large-scale real-world optimization problems. Nevertheless, as problem sizes increase, challenges like long-tail effects and degeneracy become more prevalent. Various strategies for stabilizing dual variables have demonstrated their effectiveness in mitigating these challenges. Generally, numerical tests are employed to identify the best parameter values for stabilized CG using different configurations for the same problem. This study introduces an innovative approach using machine learning (ML) to predict the best algorithm configuration, eliminating the need for extensive numerical experimentation. The core objective of this study is to predict optimal dual variables to generate improved bounds in the Restricted Master Problem of stabilized CG. By and large, this comprehensive approach represents a robust and flexible framework, optimizing algorithm configurations and expediting the convergence of the CG model. Extensive computational experiments confirm the efficacy of our ML-based approach in accurately predicting optimal dual variables and outperforming conventional methods. The practical utility is exemplified in optimizing workforce scheduling, demonstrating significant reductions in computational time across problem instances. This real-world application highlights the remarkable benefits of the smart approach in enhancing the efficiency and effectiveness of CG-based optimization solutions.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110837"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181827","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}

{"title":"A robust multi-objective optimization model for grid-scale design of sustainable cropping patterns: A case study","authors":"Nima Taheri ,&nbsp;Mir Saman Pishvaee ,&nbsp;Hamed Jahani","doi":"10.1016/j.cie.2024.110772","DOIUrl":"10.1016/j.cie.2024.110772","url":null,"abstract":"<div><div>In the face of growing population, water scarcity, and increasing food demand, there is a pressing need to shift towards optimized, resource-efficient, climate-resilient, and sustainable agricultural practices. In light of that, designing a sustainable cropping pattern that considers the procedure of resource allocation based on land capabilities and crop features is vital for ensuring long-term food production and safeguarding the delicate balance of ecosystems. Motivated by this imperative, this study proposes a comprehensive framework that integrates Geographic Information System (GIS), System Dynamics (SD), and optimization to address the sustainable design of cropping patterns. The framework assesses grid-scale land suitability, models dynamic water resource interactions, and optimizes resource allocation based on crop calendar considerations. For the first time, a dynamic crop inventory is integrated into the cropping pattern optimization process, addressing food security concerns in a comprehensive manner. In order to evaluate the effect of uncertainties on the designed system, a robust optimization model is developed based on convex sets. The results demonstrate the advantages of the robust model in situations with uncertainty. Despite a 5% reduction in profit compared to the deterministic solution, the robust design achieves a 25% decrease in irrigation, highlighting the cost of ensuring sustainability. The deterministic approach prioritizes crops based on their economic value, whereas the robust solution considers the volume of irrigation required for a sustainable design. The managerial implications emphasize the importance of prioritizing water-efficient and climate-resilient agricultural practices to guarantee long-term food security.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110772"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181735","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}

{"title":"An exact approach for the two-dimensional strip packing problem with defects","authors":"Shaowen Yao,&nbsp;Hao Zhang,&nbsp;Lijun Wei,&nbsp;Qiang Liu","doi":"10.1016/j.cie.2025.110866","DOIUrl":"10.1016/j.cie.2025.110866","url":null,"abstract":"<div><div>The paper studies the two-dimensional strip packing problem with defects (2DSPP_D), focusing on packing rectangular items orthogonally within a fixed-width, variable-height strip that includes defects. The objective is to minimize the height of the strips used. This problem is important because it appears in many real-world applications, such as cutting processes for materials like paper or steel coils, where the goal is to minimize waste, and in continuous berth allocation problems, where the objective is to minimize total unloading time. Although this problem has a wide range of practical applications, it is rarely discussed in the literature. In this paper, we present an integer programming formulation and an exact two-stage approach. In the first stage of the exact method, the 2DSPP_D is converted into a two-dimensional orthogonal placement problem (2DOPP) by fixing the strip height. In the second stage, we solve this placement problem using a Benders’ decomposition method. If the 2DOPP proves infeasible, we increase the strip height and repeat the algorithm. We employ customized preprocessing techniques, lower bounding methods, and valid inequalities to enhance the two-stage approach. Additionally, we propose a skyline-based adaptive iterative search heuristic algorithm that provides tight upper bounds for the 2DSPP_D, incorporating a randomized local search strategy and an adaptive search strategy to enhance algorithm effectiveness. Extensive computational results show that our approach proves optimal solutions for small and medium-sized benchmark instances within a reasonable time and achieves close gap values between upper and lower bounds for large benchmark instances.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110866"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180306","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}

{"title":"Personalized follow-up strategies with learning effects for disease monitoring","authors":"Mei Li ,&nbsp;Zixian Liu ,&nbsp;Xiaopeng Li ,&nbsp;Guozheng Song","doi":"10.1016/j.cie.2024.110820","DOIUrl":"10.1016/j.cie.2024.110820","url":null,"abstract":"<div><div>Effective follow-up strategies are crucial for managing patients’ risks of adverse outcomes (AOs) and associated costs. Current literature on follow-up strategy design primarily focuses on healthcare providers’ perspectives, often overlooking the significant role of patient learning behaviors in enhancing follow-up effectiveness during their healthcare journey. This paper investigates the impacts of two types of learning behaviors on follow-up strategy design. By employing the ‘virtual age’ and ‘learning parameters’, we assess the impact of follow-up services and learning behaviors on AO risks. A unified optimization model, based on patient heterogeneity, is then constructed to analyze the trade-off between follow-up services, AO risks, and the impact of patient learning behaviors. Formulated as a mixed integer nonlinear programming problem, the model is solved to determine the optimal frequency and timing of follow-up services over a planned horizon for heterogeneous patient groups. A case study focusing on pediatric type 1 diabetes mellitus patients demonstrates that learning behaviors can effectively control medical service costs while enhancing disease monitoring efficacy.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110820"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180130","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}

{"title":"Unleashing digital engineering for high-configurational systems: A taxonomy for developing digital engineering platforms","authors":"Tobias Eberhardt ,&nbsp;Dimitri Petrik ,&nbsp;Walter Schaaf ,&nbsp;Alexander Verl","doi":"10.1016/j.cie.2024.110814","DOIUrl":"10.1016/j.cie.2024.110814","url":null,"abstract":"<div><div>The significance of digital servitization in the industrial engineering domain is becoming increasingly apparent. A significant number of component and system manufacturers are attempting to incorporate digital services and products into their existing product portfolios with the aim of enhancing customer value. A promising avenue for exploration is the combination of digital engineering approaches with digital platforms, which could result in creating a digital engineering platform. However, the solution space for designing digital engineering platforms is wide, increasing the complexity for industrial organizations to choose the right configuration. To cope with this complexity in the conception phase, this paper presents a configurable model in the form of a taxonomy for such a platform, developed on the basis of a systematic literature review and demonstrated by applying it in a case study with a component and system manufacturer in the field of vacuum handling technology. The taxonomy aims to systematize the different design options for digital engineering platforms and simplify the selection of an adequate platform configuration. The results further help advance the understanding of digital platform configurations to support the digital engineering of highly configurable systems and their servitization.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110814"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181823","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}

{"title":"Exact algorithms in bar nesting: How to cut general items from linear stocks so that wastage is minimised","authors":"Rhyd Lewis ,&nbsp;Louis Bonnet","doi":"10.1016/j.cie.2024.110838","DOIUrl":"10.1016/j.cie.2024.110838","url":null,"abstract":"<div><div>This paper proposes exact, polynomial-time algorithms that solve the problem of cutting items with angled sides from a single linear stock so that wastage is minimised. In industry, this problem is called “bar nesting”. Here we give an algorithmic framework that solves several important variants of the problem, including cutting items from stocks with asymmetric cross-sections, cutting items whose sides occur on different planes, and the minimum score separation problem.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110838"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180132","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}

{"title":"Human–robot vs. human–manual teams: Understanding the dynamics of experience and performance variability in picker-to-parts order picking","authors":"Jonas Koreis","doi":"10.1016/j.cie.2024.110750","DOIUrl":"10.1016/j.cie.2024.110750","url":null,"abstract":"<div><div>Recent technological advances have enabled firms to automate an increasing number of intralogistics operations, yet manual material handling remains pivotal in several industries, particularly in retail applications where brick-and-mortar warehouses still rely on manual picker-to-parts systems. These systems, while labor-intensive, are increasingly being supplemented by technologies such as automated guided vehicles (AGVs) to enhance performance and reduce physical strain on workers. The present study analyzed a pilot test of a new industrial truck deployed as an AGV that automatically follows order pickers in their travels within the warehouse of a brick-and-mortar grocery retailer. The data set comprises 342,601 pick location visits performed in one dedicated warehouse from 01 November 2022 to 30 June 2023, with three order pickers working with an AGV in a human–robot setting and five order pickers working with a manual industrial truck in a human–manual setting, with both groups sharing the identical aisle work space. The human–robot teams demonstrated a 3.6% reduction in order picking time compared to the human–manual teams, with a significant initial performance boost that plateaued over time. Experience had different impacts on the two groups: in the human–robot teams, the benefits of experience diminished more rapidly, indicating a lower incremental gain from additional experience compared to human–manual teams. Conversely, human–manual teams showed continuous improvement in performance as experience accumulated, with each additional day of experience leading to significant gains in order picking performance. The study also highlighted the variation in performance increase in human–robot teams, suggesting that while AGVs may enhance performance, the potential for inconsistent operational methods among workers can lead to fluctuating performance outcomes. The findings provide guidance for researchers and managers in understanding the impacts of experience and automation on performance, thereby aiding in the development of targeted training programs and operational strategies to maximize the benefits of AGVs.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110750"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181313","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}

{"title":"Data-driven multi-location inventory placement in digital commerce","authors":"Yihua Wang ,&nbsp;Stefan Minner","doi":"10.1016/j.cie.2024.110842","DOIUrl":"10.1016/j.cie.2024.110842","url":null,"abstract":"<div><div>Digital commerce has become an indispensable part of global retail. Digital commerce retailers usually build large logistics networks with multiple distribution centers (DCs) to serve widespread consumers. In this paper, we study multi-location inventory placement for online retailers to fulfill customer demands. Specifically, we consider three decision-making problems: (i) in which DCs to place inventory, (ii) how to set base-stock levels for inventory-holding DCs, and (iii) from which DCs to fulfill customer demand. The main challenge is to achieve the optimal trade-off between inventory cost savings from inventory pooling and the increased demand fulfillment cost associated with placing inventory far from consumers. To investigate the trade-off, we propose a data-driven stochastic program under two different demand fulfillment policies, namely fixed and virtual pooling. We evaluate the effectiveness of the proposed method through a case study based on a real-world data set by a logistics company. The proposed method achieves an average cost reduction of 19.2% compared to the company’s current inventory placement policy. Further, we conduct ABC-XYZ analysis for more than 7,700 stock keeping units (SKUs) in the data set. The comparison of inventory placement decisions between different SKU categories suggests that digital commerce retailers should place more inventory in local DCs for SKUs with steadily high demand rates and pool more inventory at central DCs for SKUs with low demand rates and high variance. Additionally, we perform a systematic sensitivity analysis with controllable problem parameter configurations to investigate the impact of different parameters on inventory placement decisions.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110842"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181826","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}

{"title":"A comparison of reinforcement learning policies for dynamic vehicle routing problems with stochastic customer requests","authors":"Fabian Akkerman ,&nbsp;Martijn Mes ,&nbsp;Willem van Jaarsveld","doi":"10.1016/j.cie.2024.110747","DOIUrl":"10.1016/j.cie.2024.110747","url":null,"abstract":"<div><div>This paper presents directions for using reinforcement learning with neural networks for dynamic vehicle routing problems (DVRPs). DVRPs involve sequential decision-making under uncertainty where the expected future consequences are ideally included in current decision-making. A frequently used framework for these problems is approximate dynamic programming (ADP) or reinforcement learning (RL), often in conjunction with a parametric value function approximation (VFA). A straightforward way to use VFA in DVRP is linear regression (LVFA), but more complex, non-linear predictors, e.g., neural network VFAs (NNVFA), are also widely used. Alternatively, we may represent the policy directly, using a linear policy function approximation (LPFA) or neural network PFA (NNPFA). The abundance of policies and design choices complicate the use of neural networks for DVRPs in research and practice. We provide a structured overview of the similarities and differences between the policy classes. Furthermore, we present an empirical comparison of LVFA, LPFA, NNVFA, and NNPFA policies. The comparison is conducted on several problem variants of the DVRP with stochastic customer requests. To validate our findings, we study realistic extensions of the stylized problem on (i) a same-day parcel pickup and delivery case in the city of Amsterdam, the Netherlands, and (ii) the routing of robots in an automated storage and retrieval system (AS/RS). Based on our empirical evaluation, we provide insights into the advantages and disadvantages of neural network policies compared to linear policies, and value-based approaches compared to policy-based approaches.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110747"},"PeriodicalIF":6.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100572","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}