Computers & Industrial Engineering最新文献

The increasing emphasis on corporate social responsibility has led to a growing trend among firms to initiate decarbonization campaigns, aligning their sustainability efforts with profitability objectives. This paper explores the challenge of decarbonizing a supply chain wherein a buyer cooperates with an unreliable supplier that possesses private absorptive capacity and varying degrees of bargaining power. We develop models based on whether the absorptive capacity remains private information and whether the buyer has more bargaining power in determining the profit-maximizing price. Our findings indicate that the buyer may opt to avoid entering into contracts with the supplier for decarbonization if the absorptive capacity ratio falls below certain values. When contracting occurs, decarbonization has the potential to yield a mutually beneficial outcome for firms, customers, and the environment. Nevertheless, the presence of private information has negative implications for customers and the environment because it results in reduced consumer surplus and increased carbon footprint. As the buyer’s bargaining power strengthens, the likelihood of supply chain decarbonization increases, potentially leading to more consumer surplus and less carbon footprint. Finally, we discuss the effects of key model factors from a triple bottom line perspective (i.e., profit, people, and the planet).

The essential requirement for food stands as a pivotal human need, exerting significant ecological impact from production to consumption. Meat, a key dietary component, offers essential nutrients vital for human health. This paper presents a bi-objective two-stage stochastic optimization model for a green forward-reverse meat supply chain network design, addressing both economic and environmental concerns throughout the chain. In the forward flow, the supply chain manages various meat products consist of fresh, processed, and frozen meat products, ensuring their eco-efficient production and distribution. Meanwhile, in the reverse flow, waste and by-products generated during the production process are repurposed and reused. The study promotes environmental sustainability by repurposing waste, utilizing by-products, and minimizing carbon emissions. The proposed model is solved using popular exact ε-constraint method for smaller instances and Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Strength Pareto Evolutionary Algorithm 2 (SPEA2) meta-heuristic algorithms are employed for larger instances. SPEA2 outperforms both MOPSO and NASG-II, demonstrating less average gap that is 0.38% and 0.76%, respectively. Additionally, the findings reveals that an average 2.5% reduction in environmental impacts associated with an average 5% decrease in profit. The noteworthy outcomes of the research empower managers to navigate the implications of fluctuating demand effectively. Moreover, it is crucial to underscore the effects of conversion rates, particularly those associated with the manufacturing process. An excessively high conversion rate can negatively impact profitability and worsen environmental issues. Conversely, lowering the conversion rate can enhance profitability and mitigate environmental impacts.

Governments in many countries actively carry out various policies to tackle pollution problems sourced from operations and supply chain activities, aiming at reducing adverse environmental impact and promoting sustainable development. This study provides a comprehensive and well-structured review of research on operations and supply chain management within the context of environmental policy. Specifically, this study meticulously identifies 137 papers and presents the descriptive results of a bibliometric analysis on these papers. Afterwards, using a well-defined framework encompassing three types of policies and various levels of operational and supply chain management, the identified papers are further classified. For each classification, content analyses are conducted accordingly, delving into the diverse concerns and contributions of the reviewed papers from the perspectives of pricing, production planning, inventory and technology management, coordination and competition, recycling and remanufacturing activities, transportation decisions, and network design. Three aspects of sustainability performance are also considered in the analyses. Finally, this study provides future research directions from seven perspectives: research methodology, forms of regulation, dynamic games, types of contracts, sustainable target management, green behavior of supply chain members, and investment constraints. These insights in turn inform the development of policies and decision-making processes related to environmental sustainability.

The global trade disproportion results in the accumulation of containers in import-dominated ports and shortages in export-dominated ports, causing congestion and high freight costs, thus hindering maritime shipping economy development. To address these issues, this study develops a stochastic programming model considering uncertain container turnover times. The model integrates decisions for vessel deployment and empty container repositioning over multiple planning periods through a two-stage decision process, aiming to minimize the total cost, including vessel deployment, container leasing, and penalty costs for unfulfilled demand. By formulating the scenario selection problem as a p-median problem, we effectively reduce the model size. We propose an accelerated Benders decomposition algorithm which leverages the independence of sub-problems in the second stage to enable parallel computation. Numerical experiments show that our Benders decomposition algorithm improves solution speed by over 63% compared to the Gurobi optimization solver. Furthermore, our integrated optimization approach proves to be more cost-effective than the reactive method used by shipping lines, achieving an average cost savings of 0.72%. Additionally, our method of constructing turnover time scenarios to address uncertainty saves approximately 0.45% in costs compared to using the probability distribution of container turnover time.

Portfolio management is one of the unresponded problems of the Motion Pictures Industry (MPI). To design an optimal portfolio for an MPI distributor, it is essential to predict the box office of each project. Moreover, for an accurate box office prediction, it is critical to consider the effect of the celebrities involved in each MPI project, which was impossible with any precedent expert-based method. Additionally, the asymmetric characteristic of MPI data decreases the performance of any predictive algorithm. In this paper, firstly, the fame score of the celebrities is determined using a large language model. Then, to tackle the asymmetric character of MPI’s data, projects are classified. Furthermore, the box office prediction takes place for each class of projects. Finally, using a hybrid multi-attribute decision-making technique, the preferability of each project for the distributor is calculated, and benefiting from a bi-objective optimization model, the optimal portfolio is designed. To validate our approach, we conducted experiments using a dataset of movies released in the United States from 1980 to 2020 and employed the proposed approach to predict box office performance. Our results demonstrate that the proposed methodology significantly improves prediction accuracy and provides a robust framework for effective portfolio management.

Despite blockchain’s potential to enhance visibility and traceability in sustainable supply chains (SCs), its adoption is complex due to the various criteria (e.g., interoperability and cost) required for the best-fitting platform selection. This study aims to investigate conflicting criteria in the blockchain technology (BT) platform selection process for decision-making under uncertainty. We propose a three-phase decision support framework to study BT adoption considering technological, organizational, and environmental contexts. In the first phase, after exploring the evaluation criteria from multiple contexts, the developed framework incorporates uncertainty and reliability to deal with the BT platform evaluation problem. Then, fuzzy cognitive map modeling, advanced by a Z-number-based inference system, is introduced to model the causal relationships between criteria. This is followed by implementing a hybrid learning algorithm to assess the impact of each criterion on adoption decisions. Finally, the fuzzy combined compromise solution embedded in the framework prioritizes BT platforms to identify the most suitable ones for sustainable SC. The findings imply that performance efficiency, implementation costs, maintainability and operability can significantly affect the BT platform selection decisions. The outcomes offer more stable, reliable, and distinguishable solutions for the proposed problem compared to the traditional approaches. The results introduce Hyperledger and R3 Corda as the best-fitting platforms for adoption based on the identified criteria.

This study evaluates the catalytic effects of advanced manufacturing industry clusters and unified large markets on regional economic development from a computer science perspective, revealing their underlying mechanisms. It employs a Gated Recurrent Network (GRN) model optimized with Gradient Boosting Decision Tree (GBDT) technology to conduct empirical analysis through comprehensive data collection and analysis. The primary objectives are to assess these catalytic effects, highlight the importance of innovation and environmental indicators, determine the contribution levels of various factors, and test the computational fit and predictive accuracy of the model. Key findings indicate that the GBDT-GRN model demonstrates a significant improvement in data computation accuracy, ranging from 20% to 52%, and an increase in response time by 23% to 52%. The model achieves a computational fit of 92% to 99% when analyzing regional economic development. The proposed GBDT-GRN model is highly accurate and reliable in evaluating catalytic effects, providing strong support for policy-making and business decision-making. Innovation and environmental indicators play a crucial role, with varying contributions from different factors. This study offers an effective solution for sequence data prediction problems, supports policy-making and business decisions, and points to promising directions for future research.

While conventional production planning approaches prioritize short-term efficiency and economic gains, the sustainability development objectives emphasize a holistic perspective, integrating eco-friendly practices, social responsibility, and economic viability. Nevertheless, the existing literature overlooks a gap in understanding the role of socio-economic factors in labor-intensive production processes. In this regard, this research aims at investigating the impact of social factors, such as labor skill level and experience, on production planning, with a specific focus on glass wall panel manufacturing. The research integrates sustainability socioeconomics, as embodied by an empirically developed labor learning curve, with the MINLP (Mixed-Integer Nonlinear Programming) scheduling model. The results show that the integrated socio-economic scheduling approach outperforms traditional scheduling approach, reducing idle time up to 43% and promoting more balanced production distribution. Despite slightly higher upfront production costs, the integrated model offers long-term cost savings through reduced idle time and overtime, making it a viable option for companies seeking to improve productivity and worker satisfaction. The implementation of this work is recommended to maintain a sustainable, safe, and healthy work environment while also considering long-term economic benefits rather than short-term profits.

Sub-assembly is the basic stage of ship hull construction. It is necessary to optimize the scheduling of sub-assembly to shorten its assembly cycle and ensure the normal execution of subsequent processes. The scheduling problem of sub-assembly is an NP-hard problem that should take into consideration both spatial layout and temporal schedule. In this work, a mathematical model for scheduling the sub-assembly is established, and a Q-learning based hyper-heuristic with multi-spatial layout rule selection is proposed. Specifically, a spatial layout method based on multi-rule selection is proposed first. In various scenarios, distinct spatial layout rules are chosen to derive an appropriate spatial arrangement. Subsequently, a hyper-heuristic algorithm based on Q-learning is crafted to optimize the scheduling sequence and the selection of spatial layout rules. As a verification, numerical experiments are carried out in cases of different scales collected from a large shipyard. The effectiveness of the proposed algorithm is verified by comparing it with different spatial layout algorithms, various heuristic operators, existing well-known hyper-heuristic methods, and other Q-learning based scheduling methods. The results suggest that the proposed algorithm outperforms other comparison algorithms in most testing cases.