Resource Consumption Research Articles

Evaluating the Impact of Government Policies on Circular Economy Adoption in the Construction Sector

The construction sector is both essential for global economic progress and one of the largest contributors to pollution, resource depletion, and energy consumption. Given the urgent need to adopt more sustainable practices, governments in developed countries have introduced policies aimed at fostering a circular economy in this sector. These policies emphasize the use of green materials to reduce resource consumption, energy use, and greenhouse gas emissions, aligning with global sustainability goals. The waste hierarchy further governs these policies, prioritizing waste reduction, reuse, and recycling. This study analyzes the effectiveness of these government policies in promoting circular economy principles, particularly the adoption of green materials in the construction industry. Our findings reveal that while these policies have led to measurable reductions in energy consumption and emissions, the extent of green material adoption varies significantly across countries. Key factors influencing this variation include economic incentives, regulatory enforcement, and the level of industry acceptance of green technologies. One of the most important results of this study is the identification of significant gaps between policy intent and actual industry practices. In many cases, green materials, despite their favorable characteristics, have not been widely implemented due to economic, technical, and market barriers. These findings underscore the need for stronger government incentives and more consistent policy enforcement to drive broader adoption of sustainable practices. Future studies should focus on overcoming these barriers, exploring the long-term economic impacts of green material adoption, and evaluating how policy frameworks can be optimized to ensure more widespread.

Research on the Green Development of E-Commerce Economy Based on E-Commerce Platform

While innovating global trade and service models, driving profound changes in business models and consumer behaviors, the e-commerce economy is also accompanied by problems such as resource consumption and environmental pollution, which have aroused people's concern and profound thinking about sustainable development. In this context, green development has gradually become a global consensus. It has become an urgent research topic to correctly understand the relationship between them and integrate green concepts into the development of the e-commerce economy to achieve the dual goals of economic benefits and environmental sustainability. This paper selects two major e-commerce platforms, Amazon and JD, as cases, summarizes the current situation of e-commerce platforms and green development, reveals the relationship between the two, points out the challenges faced, and puts forward corresponding solutions, emphasizing the importance of technological innovation, policy guidance, consumer behavior education, and win-win cooperation. This paper reviews the state of e-commerce platforms and sustainable development, highlighting the linkage and addressing challenges with proposed solutions, underscoring the roles of tech innovation, policy, consumer education, and a collaborative strategy. This helps to highlight the vital role of e-commerce platforms in global economic growth and business innovation, increase their emphasis on environmental protection and social responsibility, and promote future paths and prospects in achieving long-term sustainable development goals.

Why aquaculture may not conserve wild fish.

We review literature on the displacement paradox and the Jevons paradox, with consideration of their implications for the potential effects of aquaculture on wild fisheries. The Jevons paradox refers to circumstances where improvements in the efficiency of resource use lead to growth in consumption and therefore undermine conservation. The displacement paradox refers to circumstances where increasing use of a potential substitute for a resource (e.g., farmed fish) does not lead to proportionate reduction in consumption of the other resource (e.g., wild fish). The literature on the displacement paradox and the Jevons paradox suggests that there may be unanticipated consequences from the rise of aquaculture that may be detrimental for fisheries conservation. Here, we present theoretical explanations, drawing on the tragedy of the commodity, for understanding the tendencies for these technological paradoxes to occur and emphasize their relevance for concerns associated with fisheries and aquaculture systems.

Sustainable Composites from Waste Polypropylene Added with Thermoset Composite Waste or Recovered Carbon Fibres

In order to limit the ever-increasing consumption of new resources for material formulations, regulations and legislation require us to move from a linear to a circular economy and to find efficient ways to recycle, reuse and recover materials. Taking into account the principles of material circularity and waste reuse, this research study aims to produce thermoplastic composites using two types of industrial waste from neighbouring companies, namely waste polypropylene (wPP) from household production and carbon-fibre-reinforced epoxy composite scrap from a pultrusion company. The industrial scrap of the carbon-fibre-reinforced epoxy composites was either machined/ground to powder (pCFRC) and used directly as a reinforcement agent or subjected to a chemical digestion process to recover the carbon fibres (rCFs). Both pCFRC and rCF, at different weight ratios, were melt-blended with wPP. Prior to melt blending, both pCFRC and rCF were analysed for morphology by scanning electron microscopy (SEM). The pCFRC powder contains epoxy resin fragments with spherical to ellipsoidal shape and carbon fibre fragments. The rCFs are clean from the matrix, but they are slightly thicker and corrugated after the matrix digestion. Further, the morphologies of wPP/pCFRC and wPP/rCF were also investigated by SEM, while the thermal behaviour, i.e., transitions and changes in crystallinity, and thermal resistance were evaluated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. The strength of the interaction between the filler (i.e., pCFRC or rCF) and the wPP matrix and the processability of these composites were assessed by rheological studies. Finally, the mechanical properties of the systems were characterised by tensile tests, and as found, both pCFRC and rCF exert reinforcement effects, although better results were obtained using rCF. The wPP/pCFRC results are more heterogeneous than those of the wPP/rCF due to the presence of epoxy and carbon fibre fragments, and this heterogeneity could be considered responsible for the mechanical behaviour. Further, the presence of both pCFRC and rCF leads to a restriction of polymer chain mobility, which leads to an overall reduction in ductility. All the results obtained suggest that both pCFRC and rCF are good candidates as reinforcing fillers for wPP and that these complex systems could potentially be processed by injection or compression moulding.

ANALYSIS OF THE DYNAMIC RELATIONSHIPS BETWEEN INDUSTRIAL, ENVIRONMENTAL FACTORS, AND THE UNEMPLOYMENT RATE. AN ECONOMETRIC APPROACH

ABSTRACT: The sustainability of socio-economic and industrial systems is a key concern, particularly within the framework of the 2030 agenda. A holistic understanding of the functioning and relationships of these systems is essential if we want to implement practical actions to achieve sustainability. This study uses econometric techniques, such as the distributed lag autoregressive model, to explore the interactions between industrial and environmental factors and the unemployment rate. The variables analyzed include working hours, wage costs, and tropospheric ozone concentration, over two periods: 2008-2020 (reduced period) and 2008-2024 (extended period). Between both periods, substantial differences have been found in the variables significantly influencing the unemployment rate. The significance of working hours and ozone (O3) concentration, determinants of the unemployment rate in the reduced period, are replaced by wage costs, determining the unemployment rate in the extended period. The results show that an initial increase in worked hours reduces unemployment, while wage increases and high levels of O3 tend to increase the unemployment rate. Granger causality analysis suggests bidirectional relationships between unemployment, wage costs, and O3 concentration, highlighting the connection between the different aspects and feedback collateral effects. The study underscores the need for balanced socioeconomic policies that promote both economic growth and environmental sustainability and the promotion of fostering of technological innovations. The importance of policies that mitigate the adverse effects of wage increases on employment is underlined, and that favor a more sustainable development model capable of decoupling economic well-being from intensive resource consumption. Keywords: Sustainability, Socioeconomic systems, ARDL techniques, Granger causality, unemployment, O3 concentration, working hours, hourly wage.

Construction and Optimization Strategies for Rural Residential Spatial Models Based on the Concept of Resource Metabolism: A Case Study of Rural Areas in the Shandong Plain

Rural communities can be conceptualized as spatial organisms interconnected by optimized resource utilization systems. Investigating the efficient utilization of rural resources and spatial construction methods grounded in resource metabolism is a pivotal step toward achieving the ecological transformation of rural spaces. This paper examined rural settlements in the Shandong Plain as a case study, exploring the relationships among three scales: village, neighborhoods, and courtyards. This analysis was based on elucidating the interaction mechanisms between “space and resource” and the integration of key resources and spatial elements. From the perspective of resource circulation and metabolism, this study aimed to elucidate the equilibrium of each resource element within three resource metabolism subsystems: the agricultural production system (core element), the ecological technology system (technological link), and the human life system (spatial carrier) in Shandong Plain’s villages, considering general climatic conditions. To achieve this, this research utilized the resource production volume, the utilization and transformation volume of resource metabolism technology facilities per unit area, and the average per capita resource consumption as fundamental measurement units. The concept of a rationing relationship is introduced to clarify resource allocation. Combining the aforementioned research on spatial resource metabolism in ecological villages in Shandong Province with the material flow analysis method, this study constructed a bottom–up spatial model of resource metabolism at three scales, courtyards, neighborhoods, and villages, under various resource metabolism scenarios. This study is anticipated to significantly contribute to the theoretical understanding of rural habitat environments, offering novel methods and perspectives for constructing ecological rural settlements.

UNDER THE INFLUENCE OF THE INDUSTRIAL REVOLUTION: A RETROSPECTIVE ANALYSIS

The article is intended to analyze the cyclicality of the development of construction, caused by fundamental qualitative changes in the basic generations of equipment, machines, technologies, vehicles and large structures in the leading sectors of the economy, i.e. industrial revolutions. The first industrial revolution created the organizational-economic, scientific-technical and institutional prerequisites for a conceptual rethinking of the principles and norms of construction and brought to life the pre-monopolist-industrial model of the construction sector (Construction 1.0). Its main feature was the active use of metal elements (cast iron, wrought iron, steel), glass, concrete and reinforced concrete in construction production. The industrial-extensive model of Construction 2.0 covers the period from the beginning of the 20th century. until the end of the 1960s and coincides with the Second Industrial Revolution. This model is distinguished by large-scale processes of territorial and spatial concentration of construction based on the accumulation of means of production, labor and capital investments in large construction organizations and complex processes of industrialization of the construction sector. From the first half of the 1970s to the first half of the 2000s, the energy-efficient construction model (Construction 3.0), brought to life by the Third Industrial Revolution, prevails in the world. This period is characterized by the systematic orientation of construction production to the optimization of thermal energy provision of objects, the rationalization of the processes of using energy resources at all stages of the construction and production process, and the minimization of the level of consumption of energy and material resources. Today, marked by the unfolding of the Fourth Industrial Revolution, is characterized by the global development of an ecosystem-type green construction model (Construction 4.0) – an environmentally responsible and resource-saving process of social reproduction of construction products. This model is based on deep integration of green and digital transition technologies and is ensured by large-scale digitization of construction works.

Systematic Analysis of Image Restoration Methods Based on Deep Learning

In today's digital age, people often use images to convey information. However, during the process of sending images, they can become damaged for various reasons, causing the images to lose their original meaning. In such cases, image repair is necessary to restore the damaged areas to their original, undamaged appearance. Traditional image repair methods often struggle to produce effective results for highly damaged images. However, image repair using deep learning techniques can address these challenges effectively. This paper will detail the impact of image repair on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Convolutional Recurrent Neural Networks (CRNN), and other models, as well as the advantages and disadvantages of each approach. From the data, it is evident that combining multiple models for image repair is the most effective method. This approach reduces the consumption of hardware resources and time required for image repair, thereby improving the overall efficiency of the process.

Changes in global trade patterns increase global inequality towards Sustainable Development Goals

Reaching the UN's sustainable development goals (SDGs) is influenced by a country's position in global value chains and its involvement in international trade. Here, we assess how changes in global trade patterns (CGTP) during 2004 and 2014 impacted 13 SDG indicators in 141 countries/regions which are further divided into four income groups. Trade pattern is characterized by the direction, composition, and magnitude of trade, indicating an economy imports what types (composition) and magnitudes of goods or services from where (direction). We find that CGTP aggravated socioeconomic and environmental inequality between countries in two ways: 1) the amount of indicators that significantly worsened due to CGTP decreased from 8 indicators (2004–2007) to 1 (2011–2014) for high-income and upper-middle-income countries, but increased from 5 to 14 for lower-middle-income and low-income countries; 2) CGTP led to a coupling of value added with most natural resource consumption and environmental pollution indicators for low-income countries, while they strengthened decoupling or reducing coupling for other countries. The findings imply one key to achieving SDGs is to address the inequality between rich and poor countries through implementing policy interventions that influence import and vertical supply chain thereby shifting the trade patterns towards environmental-economic decoupling in poor countries.

INTERNET OF THINGS UNTUK MONITORING KONDISI AIR BUDIDAYA IKAN KELOMPOK ‘TUTUT JAYA’ KOTA MALANG

This service aims to improve the skills and knowledge of catfish farmers in the "Tutut Jaya" group related to the use of IoT technology in water quality monitoring. Through the training and implementation of IoT technology, it is expected that farmers can monitor and manage water quality more efficiently, thus increasing the productivity and sustainability of catfish farming. The dedication was carried out at the Catfish Farming Group "Tutut Jaya" in Arjowinangun Village, Kedungkandang District, Malang City. The methods used include training, technology implementation, as well as monitoring and mentoring. Training was given to improve fish farmers' understanding of the importance of maintaining stable pH and water temperature. In addition, IoT technology was introduced to monitor water quality in real time, enabling rapid intervention against inappropriate water conditions. Data was obtained through surveys, interviews, and analysis of IoT sensors that measure water pH and temperature. The results of the research show a significant improvement in the quality of pond water, which contributes to the health and growth of catfish. The use of IoT technology also increases operational efficiency, reduces resource consumption, and increases catfish production. Partners show a high level of satisfaction with the application of technology and the training provided. In conclusion, the implementation of IoT technology in water quality monitoring proved to be effective in increasing the success of catfish farming, with the potential to be applied more widely in the fishing industry.

Sub-Optimal Compliance to Long-Term Inhalation Strategies and Poorer Health Care Outcomes Associated with Extended Tattoos in Adolescents with Mild-to-Moderate Bronchial Asthma.

Patients' compliance to inhalation therapy is usually sub-optimal in young asthmatics. Adolescents poorly compliant to regular anti-asthma treatments and those with tattoos (and the associated attitude) can share some personality traits and maladaptive behaviors. This relationship has not been investigated. To assess if "extended" tattoos can predict long-term compliance to regular therapy of adolescents with mild-to-moderate asthma. A 12-month retrospective observational investigation was conducted on non-smoker asthmatic adolescents of both genders. Patients assuming <70% of prescribed vilanterol/fluticasone furoate o.d. were defined as "non-compliant". Tattoo surfaces were defined as "mild" or "extended" if they were < or ≥400 cm2, respectively. The relationship between tattoos and compliance on the evolution of resources consumption at 6 and 12 months was assessed by generalized estimating equation (GEE) models at the first and second semester of the treatment period. It was found that 13.2% of compliant adolescents had mild tattoos, while 47.2% of non-compliant adolescents had mild-to-extended tattoos (odds ratio (OR) 6.91, 95% CI 2.49 to 19.17, p < 0.001). The mean annual adherence to treatment was 57.8% ± 10.1 SD expected doses in non-compliant subjects with "mild tattoos" (54.8 cm2 ± 36.9 SD), but 38.6% ± 11.4 SD expected doses in those with "extended tattoos" (568.4 cm2 ± 111.6 SD, p < 0.001). Total cost proved to be a linear trend from the lowest values of compliant patients with no/mild tattoos (EUR 65.22 at 6 months and EUR 33.63 at 12 months) to the highest values of non-compliant adolescents with extended tattoos (EUR 330.75 at 6 months and EUR 297.34 at 12 months). Tattoo extension might be used as a reliable predictor of poor compliance and higher health care costs in adolescents with mild-to-moderate asthma. Patients characterized by poor compliance to a long-term therapeutic strategy and tattooing attitude likely share some aspects of their personality profile.

Factors Affecting the Sustainability of Corporates in Polluting Sectors

Corporate sustainability performance is gaining ever greater importance. The negative impact of climate change is manifested through heavy air, water and soil pollution. Polluting sectors, as the major players, are characterized by large amounts of emissions, waste and consumption of resources, and therefore have a larger negative impact on the environment. Companies operating in polluting sectors are recognized globally as the main sources of greenhouse gas emissions; thus, their performance is widely debated. Despite their character, such companies strive for higher profitability, better financial performance and operational efficiency. However, higher financial resources create the potential for innovation investments in companies. It is widely accepted that research and experimental development (R&amp;D) expenditures enable new business ideas, models, products, services, and processes. However, while pursuing sustainability targets, financial results could be directed towards sustainability performance. The purpose of this paper is to analyze how the financial and innovation results of companies in polluting sectors interact with sustainability performance scores. For it, we have identified three essential pillars of sustainability: environmental, governance, and social. Using ordinary least squares (OLS) regressions, models were developed for each pillar of sustainability, including corporate financial performance indicators and R&amp;D expenditures. The obtained results provide the insights that a company operating in polluting sector size and turnover significantly interacts with all pillars of sustainability. However, we also found that the corporate debt ratio, earnings ratio, and current liquidity have a significant relation only with environmental and social sustainability indicators.

Recovery and Restructuring of Fine and Coarse Soil Fractions as Earthen Construction Materials

Excessive consumption of natural resources to meet the growing demands of building and infrastructure projects has put enormous stress on these resources. On the other hand, a significant quantity of soil is excavated for development activities across the globe and is usually treated as waste material. This study explores the potential of excavated soils in the Brittany region of France for its reuse as earthen construction materials. Characterization of soil recovered from building sites was carried out to classify the soils and observe their suitability for earthen construction materials. These characteristics include mainly Atterberg limits, granulometry, organic matter and optimum moisture content. Soil samples were separated into fine and coarse particles through wet sieving. The percentage of fines (particles smaller than 0.063 mm) in studied soil samples range from 28% to 65%. The methylene blue value (MBV) for Lorient, Bruz and Polama soils is 1, 1.2 and 1.2 g/100 g, and French classification (Guide de terrassements des remblais et des couches de forme; GTR) of soil samples is A1, B5 and A1, respectively. The washing of soils with lower fine content helps to recover excellent-quality sand and gravel, which are a useful and precious resource. However, residual fine particles are a waste material. In this study, three soil formulations were used for manufacturing earth blocks. These formulations include raw soil, fines and restructured soil. In restructured soil, a fine fraction of soil smaller than 0.063 mm was mixed with 15% recycled sand. Restructuring of soil fine particles helps to improve soil matrix composition and suitability for earth bricks. Compressed-earth blocks of 4 × 4 × 16 cm were manufactured at a laboratory scale for flexural strength testing by using optimum molding moisture content and compaction through Proctor normal energy. Compressive strength tests were performed on cubic blocks of size 4 × 4 × 4 cm. Mechanical testing of bricks showed that bricks with raw soil had higher resistance with a maximum of 3.4 MPa for Lorient soil. Removal of coarse particles from soil decreased the strength of bricks considerably. Restructuring of fines with recycled sand improves their granular skeleton and increases the compressive strength and durability of bricks.

Integration of Circular Economy and Urban Metabolism for a Resilient Waste-Based Sustainable Urban Environment

An unsustainable rate of resource production and consumption is evident in urban environments. The absence of innovative approaches in conjunction with the exponential urbanization and expansion of the global population will inevitably result in substantial environmental consequences. There are two emerging alternatives: circular economy (CE) and urban metabolism (UM). The integration of these principles into novel methodology casts doubt on the linear model of contemporary economic and urban systems, which includes extraction, production, utilization, and disposal. In the development of a distinctive urban framework known as circular urban metabolism, the current study has illustrated the application of these principles. We design this study to motivate urban planners and decision-makers to investigate, develop, and supervise ecologically sustainable cities. Scholars from a variety of academic disciplines, intrigued by the intricacies of urban planning, design, and administration, can foster interdisciplinary collaboration in the circular urban metabolism (CUM) region. To address the research question, we implemented a bibliometric analysis, which involved the examination of 627 pertinent research papers, utilizing the R (R 3.6.0+) statistical programming language. The results emphasize the fundamental characteristics and significance of CUM in the management of refuse. In addition, the findings underscore the importance of creating a novel framework that incorporates the principles of urban political ecology, CUM, sustainability, and the novel dimension of waste metabolism. It is the goal of this framework to emphasize the significance of recycling in the informal sector as a waste management strategy in low- and medium-income countries (LMICs).

Artificial intelligence for training and reporting infection prevention measures in critical wards.

Healthcare associated infections (HAI) are a major concern. Infections and sepsis are prevalent causes of prolonged hospitalization, healthcare resources consumption, and mortality. Infection prevention and control (IPC) measures need to be encouraged with the purpose to become standard protocol. Hand hygiene represents the most important measure to prevent the spread of infections. Poor hand hygiene is often due to: the belief that the use of gloves replaces hand hygiene, lack of good example from colleagues and superiors, sinks that are difficult to access or insufficient in number, staff often too busy / not enough time, poor knowledge of guidelines and protocols. Artificial intelligence was recently integrated in hand washing devices; this can implement training on a daily basis but also is an objective method to assess the efficacy of the IPC measures adopted. The benefit–cost ratio of this approach needs to be discussed.

Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors

This study explores the application of Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs), for detecting rice plant diseases using ARM Cortex-M microprocessors. Given the significant role of rice as a staple food, particularly in Malaysia where the rice self-sufficiency ratio dropped from 65.2% in 2021 to 62.6% in 2022, there is a pressing need for advanced disease detection methods to enhance agricultural productivity and sustainability. The research utilizes two extensive datasets for model training and validation: the first dataset includes 5932 images across four rice disease classes, and the second comprises 10,407 images across ten classes. These datasets facilitate comprehensive disease detection analysis, leveraging MobileNetV2 and FD-MobileNet models optimized for the ARM Cortex-M4 microprocessor. The performance of these models is rigorously evaluated in terms of accuracy and computational efficiency. MobileNetV2, for instance, demonstrates a high accuracy rate of 97.5%, significantly outperforming FD-MobileNet, especially in detecting complex disease patterns such as tungro with a 93% accuracy rate. Despite FD-MobileNet’s lower resource consumption, its accuracy is limited to 90% across varied testing conditions. Resource optimization strategies highlight that even slight adjustments, such as a 0.5% reduction in RAM usage and a 1.14% decrease in flash memory, can result in a notable 9% increase in validation accuracy. This underscores the critical balance between computational resource management and model performance, particularly in resource-constrained settings like those provided by microcontrollers. In summary, the deployment of CNNs on microcontrollers presents a viable solution for real-time, on-site plant disease detection, demonstrating potential improvements in detection accuracy and operational efficiency. This study advances the field of smart agriculture by integrating cutting-edge AI with practical agricultural needs, aiming to address the challenges of food security in vulnerable regions.

Assessing the impact of recycled concrete aggregates and metakaolin on the mechanical properties of pervious concrete

Population growth and urban expansion have increased impervious surfaces, leading to environmental issues such as stormwater runoff and groundwater depletion. Traditional concrete, while durable, contributes to these problems. There is a need for sustainable construction materials that can address environmental challenges and reduce the consumption of natural resources. Pervious concrete, enhanced with recycled aggregates (RCA) and supplementary cementitious materials (SCMs) like metakaolin (MK) present a potential solution. This study aims to improve the mechanical and durability properties of pervious concrete by incorporating RCA and MK, assessing their effects on workability, density, porosity, permeability, compressive strength and flexural strength. The incorporation of RCA resulted in increased aggregate porosity compared to natural aggregates. Workability was assessed using the slump test, density was measured, and porosity and permeability were evaluated. The results showed that incorporation of RCA generally decreased workability, density and strength, while the addition of MK improved these properties by refining the microstructure. Specifically, mixes with 10% MK demonstrated better performance in terms of increased compressive and flexural strengths, improved density and reduced porosity. The ANN model showed high accuracy in predicting compressive and flexural strengths. The study concludes that RCA and MK can enhance the mechanical and durability properties of pervious concrete.

Promoting Cultural Industry Management in the Context of a Green Ecological Environment

The green cultural industry is a key driving force for economic transformation and the upgrading of industrial structures. However, to achieve its development while protecting the environment, its influencing factors and the interactive relationship between the economy and the environment must be considered. This paper examines the environmental promotion effect of the green cultural industry by analyzing the interaction mechanism among these three elements, using the entropy method and coupling coordination model. The results show that the green cultural industry can develop cultural resources in line with national cultural characteristics, foster cultural innovation, promote economic growth, and reduce ecological resource consumption. At the same time, economic improvement enhances infrastructure, provides services to support the cultural industry, strengthens environmental protection, and improves ecological carrying capacity, offering greater resource support for cultural industry development.

A hybrid local search algorithm for the continuous energy-constrained scheduling problem

AbstractWe consider the continuous energy-constrained scheduling problem (CECSP). A set of jobs has to be processed on a continuous, shared resource. A schedule for a job consists of a start time, completion time, and a resource consumption profile. The goal is to find a schedule such that each job does not start before its release time, is completed before its deadline, satisfies its full resource requirement, and respects its lower and upper bounds on resource consumption during processing. The objective is to minimize the total weighted completion time. We present a hybrid local search approach, using simulated annealing and linear programming, and compare it to a mixed-integer linear programming (MILP) formulation. We show that the hybrid local search approach matches the MILP formulation in solution quality for small instances and is able to find a feasible solution for larger instances in reasonable time.

Developing a Container Ship Loading-Planning Program Using Reinforcement Learning

This study presents an optimized container-stowage plan using reinforcement learning to tackle the complex logistical challenges in maritime shipping. Traditional stowage-planning methods often rely on manual processes that account for factors like container weight, unloading order, and balance, which results in significant time and resource consumption. To address these inefficiencies, we developed a two-phase stowage plan: Phase 1 involves bay selection using a Proximal Policy Optimization (PPO) algorithm, while Phase 2 focuses on row and tier placement. The proposed model was evaluated against traditional methods, demonstrating that the PPO algorithm provides more efficient loading plans with faster convergence compared to Deep Q-Learning (DQN). Additionally, the model successfully minimized rehandling and maintained an even distribution of weight across the vessel, ensuring operational safety and stability. This approach shows great potential for enhancing stowage efficiency and can be applied to real-world shipping scenarios, improving productivity. Future work will aim to incorporate additional factors, such as container size, type, and cargo fragility, to further improve the robustness and adaptability of the stowage-planning system. By integrating these additional considerations, the system will become even more capable of handling the complexities of modern maritime logistics.