Search Results (30)

Material stocks (MS) serve as essential physical foundations for socio–economic systems, reflecting the accumulation, transformation, and consumption of resources over time and space. Positioned at the intersection of environmental and socio–economic systems, MS are increasingly recognized as leverage points for advancing sustainability. However, there is currently a lack of comprehensive overview, making it difficult to fully capture the latest developments and cutting–edge research. We adopt a systems perspective to conduct a comprehensive bibliometric and thematic review of 602 scholarly publications on MS research. The results showed that MS research encompasses has three development periods: preliminary exploration (before 2007), rapid development (2007–2016), and expansion and deepening (after 2016). MS research continues to deepen, gathering multiple teams and differentiating into diverse topics. MS research has evolved from simple accounting to intersection with socio–economic, resources, and environmental systems, and shifted from relying on statistical data to integrating high–spatio–temporal–resolution geographic big data. MS research is shifting from problem revelation to problem solving, constantly achieving new developments and improvements. In the future, it is still necessary to refine MS spatio–temporal distribution, reveal MS’s evolution mechanism, establish standardized databases, strengthen interaction with other systems, enhance problem–solving abilities, and provide powerful guidance for the formulation of dematerialization and decarbonization policies to achieve sustainable development. Full article

Lot sizing and cutting stock problems are critical for manufacturing companies seeking to optimize resource utilization and minimize waste. This paper addresses the interconnected nature of these problems, often occurring sequentially in industries involving cut items or packaging. We propose a novel mixed integer linear programming (MILP) model that integrates the capacitated lot sizing problem with the one-dimensional cutting stock problem within a multi-level manufacturing framework. The cutting stock problem is addressed using an arc flow formulation. Our model aims to minimize setup, production, holding, and waste material costs while incorporating capacity constraints, setup requirements, inventory balance, and the use of various cutting machines. The effectiveness of our model is demonstrated through numerical experiments using a commercial optimization package. While the model efficiently generates optimal solutions for most scenarios, larger instances pose challenges within the specified time limits. Sensitivity analysis is conducted to evaluate the effect of changing essential parameters of the integrated problem on model performance and to provide managerial insights for real-life applications. Full article

As the construction industry shifts from an extensive development model to one characterized by intelligent structural systems, the imperative to enhance productivity and management efficiency has emerged as a critical challenge. Conventional rebar construction processes heavily rely on manual operations—such as on-site rebar cutting, manual transcription of material lists, and decentralized processing—which are susceptible to subjective errors and often result in significant material waste. This issue is particularly pronounced in large-scale projects, where disorganized management of rebar quantities and placements exacerbates inefficiencies. To address these challenges, this study proposes an integrated approach that synergistically combines a genetic algorithm-based rebar-cutting optimization model with BIM technology, thereby optimizing rebar management throughout the construction process. The research is structured into two primary components. Firstly, a one-dimensional mathematical model for rebar-cutting optimization is developed, incorporating an innovative real-number encoding strategy within the genetic algorithm framework to maximize material utilization. A case study conducted on a pump station project reveals that the utilization rates for 32 mm and 16 mm rebar reach 86.76% and 93.90%, respectively, significantly exceeding the industry standard of 80%. Secondly, an automated batch modeling tool is developed using C# and the Revit API, which enables the efficient generation of rebar components; a unique coding system is employed to establish a bidirectional mapping between the digital model and the physical rebar, ensuring precise positioning and effective information management. Overall, this integrated method—encompassing rebar-cutting optimization, digital modeling, and on-site intelligent management—not only mitigates material waste and reduces production costs but also markedly enhances construction efficiency and accuracy in complex projects, thereby providing robust technical support for the seamless integration of intelligent construction and industrialized building practices. Full article

This study addresses the two-dimensional weakly homogeneous Bin Packing Problem (2D-BPP) in the context of robotic packing, where items must be arranged in a manner feasible for robotic manipulation. Traditional heuristics for this NP-hard problem often lack adaptability across diverse datasets, while metaheuristics typically suffer from slow convergence. To overcome these limitations, we propose a novel neural-driven constructive heuristic. The method employs a population of simple feed-forward neural networks, which are trained using black-box optimization via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting neural network dynamically scores candidate placements within the constructive heuristic. Unlike conventional heuristics, the approach adapts to instance-specific characteristics without relying on predefined rules. Evaluated on datasets generated by 2DCPackGen and real-world logistic scenarios, the proposed method consistently outperforms benchmark heuristics such as MaxRects and Skyline, reducing the average number of bins required across various item types and demand ranges. The most significant improvements occur in complex instances, with up to 86% of 2DCPackGen cases yielding superior results. This heuristic offers a flexible and extremely fast, data-driven solution to the algorithm selection problem, demonstrating robustness and potential for broader application in combinatorial optimization while avoiding the scalability issues of reinforcement learning-based methods. Full article

The Cutting Stock Problem (CSP) is an optimization challenge that involves dividing large objects into smaller components while considering various managerial objectives. The problem’s complexity can differ based on factors such as object dimensionality, the number of cutting stages required, and any technological constraints. The demand for coils of varying sizes and quantities necessitates intermediate splitting and slitting stages to produce the finished rolls. Additionally, relationships between orders are affected by dimensional variations at each stage of processing. This specific variant of the problem is known as the One-and-a-Half Dimensional Two-Stage Cutting Stock Problem (1.5-D TSCSP). To address the 1.5-D TSCSP, two algorithmic approaches were developed: the Generate-and-Solve (G&S) method and a hybrid Row-and-Column Generation (R&CG) approach. Both aim to minimize total trim loss while navigating the complexities of the problem. Inspired by existing problems in the literature for simpler versions of the problem, a set of randomly generated test cases was prepared, as detailed in this paper. An implementation of the two approaches was used to obtain solutions for the generated test campaign. The simpler G&S approach demonstrated superior performance in solving smaller instances of the problem, while the R&CG approach exhibited greater efficiency and provided superior solutions for larger instances. Full article

As an effective solution to the first- and last-mile logistics of door-to-door intermodal container transportation, inland container transportation involves transporting containers by truck between terminals, depots, and customers within a local area. This paper is the first to focus specifically on the inland container transportation problem with limited depot capacity, where the storage of empty containers is constrained by physical space limitations. To reflect a more realistic scenario, we also consider the initial stock levels of empty containers at the depot. The objective of this problem is to schedule trucks to fulfill inland container transportation orders such that the overall cost is minimum and the depot is neither out of stock or over stocked at any time. A novel graphical representation is introduced to model the constraints of empty containers and depot capacity in a linear form. This problem is then mathematically modeled as a mixed-integer linear programming formulation. To avoid discretizing the time horizon and effectively achieve the optimal solution, we design a tailored branch-and-price-and-cut algorithm where violated empty container constraints for critical times are dynamically integrated into the restricted master problem. The efficiency of the proposed algorithm is enhanced through the implementation of several techniques, such as a heuristic label-setting method, decremental state-space relaxation, and the utilization of high-quality upper bounds. Extensive computational studies are performed to assess the performance of the proposed algorithm and justify the introduction of enhancement strategies. Sensitivity analysis is additionally conducted to investigate the implications of significant influential factors, offering meaningful managerial guidance for decision-makers. Full article

This paper addresses the one-dimensional cutting stock problem, focusing on minimizing total stock usage. Most procedures that deal with this problem reside on linear programming methods, heuristics, metaheuristics, and hybridizations. These methods face drawbacks like handling only low-complexity instances or requiring extensive parameter tuning. To address these limitations we develop a Petri-net model to construct cutting patterns. Using the filtered beam search algorithm, the reachability tree of the Petri net is constructed level by level from its root node to find the best solution, pruning the nodes that worsen the solution as the search progresses through the tree. Our algorithm is compared with the Least Lost Algorithm and the Generate and Solve algorithm over five datasets of instances. These algorithms share some characteristics with ours and have proven to be effective and efficient. Experimental results demonstrate that our algorithm effectively finds optimal and near-optimalsolutions for both low and high-complexity instances. These findings confirm that Petri nets are suitable for modeling and solving the one-dimensional cutting stock problem. Full article

Today, a significant part of production wells’ stock has a high water cut percentage of 90% and above. Obviously, for this reason, the need to develop new and improved existing technologies for water shut-off in wells increases every year. Physico-chemical methods of water shut-off are based on the application of special reagents and compositions that plug the pathways of water inflow to the well. Depending on the mechanism and specific features of water barrier formation, isolation methods are divided into selective and non-selective. This article investigates the possibility of using hydrolyzed polyacrylonitrile as a gel-forming and precipitation-forming reagent for water shut-off technologies in production wells. A surfactant–polymer composition for the isolation of water inflow in production wells in objects with high salinity in formation water, possessing physical and chemical selectivity and providing permeability reduction only in water-saturated intervals, is proposed. The developed composition is the invert emulsion, which makes it possible to carry out treatment at a distance from the well and solve the problem of possible premature gel formation directly in the wellbore. The lowest effective concentration of HPAN in an aqueous solution for use as a gel-forming and sedimentation reagent was determined experimentally (5.0 wt% and more). The interaction of the polymer solution with a chromium crosslinker allows obtaining structured gels in the whole volume of the system. The structure of the gels was evaluated using the Sydansk classifier with the assignment of a letter code from A to J. It was experimentally proved that the structure of the obtained gels depends on the temperature and content of the crosslinking agent in the system; the more crosslinking agent in the composition of the system, the stronger the structure of the resulting gel. The optimal ratio of polymer and crosslinking agent to obtain a strong gel was obtained, which amounted to 5:1 by weight of dry polymer powder. For the HPAN concentration of 5 wt% according to the Sydansk classifier, the gel structure had the code “H”—slightly deformable non-flowing gel. The dependence of the volume of gel sediment obtained because of the interaction with mineralized water on the polymer concentration was studied. It was proved that an increase in the concentration of hydrolyzed polyacrylonitrile in the solution, as well as an increase in the concentration of calcium ions in mineralized water, leads to a larger volume of the resulting gel or precipitate and to the strengthening of the gel structure. The results of rheological studies of the developed composition, as well as experiments on thermal stability, are presented. The results of filtration tests on bulk reservoir models demonstrated the selectivity of the developed composition. The obtained value of the residual resistance factor for the oil-saturated low-permeability model was 1.49 units; the value of the residual resistance factor for the water-saturated high-permeability model was 18.04 units. The ratio of the obtained values of the residual resistance factor, equal to 0.08 (much less than 1), can characterize the developed composition as a selective material for water shut-off in producing wells. Existing technologies for water shut-off based on HPAN do not allow for making a treatment at a distance from the well and require the use of technological solutions to prevent premature gel sedimentation in the well. The developed composition makes it possible to solve the problem of premature gelation. In addition, the composition can form a blocking screen in highly permeable water-saturated zones. The development can be useful for deposits with difficult conditions (high mineralization in reservoir waters, boreholes with a horizontal end, elevated temperatures up to 80 °C). Full article

Cutting problems consist of cutting a set of objects available in stock in order to produce the desired items in specified quantities and sizes. The cutting process can generate leftovers (which can be reused in the case of new demand) or losses (which are discarded). This paper presents a tree-based heuristic method for minimizing the number of cut bars in the one-dimensional cutting process, satisfying the item demand in an unlimited bar quantity of just one type. The results of simulations are compared with the $RG{R}_{L1}$ algorithm and with the limiting values for this considered type of problem. The results show that the proposed heuristic reduces processing time and the number of bars needed in the cutting process, while it provides a larger leftover (by grouping losses) for the one-dimensional cutting stock problem. The heuristic contributes to reduction in raw materials or manufacturing costs in industrial processes. Full article

In this paper, we propose a grain volume measurement method based on binocular structured light to address the need for fast and high-precision grain volume measurement in grain stocks. Firstly, we utilize speckle structured light imaging to tackle the image matching problem caused by non-uniform illumination in the grain depot environment and the similar texture of the grain pile surface. Secondly, we employ a semi-global stereo matching algorithm with census transformation to obtain disparity maps in grain bins, which are then converted into depth maps using the triangulation principle. Subsequently, each pixel in the depth map is transformed from camera coordinates to world coordinates using the internal and external parameter information of the camera. This allows us to construct 3D cloud data of the grain pile, including the grain warehouse scene. Thirdly, the improved European clustering method is used to achieve the segmentation of the three-dimensional point cloud data of the grain pile and the scene of the grain depot, and the pass-through filtering method is used to eliminate some outliers and poor segmentation points generated by segmentation to obtain more accurate three-dimensional point cloud data of the grain pile. Finally, the improved Delaunay triangulation method was used to construct the optimal topology of the grain surface continuous triangular mesh, and the nodes of the grain surface triangular mesh were projected vertically to the bottom of the grain warehouse to form several irregular triangular prisms; then, the cut and complement method was used to convert these non-plane triangular prisms into regular triangular prisms that could directly calculate the volume. The measured volume of the pile is then obtained by calculating the volume of the triangular prism. The experimental results indicate that the measured volume has a relative error of less than 1.5% and an average relative error of less than 0.5%. By selecting an appropriate threshold, the relative standard deviation can be maintained within 0.6%. The test results obtained from the laboratory test platform meet the requirements for field inspection of the granary. Full article

The article deals with the problem of calculating the volume calendar plan of a paper mill. The presented mathematical model and methods make it possible to schedule paper production orders between several paper machines (PM) to even their loading, devise cutting plans for each winder and arrange the order of their implementation. When forming cutting plans, orders are grouped in accordance with such parameters as grammage, roll diameter, core diameter, product type and number of layers. Deadlines and volumes in customer orders are taken into account. The cutting plans for each winder account for the allowable roll width limits and the maximum number of knives. To find the optimal schedule, a combination of the following criteria is used: minimal trim loss, minimal changes to the knives’ setup and smooth transitions by product grades. Solution algorithms are presented that use a combination of the simplex method, the column generation, the branch and bound methods, the greedy algorithm and the local search procedure. We tested the solution approach on real production data from a paper mill in European Russia and obtained the production sequence that better matches deadlines in customer orders compared to the plan devised manually by production planners. Full article

The inventory level has a significant influence on the cost of process scheduling. The stochastic cutting stock problem (SCSP) is a complicated inventory-level scheduling problem due to the existence of random variables. In this study, we applied a model-free on-policy reinforcement learning (RL) approach based on a well-known RL method, called the Advantage Actor-Critic, to solve a SCSP example. To achieve the two goals of our RL model, namely, avoiding violating the constraints and minimizing cost, we proposed a two-stage discount factor algorithm to balance these goals during different training stages and adopted the game concept of an episode ending when an action violates any constraint. Experimental results demonstrate that our proposed method obtains solutions with low costs and is good at continuously generating actions that satisfy the constraints. Additionally, the two-stage discount factor algorithm trained the model faster while maintaining a good balance between the two aforementioned goals. Full article

Since the requirement for a material cutting process occurs in a wide variety of applied contemporary manufacturing, the cutting stock problem plays a critical role in optimizing the amount of raw material utilized in everyday production operations. In this paper, we address the two-dimension strip-cutting problem and implement the graph compression technique to improve the performance of the arc-flow formulation. The number of variables of the obtained mathematical model are substantially reduced. A comparative study on a large set of benchmark instances shows that our compressed model yields very good results for the non-unitary item demand case in contrast to the state-of-the-art mathematical models. Moreover, improved bounds are provided for 24 unsolved benchmark instances, among which 8 have been solved to optimality. Full article

It is well known that the one-dimensional cutting stock problem (1DCSP) is a combinatorial optimization problem with nondeterministic polynomial (NP-hard) characteristics. Heuristic and genetic algorithms are the two main algorithms used to solve the cutting stock problem (CSP), which has problems of small scale and low-efficiency solutions. To better improve the stability and versatility of the solution, a mathematical model is established, with the optimization objective of the minimum raw material consumption and the maximum remaining material length. Meanwhile, a novel algorithm based on deep reinforcement learning (DRL) is proposed in this paper. The algorithm consists of two modules, each designed for different functions. Firstly, the pointer network with encoder and decoder structure is used as the policy network to utilize the underlying mode shared by the 1DCSP. Secondly, the model-free reinforcement learning algorithm is used to train network parameters and optimize the cutting sequence. The experimental data show that the one-dimensional cutting stock algorithm model based on deep reinforcement learning (DRL-CSP) can obtain the approximate satisfactory solution on 82 instances of 3 data sets in a very short time, and shows good generalization performance and practical application potential. Full article

Grafting is one of the main techniques used in intensive tomato cultivation to address abnormal weather, diseases, insect pests, and continuous cropping problems. In recent years, due to agricultural labor constraints, the industry has needed to make use of more machinery. In this study, a clamping and cutting arrangement device for Solanaceae scion and stock seedlings was developed; it included a UR5 robotic arm to realize the automatic clamping and cutting of multiple seedlings, to facilitate the mechanized grafting of seedlings. The clamping success rate of eggplant and tomato seedlings using this device was 99%. When working with four seedlings in a single operation, the oblique-cutting success rates for eggplant and tomato seedlings were 97.76% and 94.55%, respectively. Moreover, 404 eggplant seedlings and 384 tomato seedlings could be worked with in 1 h. After the splicing and grafting process had been completed, the survival rate of the grafted seedlings was 85%. The simple device structure could be used by the industry to assist operators in clipping and cutting seedlings, reduce the complexity of grafting operations, shorten training times, address the problem of labor constraints in Taiwan’s agricultural industry, and improve product quality and efficiency. Full article