Search Results (35)

Quantum finance represents a pivotal and cutting-edge application domain within the burgeoning field of quantum computing. In this work, we propose a two-step quantum approximate optimization algorithm (two-step QAOA) for portfolio optimization and risk assessment. The algorithm initiates by formulating the stock selection problem as a quadratic unconstrained binary optimization (QUBO) problem and employs a classical-quantum hybrid method to find the ground state of the Hamiltonian. We then introduce an energy-based characteristic indicator $U\in [0,1)$, which quantitatively evaluates portfolio performance under customizable investment preferences, effectively capturing the trade-off between expected return and risk. The number of qubits required scales with the number of stocks N in the pool, and the number of Hamiltonian terms is $\mathcal{O}\left(N^2\right)$. Numerical simulations show that the algorithm provides consistent and reasonable assessment results on both training and test datasets under different investment preferences (aggressive or conservative), validating the capability of the characteristic indicator to extract intrinsic information from the portfolios. Additionally, by incorporating warm-starting and digitized counterdiabatic techniques, the algorithm achieves improved scalability and faster convergence. Our work presents a flexible and practical algorithmic framework for applying quantum computing in the financial domain. Full article

This paper proposes adaptive sequence-based heuristics for solving rectangular two-dimensional guillotine Cutting and Packing Problems (CPPs). These problems are essential in various industrial sectors, aiming to maximise resource utilisation by selecting profitable item subsets or minimise waste by using the fewest possible identical large objects. The core methodology is grounded in the principle that if a specific item sequence generates a high-quality solution, incremental adjustments to that sequence can yield even better outcomes. By iteratively refining item ordering through the BubbleSearch method, the heuristics balance search intensification with the diversification of the solution space. Extensive computational experiments were conducted on benchmark datasets, including SET1, ATP, and CLASS, across multiple problem variants such as the Single Stock-Size Cutting Stock Problem (SSSCSP) and the Single Large Object Placement Problem (SLOPP). The results confirm that these heuristics and their extension with path relinking consistently deliver optimal or near-optimal solutions. These heuristics achieve high performance in computational times that are significantly shorter than existing state-of-the-art methods, demonstrating their robustness, flexibility, and suitability for software transferability and real-world industrial adoption. Full article

Column generation is a fundamental technique for solving large-scale combinatorial optimization problems such as unit commitment and vehicle routing, yet its performance is often limited by dual oscillation. This study explores the intrinsic cause of this phenomenon from the perspective of shadow price theory and demonstrates that dual oscillation arises from the lack of marginal interpretability of Lagrange multipliers when multiple dual solutions coexist. To address this issue, an improved column generation framework is proposed in which traditional multipliers are replaced with minimum-norm multipliers that possess clear economic meaning and act as directional shadow prices. A generalized pricing subproblem is formulated, and partial minimum-norm multipliers are obtained through convex quadratic optimization to guide column generation. Numerical experiments on a simplified single-period unit commitment case and large-scale cutting stock problems showed that the proposed approach eliminated invalid column generation and achieved speedy convergence to the optimal solution within only two iterations for the unit commitment case, and the classical column generation exhibited slow convergence with dual oscillation in large-scale scenarios while the improved algorithm achieved fast and stable convergence. The results indicate that the stabilization method enhances the consistency of dual variables and provides a more robust foundation for the theoretical and practical development of column generation algorithms. Full article

Short-term trading presents a high-dimensional prediction problem, where the profitability of trading signals depends not only on model accuracy but also on how financial labels are defined and aligned with market dynamics. Traditional approaches often apply uniform modeling choices across assets, overlooking the asset-specific nature of volatility, liquidity, and market response. In this work, we introduce a structured, label-aware machine learning pipeline aimed at maximizing short-term trading profitability across four major benchmarks: S&P 500 (SPX), NASDAQ-100 (NDX), Dow Jones Industrial Average (DJI), and the Tadāwul All-Share Index (TASI and twelve of their most actively traded constituents). Our solution systematically evaluates all combinations of six model types (logistic regression, support vector machines, random forest, XGBoost, 1-D CNN, and LSTM), eight look-ahead labeling windows (3 to 10 days), and four feature subset sizes (44, 26, 17, 8 variables) derived through Random Forest permutation-importance ranking. Backtests are conducted using realistic long/flat simulations with zero commission, optimizing for Percentage Profit and Profit Factor on a 2005–2021 train/2022–2024 test split. The central contribution of the framework is a labeling-aware search mechanism that assigns to each asset its optimal combination of model type, look-ahead horizon, and feature subset based on out-of-sample profitability. Empirical results show that while XGBoost performs best on average, CNN and LSTM achieve standout gains on highly volatile tech stocks. The optimal look-ahead window varies by market from 3-day signals on liquid U.S. shares to 6–10-day signals on the less-liquid TASI universe. This joint model–label–feature optimization avoids one-size-fits-all assumptions and yields transferable configurations that cut grid-search cost when deploying from index level to constituent stocks, improving data efficiency, enhancing robustness, and supporting more adaptive portfolio construction in short-horizon trading strategies. Full article

In the manufacturing of iron core for high-power transformers, a cutting stock problem arises where large-width silicon steel coils must be cut into narrower coils, known as strips. Typically, the required length of each strip far exceeds that of a single coil. Therefore, the problem necessitates additional consideration of how to split the strips and arrange them on the large coils, with the goal of minimizing the total number of strips. In this paper, we propose a discrete artificial bee colony algorithm to address this problem. The algorithm replaces the stochastic roulette wheel with biased selection in the onlooker bee phase and introduces partially mapped crossover in both the onlooker and scout bee phases. These enhancements facilitate more effective utilization of information from high-quality solutions, thereby improving the algorithm’s stability and its capacity to obtain higher-quality results. Experimental results show that compared to existing methods reported in the literature, the proposed approach reduces the total number of strips by an average of over 3.9% and 7.6% for Set 2 and Set 3, respectively, while also exhibiting a faster convergence rate than other competitive algorithms. Full article

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