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With the extensive application of laser cutting, the proper planning of the cutting path has a significate impact on industrial production, like clothing, metalware, and furniture. This paper proposes heuristic approaches for the two-dimensional cutting path problem, which aims to find the shortest path to cut pieces from the master plate. Two cutting methods, the arc routing method, and the node routing method, are studied based on whether the piece must be cut separately. A two-step heuristic approach is proposed for the arc routing method. A model based on a generalized traveling salesman problem (GTSP) and a variable neighborhood search (VNS) is introduced for the node routing method. The results show that the proposed two-step heuristic quickly finds the optimal or near-optimal solution. CPLEX using the GTSP model can solve small-size instances, while the VNS can find the solution with good quality in a reasonable time for medium-size instances. In addition, the model and approaches are also tested on larger instances with more than 250 pieces and 20,000 nodes. The results show that the proposed heuristics can handle these instances within a reasonable time. • A two-step heuristic is proposed for the arc routing method. • A new model is proposed for the node routing method. • A variable neighborhood search is introduced for the node routing method. • The two-step heuristic quickly finds the optimal or near-optimal solution. [ABSTRACT FROM AUTHOR]

In Birgin et al. (2020) the multi-period two-dimensional non-guillotine cutting stock problem with usable leftovers was introduced. At each decision instant, the problem consists in determining a cutting pattern for a set of ordered items using a set of objects that can be purchased or can be leftovers of previous periods; the goal being the minimization of the overall cost of the objects up to the considered time horizon. Among solutions with minimum cost, a solution that maximizes the value of the leftovers at the end of the considered horizon is sought. A forward-looking matheuristic approach that applies to this problem is introduced in the present work. At each decision instant, the objects and the cutting pattern that will be used is determined, taking into account the impact of this decision in future states of the system. More specifically, for each potentially used object, an attempt is made to estimate the utilization rate of its leftovers and thereby determine whether the object should be used or not. The introduced approach is compared with an exact off-the-shelf commercial solver and a myopic technique. Numerical experiments show the efficacy of the proposed approach. • A two-dimensional non-guillotine cutting stock problem is addressed. • Considered problem is multi-period and includes the usage of leftovers. • A forward-looking matheuristic approach is introduced. • Proposed method greatly improves greedy applicable methods. • The work opens a wide range of future research alternatives. [ABSTRACT FROM AUTHOR]

• A framework to select improvement heuristics for the rectangular 2D-SPP; • Classification models fitted with supervised machine learning techniques; • 15,666 instances were used to represent the rectangular 2D-SPP characteristics; • The predictions can reduce the computational processing time to solve instances; • Framework is consistent and can be applied for other problems. Defining the algorithm capable of best fit the characteristics observed for a problem is a complex task in the context of combinatorial optimization problems. As a decision-making process, one of the most practical and useful ways to treat and solve algorithm selection problems is using supervised machine learning techniques to search for patterns between explanatory variables characteristics of the problem and the algorithms available to be selected. The present article deals with the development of a framework to fit classification models based on supervised machine learning techniques to select improvement heuristics for the rectangular 2D strip packing problem (2D-SPP) with 90-degrees rotation. Classification models were fitted to predict the best improvement heuristic for constructive heuristics bottom-left, bottom-left-fill, best-fit, best-fit with bottom-left-fill, fast-heuristic, and fast-heuristic with bottom-left-fill, using 19 features provided by problem characteristics. A total of 15,666 benchmark problem instances from the literature were used to represent the rectangular 2D-SPP characteristics variations found in real-world applications to train and test the fitted classification models. The framework proved to be consistent to predict improvement heuristics with acceptable accuracy, being able to be applied for the prediction of other cutting and packing problems algorithms. [ABSTRACT FROM AUTHOR]

• A special 2D cutting stocking problem arisen in transformer industry is studied. • A constructive heuristic algorithm is proposed and its time complexity is given. • Three powerful metaheuristic algorithms are used for searching better solution. • 135 instances from real-world problem are used to test the algorithms' performance. This paper investigates a special cutting stock problem arisen in the production of iron core from the transformer manufacturing industry, in which a set of strips are cut from coiled stocks with multiple sizes. This problem has three main characteristics: the needed strips can be split into sub strips, the guillotine-cut, and the peculiar optimization objective, i.e., minimizing the total number of strips. The specific constraints prevent the direct application of procedures developed for the standard cutting stock problem. Hence, we developed a new constructive heuristic, called placer, which builds a layout starting from an ordered list representing stocks and needed strips. This polynomial procedure contains three helpful strategies, namely, double-layer, searching, and randomizing. Based on this procedure, we design a randomized multi-start algorithm to solve the problem and integrate the placer into three well-known metaheuristics, i.e., biased genetic algorithm, variable neighborhood search, and grey wolf optimization. To evaluate the performance of the proposed algorithms, computational experiments are conducted on 135 test instances generated from real-world data provided by the transformer manufacturing company. [ABSTRACT FROM AUTHOR]

By shaping investors' perceptions and assessments of the stock, research reports have significant impacts on the stock market. Due to the limitations of text mining technology, it is difficult for researchers to effectively utilize long research reports, and most studies mainly focus on investor sentiment. However, due to the lack of appropriate open-domain toolkits, the annotations of sentiment often require expensive manual labeling. In addition, most existing studies have shown the success of using textual data as a supplement to historical price data in short-term forecasting, but not in long-term forecasting. To cover this gap and solve the problem of difficult annotations, we introduce a novel knowledge-driven approach for long-term stock movement prediction based on Chinese research reports. In detail, a new long-term Stock Movement Prediction dataset composed of Research Reports is proposed, namely SMPRR. It is mainly composed of long, formal, and professional research reports and historical prices. Furthermore, we propose the Multi-module Feature Fusion method based on the pre-trained language model FinBERT (MFF-FinBERT), which can effectively fuse textual features from research reports. The experiment results show that the proposed model has achieved better performance than existing methods in the forecasting of one-year stock movements, and the accuracy reaches 79.2%. The results also indicate that the basic information of stocks plays an important role in long-term forecasting, which is in line with the theory of value investing. • Predicting long-term stock movements with fused textual features of research reports. • Constructing a dataset composed of research reports and historical prices. • Proposing a multi-module feature fusion method based on pre-trained language models. • The basic information of stocks plays an important role in long-term forecasting. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the two-dimensional cutting/packing problem with items that correspond to simple polygons that may contain holes are studied in which we propose algorithms based on no-fit polygon computation. We present a GRASP based heuristic for the 0/1 version of the knapsack problem, and another heuristic for the unconstrained version of the knapsack problem. This last heuristic is divided in two steps: first it packs items in rectangles and then use the rectangles as items to be packed into the bin. We also solve the cutting stock problem with items of irregular shape, by combining this last heuristic with a column generation algorithm. The algorithms proposed found optimal solutions for several of the tested instances within a reasonable runtime. For some instances, the algorithms obtained solutions with occupancy rates above 90% with relatively fast execution time. [Copyright &y& Elsevier]

In this paper, we address the Constrained Three-dimensional Guillotine Cutting Problem (C3GCP), which consists of cutting a larger cuboid block (object) to produce a limited number of smaller cuboid pieces (items) using orthogonal guillotine cuts only. This way, all cuts must be parallel to the object's walls and generate two cuboid sub-blocks, and there is a maximum number of copies that can be manufactured for each item type. The C3GCP arises in industrial manufacturing settings, such as the cutting of steel and foam for mattresses. To model this problem, we propose a new compact mixed-integer non-linear programming (MINLP) formulation by extending its two-dimensional version, and develop a mixed-integer linear programming (MILP) version. We also propose a new model for a particular case of the problem which considers 3-staged patterns. As a solution method, we extend the algorithm of Wang (1983) to the three-dimensional case. We emphasise that the C3GCP is different from 3D packing problems, namely from the Container Loading Problem, because of the guillotine cut constraints. All proposed approaches are evaluated through computational experiments using benchmark instances. The results show that the approaches are effective on different types of instances, mainly when the maximum number of copies per item type is small, a situation typically encountered in practical settings with low demand for each item type. These approaches can be easily embedded into existing expert systems for supporting the decision-making process. • We address the constrained three-dimensional guillotine placement problem. • We present ILP formulations for non-staged and 3-staged patterns. • We present a binary tree-search algorithm for non-staged and 3-staged patterns. • Optimal or near-optimal solutions are obtained in reasonable processing times. • These approaches can be useful for different industrial cutting processes. [ABSTRACT FROM AUTHOR]