A deterministic algorithm for generating optimal three- stage layouts of homogenous strip pieces

Purpose: The time required by the algorithms for general layouts to solve the large-scale two-dimensional cutting problems may become unaffordable. So this paper presents an exact algorithm to solve above problems. Design/methodology/approach: The algorithm uses the dynamic programming algorithm to generate the optimal homogenous strips, solves the knapsack problem to determine the optimal layout of the homogenous strip in the composite strip and the composite strip in the segment, and optimally selects the enumerated segments to compose the three-stage layout. Findings: The algorithm not only meets the shearing and punching process need, but also achieves good results within reasonable time. Originality/value: The algorithm is tested through 43 large-scale benchmark problems. The number of optimal solutions is 39 for this paper’s algorithm; the rate of the rest 4 problem’s solution value and the optimal solution is 99. 9%, and the average consumed time is only 2. 18seconds. This paper’s pattern is used to simplify the cutting process. Compared with the classic three-stage, the two-segment and the T-shape algorithms, the solutions of the algorithm are better than that of the above three algorithms. Experimental results show that the algorithm to solve a large-scale piece packing quickly and efficiency.Peer Reviewe

Geometric process planning in rough machining

This thesis examines geometric process planning in four-axis rough machining. A review of existing literature provides a foundation for process planning in machining; efficiency (tool path length) is identified as a primary concern. Emergent structures (thin webs and strings) are proposed as a new metric of process robustness. Previous research efforts are contrasted to establish motivation for improvements in these areas in four-axis rough machining. The original research is presented as a journal article. This research develops a new methodology for quickly estimating the remaining stock during a plurality of 2 y D cuts defined by their depth and orientation relative to a rotary fourth axis. Unlike existing tool path simulators, this method can be performed independently of (and thus prior to) tool path generation. The algorithms presented use polyhedral mesh surface input to create and analyze polygonal slices, which are again reconstructed into polyhedral surfaces. At the slice level, nearly all operations are Boolean in nature, allowing simple implementation. A novel heuristic for polyhedral reconstruction for this application is presented. Results are shown for sample components, showing a significant reduction in overall rough machining tool path length. The discussion of future work provides a brief discussion of how this new methodology can be applied to detecting thin webs and strings prior to tool path planning or machining. The methodology presented in this work provides a novel method of calculating remaining stock such that it can be performed during process planning, prior to committing to tool path generation

Formal descriptions of material manipulations: an exploration with cuts and shadows

Shape computation in design is never purely limited to visual aspects and ideally includes material aspects as well. The physicality of designing introduces a wide range of variables for designers to tackle within the design process. We present a simple design exercise realised in four stages where we physically manipulate perforated cardboard sheets as a case to make material variables explicit in the computation. The emphasis is on representing sensory aspects rather than easily quantifiable properties more suitable for simulations. Our explorations demonstrate the use of visual rules to represent actions, variables and form as well as how to control the variables to create new results, both desired and surprising, in materially informed ways

The Spatial Hierarchy of Technological Change and Economic Development in Europe

This paper discusses the possibility of a spatial hierarchy of innovation and growth dynamics in Europe. A spatial hierarchy is understood as a geographical clustering of regions, where important differences exist in terms of innovation and growth dynamics between the clusters. The literature on regional growth and innovation is briefly scanned. After this, a database on European regional growth and innovation dynamics is presented. Spatial correlation analysis and spatial principal components analysis are used to explore the possibility of a spatial hier-archy in Europe. The results point to a hierarchy consisting of four groups: South Europe, East Europe, and two groups in West and North Europe. Growth and innovation performance in these clusters is discussed, and some policy conclusions are drawn.Technological Change, Economic Development, Europe, Geographical Distribution, Government Policy

Data-Driven Robust Optimization

The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests. The approach is flexible and widely applicable, and robust optimization problems built from our new sets are computationally tractable, both theoretically and practically. Furthermore, optimal solutions to these problems enjoy a strong, finite-sample probabilistic guarantee. \edit{We describe concrete procedures for choosing an appropriate set for a given application and applying our approach to multiple uncertain constraints. Computational evidence in portfolio management and queuing confirm that our data-driven sets significantly outperform traditional robust optimization techniques whenever data is available.Comment: 38 pages, 15 page appendix, 7 figures. This version updated as of Oct. 201

Novel approaches to container loading: from heuristics to hybrid tabu search

A thesis submitted for the degree of Doctor of Philosophy of the University ofBedford shireThis work investigates new approaches to the container loading problem which address the issue of how to load three-dimensional, rectangular items (e.g. boxes) into the container in such a way that maximum utilisation is made of the container space. This problem occurs in several industry sectors where the loading approach places cargo effectively into aeroplanes, ships, trailers or trucks in order to save considerable cost. In carrying out this work, the investigation starts by developing a new heuristic approach to the two-dimensional bin packing problem, which has lower complexity than container loading in the aspects of constraints and geometry. A novel approach, including the heuristic strategies and handling method for remaining areas, is developed that can produce good results when testing with benchmark and real world data. Based on the research for two-dimensional bin packing, a novel heuristic approach is developed to deal with the container loading problem with some practical constraints. The heuristic approach to container loading also includes heuristic strategies and the handling of remaining spaces. The heuristic strategies construct effective loading arrangements where combinations of identical or different box types are loaded in blocks. The handling method for remaining spaces further improves the loading arrangements through the representation, partitioning and merging of remaining spaces. The heuristic approach obtains better volume utilisation and the highest stability compared with other published heuristic approaches. However, it does not achieve as high a volume utilisation as metaheuristic approaches, e.g. genetic algorithms and tabu search.To improve volume utilisation, a new hybrid heuristic approach to the container loading problem is further developed based on the tabu search technique which covers the encoding, evaluation criterion and configuration of neighbourhood and candidate solutions. The heuristic strategies as well as the handling method for remaining spaces developed in the heuristic approach are used in this new hybrid tabu search approach. It is shown that the hybrid approach has better volume utilisation than the published approaches under the condition that all loaded boxes with one hundred per cent support from below. In addition, the experimental results show that both the heuristic and hybrid tabu search approaches can also be applied to the multiple container loading problem

The Bright Side of MAUP: an Enquiry on the Determinants of Industrial Agglomeration in the United States

Using county employment data for US and two appositely developed zoning algorithms, I compare the industrial concentration of manufacturing sectors calculated following the standard metropolitan and micropolitan statistical areas definition with two other counterfactuals, obtained by “gerrymandering” the original sample of counties. The methodology allows i) to obtain an unbiased estimate of industrial agglomeration which significantly improves on existing indices, and ii) to provide a ranking of industries according to their responsiveness to labour market determinants of agglomeration. Results show that labour market determinants explain one quarter of the variation of spatial agglomeration across industries.Industrial Agglomerations, MAUP, Industrial Concentration