Mathematische Logik

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Jostle heuristics for the 2D-irregular shapes bin packing problems with free rotation

The paper investigates the two-dimensional irregular packing problem with multiple homogeneous bins (2DIBPP). The literature on irregular shaped packing problems is dominated by the single stock sheet strip packing problem. However, in reality manufacturers are cutting orders over multi-stock sheets. Despite its greater relevance, there are only a few papers that tackle this problem in the literature. A multi-stock sheet problem has two decision components; the allocation of pieces to stock sheets and the layout design for each stock sheet. In this paper, we propose a heuristic method that addresses both the allocation and placement problems together based on the Jostle algorithm. Jostle was first applied to strip packing. In order to apply Jostle to the bin packing problem we modify the placement heuristic. In addition we improve the search capability by introducing a diversification mechanism into the approach. Furthermore, the paper presents alternative strategies for handling rotation of pieces, which includes a restricted set of angles and unrestricted rotation. Very few authors permit unrestricted rotation of pieces, despite this being a feature of many problems where the material is homogeneous. Finally, we investigate alternative placement criteria and show that the most commonly applied bottom left criteria does not perform as well as other options. The paper evaluates performance of each algorithm using different sets of instances considering convex and non-convex shapes. Findings of this study reveal that the proposed algorithms can be applied to different variants of the problem and generate significantly better results

Dealing with Nonregular Shapes Packing

This paper addresses the irregular strip packing problem, a particular two-dimensional cutting and packing problem in which convex/nonconvex shapes (polygons) have to be packed onto a single rectangular object. We propose an approach that prescribes the integration of a metaheuristic engine (i.e., genetic algorithm) and a placement rule (i.e., greedy bottom-left). Moreover, a shrinking algorithm is encapsulated into the metaheuristic engine to improve good quality solutions. To accomplish this task, we propose a no-fit polygon based heuristic that shifts polygons closer to each other. Computational experiments performed on standard benchmark problems, as well as practical case studies developed in the ambit of a large textile industry, are also reported and discussed here in order to testify the potentialities of proposed approach

Mathematical Logic: Proof Theory, Constructive Mathematics

The workshop “Mathematical Logic: Proof Theory, Constructive Mathematics” was centered around proof-theoretic aspects of core mathematics and theoretical computer science as well as homotopy type theory and logical aspects of computational complexity

Rethinking the notion of oracle: A link between synthetic descriptive set theory and effective topos theory

We present three different perspectives of oracle. First, an oracle is a blackbox; second, an oracle is an endofunctor on the category of represented spaces; and third, an oracle is an operation on the object of truth-values. These three perspectives create a link between the three fields, computability theory, synthetic descriptive set theory, and effective topos theory

An iterated local search algorithm based on nonlinear programming for the irregular strip packing problem

The irregular strip packing problem is a combinatorial optimization problem that requires to place a given set of two-dimensional polygons within a rectangular container so that no polygon overlaps with other polygons or protrudes from the container, where each polygon is not necessarily convex. The container has a fixed width, while its length can change so that all polygons are placed in it. The objective is to find a layout of the set of polygons that minimizes the length of the container. We propose an algorithm that separates overlapping polygons based on nonlinear programming, and an algorithm that swaps two polygons in a layout so as to find their new positions in the layout with the least overlap. We incorporate these algorithms as components into an iterated local search algorithm for the overlap minimization problem and then develop an algorithm for the irregular strip packing problem using the iterated local search algorithm. Computational comparisons on representative instances disclose that our algorithm is competitive with other existing algorithms. Moreover, our algorithm updates several best known results

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Nesting Problems : Exact and Heuristic Algorithms

Nesting problems are two-dimensional cutting and packing problems involving irregular shapes. This thesis is focused on real applications on Nesting problems such as the garment industry or the glass cutting. The aim is to study different mathematical methodologies to obtain good lower bounds by exact procedures and upper bounds by heuristic algorithms. The core of the thesis is a mathematical model, a Mixed Integer Programming model, which is adapted in each one of the parts of the thesis. This study has three main parts: first, an exact algorithm for Nesting problems when rotation for the pieces is not allowed; second, an Iterated Greedy algorithm to deal with more complex Nesting problems when pieces can rotate at several angles; third, a constructive algorithm to solve the two-dimensional irregular bin packing problem with guillotine cuts. This thesis is organized as follows. The first part is focused on developing exact algorithms. In Chapter 2 we present two Mixed Integer Programming (MIP) models, based on the Fischetti and Luzzi MIP model. We consider horizontal lines in order to define the horizontal slices which are used to separate each pair of pieces. The second model, presented in Section 2.3, uses the structure of the horizontal slices in order to lift the bound constraints. Section 2.5 shows that if we solve these formulations with CPLEX, we obtain better results than the formulation proposed by Gomes and Oliveira. The main objective is to design a Branch and Cut algorithm based on the MIP, but first a Branch and Bound algorithm is developed in Chapter 3. Therefore, we study different branching strategies and design an algorithm which updates the bounds on the coordinates of the reference point of the pieces in order to find incompatible variables which are fixed to 0 in the current branch of the tree. The resulting Branch and Bound produces an important improvement with respect to previous algorithms and is able to solve to optimality problems with up to 16 pieces in a reasonable time. In order to develop the Branch and Cut algorithm we have found several classes of valid inequalities. Chapter 4 presents the different inequalities and in Chapter 5 we propose separation algorithms for some of these inequalities. However, our computational experience shows that although the number of nodes is reduced, the computational time increases considerably and the Branch and Cut algorithm becomes slower. The second part is focused on building an Iterated Greedy algorithm for Nesting problems. In Chapter 6 a constructive algorithm based on the MIP model is proposed. We study different versions depending on the objective function and the number of pieces which are going to be considered in the initial MIP. A new 11 idea for the insertion is presented, trunk insertion, which allows certain movements of the pieces already placed. Chapter 7 contains different movements for the local search based on the reinsertion of a given number of pieces and compaction. In Chapter 8 we present a math-heuristic algorithm, which is an Iterated Greedy algorithm because we iterate over the constructive algorithm using a destructive algorithm. We have developed a local search based on the reinsertion of one and two pieces. In the constructive algorithm, for the reinsertion of the pieces after the destruction of the solution and the local search movements, we use several parameters that change along the algorithm, depending on the time required to prove optimality in the corresponding MIP models. That is, somehow we adjust the parameters, depending on the difficulty of the current MIP model. The computational results show that this algorithm is competitive with other algorithms and provides the best known results on several known instances. The third part is included in Chapter 9. We present an efficient constructive algorithm for the two dimensional irregular bin packing problem with guillotine cuts. This problem arises in the glass cutting industry. We have used a similar MIP model with a new strategy to ensure a guillotine cut structure. The results obtained improve on the best known results. Furthermore, the algorithm is competitive with state of the art procedures for rectangular bin packing problems

Cutting path as a Rural Postman Problem: solutions by Memetic Algorithms

The Rural Postman Problem (RPP) is a particular Arc Routing Problem (ARP) which consistsof determining a minimum cost circuit on a graph so that a given subset of required edges is traversed. TheRPP is an NP-hard problem with significant real-life applications. This paper introduces an originalapproach based on Memetic Algorithms - the MARP algorithm - to solve the RPP and, also deals with aninteresting Industrial Application, which focuses on the path optimization for component cuttingoperations. Memetic Algorithms are a class of Metaheuristics which may be seen as a population strategythat involves cooperation and competition processes between population elements and integrates "socialknowledge", using a local search procedure. The MARP algorithm is tested with different groups ofinstances and the results are compared with those gathered from other publications. MARP is also used inthe context of various real-life applications