Exploring the Local Orthogonality Principle

Nonlocality is arguably one of the most fundamental and counterintuitive aspects of quantum theory. Nonlocal correlations could, however, be even more nonlocal than quantum theory allows, while still complying with basic physical principles such as no-signaling. So why is quantum mechanics not as nonlocal as it could be? Are there other physical or information-theoretic principles which prohibit this? So far, the proposed answers to this question have been only partially successful, partly because they are lacking genuinely multipartite formulations. In Nat. Comm. 4, 2263 (2013) we introduced the principle of Local Orthogonality (LO), an intrinsically multipartite principle which is satisfied by quantum mechanics but is violated by non-physical correlations. Here we further explore the LO principle, presenting new results and explaining some of its subtleties. In particular, we show that the set of no-signaling boxes satisfying LO is closed under wirings, present a classification of all LO inequalities in certain scenarios, show that all extremal tripartite boxes with two binary measurements per party violate LO, and explain the connection between LO inequalities and unextendible product bases.Comment: Typos corrected; data files uploade

The split delivery vehicle routing problem with three-dimensional loading constraints

 The Split Delivery Vehicle Routing Problem with three-dimensional loading constraints (3L-SDVRP) combines vehicle routing and three-dimensional loading with additional packing constraints. In the 3L-SDVRP splitting deliveries of customers is basically possible, i.e. a customer can be visited in two or more tours. We examine essential problem features and introduce two problem variants. In the first variant, called 3L-SDVRP with forced splitting, a delivery is only split if the demand of a customer cannot be transported by a single vehicle. In the second variant, termed 3L-SDVRP with optional splitting, splitting customer deliveries can be done any number of times. We propose a hybrid algorithm consisting of a local search algorithm for routing and a genetic algorithm and several construction heuristics for packing. Numerical experiments are conducted using three sets of instances with both industrial and academic origins. One of them was provided by an automotive logistics company in Shanghai; in this case some customers per instance have a total freight volume larger than the loading space of a vehicle. The results prove that splitting deliveries can be beneficial not only in the one-dimensional case but also when goods are modeled as three-dimensional items

Algorithms and data structures for three-dimensional packing

Cutting and packing problems are increasingly prevalent in industry. A well utilised freight vehicle will save a business money when delivering goods, as well as reducing the environmental impact, when compared to sending out two lesser-utilised freight vehicles. A cutting machine that generates less wasted material will have a similar effect. Industry reliance on automating these processes and improving productivity is increasing year-on-year. This thesis presents a number of methods for generating high quality solutions for these cutting and packing challenges. It does so in a number of ways. A fast, efficient framework for heuristically generating solutions to large problems is presented, and a method of incrementally improving these solutions over time is implemented and shown to produce even higher packing utilisations. The results from these findings provide the best known results for 28 out of 35 problems from the literature. This framework is analysed and its effectiveness shown over a number of datasets, along with a discussion of its theoretical suitability for higher-dimensional packing problems. A way of automatically generating new heuristics for this framework that can be problem specific, and therefore highly tuned to a given dataset, is then demonstrated and shown to perform well when compared to the expert-designed packing heuristics. Finally some mathematical models which can guarantee the optimality of packings for small datasets are given, and the (in)effectiveness of these techniques discussed. The models are then strengthened and a novel model presented which can handle much larger problems under certain conditions. The thesis finishes with a discussion about the applicability of the different approaches taken to the real-world problems that motivate them

A Combinatorial Approach to Nonlocality and Contextuality

So far, most of the literature on (quantum) contextuality and the Kochen-Specker theorem seems either to concern particular examples of contextuality, or be considered as quantum logic. Here, we develop a general formalism for contextuality scenarios based on the combinatorics of hypergraphs which significantly refines a similar recent approach by Cabello, Severini and Winter (CSW). In contrast to CSW, we explicitly include the normalization of probabilities, which gives us a much finer control over the various sets of probabilistic models like classical, quantum and generalized probabilistic. In particular, our framework specializes to (quantum) nonlocality in the case of Bell scenarios, which arise very naturally from a certain product of contextuality scenarios due to Foulis and Randall. In the spirit of CSW, we find close relationships to several graph invariants. The recently proposed Local Orthogonality principle turns out to be a special case of a general principle for contextuality scenarios related to the Shannon capacity of graphs. Our results imply that it is strictly dominated by a low level of the Navascu\'es-Pironio-Ac\'in hierarchy of semidefinite programs, which we also apply to contextuality scenarios. We derive a wealth of results in our framework, many of these relating to quantum and supraquantum contextuality and nonlocality, and state numerous open problems. For example, we show that the set of quantum models on a contextuality scenario can in general not be characterized in terms of a graph invariant. In terms of graph theory, our main result is this: there exist two graphs $G_1$ and $G_2$ with the properties \begin{align*} \alpha(G_1) &= \Theta(G_1), & \alpha(G_2) &= \vartheta(G_2), \\[6pt] \Theta(G_1\boxtimes G_2) & > \Theta(G_1)\cdot \Theta(G_2),& \Theta(G_1 + G_2) & > \Theta(G_1) + \Theta(G_2). \end{align*}Comment: minor revision, same results as in v2, to appear in Comm. Math. Phy

Solving the Pickup and Delivery Problem with 3D Loading Constraints and Reloading Ban

In this paper, we extend the classical Pickup and Delivery Problem (PDP) to an integrated routing and three-dimensional loading problem, called PDP with 3D loading constraints (3L-PDP). A set of routes of minimum total length has to be determined such that each request is transported from a loading site to the corresponding unloading site. In the 3L-PDP, each request is given as a set of 3D rectangular items (boxes) and the vehicle capacity is replaced by a 3D loading space. This paper is the second one in a series of articles on 3L-PDP. In both articles we investigate which constraints will ensure that no reloading effort will occur, i.e. that no box is moved after loading and before unloading. In this paper, the focus is laid on the so-called reloading ban, a packing constraint that ensures identical placements of same boxes in different packing plans. We propose a hybrid algorithm for solving the 3L-PDP with reloading ban consisting of a routing and a packing procedure. The routing procedure modifies a well-known large neighborhood search for the 1D-PDP. A tree search heuristic is responsible for packing boxes. Computational experiments were carried out using 54 3L-PDP benchmark instances

Modelling and Optimisation of Space Allocation and layout Problems

This thesis investigates the development of optimisation-based, decision-making frameworks for allocation problems related to manufacturing, warehousing, logistics, and retailing. Since associated costs with these areas constitute significant parts to the overall supply chain cost, mathematical models of enhanced fidelity are required to obtain optimal decisions for i) pallet loading, ii) assortment, and iii) product shelf space, which will be the main research focus of this thesis. For the Manufactures Pallet loading problems (MPLP), novel single- and multi-objective Mixed Integer Linear Programming (MILP) models have been proposed, which generate optimal layouts of improved 2D structure based on a block representation. The approach uses a Complexity Index metric, which aids in comparing 2 pallet layouts that share the same pallet size and number of boxes loaded but with different box arrangements. The proposed algorithm has been tested against available data-sets in literature. In the area of Assortments (optimal 2D packing within given containers) , an iterative MILP algorithm has been developed to provide a diverse set of solutions within pre-specified range of key performance metrics. In addition, a basic software prototype, based on AIMMS platform, has been developed using a user-friendly interface so as to facilitate user interaction with a visual display of the solutions obtained. In Shelf- Space Allocation (SSAP) problem, the relationship between the demand and the retailer shelf space allocated to each item is defined as space elasticity. Most of existing literature considers the problem with stationary demand and fixed space elasticities. In this part of the thesis, a dynamic framework has been proposed to forecast space elasticities based on historical data using standard time-series methodologies. In addition, an optimisation mathematical model has been implemented using the forecasted space elasticities to provide the retailer with optimal shelf space thus resulting into closer match between supply and demand and increased profitability. The applicability and effectiveness of the proposed framework is demonstrated through a number of tests and comparisons against literature data-sets

Unpublished research reports; a problem in bibliographical control

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