Integration of constraint programming and linear programming techniques for constraint satisfaction problem and general constrained optimization problem.

Wong Siu Ham.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 131-138).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgments --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation for Integration --- p.2Chapter 1.2 --- Thesis Overview --- p.4Chapter 2 --- Preliminaries --- p.5Chapter 2.1 --- Constraint Programming --- p.5Chapter 2.1.1 --- Constraint Satisfaction Problems (CSP's) --- p.6Chapter 2.1.2 --- Satisfiability (SAT) Problems --- p.10Chapter 2.1.3 --- Systematic Search --- p.11Chapter 2.1.4 --- Local Search --- p.13Chapter 2.2 --- Linear Programming --- p.17Chapter 2.2.1 --- Linear Programming Problems --- p.17Chapter 2.2.2 --- Simplex Method --- p.19Chapter 2.2.3 --- Mixed Integer Programming Problems --- p.27Chapter 3 --- Integration of Constraint Programming and Linear Program- ming --- p.29Chapter 3.1 --- Problem Definition --- p.29Chapter 3.2 --- Related works --- p.30Chapter 3.2.1 --- Illustrating the Performances --- p.30Chapter 3.2.2 --- Improving the Searching --- p.33Chapter 3.2.3 --- Improving the representation --- p.36Chapter 4 --- A Scheme of Integration for Solving Constraint Satisfaction Prob- lem --- p.37Chapter 4.1 --- Integrated Algorithm --- p.38Chapter 4.1.1 --- Overview of the Integrated Solver --- p.38Chapter 4.1.2 --- The LP Engine --- p.44Chapter 4.1.3 --- The CP Solver --- p.45Chapter 4.1.4 --- Proof of Soundness and Completeness --- p.46Chapter 4.1.5 --- Compared with Previous Work --- p.46Chapter 4.2 --- Benchmarking Results --- p.48Chapter 4.2.1 --- Comparison with CLP solvers --- p.48Chapter 4.2.2 --- Magic Squares --- p.51Chapter 4.2.3 --- Random CSP's --- p.52Chapter 5 --- A Scheme of Integration for Solving General Constrained Opti- mization Problem --- p.68Chapter 5.1 --- Integrated Optimization Algorithm --- p.69Chapter 5.1.1 --- Overview of the Integrated Optimizer --- p.69Chapter 5.1.2 --- The CP Solver --- p.74Chapter 5.1.3 --- The LP Engine --- p.75Chapter 5.1.4 --- Proof of the Optimization --- p.77Chapter 5.2 --- Benchmarking Results --- p.77Chapter 5.2.1 --- Weighted Magic Square --- p.77Chapter 5.2.2 --- Template design problem --- p.78Chapter 5.2.3 --- Random GCOP's --- p.79Chapter 6 --- Conclusions and Future Work --- p.97Chapter 6.1 --- Conclusions --- p.97Chapter 6.2 --- Future work --- p.98Chapter 6.2.1 --- Detection of implicit equalities --- p.98Chapter 6.2.2 --- Dynamical variable selection --- p.99Chapter 6.2.3 --- Analysis on help of linear constraints --- p.99Chapter 6.2.4 --- Local Search and Linear Programming --- p.99Appendix --- p.101Proof of Soundness and Completeness --- p.101Proof of the optimization --- p.126Bibliography --- p.13

A progressive stochastic search method for solving constraint satisfaction problems.

Bryan Chi-ho Lam.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 163-166).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 2 --- Background --- p.4Chapter 2.1 --- Constraint Satisfaction Problems --- p.4Chapter 2.2 --- Systematic Search --- p.5Chapter 2.3 --- Stochastic Search --- p.6Chapter 2.3.1 --- Overview --- p.6Chapter 2.3.2 --- GENET --- p.8Chapter 2.3.3 --- CSVC --- p.10Chapter 2.3.4 --- Adaptive Search --- p.12Chapter 2.4 --- Hybrid Approach --- p.13Chapter 3 --- Progressive Stochastic Search --- p.14Chapter 3.1 --- Progressive Stochastic Search --- p.14Chapter 3.1.1 --- Network Architecture --- p.15Chapter 3.1.2 --- Convergence Procedure --- p.16Chapter 3.1.3 --- An Illustrative Example --- p.21Chapter 3.2 --- Incremental Progressive Stochastic Search --- p.23Chapter 3.2.1 --- Network Architecture --- p.24Chapter 3.2.2 --- Convergence Procedure --- p.24Chapter 3.2.3 --- An Illustrative Example --- p.25Chapter 3.3 --- Heuristic Cluster Selection Strategy --- p.28Chapter 4 --- Experiments --- p.31Chapter 4.1 --- N-Queens Problems --- p.32Chapter 4.2 --- Permutation Generation Problems --- p.53Chapter 4.2.1 --- Increasing Permutation Problems --- p.54Chapter 4.2.2 --- Random Permutation Generation Problems --- p.75Chapter 4.3 --- Latin Squares and Quasigroup Completion Problems --- p.96Chapter 4.3.1 --- Latin Square Problems --- p.96Chapter 4.3.2 --- Quasigroup Completion Problems --- p.118Chapter 4.4 --- Random CSPs --- p.120Chapter 4.4.1 --- Tight Random CSPs --- p.139Chapter 4.4.2 --- Phase Transition Random CSPs --- p.156Chapter 5 --- Concluding Remarks --- p.159Chapter 5.1 --- Contributions --- p.159Chapter 5.2 --- Future Work --- p.16

An investigation of novel approaches for optimising retail shelf space allocation

This thesis is concerned with real-world shelf space allocation problems that arise due to the conflict of limited shelf space availability and the large number of products that need to be displayed. Several important issues in the shelf space allocation problem are identified and two mathematical models are developed and studied. The first model deals with a general shelf space allocation problem while the second model specifically concerns shelf space allocation for fresh produce. Both models are closely related to the knapsack and bin packing problem. The thesis firstly studies a recently proposed generic search technique, hyper-heuristics, and introduces a simulated annealing acceptance criterion in order to improve its performance. The proposed algorithm, called simulated annealing hyper-heuristics, is initially tested on the one-dimensional bin packing problem, with very promising and competitive results being produced. The algorithm is then applied to the general shelf space allocation problem. The computational results show that the proposed algorithm is superior to a general simulated annealing algorithm and other types of hyper-heuristics. For the test data sets used in the thesis, the new approach solves every instance to over 98% of the upper bound which was obtained via a two-stage relaxation method. The thesis also studies and formulates a deterministic shelf space allocation and inventory model specifically for fresh produce. The model, for the first time, considers the freshness condition as an important factor in influencing a product's demand. Further analysis of the model shows that the search space of the problem can be reduced by decomposing the problem into a nonlinear knapsack problem and a single-item inventory problem that can be solved optimally by a binary search. Several heuristic and meta-heuristic approaches are utilised to optimise the model, including four efficient gradient based constructive heuristics, a multi-start generalised reduced gradient (GRG) algorithm, simulated annealing, a greedy randomised adaptive search procedure (GRASP) and three different types of hyper-heuristics. Experimental results show that the gradient based constructive heuristics are very efficient and all meta-heuristics can only marginally improve on them. Among these meta-heuristics, two simulated annealing based hyper-heuristic performs slightly better than the other meta-heuristic methods. Across all test instances of the three problems, it is shown that the introduction of simulated annealing in the current hyper-heuristics can indeed improve the performance of the algorithms. However, the simulated annealing hyper-heuristic with random heuristic selection generally performs best among all the other meta-heuristics implemented in this thesis. This research is funded by the Engineering and Physical Sciences Research Council (EPSRC) grant reference GR/R60577. Our industrial collaborators include Tesco Retail Vision and SpaceIT Solutions Ltd

Problèmes de Tournées de Véhicules avec Synchronisation de Ressources

This dissertation focuses on vehicle routing problems, one of the major academic problems in logistics. We address NP-Hard problems that model some real world situations particularly those with different temporal constraints including time windows, visit synchronization and service balance.The aim of this research is to develop new algorithms for the considered problems, investigate their performance and compare them with the literature approaches. Two cases are carried out. The first case studies the Vehicle Routing Problem with Time Windows (VRPTW). We propose new lower bound methods for the number of vehicles. Then we present a Particle Swarm Optimization algorithm dealing with the Solomon objective. The second case studies the Vehicle Routing Problem with Time Windows and Synchronized Visits (VRPTWsyn). Both exact methods and heuristics are proposed and compared to the literature approaches.Cette thèse porte sur la résolution de problèmes de transport qui intègrent des contraintes temporelles considérant les fenêtres de temps, la synchronisation des visites et l’équilibrage des services. Ces problèmes trouvent plusieurs applications dans le monde réel.L’objectif de nos recherches est l’élaboration de nouvelles méthodes de résolution pour les problèmes considérés en examinant leur performance avec une étude comparative par rapport aux différentes approches de la littérature. Deux variantes sont traitées. Le premier cas étudie le Problème de Tournées de Véhicules avec Fenêtres de Temps (VRPTW). Nous proposons de nouveaux prétraitements et bornes inférieures pour déterminer le nombre de véhicules nécessaires en s’inspirant de travaux menés en ordonnancement (raisonnement énergétique) et d’autres problèmes combinatoires comme la clique maximum et les problèmes de bin-packing. Nous présentons également un algorithme d’optimisation par essaim particulaire qui traite de la minimisation du nombre de véhicules puis de celle du temps de trajet total. Le deuxième cas étudie le Problème de Tournées de Véhicules avec des Fenêtres de Temps et des Visites Synchronisées (VRPTWSyn). Nous proposons plusieurs méthodes basées sur des approches heuristiques et des formulations linéaires avec l’incorporation d’inégalités valides pour tenir compte de la contrainte de synchronisation

Scalable allocation of safety integrity levels in automotive systems

The allocation of safety integrity requirements is an important problem in modern safety engineering. It is necessary to find an allocation that meets system level safety integrity targets and that is simultaneously cost-effective. As safety-critical systems grow in size and complexity, the problem becomes too difficult to be solved in the context of a manual process. Although this thesis addresses the generic problem of safety integrity requirements allocation, the automotive industry is taken as an application example.Recently, the problem has been partially addressed with the use of model-based safety analysis techniques and exact optimisation methods. However, usually, allocation cost impacts are either not directly taken into account or simple, linear cost models are considered; furthermore, given the combinatorial nature of the problem, applicability of the exact techniques to large problems is not a given. This thesis argues that it is possible to effectively and relatively efficiently solve the allocation problem using a mixture of model-based safety analysis and metaheuristic optimisation techniques. Since suitable model-based safety analysis techniques were already known at the start of this project (e.g. HiP-HOPS), the research focuses on the optimisation task.The thesis reviews the process of safety integrity requirements allocation and presents relevant related work. Then, the state-of-the-art of metaheuristic optimisation is analysed and a series of techniques, based on Genetic Algorithms, the Particle Swarm Optimiser and Tabu Search are developed. These techniques are applied to a set of problems based on complex engineering systems considering the use of different cost functions. The most promising method is selected for investigation of performance improvements and usability enhancements. Overall, the results show the feasibility of the approach and suggest good scalability whilst also pointing towards areas for improvement

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma