19 research outputs found
Proceedings of the third International Workshop of the IFIP WG5.7
Contents of the papers presented at the international workshop deal with the wide variety of new and computer-based techniques for production planning and control that has become available to the scientific and industrial world in the past few years: formal modeling techniques, artificial neural networks, autonomous agent theory, genetic algorithms, chaos theory, fuzzy logic, simulated annealing, tabu search, simulation and so on. The approach, while being scientifically rigorous, is focused on the applicability to industrial environment
Implementasi Algoritma Ant Colony Optimization (ACO) Pada Pencarian Jalur Terpendek Automatic Teller Machine (ATM) Di Kota Palu
Penelitian ini bertujuan untuk membangun sebuah sistem yang dapat memberikan informasi lokasi ATM di Kota Palu, dan sekaligus memberikan petunjuk jalur terpendek dengan menggunakan algoritma Ant Colony Optimization (ACO) system (ACS), yang merupakan variasi algoritma Ant Colony Optimization (ACO), dalam mencari makanan setiap setiap semut akan berusaha mencari jalur terpendek dari sarang ke tempat makanan. Kemudian semut tersebut akan meninggalkan pheromone di jalur yang dilaluinya .Pada proses awal algoritma ini adalah menginisialisasi penggunaan parameter yang tepat sesuai kasus yang akan diselesaikan .Pada proses ini semut akan memilihmelakukan eksploitasi atau eksplorasi rute yang akan di kunjungi. Eksploitasi berarti semut hanya akan mengunjungi ruas-ruas simpul yang memiliki pheromone yang tinggi dengan bobot jarak yang kecil sedangkan eksporasi berarti semut bias saja mengunjungi ruas ruas simpul yang memiliki pheromone yang rendah dengan bobot jarak besar. Proses perhitungan ACO di implementasikan ke dalam sistem yangtelah di buat menggunakan aplikasi Android studio versi 3.3 dengan memanfaatkan prosedur algoritma ACO, penelitian ini menggunakan data sebanyak 60 lokasi ATM dikota Pal
The cooperative effects of channel length-bias, width asymmetry, gradient steepness, and contact-guidance on fibroblasts’ directional decision making
Cell migration in complex micro-environments, that are similar to tissue pores, is important for predicting locations of tissue nucleation and optimizing scaffold architectures. Firstly, how fibroblast cells - relevant to tissue engineering, affect each other’s directional decisions when encountered with a bifurcation of different channel lengths was investigated. It was found that cell sequence and cell mitosis influence the directional choices that the cells made while chemotaxing. Specifically, the fibroblasts chose to alternate between two possible paths - one longer and the other shorter - at a bifurcation. This finding was counter-intuitive given that the shorter path had a steeper chemoattractant gradient, and would thus be expected to be the preferred path, according to classical chemotaxis theory. Hence, a multiscale image-based modeling was performed in order to explain this behavior. It showed that consumption of the chemotactic signals by the neighboring cells led to the sequence-dependent directional decisions. Furthermore, it was also found that cellular division led to daughter cells making opposite directional choices from each other; even it meant that one of the daughter cells had to move against the chemotactic gradient, and overcome oncoming traffic of other cells.
Secondly, a comparison of the effects of the various directional cues on the migration of individual fibroblast cells: including the chemoattractant concentration gradient, the channel width, and the contact-guidance was provided. Simple bifurcated mazes with two branches of different widths were created and fibroblasts were allowed to travel across these geometries by introducing a gradient of PDGF-BB at the ‘exit’ of the device. By incorporating image-based modeling methodology into the experimental approach, an insight into (i) how individual cells make directional decisions in the presence of complex migration cues and (ii) how the cell-cell interaction influences it was provided. It was found that a larger width ratio between the two bifurcated branches outdoes a gradient difference in attracting the cells. Also, when cells encounter a symmetric bifurcation (i.e., no difference between the branch widths), the gradient is predominant in deciding which path the cell will take. Then, in a symmetrical gradient field (i.e., inside a bifurcation of similar branch widths, and in the absence of any leading cells), the contact guidance is important for guiding the cells in making directional choices. Finally, these directional cues were ranked according to the order from the most importance to the least: vast gradient difference between the two branches, channel width bias, mild gradient difference, and contact-guidance
Evolving comprehensible and scalable solvers using CGP for solving some real-world inspired problems
My original contribution to knowledge is the application of Cartesian Genetic Programming to design some scalable and human-understandable metaheuristics automatically; those find some suitable solutions for real-world NP-hard and discrete problems. This technique is thought to possess the ability to raise the generality of a problem-solving process, allowing some supervised machine learning tasks and being able to evolve non-deterministic algorithms. \\
Two extensions of Cartesian Genetic Programming are presented. Iterative My original contribution to knowledge is the application of Cartesian Genetic Programming to design some scalable and human-understandable metaheuristics automatically; those find some suitable solutions for real-world NP-hard and discrete problems. This technique is thought to possess the ability to raise the generality of a problem-solving process, allowing some supervised machine learning tasks and being able to evolve non-deterministic algorithms. \\
Two extensions of Cartesian Genetic Programming are presented. Iterative Cartesian Genetic Programming can encode loops and nested loop with their termination criteria, making susceptible to evolutionary modification the whole programming construct. This newly developed extension and its application to metaheuristics are demonstrated to discover effective solvers for NP-hard and discrete problems. This thesis also extends Cartesian Genetic Programming and Iterative Cartesian Genetic Programming to adapt a hyper-heuristic reproductive operator at the same time of exploring the automatic design space. It is demonstrated the exploration of an automated design space can be improved when specific types of active and non-active genes are mutated. \\
A series of rigorous empirical investigations demonstrate that lowering the comprehension barrier of automatically designed algorithms can help communicating and identifying an effective and ineffective pattern of primitives. The complete evolution of loops and nested loops without imposing a hard limit on the number of recursive calls is shown to broaden the automatic design space. Finally, it is argued the capability of a learning objective function to assess the scalable potential of a generated algorithm can be beneficial to a generative hyper-heuristic
GPU accelerated Hungarian algorithm for traveling salesman problem
In this thesis, we present a model of the Traveling Salesman Problem (TSP) cast in a quadratic assignment problem framework with linearized objective function and constraints. This is referred to as Reformulation Linearization Technique at Level 2 (or RLT2). We apply dual ascent procedure for obtaining lower bounds that employs Linear Assignment Problem (LAP) solver recently developed by Date(2016). The solver is a parallelized Hungarian Algorithm that uses Compute Unified Device Architecture (CUDA) enabled NVIDIA Graphics Processing Units (GPU) as the parallel programming architecture. The aim of this thesis is to make use of a modified version of the Dual Ascent-LAP solver to solve the TSP.
Though this procedure is computational expensive, the bounds obtained are tight and our experimental results confirm that the gap is within 2% for most problems. However, due to limitations in computational resources, we could only test problem sizes N < 30. Further work can be directed at theoretical and computational analysis to test the efficiency of our approach for larger problem instances
Roteirização dinâmica de veículos em áreas urbanas congestionadas
Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2011Problemas dinâmicos de roteirização de veículos têm recebido crescente atenção dos pesquisadores, em função da rápida evolução das tecnologias de telecomunicação, do tratamento da informação e dos avanços observados nas técnicas de análise, otimização e computação. Nos centros urbanos sujeitos a congestionamentos de tráfego elevados e imprevisíveis, os operadores logísticos costumam alocar, muitas vezes, um número excessivo de tarefas aos seus veículos, acarretando o não cumprimento de atividades programadas ao fim da jornada diária, situação essa que leva ao não cumprimento dos compromissos logísticos assumidos com seus clientes. No presente estudo é apresentado um método de roteirização dinâmica em que parte das tarefas em excesso, que venham a ocorrer nos roteiros programados, é transferida para um veículo auxiliar, que efetua, assim, um roteiro dinâmico constituído pelas atividades provenientes dos veículos regulares. Para validar o modelo proposto foi utilizada simulação na definição dos parâmetros mais relevantes e foram comparados resultados obtidos entre um procedimento de roteirização estática com o procedimento de roteirização dinâmica proposto. Os resultados obtidos apresentaram um aumento considerável do nível de serviço com a adoção do modelo propost
GAPS : a hybridised framework applied to vehicle routing problems
In this thesis we consider two combinatorial optimisation problems; the Capacitated Vehicle Routing Problem (CVRP) and the Capacitated Arc Routing Problem (CARP). In the CVRP, the objective is to find a set of routes for a homogenous fleet of vehicles, which must service a set of customers from a central depot. In contrast, the CARP requires a set of routes for a fleet of vehicles to service a set of customers at the street level of an intercity network. After a comprehensive discussion of the existing exact and heuristic algorithmic techniques presented in the literature for these problems, computational experiments to provide a benchmark comparison of a subset of algorithmic implementations for these methods are presented for both the CVRP and CARP, run against a series of dataset instances from the literature. All dataset instances are re-catalogued using a standard format to overcome the difficulties of the different naming schemes and duplication of instances that exist between different sources. We then present a framework, which we shall call Genetic Algorithm with Perturbation Scheme (GAPS), to solve a number of combinatorial optimisation problems. The idea is to use a genetic algorithm as a container framework in conjunction with a perturbation or weight coding scheme. These schemes make alterations to the underlying input data within a problem instance, after which the changed data is fed into a standard problem specific heuristic and the solution obtained decoded to give a true solution cost using the original unaltered instance data. We first present GAPS in a generic context, using the Travelling Salesman Problem (TSP) as an example and then provide details of the specific application of GAPS to both the CVRP and CARP. Computational experiments on a large set of problem instances from the literature are presented and comparisons with the results achieved by the current state of the art algorithmic approaches for both problems are given, highlighting the robustness and effectiveness of the GAPS framework.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Large-Scale Linear Programming
During the week of June 2-6, 1980, the System and Decision Sciences Area of the International Institute for Applied Systems Analysis organized a workshop on large-scale linear programming in collaboration with the Systems Optimization Laboratory (SOL) of Stanford University, and co-sponsored by the Mathematical Programming Society (MPS). The participants in the meeting were invited from amongst those who actively contribute to research in large-scale linear programming methodology (including development of algorithms and software).
The first volume of the Proceedings contains five chapters. The first is an historical review by George B. Dantzig of his own and related research in time-staged linear programming problems. Chapter 2 contains five papers which address various techniques for exploiting sparsity and degeneracy in the now standard LU decomposition of the basis used with the simplex algorithm for standard (unstructured) problems. The six papers of Chapter 3 concern aspects of variants of the simplex method which take into account through basis factorization the specific block-angular structure of constraint matrices generated by dynamic and/or stochastic linear programs. In Chapter 4, five papers address extensions of the original Dantzig-Wolfe procedure for utilizing the structure of planning problems by decomposing the original LP into LP subproblems coordinated by a relatively simple LP master problem of a certain type. Chapter 5 contains four papers which constitute a mini-symposium on the now famous Shor-Khachian ellipsoidal method applied to both real and integer linear programs. The first chapter of Volume 2 contains three papers on non-simplex methods for linear programming. The remaining chapters of Volume 2 concern topics of present interest in the field. A bibliography a large-scale linear programming research completes Volume 2