Optimización de las operaciones portuarias mediante simulación y metodología de superficie de respuesta

Este artículo presenta los resultados de la utilización de herramientas de optimización estocástica, mediante simulación y metodología de superficie de respuesta, en un caso real del ámbito logístico portuario. El problema en estudio corresponde al dimensionamiento del área de transferencia de contenedores de un nuevo muelle en el puerto de Coronel, en la octava región del país. Básicamente la problemática implica definir los requerimientos de equipamiento necesarios (grúas portacontenedores y camiones) que permitan el cumplimiento de estándares de utilización y eficiencia del proceso de carga y descarga de contenedores al menor costo de inversión posible. La metodología utilizada para resolver el problema consiste en la creación de un modelo de simulación de eventos discreta, que imita el comportamiento del área de transferencia de contenedores. Los resultados de diversos experimentos generados por el modelo de simulación permiten levantar información suficiente para la aplicación de técnicas de Diseño Experimental que finalmente sirven para la estimación de una superficie de respuesta que aproxime el comportamiento del proceso en estudio. Finalmente las ecuaciones estimadas se utilizaron para la aplicación de un proceso de optimización con el fin de determinar los requerimientos de equipamiento necesarios para lograr una optimización en el uso de los recursos. Los resultados obtenidos permiten dimensionar los requerimientos de equipamiento para dos escenarios diferentes, incorporando dentro del proceso de optimización la aleatoriedad propia de este tipo de sistemas.This article presents the results of the utilization of stochastic optimization tools, through simulation and response surface methodology, to a real case of harbor logistics. The problem under consideration corresponds to the sizing of the container transfer area of a new pier in the port of Coronel, in the eighth region of the country. Basically the problem involves defining the equipment requirements necessary (cranes and tractors) in order to comply with utilization and efficiency standards of the containers loading and unloading process at the lowest possible investment cost. The methodology used to solve the problem is the creation of a discrete event simulation model, which mimics the behavior of the area of containers transfer. The results of various experiments generated by the simulation model allows to gather sufficient information to apply Experimental Design techniques that are finally used to estimate a response surface that approximates the behavior of the process under study. Finally, the estimated equations were used for the implementation of an optimization process in order to determine equipment requirements needed to achieve an optimal use of resources. The results permit the sizing of equipment requirements for two different scenarios, incorporating into the optimization process the natural randomness of this type of systems

A general framework on the computing budget allocation problem

Master'sMASTER OF ENGINEERIN

Modified Selection Mechanisms Designed to Help Evolution Strategies Cope with Noisy Response Surfaces

With the rise in the application of evolution strategies for simulation optimization, a better understanding of how these algorithms are affected by the stochastic output produced by simulation models is needed. At very high levels of stochastic variance in the output, evolution strategies in their standard form experience difficulty locating the optimum. The degradation of the performance of evolution strategies in the presence of very high levels of variation can be attributed to the decrease in the proportion of correctly selected solutions as parents from which offspring solutions are generated. The proportion of solutions correctly selected as parents can be increased by conducting additional replications for each solution. However, experimental evaluation suggests that a very high proportion of correctly selected solutions as parents is not required. A proportion of correctly selected solutions of around 0.75 seems sufficient for evolution strategies to perform adequately. Integrating statistical techniques into the algorithm?s selection process does help evolution strategies cope with high levels of noise. There are four categories of techniques: statistical ranking and selection techniques, multiple comparison procedures, clustering techniques, and other techniques. Experimental comparison of indifference zone selection procedure by Dudewicz and Dalal (1975), sequential procedure by Kim and Nelson (2001), Tukey?s Procedure, clustering procedure by Calsinki and Corsten (1985), and Scheffe?s procedure (1985) under similar conditions suggests that the sequential ranking and selection procedure by Kim and Nelson (2001) helps evolution strategies cope with noise using the smallest number of replications. However, all of the techniques required a rather large number of replications, which suggests that better methods are needed. Experimental results also indicate that a statistical procedure is especially required during the later generations when solutions are spaced closely together in the search space (response surface)

Multiobjective Simulation Optimization Using Enhanced Evolutionary Algorithm Approaches

In today\u27s competitive business environment, a firm\u27s ability to make the correct, critical decisions can be translated into a great competitive advantage. Most of these critical real-world decisions involve the optimization not only of multiple objectives simultaneously, but also conflicting objectives, where improving one objective may degrade the performance of one or more of the other objectives. Traditional approaches for solving multiobjective optimization problems typically try to scalarize the multiple objectives into a single objective. This transforms the original multiple optimization problem formulation into a single objective optimization problem with a single solution. However, the drawbacks to these traditional approaches have motivated researchers and practitioners to seek alternative techniques that yield a set of Pareto optimal solutions rather than only a single solution. The problem becomes much more complicated in stochastic environments when the objectives take on uncertain (or noisy ) values due to random influences within the system being optimized, which is the case in real-world environments. Moreover, in stochastic environments, a solution approach should be sufficiently robust and/or capable of handling the uncertainty of the objective values. This makes the development of effective solution techniques that generate Pareto optimal solutions within these problem environments even more challenging than in their deterministic counterparts. Furthermore, many real-world problems involve complicated, black-box objective functions making a large number of solution evaluations computationally- and/or financially-prohibitive. This is often the case when complex computer simulation models are used to repeatedly evaluate possible solutions in search of the best solution (or set of solutions). Therefore, multiobjective optimization approaches capable of rapidly finding a diverse set of Pareto optimal solutions would be greatly beneficial. This research proposes two new multiobjective evolutionary algorithms (MOEAs), called fast Pareto genetic algorithm (FPGA) and stochastic Pareto genetic algorithm (SPGA), for optimization problems with multiple deterministic objectives and stochastic objectives, respectively. New search operators are introduced and employed to enhance the algorithms\u27 performance in terms of converging fast to the true Pareto optimal frontier while maintaining a diverse set of nondominated solutions along the Pareto optimal front. New concepts of solution dominance are defined for better discrimination among competing solutions in stochastic environments. SPGA uses a solution ranking strategy based on these new concepts. Computational results for a suite of published test problems indicate that both FPGA and SPGA are promising approaches. The results show that both FPGA and SPGA outperform the improved nondominated sorting genetic algorithm (NSGA-II), widely-considered benchmark in the MOEA research community, in terms of fast convergence to the true Pareto optimal frontier and diversity among the solutions along the front. The results also show that FPGA and SPGA require far fewer solution evaluations than NSGA-II, which is crucial in computationally-expensive simulation modeling applications

Continuous optimization via simulation using Golden Region search

Simulation Optimization (SO) is the use of mathematical optimization techniques in which the objective function (and/or constraints) could only be numerically evaluated through simulation. Many of the proposed SO methods in the literature are rooted in or originally developed for deterministic optimization problems with available objective function. We argue that since evaluating the objective function in SO requires a simulation run which is more computationally costly than evaluating an available closed form function, SO methods should be more conservative and careful in proposing new candidate solutions for objective function evaluation. Based on this principle, a new SO approach called Golden Region (GR) search is developed for continuous problems. GR divides the feasible region into a number of (sub) regions and selects one region in each iteration for further search based on the quality and distribution of simulated points in the feasible region and the result of scanning the response surface through a metamodel. The experiments show the GR method is efficient compared to three well-established approaches in the literature. We also prove the convergence in probability to global optimum for a large class of random search methods in general and GR in particular

Optimization under uncertainty with application to data clustering

A new optimization technique with uncertainty that extends the pure nested partition (NP) algorithm is presented in this thesis. This method is called the nested partition with inheritance. The basic idea of a NP algorithm is very simple. At each iteration, the most promising region is partitioned and the performance of the partitioned region is evaluated using sampling. Based on the performance evaluation, the most promising region is chosen for the next iteration. These procedures are repeated until it satisfies the termination condition.;Even though the pure NP method guarantees the convergence to the optimal solution, it has several shortcomings. To handle these shortcomings, two extensions to the pure NP are suggested. To rigorously determine the required sample effort, some statistical selection methods are implemented, which include the Nelson Matejcik procedure, the Rinott procedure, and the Dudewicz and Dalal procedure, as well as a subset procedure. In addition, Genetic Algorithms (GAs) are used to speed convergence and to overcome the difficulty in the backtracking stage of the NP algorithm.;As an application of the new methodology, this work also suggests the methods to be applied to a data clustering problem. This is a very hard problem with two of the main difficulties being lack of scalability with respect to amount of data and problems with high dimensionality. The new algorithms are found to be effective for solving this problem. Random sampling enhances scalability and the iterative partitioning addresses the dimensionality

A SURVEY OF RANKING, SELECTION, AND MULTIPLE COMPARISON PROCEDURES FOR DISCRETE-EVENT SIMULATION

Discrete-event simulation models are often constructed so that an analyst may compare two or more competing design alternatives. This paper presents a survey of the literature for two widely-used statistical methods for selecting the best design from among a finite set of k alternatives: ranking and selection (R&S) and multiple comparison procedures (MCPs). A comprehensive survey of each topic is presented along with a summary of recent unified R&S-MCP approaches. In addition, an example of the application of Nelson and Matejcik’s (1995) combined R&S-MCP procedure is given

Modeling and analyzing army air assault operations via simulation

Cataloged from PDF version of article.It is very important to use combat simulations in personel training and as a scientific decision tool in developed countries. The use of simulation and analysis methodologies gives opportunity to the staff officers and the commanders to foresee the results of their plans and to take some precautions accordingly. Different combat scenarios can be tried without deploying the units to the combat area and getting losts, costs and risks. As one of the most complicated and decisive operation in the way to victory “Air assault operations” are high risk, high payoff operations, that, when properly planned and vigorously executed, allow commanders to take the initiative of the combat area. The use of Air Assault Operations Simulation Model (AAOSM) allows planners: (1) to build models of air assault operations early in the decision process and refine those models as their decision process evolve, (2) perform “Bottleneck analysis” of the preplanned operation using statistical procedures and take some precautions accordingly. (3) perform “Risk management” of the operation before conducting the real one. AAOSM is created by using ARENA 3.0 simulation program and SIMAN programming languauge.The outputs of the model is analysed using experimental design procedures and the significant factors that are significant to the outputs are analysed. Moreover, the best scenarios are evaluated in different weather and terrain conditions and different refuelling and maintenance configurations.Virlan, GökhanM.S

Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems

A new class of algorithms is introduced and analyzed for bound and linearly constrained optimization problems with stochastic objective functions and a mixture of design variable types. The generalized pattern search (GPS) class of algorithms is extended to a new problem setting in which objective function evaluations require sampling from a model of a stochastic system. The approach combines GPS with ranking and selection (R&S) statistical procedures to select new iterates. The derivative-free algorithms require only black-box simulation responses and are applicable over domains with mixed variables (continuous, discrete numeric, and discrete categorical) to include bound and linear constraints on the continuous variables. A convergence analysis for the general class of algorithms establishes almost sure convergence of an iteration subsequence to stationary points appropriately defined in the mixed-variable domain. Additionally, specific algorithm instances are implemented that provide computational enhancements to the basic algorithm. Implementation alternatives include the use modern R&S procedures designed to provide efficient sampling strategies and the use of surrogate functions that augment the search by approximating the unknown objective function with nonparametric response surfaces. In a computational evaluation, six variants of the algorithm are tested along with four competing methods on 26 standardized test problems. The numerical results validate the use of advanced implementations as a means to improve algorithm performance