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Simulation optimization has been one of the most important topics in recent years. It is especially useful when the objective function cannot be computed directly or some of the parameters are stochastic. Buffer allocation problem is one of the important problems related to production lines. In this problem the objective is to determine the buffers between workstations so that the throughput is maximized. Non-linear and combinatorial nature of the problem, stochastic factors like machine breakdown or processing times and most importantly lack of a distinct relation for the objective function are the reasons of using simulation ... 

Digital computer simulation and employing the concepts of the experimental design, and other analytical tools for evaluating its output have attracted many of the scientists and researchers interests in the recent decades. The importance of this topic has been increasing and more related analytical tools have been introduced to the scientific literature. One of the powerful tools for simplifying accelerating the optimization process of simulation results, are the use of the metamodels. Use of these powerful tools becomes more eminent when the simulation runs are expensive. By the use of the metamodels the needs of conducting sampling for obtaining some more new points from direct simulating... 

In this thesis, the problem of the hotel revenue management with the customer choice behaiver has been studied . In this model , each customer has a preference order among the set of products. He purchased or booked his products according to his ordered list of preferences. Because the high number of hotel products are assumed, each customer can only choices different fare classes and the list of the preference customers is made up of different fare classes. Two different approaches have been used to solve this problem . The first approach is The linear programming model in which α percent of the capacity is allocated to business customers and the remaining capacity to leisuer customers... 

Huge availability of data in last decade has raised the opportunity to use data for decision making. The idea of using existing data to achieve more coherent reality solution has led to a branch of optimization called data-driven optimization. Presence of uncertain variables makes it crucial to design robust optimization methods for this area. On the other hand, in many real-world problems, the closed-form of the objective function is not available and a meta-model based framework is necessary. Motivated by this, we are using a Gaussian process in a Bayesian optimization framework to design a method that is consistent with the data in predefined confidence level. The goodness of the... 

In this research we will develop an algorithm to find the optimal robust solution via simulation optimization by using an artificial neural network metamodel. Following Taghuchi, in design phase of the algorithm, we will discriminate between decision or control variables and environmental or noise variables. To arrive of the best new solution in every iteration, the algorithm will use a symmetrical probabilistic distribution about the optimum point of the previous iteration. In comparison with the existing methods, our algorithm displays an improvement in results when applied to such problems as single channel queueing system problem and economic order quantity problem  

Project Scheduling is one of the most important branches of operations research and management science. In this paper, project scheduling problem with a deterministic duration of activities is studied. Resources are considered nonrenewable ones which are perishable. First of all, the project scheduling problem has been studied with a rate of corruption of inventory. Then, a rate of corruption with three parameters for Weibull is considered. Cost function for purchasing a renewable Perishable resource, has been assumed that has growing discounts for the purchase of certain health differently. Project costs are included purchase costs and maintenance costs. The objective function is the... 

In this work, we propose a metamodel-based simulation optimization algorithm using a novel hybrid sequential experimental design. The algorithm starts with a metamodel construction phase in which at each stage, a sequential experimental design is used to select a new sample point from the search space using a hybrid exploration-exploitation search strategy. Based on the available design points at each stage, a metamodel is constructed using Artificial Neural Network (ANN) and Kriging interpolation techniques. The resulting metamodel is then used in the optimization process to evaluate new solutions. We use Imperialist Competitive Algorithm (ICA) which is a powerful population-based... 

simulation models are free of any restricting assumptions which may normally be considered in a stochastic system, so simulation is considered as one of the most popular tools that can be applied toward analysis of behavior of stochastic systems which are complex.
In order to analyze such problems and determine the best combination of input variables to optimize the system performance criterion, simulation optimization methods were introduced. The most important issue in these problems is that simulation models are usually considered as the black-box models in which, the output function is not usually expressed explicitly.
This work reviews different methods which developed... 

In this thesis we propose a new metamodel based simulation optimization algorithm using sequential design of experiments. The main objective is to have a new method which can be used without deep knowledge of different kinds of metamodels, optimization techniques and design of experiments. The method uses three metamodels simulataneously and gradually adapts to the best metamodel. In each iteration, some points are chosen as candidates for future simulation. These points are ranked based on the quality of metamodel prediction and their placement among simulated points, the best point will be chosen for simulation. Comparing the proposed algorithm with some of the popular simulation... 

Sales promotion plays an important role in increasing the profit, attraction, and retention of consumers. Temporary discounts are a popular promotional tactic that is applied in diverse situations. In this study, we examine some situations to determine optimal decisions. In the first scenario, promotions are implemented during predetermined periods, and the seller determines optimal pricing to achieve two separate objectives: maximizing profit and managing demand (clearance sales). We propose generalized inverse optimization models that determine discounted prices to make the given promotion timing as close to optimal as possible. The efficacy of our approach is demonstrated through... 

In this research, the problem of locating fire stations and allocating equipment has been studied and a simulation-optimization approach has been presented to solve the problem. The mathematical models of this research were developed based on the idea of the randomness of the covered demand and the maximum expected coverage model. In these models, the issue of non-availability of equipment to cover accidents, the random nature of accidents, various fire incidents and the equipment needed to cover them are considered. Two mathematical models with deterministic and non-deterministic approach with different scenarios for demand are proposed. The non-deterministic model is developed with the aim... 

Following the recent developments in the field of decision making, a considerable number of problems involved with stochastic systems can be thought of whose analysis depends on a set of intricate mathematical relations. In such cases, simulation is one of the most popular tools that can be applied toward analysis of behavior of such stochastic systems. Not only does not the simulation model rely on such intricate mathematical relations, it also enjoys the added advantage of being free of any restricting assumptions which may normally be considered in a stochastic system.To analyze such problem, one may aim at determining the best combination of input variables to optimize the system...