Sharif Digital Repository

Humanitarian logistics is regarded as a key area for improved disaster management efficiency and effectiveness. In this study, a multi-objective integrated logistic model is proposed to locate disaster relief centers while taking into account network costs and responsiveness. Because this location problem is NP-hard, we present a genetic approach to solve the proposed model  

Neural Networks (FFNNs) that are trained with the Levenberg-Marquardt back-propagation algorithm were used. Models were evaluated using mean squared error (MSE) and coefficient of determination (R2) as statistical performance parameters. In traffic noise modelling, the noise level at a receptor position due to the source of traffic emission is modelled as a function of the traffic conditions, road gradient, road dimensions, speed and height of buildings around the road. The curse of dimensionality problems is caused by the large number of input variables in the ANN model. The Hybrid Genetic Algorithm-Gamma Test (GA-GT) as a data pre-processing method for determining adequate model inputs was... 

Control charts are statistical tools to monitor a process or a product. However, some processes cannot be controlled by monitoring a characteristic; instead, they need to be monitored using profiles. Economic-statistical design of profile monitoring means determining the parameters of a profile monitoring scheme such that total costs are minimized while statistical measures maintain proper values. While varying sampling interval usually increases the effectiveness of profile monitoring, economic-statistical design of variable sampling interval (VSI) profile monitoring is investigated in this paper. An extended Lorenzen–Vance function is used for modeling total costs in VSI model where the... 

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg–Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the... 

We provide a mixed-integer nonlinear programming (MINLP) model for a location-allocation-routing problem in a transportation network with links carrying the travel times among the nodes in the network. The travel time between two nodes is considered to be intelligent, that is, since the travelling population in a link can affect the travel time, here we consider the impact of the travelling population on the travel time of the link. This way, depending on how the population is distributed in the network, the travel times of the links may change. The problem is to find an optimal locations of server node(s), allocation of existing demand nodes in the network to the server(s) and allocation of... 

In today's business transactions, sometimes customers are allowed to pay in a grace period, i.e., permissible delay in payment occurs. This policy is advantageous both for the suppliers and for the customers. This paper formulates a multi-product economic order quantity (EOQ) problem with an order-quantity-dependent permissible delay in payment. In this problem, the shortage is backlogged and there is a warehouse constraint. We show that the model of the problem is a constrained nonlinear-integer-program and propose a genetic algorithm (GA) to solve it. Moreover, a statistical approach is employed to calibrate the parameters of the GA. A numerical example is presented at the end to not only... 

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg–Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the... 

The multi-carrier microgrid (MCMG) is a restricted district comprising convertors and energy storage systems (ESSs) that are used to fulfill various energy demands. The structure and optimal operation of these MCMGs with regard to fulfilling multi-carrier demands are presented in relation to their rapid spread. In this paper, a two-stage optimum planning and design method for an MCMG is presented in the planning horizon. The investment and operation (fuel and maintenance) costs are considered concurrently to find the optimal type and size of components over the planning horizon. At the first stage, the genetic algorithm (GA) is applied to determine the optimal type and size of components,... 

The learning effects of the workers and machine deterioration in an aggregate production planning (APP) problem have not been taken into account in the literature yet. These factors affect the performance of any real-world production system and require attention. In this paper, a bi-objective optimization model is developed for an APP problem with labor learning effect and machine deterioration. The first objective of this model maximizes the profit by improving learning and reducing the failure cost of the system. The second objective function minimizes the costs associated with repairs and deterioration, which depend on the failure rate of the machines in the production periods. The aim of... 

In this document, allocation of different types of vehicles for transporting products from a manufacturing firm to its depots is considered. The problem involves a limited number of vehicles of various capacities, with the fixed and variable costs as well as a discount mechanism. The objective is defined as the minimisation of the total transportation costs. A mathematical model in the form of nonlinear integer programming is developed and converted to the linear zero-one programme. Due to the NP hard complexity of the proposed mathematical model, a Prufer-based genetic algorithm capable of solving large instances is developed. The efficiency of the proposed algorithm is compared with the... 

While in multi-periodic inventory control problems the usual assumption are that the orders are placed at the beginning of each period (periodic review) or depending on the inventory level they can happen at any time (continuous review), in this research, we relax these assumptions and assume that the periods between two replenishments of the products are independent and identically distributed random variables. Furthermore, assuming the purchasing price are triangular fuzzy variables, the quantities of the orders are of integer-type and that there are space, budget and service level constraints, incremental discount is considered to purchase products and a combination of back-order and... 

In this research, a maximal covering location problem (MCLP) with real-world constraints such as multiple types of facilities and vehicles with different setup costs is taken into account. An original mixed integer linear programming (MILP) model is constructed in order to find the optimal solution. Since the problem at hand is shown to be NP-hard, a constructive heuristic method and a meta-heuristic approach based on genetic algorithm (GA) are developed to solve the problem. To find the most effective solution technique, a set of problems of different sizes is randomly generated and solved by the proposed solution methods. Computational results demonstrate that the heuristic method is... 

In this paper, we consider the multi-product multi-constraint newsboy problem with random fuzzy demands and total discount. The demand of the products is often stochastic in the real word but the estimation of the parameters of distribution function may be done by fuzzy manner. So an appropriate option to modeling the demand of products is using the random fuzzy variable. The objective function of proposed model is to maximize the expected profit of newsboy. We consider the constraints such as warehouse space and restriction on quantity order for products, and restriction on budget. We also consider the batch size for products order. Finally we introduce a random fuzzy multi-product... 

In this chapter, we introduce Symbiotic Evolutionary Algorithm (SEA) as a template for search and optimization based on partially specified chromosomes and symbiotic combination operator. We show that in contrast to genetic algorithms with traditional recombination operators, this template will not be bound to linkage problems. We present three implementations of this template: first, as a pure algorithm for search and optimization, second, as an artificial immune system, and third, as an algorithm for classifier rule base evolution, and compare implementation results and feature lists with similar algorithms. © 2008 Springer-Verlag Berlin Heidelberg  

The effects of various process parameters on caustic current efficiency (CCE) in a zero-gap oxygen-depolarized chlor-alkali cell employing a state-of-the-art silver plated nickel screen electrode (ESNS®) were studied. For doing a thorough research, we selected the process parameters from both cathodic and anodic compartments. Seven process parameters were studied including anolyte pH, temperature, flow rate and brine concentration from the anode side, oxygen temperature and flow rate from the cathode side and the applied current density. The effect of these parameters on CCE was determined quantitatively. A feed forward neural network model with the Levenberg-Marquardt (LM) back propagation... 

This paper considers a project scheduling problem with the objective of minimizing resource availability costs required to execute the activities in a project by a given project deadline. The project contains activities interrelated by finish-start-type precedence relations with a time lag of zero, which require a set of renewable resources. Two metaheuristics, path relinking and genetic algorithm, are developed to tackle this problem in which a schedule is created with a precedence feasible priority list given to the schedule generation scheme. In these procedures, each new generation of solutions are created using the combination of current solutions. Comparative computational results... 

Kinetic modeling is an important issue, whose objective is the accurate determination of the rates of various reactions taking place in a reacting system. This issue is a pivotal element in the process design and development particularly for novel processes which are based on reactions taking place between various types of species. The Fischer Tropsch (FT) reactions have been used as the kinetic modeling bench mark. General kinetic models for FT, Water-Gas-Shift (WGS) and overall rates based on Langmuir-Hinshelwood-Hougen- Watson (LHHW) type have been considered and their optimum parameters have been obtained by Genetic Algorithms. The study shows the obtained model outperforms the other... 

Fault coverage and test time have important roles in using Built in self-test (BIST). Two parameters are crucial and effective in BIST design: LFSR's polynomial (or configuration) and its initial seed. In this paper we propos a practical method for finding near optimal LFSR with genetic algorithm (GA) and show that LFSR is a good TPG compared with other TPGs. In this method, the candidate seeds are achieved through a deterministic approach, and an evolutionary approach is employed to obtain the LFSR configurations for the desired fault coverage under test time constraint. The configurations are encoded in binary chromosomes. The evolution process evolves the fittest configurations to achieve... 

A new approach for PID tuning, based on GA (Genetic algorithm) and Internal Model Control (IMC) technique, is presented in this paper. PID tuning is based on using Method. The IMC technique reduces the number of parameters that must be tuned for a multivariable system using PID controller. The algorithm uses GA for optimal determination of IMC variables. Simulation results present the good performance of the proposed method. © 2007 IEEE  

Recombination in the Genetic Algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations hopefully producing an offspring that has the good characteristics of both parents. Symbiotic Combination is formerly introduced as an alternative for sexual recombination operator to overcome the need of explicit design of recombination operators in GA all. This paper presents an optimization algorithm based on using this operator in Tabu Search. The algorithm is benchmarked on two problem sets and is compared with standard genetic algorithm and symbiotic evolutionary adaptation model, showing success rates higher than both cited...