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Showing 90 results for Optimization

A. Shidfar, M. Garshasbi ,
Volume 18, Issue 1 (1-2007)
Abstract

 Abstract: This study deals with modeling of heat flux at the external surface of combustion chamber wall in an internal combustion (IC) engine as a function of crank angle. This investigation results in an inverse heat conduction problem in the cylinder wall. Alifanov regularization method is used for solving this inverse problem. This problem study as an optimization problem in which a squared residual unctional is minimized with the conjugate gradient method. This algorithm tests for an example in some cases and numerical results are shown.

 

 


H. Golestanian ,
Volume 18, Issue 4 (12-2007)
Abstract

Abstract: This paper presents the results of experimental determination of fiber bed permeability variation with porosity. Flow measurement experiments were designed to measure fiber mat permeability for fiber beds with various fiber volume fractions. Woven fiberglass, chopped fiberglass, and carbon fiber mats were used as reinforcements. The effects of reinforcement type and porosity on fiber bed permeability were investigated. Fiber mat permeability of woven mats showed large degrees of anisotropy, whereas chopped fiberglass mats had isotropic permeability. In all cases perform permeability increased with fiber bed porosity. Fiber mat permeability of woven carbon was found to be about four times lower than that of woven fiberglass mats at the same porosity. This lower permeability results in longer injection time and higher manufacturing cost for composite parts made with carbon fiber mats. The results of this investigation could be employed in process/product optimization in Resin Transfer Molding (RTM) processes.

 


M.b Aryanezhad , A. Roghanian ,
Volume 19, Issue 1 (3-2008)
Abstract

Abstract: Bi-level programming, a tool for modeling decentralized decisions, consists of the objective(s) of the leader at its first level and that is of the follower at the second level. Three level programming results when second level is itself a bi-level programming. By extending this idea it is possible to define multi-level programs with any number of levels. Supply chain planning problems are concerned with synchronizing and optimizing multiple activities involved in the enterprise, from the start of the process, such as procurement of the raw materials, through a series of process operations, to the end, such as distribution of the final product to customers.  Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using “multi-level programming” principles. This paper studies a “bi-level linear multi-objective decision making” model in with “interval” parameters and presents a solution method for solving it this method uses the concepts of tolerance membership function and multi-objective multi-level optimization when all parameters are imprecise and interval .

  


M.b. Aryanezhad , E. M.b.aryanezhad & E.roghanian ,
Volume 19, Issue 1 (3-2008)
Abstract

  Bi-level programming, a tool for modeling decentralized decisions, consists of the objective(s) of the leader at its first level and that is of the follower at the second level. Three level programming results when second level is itself a bi-level programming. By extending this idea it is possible to define multi-level programs with any number of levels. Supply chain planning problems are concerned with synchronizing and optimizing multiple activities involved in the enterprise, from the start of the process, such as procurement of the raw materials, through a series of process operations, to the end, such as distribution of the final product to customers.

  Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using “multi-level programming” principles. This paper studies a “bi-level linear multi-objective decision making” model in with “interval” parameters and presents a solution method for solving it this method uses the concepts of tolerance membership function and multi-objective multi-level optimization when all parameters are imprecise and interval .

 


A. Shariat Mohaymany, M. Khodadadiyan,
Volume 19, Issue 3 (7-2008)
Abstract

 

Abstract: The shipments of hazardous materials (HAZMATs) induce various risks to the road network. Today, one of the major considerations of transportation system managers is HAZMATs shipments, due to the increasing demand of these goods (because it is more used in industry, agriculture, medicine, etc.), and the rising number of incidents that are associated to hazardous materials. This paper presents a tool for HAZMATs transportation authorities and planners that would reduce the risk of the road network by identifying safe and economic routes for HM transshipment. Using the proposed linear integer programming model, the HM management system could determine an optimal assignment for all origin–destination pairs for various hazardous materials in a transportation network and so reduce the vulnerability due to HAZMATs releases such as population and environmental vulnerability. The model is implemented and evaluated for the hazardous materials routing within Fars, Yazd, Isfahan, and Chaharmaha-o-Bakhtiyari provinces of Iran. The branch-and-bound algorithm is applied to solve the model using the Lingo software package.
F. Rashidinejad, M. Osanloo , B. Rezai ,
Volume 19, Issue 5 (7-2008)
Abstract

Cutoff grade is a grade used to assign a destination label to a parcel of material. The optimal cutoff grades depend on all the salient technological features of mining, such as the capacity of extraction and of milling, the geometry and geology of the orebody, and the optimal grade of concentrate to send to the smelter. The main objective of each optimization of mining operation is to maximize the net present value of the whole mining project, but this approach without consideration of environmental issues during planning is not really an optimum design. Lane formulation among the all presented algorithms is the most commonly used method for optimization of cutoff grades. All presented models for optimum cutoff grades are ore-oriented and in none of them the costs related to waste materials which must to be minimized during the mine life are considered. In this paper, after comparison of traditional and modern approaches for cutoff grade optimization in open pit mines, a real case study is presented and discussed to ensure optimality of the cutoff grades optimization process.


M. Sedighi , M. Noorani Azad,
Volume 19, Issue 5 (7-2008)
Abstract

Along with increasingly development of CAD/CAM software and their application in various industries, minimizing of the machining time is found to be more important. In this paper, firstly the concerning subjects are discussed regarding classification of the optimization techniques. These are programming techniques, high speed machining techniques and feed rate optimization techniques. As a case study, an NC code was generated for machining of a plastic die by means of a dedicated software and the die was machined conventionally. Then the workpiece was machined using optimization techniques. Finally times taken for two approaches have been compared. The result shows machining time after optimization has been reduced considerably (64%).

 

Keywords:
A. Golbabai, M. Mammadov , S. Seifollahi ,
Volume 19, Issue 6 (8-2008)
Abstract

A new learning strategy is proposed for training of radial basis functions (RBF) network. We apply two different local optimization methods to update the output weights in training process, the gradient method and a combination of the gradient and Newton methods. Numerical results obtained in solving nonlinear integral equations show the excellent performance of the combined gradient method in comparison with gradient method as local back propagation algorithms.


, , ,
Volume 20, Issue 1 (5-2009)
Abstract

Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrated via real data of a purchase system in order to simulate the system’s behavior.
Kamran Shahanaghi, Hamid Babaei , Arash Bakhsha,
Volume 20, Issue 1 (5-2009)
Abstract

In this paper we focus on a continuously deteriorating two units series equipment which its failure can not be measured by cost criterion. For these types of systems avoiding failure during the actual operation of the system is extremely important. In this paper we determine inspection periods and maintenance policy in such a way that failure probability is limited to a pre-specified value and then optimum policy and inspection period are obtained to minimize long-run cost per time unit. The inspection periods and maintenance policy are found in two phases. Failure probability is limited to a pre-specified value In the first phase, and in the second phase optimum maintenance thresholds and inspection periods are obtained in such a way that minimize long-run expected.
Hamed. R. Tareghian , Madjid Salari,
Volume 20, Issue 3 (9-2009)
Abstract

The dynamic nature of projects and the fact that they are carried out in changing environments, justify the need for their periodic monitoring and control. Collection of information about the performance of projects at control points costs money. The corrective actions that may need to be taken to bring the project in line with the plan also costs money. On the other hand, penalties are usually imposed when due to “no monitoring” policies projects are delivered later than expected. Thence, this paper addresses two fundamental questions in this regard. First question concerns the optimal frequency of control during the life cycle of a project. The second question concerns the optimal timing of control points. Our solution methodology consists of a simulation-optimization model that optimizes the timing of control points using the attraction-repulsion mechanisms borrowed from the electromagnetism theory. A mathematical model is also used to optimally expedite the remaining part of the project when possible delays are to be compensated.
M. Yaghini, N. Ghazanfari,
Volume 21, Issue 2 (5-2010)
Abstract

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the optimization property of tabu search and the local search capability of k-means algorithm together. The contribution of proposed algorithm is to produce tabu space for escaping from the trap of local optima and finding better solutions effectively. The Tabu-KM algorithm is tested on several simulated and standard datasets and its performance is compared with k-means, simulated annealing, tabu search, genetic algorithm, and ant colony optimization algorithms. The experimental results on simulated and standard test problems denote the robustness and efficiency of the algorithm and confirm that the proposed method is a suitable choice for solving data clustering problems.



Volume 21, Issue 3 (9-2010)
Abstract

  In this paper an Ant Colony (ACO) algorithm is developed to solve aircraft recovery while considering disrupted passengers as part of objective function cost. By defining the recovery scope, the solution always guarantees a return to the original aircraft schedule as soon as possible which means least changes to the initial schedule and ensures that all downline affects of the disruption are reflected. Defining visibility function based on both current and future disruptions is one of our contributions in ACO which aims to recover current disruptions in a way that cause less consequent disruptions. Using a real data set, the computational results indicate that the ACO can be successfully used to solve the airline recovery problem .


Mona Ahmadi Rad, Mohammadjafar Tarokh, Farid Khoshalhan ,
Volume 22, Issue 1 (3-2011)
Abstract

  This article investigates integrated production-inventory models with backorder. A single supplier and a single buyer are considered and shortage as backorder is allowed for the buyer. The proposed models determine optimal order quantity, optimal backorder quantity and optimal number of deliveries on the joint total cost for both buyer and supplier. Two cases are discussed: single-setup-single-delivery (SSSD) case and single-setup-multiple-deliveries (SSMD) case. Two algorithms are applied for optimizing SSMD case: Gradient search and particle swarm optimization (PSO) algorithms. Finally, numerical example and sensitivity analysis are provided to compare the total cost of the SSSD and SSMD cases and effectiveness of the considered algorithms. Findings show that the policy of frequent shipments in small lot sizes results in less total cost than single shipment policy .


M. Yaghini, M. Momeni, M. Sarmadi ,
Volume 22, Issue 1 (3-2011)
Abstract

  The traveling salesman problem is a well-known and important combinatorial optimization problem. The goal of this problem is to find the shortest Hamiltonian path that visits each city in a given list exactly once and then returns to the starting city. In this paper, for the first time, the shortest Hamiltonian path is achieved for 1071 Iranian cities. For solving this large-scale problem, two hybrid efficient and effective metaheuristic algorithms are developed. The simulated annealing and ant colony optimization algorithms are combined with the local search methods. To evaluate the proposed algorithms, the standard problems with different sizes are used. The algorithms parameters are tuned by design of experiments approach and the most appropriate values for the parameters are adjusted. The performance of the proposed algorithms is analyzed by quality of solution and CPU time measures. The results show high efficiency and effectiveness of the proposed algorithms .


Meysam Zareiee, Abbas Dideban, Ali A. Orouji ,
Volume 22, Issue 2 (6-2011)
Abstract

 

  Discrete event system,

  Supervisory control,

  Petri Net, Constraint

 

This paper presents a method to manage the time in a manufacturing system for obtaining an optimized model. The system in this paper is modeled by the timed Petri net and the optimization is performed based on the structural properties of Petri nets. In a system there are some states which are called forbidden states and the system must be avoided from entering them. In Petri nets, this avoidance can be performed by using control places. But in a timed Petri net, using control places may lead to decreasing the speed of systems. This problem will be shown on a manufacturing system. So, a method will be proposed for increasing the speed of the system without using control places .


Mohammad Saber Fallahnezhad, Hasan Hosseini Nasab,
Volume 22, Issue 3 (9-2011)
Abstract

 In this research, a new control policy for the acceptance sampling problem is introduced. Decision is made based on the number of defectives items in an inspected batch. The objective of the model is to find a constant control level that minimizes the total costs, including the cost of rejecting the batch, the cost of inspection and the cost of defective items. The optimization is performed by approximating the negative binomial distribution with Poisson distribution and using the properties of binomial distribution. A solution method along with numerical demonstration on the application of the proposed methodology is presented. Furthermore, the results of sensitivity analysis show that the proposed method needs a large sample size .


E. Teimoury, I.g. Khondabi , M. Fathi ,
Volume 22, Issue 3 (9-2011)
Abstract

 

  Discrete facility location,

  Distribution center,

  Logistics,

  Inventory policy,

  Queueing theory,

  Markov processes,

The distribution center location problem is a crucial question for logistics decision makers. The optimization of these decisions needs careful attention to the fixed facility costs, inventory costs, transportation costs and customer responsiveness. In this paper we study the location selection of a distribution center which satisfies demands with a M/M/1 finite queueing system plus balking and reneging. The distribution center uses one for one inventory policy, where each arrival demand orders a unit of product to the distribution center and the distribution center refers this demand to its supplier. The matrix geometric method is applied to model the queueing system in order to obtain the steady-state probabilities and evaluate some performance measures. A cost model is developed to determine the best location for the distribution center and its optimal storage capacity and a numerical example is presented to determine the computability of the results derived in this study .


M. Mohammadi, R. Tavakkoli-Moghaddam, A. Ghodratnama , H. Rostami ,
Volume 22, Issue 3 (9-2011)
Abstract

 

  Hub covering location problem, Network design,

  Single machine scheduling, Genetic algorithm,

  Shuffled frog leaping algorithm

 

Hub location problems (HLP) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc.) through special facilities or transshipment points called hubs. In this paper, we consider a central mine and a number of hubs (e.g., factories) connected to a number of nodes (e.g., shops or customers) in a network. First, the hub network is designed, then, a raw materials transportation from a central mine to the hubs (i.e., factories) is scheduled. In this case, we consider only one transportation system regarded as single machine scheduling. Furthermore, we use this hub network to solve the scheduling model. In this paper, we consider the capacitated single allocation hub covering location problem (CSAHCLP) and then present the mixed-integer programming (MIP) model. Due to the computational complexity of the resulted models, we also propose two improved meta-heuristic algorithms, namely a genetic algorithm and a shuffled frog leaping algorithm in order to find a near-optimal solution of the given problem. The performance of the solutions found by the foregoing proposed algorithms is compared with exact solutions of the mathematical programming model .



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