فهرست مطالب

Scientia Iranica
Volume:20 Issue: 3, 2013

  • Transactions E: Industrial Engineering
  • تاریخ انتشار: 1392/06/02
  • تعداد عناوین: 21
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  • Mahdi Bashiri, M.H. Bakhtiarifar Pages 793-780
    The one median location problem with stochastic demands can be solved as a deterministic problem by considering the mean of weights as demands. There are also some other approaches in consideration of this problem. However, it is better to find the probability for each node that shows the chance of the node being in the optimal location, especially when demands are correlated to each other. With this approach, alternative answers with their optimality probability can be found. In small networks with a few nodes, it is not so difficult to solve the problem, because a multivariate normal probability for each node should be calculated. But, when the number of nodes increases, not only do the number of probability calculations increase, but also, the computation time for each multivariate normal distribution grows exponentially. In this paper, a meta-heuristic algorithm, based on modified Simulated Annealing (SA), with consideration of a short term memory module is proposed to find the optimality probability more efficiently. The algorithm was performed on some sample networks with correlated demands.
  • M. Seifbarghy, M. Amiri, M. Heydari Pages 801-810
    The inventory system under consideration consists of one central warehouse and a few non-identical retailers controlled by a continuous review inventory policy (image). The retailers face an independent Poisson demand. Order transportation time from the central warehouse to each retailer is assumed to be constant. Also, the lead time for replenishing orders from an external supplier is assumed to be constant for the warehouse. Unsatisfied demands are assumed to be lost at the retailers and unsatisfied retailer orders are backordered at the warehouse. The cost function of the system was estimated utilizing a Response Surface Method (RSM) in the case of four retailers, and, in this regard, two linear and nonlinear regression models were developed. The optimal reorder points for given batch sizes in all installations were obtained from optimizing the estimated cost function. The estimation accuracy was assessed through simulation. The results indicate that the nonlinear regression model outperforms the linear one.
  • A.A. Taleizadeh, S.Gh. Jalali, Nainih., M. Weet., C. Kuo Pages 811-823
    This study develops an imperfect, multi-product production system with rework. Our objective is to minimize the joint total cost of the system, subject to service level and budget constraints. Since the objective function in the problem is convex, the existence of global optimality is assured. A solution procedure is proposed and numerical examples are provided to demonstrate the applicability of the model in real-world manufacturing problems. Finally, sensitivity analyses are provided to provide managerial insights, as well as to investigate the interdependency of the parameters.
  • Behrouz Afshar, Nadjafi, Amir Rahimi, Hamid Karimi Pages 824-831
    The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) is one of the most important problems in the context of project scheduling. This paper involves a Mode identity and Resource Constrained Project Scheduling Problem (MIRCPSP) to minimize the project makespan. This problem is a more realistic model and a general case of a multi-mode resource constrained project scheduling problem in which the set of project activities is partitioned into disjoint subsets, while all activities forming one subset have to be processed in the same mode. The problem formed in this way is NP-hard, forcing us to develop a meta-heuristic algorithm, namely, the Genetic Algorithm (GA), to obtain a global optimum solution or at least a satisfying one. In addition, the Taguchi experimental design is employed as a statistical optimization technique to calibrate the effective parameters of GA in order to have a robust algorithm. The meta-heuristic algorithm is compared with an exact branch and bound procedure on the basis of a computational experiment performed on a data set constructed by the image project generator. Obtained results show that the performance of the proposed genetic algorithm is quite satisfactory.
  • A.A. Najafi, H. Karimi, A. Chambari, Fatemeh Azimi Pages 832-838
    The redundancy allocation problem is one of the main branches of reliability optimization problems. Traditionally, the redundancy allocation model has focused mainly on maximizing system reliability at a predetermined time. Hence, in this study, we develop a more realistic model, such that the mean time to failure of a system is maximized. To overcome the structural complexity of the model, the Monte Carlo simulation method is applied. Two metaheuristics, Simulated Annealing (SA) and Genetic Algorithm (GA), are proposed to solve the problem. In addition, the design of experiments and response surface methodology are employed for tuning the GA and SA parameters. The metaheuristics are compared, based on their computation time and accuracy, in 30 test problems. Finally, the results are analyzed and discussed, and some conclusions are drawn.
  • Z. Hussain, J. Shabbir Pages 839-845
    This paper focuses on presenting a generalization of the scrambled response models of Hussain and Shabbir [Hussain, Z. and Shabbir, J. “On estimation of mean of a sensitive quantitative variable”, InterStat, (#006), (2007)] and Gjestvang and Singh [Gjestvang, C.R. and Singh, S. “An improved randomized response model: estimation of mean”, Journal of Applied Statistics, 36(12), pp. 1361–1367 (2009)]. The suggested generalization is helpful in procuring honest data on socially undesirable characteristics. The suggested estimator is found to be unconditionally more efficient in terms of variablity. From a privacy point of view, comparison of the proposed class of models is made using the privacy protection measure by Zaizai et al. [Zaizai, Y., Jingu, W. and Junfeng, L. “An efficiency and protection based comparison among the quantitative randomized response strategies”, Communications in Statistics-Theory and Methods, 38, pp. 400–408 (2009)]. Unlike many scrambled response models, the proposed class of models is free from the need of known parameters of scrambling variables. The relative numerical efficiency of the proposed model is simulated for some fixed values of the parameters. The practical application of the proposed model is also studied through a small scale survey.
  • M. Hoseininia, M.M. Seyyed Esfahani, F. Didehvar, A. Haghi Pages 846-854
    This paper investigates inventory management in a multi channel distribution system consisting of a manufacturer, with a single product, and an arbitrary number of retailers that face stochastic demand. Existence of the pure Nash equilibrium is proved and parameter restriction, which implies its uniqueness, is derived. Also, the Stackelberg game, where the manufacturer plays the role of leader is discussed. Under specified parameter restrictions which guarantee profitability, a sufficient condition for the uniqueness of the Stackelberg equilibrium is obtained. In addition, a comparison with a simultaneous move game is made. The results show that when whole prices are equal to production cost, the manufacturer carries more inventory than in the simultaneous move game.
  • A. Sharafi, M. Aminnayeri, Amirhossein Amiri Pages 855-860
    In some process control applications, the quality of a product or process can be characterized by a relationship between two or more variables, which is typically referred to as a profile. Moreover, in some situations, the dependent variable is a count, which can be modeled as a Poisson regression of one explanatory variable. We refer to this as Poisson regression profiles. Control chart signals do not indicate the real time of process changes, so estimators are applied to indicate the time when a change in the process takes place, which is referred to as the change point. In this paper, we propose the use of an MLE estimator to identify the real time of a step change in phase II monitoring of Poisson regression profiles. The results reveal that the change point estimator is effective in identifying step shifts in the process parameters.
  • F. Jolai, H. Asefi, M. Rabiee, P. Ramezani Pages 861-872
    This paper focuses on solving the bi-objective problem of no-wait two-stage flexible flow shop scheduling. The objectives considered in this study are minimum makespan (image), as well as maximum tardiness of jobs (image). This problem is known as NP-hard. Hence, three bi-objective optimization methods based on simulated annealing, called CWSA (classical weighted simulated annealing), NWSA (normalized weighted simulated annealing), and FSA (fuzzy simulated annealing), are developed to solve the problem with the goal of finding approximations of the optimal Pareto front. Due to the fact that meta-heuristic algorithms are very vigilant of parameter values, we proposed a new reliable method, by mixing the Taguchi method and a Multi-Objective Decision Making (MODM) approach, for achieving our purpose. The algorithms are evaluated by solving both small and large scale problems. The performances are evaluated in terms of a relative deviation index. Finally, the result of the study is discussed and concluded, and potential areas of further study are highlighted.
  • J., Q. Wangh., Y. Zhangs., C. Ren Pages 873-878
    After definition of the discrete grey stochastic variable and its expected value, the expected probability degree is defined. For multi-criteria decision-making problems, in which the criteria weights are incompletely certain and the criteria values of alternatives are in the form of grey stochastic variables, a grey stochastic multi-criteria decision-making approach is proposed. In this method, the evaluation value of each alternative under each criterion can be transformed to comprise the expected probability degree judgment matrix, based on which, a non-linear programming model can be enacted. In the end, the genetic algorithm is used to solve the model to attain the criteria weights, and the ranking of alternatives can be produced consequently. The feasibility and validity of this approach are illustrated by an example.
  • M.H. Fazel Zarandi, R. Gamasaee Pages 879-899
    The purpose of this paper is to evaluate and reduce the bullwhip effect in fuzzy environments by means of type-2 fuzzy methodology. In order to reduce the bullwhip effect in a supply chain, we propose a new method for demand forecasting. First, the demand data of a real steel industry in Canada is clustered with an interval type-2 fuzzy image-regression clustering algorithm. Then, a novel interval type-2 fuzzy hybrid expert system is developed for demand forecasting. This system uses Fuzzy Disjunctive Normal Forms (FDNF) and Fuzzy Conjunctive Normal Forms (FCNF) for the aggregation of antecedents. An interval type-2 fuzzy order policy is developed to determine orders in the supply chain. Then, the results of the proposed method are compared with the type-1 fuzzy expert system as well as the type-1 fuzzy time series method in the literature. The results show that the bullwhip effect is significantly reduced; also, the system has less error and high accuracy.
  • M. Ozkok Pages 900-908
    In the production environment, there are many disruptions, such as machine breakdown, rush orders, and so on. Under these circumstances, the shipyards have to meet customer demand. If this is not done, the shipyard may face customer loss, since they cannot meet the deadline. So, it is very important to understand the effects of machine breakdown on system throughput. In this study, the hull production system of a shipyard situated in Turkey has been considered. In the first step of the study, the structure of the double bottom block, which is produced in the shipyard production system, and the workstations, which constitute the production system, were identified. Secondly, the simulation model of the production system was created using simulation software. By creating some machine breakdowns for various work stations in the production system, the effects of breakdown on system throughput have been investigated. As a result of the study, the critical rates of machine breakdown, which have affected system throughput, have been determined. The main contribution of the study is to allow production engineers to take measures against machine breakdowns in advance, so that target throughput can be reached.
  • Mahdi Bashiri, Amirhossein Amiri, Mohammad Hadi Doroudyan, Ali Asgari Pages 909-918
    Control charts are widely used for monitoring the quality of a product or a process. Their implementation cost motivates researchers to design them with the lowest cost and most desirable statistical properties. Usually, the cost function is optimized subject to statistical properties. However, the cost function also depends on statistical properties, and minimizing it as the only objective is not an efficient method of economic statistical design of control charts. In this paper, cost function, as well as statistical properties, including probability of Type I error, power of image control chart, and Average Time to Signal (ATS), are considered as objectives; the corresponding constraints are also used. Then, a Multi-Objective Genetic Algorithm for Economic Statistical Design (MOGAESD) is proposed for identifying the Pareto optimal solutions of control chart design. The preferred solution is selected by the designer. The performance of the proposed method is compared through some numerical examples reported in the literature. The results show that the proposed approach is effective.
  • N. Ghaffari, Nasab, S. Ghazanfar Ahari, M. Ghazanfari Pages 919-930
    The location-routing problem (LRP) is established as a new research area in the context of location analysis, which deals simultaneously with two problems of locating the facilities and designing the travel routes for vehicles among established facilities and existing demand points. In this paper, the location-routing problem with fuzzy demands (LRPFD) is considered which may arise in many real life situations in logistics management, and a fuzzy chance constrained program is designed to model it, based on the fuzzy credibility theory. A hybrid simulated annealing (SA) based heuristic incorporated with stochastic simulation is developed and proposed to solve the problem. The efficiency of the solution procedure is demonstrated via comparing its performance with those of some other existing solution procedures from literature using a standard benchmark set of test problems.
  • E. Nabipoor Afruzi, E. Roghanian, A.A. Najafi, M. Mazinani Pages 931-944
    One of the most important problems in project scheduling applications is the discrete time-cost tradeoff problem (DTCTP). Due to real world resource-constraint situations in projects, this model is expanded to a multi-mode resource-constrained DTCTP model. In this model, each activity has multi modes to be performed. In each mode, the required resources for performing the activity are different, at least in one type. In addition, each activity can be done in either normal or crashing ways in each performing mode. The project cost consists of both direct and indirect costs. In this paper, we present the adjusted fuzzy dominance genetic algorithm to solve our proposed model. We explain the elements of the algorithm and solve some problems generated for this model, including large and small sizes, by this algorithm. The performance of our proposed algorithm is evaluated by comparison with four well-known algorithms; NSGAII, NRGA, PAES, MOIWO. The obtained results show the effectiveness of the proposed algorithm.
  • Abdolhamid Eshraghniaye Jahromi, Zohreh Besharati Rad Pages 945-957
    An electric power supply is the backbone of development in advanced as well as in developing economies. An integral part of ensuring a secure power supply system is a power communication system. Due to the high and sustained performance requirements of power communication systems, electric companies prefer to construct their own communication networks privately rather than relying solely on a public communication system. The focus of this paper is on the optimal topological design of a power communication network. Based on advanced optimization models in public communication networks, and taking into account the specific Quality of Service, as demanded by various applications, such as protection, SCADA, voice, etc., an optimization model (PC/ISO) has been developed. The PC/ISO requires tedious numerical processing. Hence, a Genetic Algorithm (GA) is proposed to solve the optimization problem. In order to demonstrate the application of the proposed model for a power system communication network design and in order to evaluate GA solver results, a case study on designing the optimal communication network topology of one of the Iranian Regional Electric Companies has been conducted. The results suggest that the PC/ISO model and its GA solver are entirely viable and offer a simple, accurate, and cost effective solution.
  • R. Noorossana, M. Aminnayeri, H. Izadbakhsh Pages 958-966
    In certain statistical process control applications, the quality of a process or a product can be characterized by a function commonly referred to as a profile. Some potential applications of profile monitoring are cases where the quality characteristic of interest can be modelled using dichotomous or polytomous variables. Polytomous variables, especially ordinal variables, have various applications. An ordinal (or ordered) variable is a categorical variable, whose values are related in a greater/lesser sense. In this paper, which is the first investigation on ordinal profiles, we propose four methods for monitoring a profile when the process/service output is an ordinal response variable. Ordinal Logistic Regression (OLR) provides the basis for our profile model. These four methods are: Multivariate Exponentially Weighted Moving Average (MEWMA), image statistics, Exponentially Weighted Moving Average (EWMA) with image statistic, and a combination of the last two statistics that are used to monitor OLR profiles in phase II. Performances of these four methods are evaluated using An Average Run Length (ARL) criterion. Two different case studies involving customer satisfaction in the tourist industry and sensory measurements of an electronic nose are used to demonstrate application of the proposed methods in practice.
  • R. Venkata Rao, V.D. Kalyankar Pages 967-974
    The primary objective in multi-pass turning operations is to produce products with low cost and high quality, with a lower number of cuts. Parameter optimization plays an important role in achieving this goal. Process parameter optimization in a multi-pass turning operation usually involves the optimal selection of cutting speed, feed rate, depth of cut and number of passes. In this work, the parameter optimization of a multi-pass turning operation is carried out using a recently developed advanced optimization algorithm, named, the teaching–learning-based optimization algorithm. Two different examples are considered that have been attempted previously by various researchers using different optimization techniques, such as simulated annealing, the genetic algorithm, the ant colony algorithm, and particle swarm optimization, etc. The first example is a multi-objective problem and the second example is a single objective multi-constrained problem with 20 constraints. The teaching–learning-based optimization algorithm has proved its effectiveness over other algorithms.
  • A. Mahmoudi, H. Shavandi Pages 975-982
    In this article, we propose a bi-objective model for the pricing–queuing problem under a fuzzy environment. We consider two
    Objectives
    maximizing the profit function and minimizing the waiting time in queue. Imagine a firm which sells a product in a channel providing after sales services. The sales price and warranty length affect customer demand. We formulate the demand function as a fuzzy system considering the sales price and warranty length. The firm optimizes the sales price and warranty length, as well as waiting time, in the queue of after sales services, to maximize its revenues and minimize waiting time. To solve the derived model, we develop a hybrid solution method of a fuzzy system and a genetic algorithm. Finally, the numerical analysis is done to show the reasonable performance of the solution method and results.
  • S. Davari, M.H. Fazel Zarandi, I.B. Turksen Pages 983-991
    Facility location is a prime decision to be made in many organizations around the globe. The hub location problem is one of the main variants of the facility location problem, with applications in telecommunications, the airline industry, and etc. In this paper, we deal with an incomplete hub-covering network design problem, where the exact locations of demands are unknown and are estimated as fuzzy variables. An earlier model in the hub location literature has been modified to address the uncertainty in the problem. In order to solve this problem, an efficient simulation-embedded Variable Neighborhood Search (VNS) has been designed and its performance has been validated using the well-known CAB dataset.
  • S. Rouhani, Ahad Zare Ravasan Pages 992-1001
    The Enterprise Resource Planning system (ERP) has been pointed out as a new information systems paradigm. However, achieving a proper level of ERP success relies on a variety of factors that are related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation success based on organizational profiles has been discussed. As with the need to create the expectations of organizations of ERP, an expert system was developed by exploiting the Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP success. The expert system role is in preparation to obtain data from the new enterprises that wish to implement ERP, and to predict the probable system success level. To this end, factors of organizational profiles are recognized and an ANN model is developed. Then, they are validated with 171 surveyed data obtained from Middle East-located enterprises that experienced ERP. The trained expert system predicts, with an average correlation coefficient of 0.744, which is respectively high, and supports the idea of dependency of ERP success on organizational profiles. Besides, a total correct classification rate of 0.685 indicates good prediction power, which can help firms predict ERP success before system implementation.