International Journal of Engineering
Volume:26 Issue: 9, Sep 2013

In this paper, a remedial measure is first proposed to eliminate the effect of autocorrelation in phase-ІІ monitoring of autocorrelated polynomial profiles, where there is a first order autoregressive (AR(1)) relation between the error terms in each profile. Then, a control chart based on the generalized linear test (GLT) is proposed to monitor the coefficients of polynomial profiles and an R-chart is used to monitor the error variance, the combination of which is called GLT/R chart. The performance of the proposed GLT/R chart is evaluated by comparing it to ones of prevalent methods including multivariate T2, EWMA/R and T2 residual control charts, in terms of the average run length (ARL) criterion. Furthermore, an estimator based on the likelihood ratio approach is proposed to estimate the change point in the parameters of autocorrelated polynomial profiles. The results of extensive simulation experiments show good performances of the proposed estimator.

Design of Cellular Manufacturing System involves four major decisions: Cell formation (CF), Group layout (GL), Group scheduling (Gs) and Resource assignment (RA). These problems should be regarded, concurrently, in order to obtain an optimal solution in a CM environment. In this paper a two stage heuristic procedure is proposed for CF and RA decisions problem. The solution approach contains a heuristic multivariate clustering technique as the first stage to find the best machine-cluster center distances. Next in the second stage a new mathematical model based on extracted distances and also worker related issues, including salary, hiring, firing and cross-training is proposed. In order to verify and validate the performance of proposed approach a mathematical model considering the inter-intra cell part trips and also operator related issues are developed and some numerical examples are solved using Lingo Software. The analysis of results verifies the solution approach in both optimality and computational time aspects.

Energy simulation is a vital part of energy policy of a country, especially for a developing country like China where energy consumption is growing very rapidly. The present study has been conducted to simulate the total primary energy consumption in residential sector in rural areas in Chongqing by using macro and micro drivers including population size, number of households, persons per household, fuel types, end-use devices and their intensities. The study finds the energy intensity of end-use device in rural areas in Chongqing is 1166.15kwh/household/year. About 11.02% energy consumes for lighting, 16.53% for space heating and cooling, 58.71% for cooking and water heating, and 13.74% for other end-use devices in the studied areas. The sharing of fuels are LPG 18.15%, coal 23.54%, firewood 21.13% and electricity 37.18% that are used as primary fuel. The study finds the total residential energy consumption is43.940X109 kWh/year in rural areas of Chongqing in 2012. The study has also conducted to forecast the energy consumption during 2000-2020 by using two Grey Model such as GM (1,1) and DGM (2,1) in Chongqing. The GM (1,1) uses a first order differential equation to characterize an unknown system in which the irregular data of system can become regular sequences which it can identify the uncertainties of system and predict the variables of it. DGM (2,1) model is a new grey model which is constructed by grey derivative and second-order grey derivative. The five years average growth rate of total energy consumption in Chongqing during 2011-2015 and 2016-2020 are 51.70% and 195.04% respectively comparing to 2010 by GM (1,1) model, whereas 70.54% and 330.23% respectively by DGM (2,1) model during the designated time period. The higher accuracy has been found in GM (1,1) model than in DGM (2,1) model.

In this paper, a supply chain network design problem is explained which contains environmental concerns in arcs and nodes of network. It is assumed that there are some routes such as road, rail and etc. in each pair of nodes. In this model decision variables are choosing facilities to open, environmental investment level in each facility and flow of products between nodes in each route. A multi-objective optimization model is proposed in this paper to capture the trade-off between the total cost and the environment influence. Some simulated numerical examples are considered to evaluate the model and the solution approach. The comparisons verify the suitability of the model and solution approach.

The main problem is less efficiency and blocking during sugarcane harvesting in hilly areas. This paper researched the cutting and transporting system of a small sugarcane harvester using virtual prototype technology. The dynamics simulation analyses were carried out to study the transporting status with different friction coefficients between the sugarcane and the spiral and different numbers of the rubber around the drum. The virtual test results show that increasing the friction coefficient can enhance the transporting speed of the sugarcane, and adding more rubbers on the drum can also increase the speed further. Then, the paper analyzed the logistic process of the cut sugarcane with different friction coefficients between the sugarcane and the spiral and four rubbers mounted on the drum based on the high-speed photography in the field simulation test. The results also show that the transporting speed of the cut sugarcane can increase 40% when the friction coefficient and the rubbers are added. The simulation and field test results verify that the virtual prototype technology can provide reference for the development of the physical prototype.

A method for constructing confidence intervals on parameters of a continuous probability distribution is developed in this paper. The objective is to present a model for an uncertainty represented by parameters of a probability density function. As an application, confidence intervals for the two parameters of the Weibull distribution along with their joint confidence interval are derived. The model admits complete data, as well as censored data. The estimation accuracy of the proposed model is compared to those of the existing procedures by a numerical method. The validation analysis shows that the estimation accuracy of the proposed model lead to an encouraging conclusion. It is also shown that improper use of available information in the data that affects the width of the confidence intervals obtained by the existing procedures does not affect the coverage of the proposed confidence interval method.

In this paper, a comprehensive model for hub location-routing problem is proposed which no network structure other than connectivity is imposed on backbone (i.e. network between hub nodes) and tributary networks (i.e. networks which connect non-hub nodes to hub nodes). This model is applied in public transportation, telecommunication and banking networks. In this model locating and routing is considered simultaneously and it has a multiple allocation strategy to allocate non-hub nodes to hub nodes. In addition, non-hub nodes can connect directly to each other. The objective of the proposed model is minimizing costs of establishing network and transferring flows. To expedite solving the proposed model and improve the lower bound, which gain from linear relaxation, a number of preprocessing tests and valid inequalities are presented which have relatively good performance in the proposed model. Their performance is analyzed by implementing them on the test problems. Results show that using all preprocessing tests and valid inequalities is the best approach to solve the problem among all proposed approaches in this paper.

This study presents a capacitated multi-facility location-allocation problem with stochastic demands based on a well-known distribution function. In this discrete environment, besides the capacitated facilities, we can employee the capacitated sub-source of each facility for satisfying demands of customers. The objective function is to find the optimal locations of facilities among a finite number of potential locations and optimal allocation of the demand points (customers) to the operated facilities so that the total sum of establishment costs of the facilities, costs of allocation the costumers to the operated facilities and the expected values of servicing and outsourcing costs are minimized. To display the applicability of the model, a numerical example is provided and computational results are reported.

The solution of single-objective unit commitment problems for generation companies participating in deregulated markets may not directly be implementable mainly because of neglecting some conflicting secondary objectives arising from policy-making at internal/external environment. Benefiting an efficient multi-objective approach to improve the applicability of price-based unit commitment solutions, a novel mixed integer linear preemptive goal programming model is developed in which several complementary objectives with lower relative importances are also incorporated. Non-linear characteristic curves of generating units are approximated by piece-wise linear ones. The experimental results inspiring by a real case demonstrate the efficiency of our approach. An important capability of the model is that it can easily and efficiently be matched with a various line of unit commitment problems.

A redundancy allocation problem (RAP) is a well-known NP-hard problem that involves the selection of elements and redundancy levels to maximize the system reliability under various system-level constraints. In many practical design situations, reliability apportionment is complicated because of the presence of several conflicting objectives that cannot be combined into a single-objective function. As telecommunications, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. In this paper, a new hybrid multi-objective imperialist competition algorithm (HMOICA) based on imperialist competitive algorithm (ICA) and genetic algorithm (GA) is proposed for the first time in multi-objective redundancy allocation problems. In the multi-objective formulation, the system reliability is maximized while the cost and volume of the system are minimized simultaneously. Additionally, a response surface methodology (RSM) is employed to tune the ICA parameters. The proposed HMOICA is validated by some examples with analytical solutions. It shows its superior performance compared to a non-dominated sorting genetic algorithm (NSGA-II) and Pareto archive evolution strategy algorithm (PAES). Finally, the conclusion is given.

This paper presents a new mathematical model, in which the location of hubs is fixed and their capacity is determined based on facilities and factories allocated to it. In order to feed the client's nodes, different types of vehicles of different capacities are considered, in which the clients are allocated to hubs, and types and numbers of vehicles are allocated to the factory's facilities. To come up with solutions we propose to use three meta-heuristics, namely genetic algorithm, particle swarm optimization, and simulated annealing. The efficiency and computational results of the foregoing algorithms are compared with one another. Finally, the conclusion is presented.

The present paper investigates the reliability and sensitivity analysis of a coal handling unit of a thermal power plant using a probabilistic approach. Coal handling unit is the main block of a thermal power plant and it is necessary for a good function of a power plant that its power supply, which is dealt in coal handling unit, must function continuously without any obstacle. The configuration of the coal handling system consists of two subsystems connected in series, also each subsystem have two units in parallel configuration. Failure and repair rate of both the subsystems are taken constant. With the help of Laplace transforms and differential equations, the transition state probabilities, availability, reliability, MTTF, sensitivity analysis and cost-effectiveness of the system have been evaluated.

The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, thousands of jobs should be scheduled in a long planning horizon. In this paper, we designed an expert model for achieving better performance of real-time scheduling tasks in a flexible manufacturing system (FMS). The proposed expert model is comprised of two set of modules, namely FMS simulator and decision (control) modules. Information is translated from the first set of modules to the second in two phases. First, a feed-forward neural network as a supervised machine learning mechanism is set to capture the queuing attributes of the shop and train in initialization and pre-run mode. Second, system states (in real run) are interpreted to the control module which is comprised of interconnected online learning activation function and a feed-forward neural net and then the best strategy is selected. Therefore, an interactive discrete-event simulation model with control module is implemented in order to evaluate different scenarios and reduce the computational time and complexity. Eventually, presented procedure is benchmarked through the simulation modeling of a triple-stage-triple-machine flexible flowshop with some embedded stochastic concept. Results support our proposed methodology and follow our overall argument.

The present study deals with a robot safety system composed of standby robot units and inbuilt safety unit. When the main operative unit fails, it is replaced by the standby robot unit available in the system. The concept of reboot delay is also incorporated in this study according to which the robot unit is rebooted if it is not successfully recovered. The recovery and reboot times of failed units, life time of the operative as well as standby units and the repair time are assumed be exponentially distributed. Furthermore, the repair time of partially-failed unit of total system failure is assumed to be arbitrarily distributed. The expressions for the state probabilities, availability, reliability and mean time to failure are derived with the help of Markovian and supplementary variable methods. The occurrence of standby units, imperfect coverage and reboot demonstrates the significant impact on the robot system reliability, availability and mean time to failure. A numerical illustration has been provided to validate the present model as well as to demonstrate the effects of various parameters on the performance measures of the robot safety system.

In present study, a three-step multi-objective optimization algorithm of cyclone separators is catered for the design objectives. First, the pressure drop (Dp) and collection efficiency (h) in a set of cyclone separators are numerically evaluated. Secondly, two meta models based on the evolved Group Method of Data Handling (GMDH) type neural networks are regarded to model the Dp and h as the required functions of geometrical characteristics. Finally, a multi-objective (MO) algorithm based on hybrid of Particle Swarm Optimization (PSO), multiple crossover and mutation operator are used for Pareto based optimization of cyclones considering two conflicting objectives Dp and h. By comparing the Pareto results of MOPSO with that of multi-objective genetic algorithms (NSGA II) regarding Pareto based multi-objective optimization of the obtained polynomial meta-models, it is shown that there are some interesting and important relationships as useful optimal design principles involved in the performance of cyclone separators.