JOURNAL OF INDUSTRIAL ENGINEERING INTERNATIONAL
Year:2013 | Volume:9 | Issue:9|Papers Count:8

In this paper, a new method is proposed to optimize a multi-response optimization problem based on the Taguchi method for the processes where controllable factors are the smaller-the-better (STB) -type variables and the analyzer desires to find an optimal solution with smaller amount of controllable factors. In such processes, the overall output quality of the product should be maximized while the usage of the process inputs, the controllable factors, should be minimized. Since all possible combinations of factors' levels, are not considered in the Taguchi method, the response values of the possible unpracticed treatments are estimated using the artificial neural network (ANN). The neural network is tuned by the central composite design (CCD) and the genetic algorithm (GA). Then data envelopment analysis (DEA) is applied for determining the efficiency of each treatment. Although the important issue for implementation of DEA is its philosophy, which is maximization of outputs versus minimization of inputs, this important issue has been neglected in previous similar studies in multi-response problems. Finally, the most efficient treatment is determined using the maximin weight model approach. The performance of the proposed method is verified in a plastic molding process. Moreover a sensitivity analysis has been done by an efficiency estimator neural network. The results show efficiency of the proposed approach.

Risk analysis of production system, while the actual and appropriate data is not available, will cause wrong system parameters prediction and wrong decision making. In uncertainty condition, there are no appropriate measures for decision making. In epistemic uncertainty, we are confronted by the lack of data. Therefore, in calculating the system risk, we encounter vagueness that we have to use more methods that are efficient in decision making. In this research, using Dempster-Shafer method and risk assessment diagram, the researchers have achieved a better method of calculating tools failure risk. Traditional statistical methods for recognizing and evaluating systems are not always appropriate, especially when enough data is not available. The goal of this research was to present a more modern and applied method in real world organizations. The findings of this research were used in a case study, and an appropriate framework and constraint for tools risk were provided. The research has presented a hopeful concept for the calculation of production systems' risk, and its results show that in uncertainty condition or in case of the lack of knowledge, the selection of an appropriate method will facilitate the decision-making process.

This study is concerned with threshold F-policy and N-policy for controlling the arrivals and service in the queueing scenario of a machining system, having active and redundant components. For both F-policy and N-policy models, the queue size distributions are determined by the recursive method. Various performance measures, namely the average number of failed units in the system, probability that the server is busy or idle in the system, etc., are established using the queue size distribution.

The integration of marketing and demand with logistics and inventories (supply side of companies) may cause multiple improvements, it can revolutionize the management of the revenue of rental companies, hotels, and airlines. In this paper, we develop a multi-objective pricing-inventory model for a retailer. Maximizing the retailer's profit and the service level are the objectives, and shortage is allowed. We present the model under stochastic lead time with uniform and exponential distributions. Since pricing is important and influences demand, the demand is considered as a general function of price. The multiple-objective optimization model is solved using the weighting method as well as the L-P metric method. Concerning the properties of a nonlinear model, a genetic algorithm is taken into account to find the optimal solutions for the selling price, lot size, and reorder point. Finally, numerical examples with sensitivity analysis regarding key parameters are provided.

The ability to evaluate project proposals, assessing future success, and organizational value is critical to overall business performance for most enterprises. Yet, predicting project success is difficult and often unreliable. A four-year field study shows that the effectiveness of available methods for evaluating and selecting large, complex project depends on the specific project type, organizational culture, and managerial skills. This paper examines the strength and limitations of various evaluation methods. It also shows that, especially in complex project situations, the decision-making process has to go beyond the application of just analytical methods, but has to incorporate both quantitative and qualitative measures into a combined rational judgmental evaluation process. Equally important, the evaluation process must be effectively linked among functional support groups and with senior management in order to strategically align the project proposal and to unify the evaluation team and stakeholder community behind the mission objectives. All of this requires leadership and managerial skills in planning, organizing, and communicating. The paper suggests specific leadership actions, organizational conditions, and managerial processes for evaluating complex project proposals toward future value and success.

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change, a linear trend and a known multiple number of changes in the Poisson rate. The Markov chain Monte Carlo is used to obtain posterior distributions of the change point parameters and corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the well-known c-, Poisson exponentially weighted moving average (EWMA) and Poisson cumulative sum (CUSUM) control charts for different change type scenarios. We also apply the Deviance Information Criterion as a model selection criterion in the Bayesian context, to find the best change point model for a given dataset where there is no prior knowledge about the change type in the process. In comparison with built-in estimators of EWMA and CUSUM charts and ML based estimators, the Bayesian estimator performs reasonably well and remains a strong alternative. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

Competition in the electric service industry is highlighting the importance of a number of issues affecting the nature and quality of customer service. The quality of service (s) provided to electricity customers may be enhanced by competition, if doing so offers service suppliers a competitive advantage. On the other hand, service quality offered to some consumers could decline if utilities focus their attention on those customers most likely to exercise choice, while reducing effort and investment to serve customers less likely to choose alternatives. Service quality is defined as the way in which the utility interacts with and responds to the needs of its customers. To achieve maximum consumer satisfaction in electricity service, This paper has designed a framework by QFD by measuring service quality of electricity utility sector in ANN and also find interrelationship between these design requirements by ISM.

An employee transporting problem is described and a set partitioning model is developed. An investigation of the model leads to a knapsack problem as a surrogate problem. Finding a partition corresponding to the knapsack problem provides a solution to the problem. An exact algorithm is proposed to obtain a partition (subset-vehicle combination) corresponding to the knapsack solution. It requires testing and matching too many alternatives to obtain a partition. The sweep algorithm is implemented in obtaining a partition (subset-vehicle combination) in an efficient manner. Illustrations are provided to show how the algorithms obtain solutions.