Journal Of Industrial Engineering International
Volume:7 Issue: 3, Jun 2011

Struggling in world''s competitive markets, industries are attempting to upgrade their technologies aiming at improving the quality and minimizing the waste and cutting the price. Industry tries to develop their technology in order to improve quality via proactive quality control. This paper studies the possible paint quality in order to reduce the defects through neural network techniques in auto industry production lines. The inputs as effective factor in paint spray process identified for each thin layer on a plate. In the paint shop, defects generate that correlate with film thickness in paint process. In this work, a sheet of metal in demonstrated 50*20 using as a sample when Saipa Company, Iranian Auto Market Leader, is considered as a case study. In the present paper two models of NN is presented. The first model shows prediction of film thickness by10 input for bell, air layers and 12 inputs variables for dry film thickness or final paint thickness and 6 output points for three layers and second model is predicting of paint appearing uniformity by average and standard deviation of film thickness. Finally the application of Neural Network and statistical method (Regression) in predicting paint thickness and the comparison of the results are presented.

Scheduling of production in Flexible Manufacturing Systems (FMSs) has been extensively investigated over the past years and it continues to attract the interest of both academic researchers and practitioners. The generation of new and modified production schedules is becoming a necessity in today’s complex manufacturing environment. Genetic algorithms are used in this paper to obtain an initial schedule. Uncertainties in the production environment and modeling limitations inevitably result in deviations from the generated schedules. This makes rescheduling or reactive scheduling essential. One of the four different types of uncertainties that normally cause discrepancies between the actual output and the planned output is considered in this paper. These include unforeseen machine break-downs, increased order priority, rush orders arrival and order cancellations. In this paper, the current status of the shop is considered while rescheduling. The proposed algorithms revise only those operations that must be rescheduled and can, therefore, be used in conjunction with the existing scheduling methods to improve the efficiency of flexible manufacturing systems.

ToToday’s market place is increasingly dynamic and volatile. In this area supply network issues recently have attracted a lot of attention from industrial practitioners and academics worldwide. Chaos theory is the study of complex, nonlinear, dynamic systems. For chaotic systems, a tiny change in conditions may result in an enormous change in system output, whereas substantial changes in conditions may be absorbed without significant effect to the system’s output. In this paper, the researchers have investigated the effect of supply network design on the chaotic behavior of supply network from retailer’s inventory point of view. The multi-level supply network is characterized by extension of the widely known beer distribution model. System dynamic approach is implemented to study the supply network behavior. Furthermore, we use Lyapunov exponents to quantify the system chaos. Simulation outputs and its analysis indicate that the number of distributors and wholesalers have a significantly effect on making the system behavior chaotic.

In this ever changing business scenario, success of the company lies in understanding the customer preferences, tastes and anticipating the changes required in existing or new products being offered. It was desired to develop a model which allows decision makers to decompose a complex problem in a hierarchical structure to show the relationship of the goals, objectives, criteria and alternatives. The paper contributes a method for evaluation and analysis of customer as well as technical data using a synergistic combination of techniques like analytical network process (ANP) with quality function deployment (QFD) to evaluate most satisfying design for customers. A case study for the development of cars has been presented here to illustrate and validate the proposed approach. This study has been performed for the assessment of strategies to synthesize qualitative and quantitative factors in decision making keeping reasonable checks on consistency. It finds the priority rating of criteria, weighs them down to lower levels, checks for inconsistencies between relationships and correlation matrices, weighs interdependencies in engineering characteristics, evaluates the modified house of quality and establishes the overall contribution of engineering characteristics to satisfy customer needs.

In this paper, the researchers have investigated a Concatenated Robot Move (CRM) sequence problem and Minimal Part Set (MPS) schedule problem with different setup times for two-machine robotic cell. They have focused on simultaneous solving of CRM sequence and MPS schedule problems with different loading and unloading times. They have applied a Simulated Annealing (SA) algorithm to provide a good solution rapidly. A domination rule has been developed and used in SA algorithm that strongly reduces the search space and increases speed and solution''s quality of the algorithm. Numerical experiments indicate the results of the proposed SA from two points of view: quality of solution and consumed time.

This paper assumes the cell formation problem as a distributed decision network. It proposes an approach based on application and extension of information theory concepts, in order to analyze informational complexity in an agent- based system, due to interdependence between agents. Based on this approach, new quantitative concepts and definitions are proposed in order to measure the amount of the information in an agent, based on Shannon entropy and its complement in possibility theory, U uncertainty. The paper presents an agent-based model of production system as a graph composed of decision centers. The application of the proposed approach is in analyzing and assessing a measure to the production system structure efficiency, based on informational communication view. Information flow in cells and grouping algorithm are investigated in this paper.

In today’s highly competitive manufacturing environment, an effective supplier selection process is very important for the success of any business organization. Selection of the best supplier is always a difficult task for the purchasing manager. Suppliers have varied strengths and weaknesses which require careful assessment by the purchasing manager before selecting and ranking them. Any supplier selection procedure is to be based on the establishment of various criteria which may include quality, cost efficiency, delivery dependability, volume flexibility, information and customer service. This paper attempts to solve the supplier selection problem using two most potential multi-criteria decision-making (MCDM) approaches and compares their relative performance for a given organizational environment. The first MCDM approach is ‘Vlse Kriterijumska Optimizacija Kompromisno Resenje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. These two methods are used to rank the alternative suppliers, for whom several requirements are considered simultaneously. Two real-time examples are cited in order to demonstrate and validate the effectiveness and flexibility of these two MCDM methods. The rankings of the alternative suppliers as obtained using these two MCDM methods almost match with those as derived by the past researchers.

During 21st century, manufacturing success and survival are becoming more difficult to ensure this fact is originated in the emergency of new business era that has «change» as one of its major characteristics. Change in business environment and uncertainly have entered management study and research for the last two decades. Agility enhances the organization ability to provide high quality products and services, and is, therefore crucial to organization competitiveness. The paper briefly reviews the literature in this field and offers a model of agility based on leadership, human resource, organizational structure, organizational culture, process and system in the organization. This paper discusses the concept and the development of the methodology to achieve effective factors on organizational agility. An introduction of subject is given and followed by a detailed discussion of the proposed factor analysis methodology. One of the defining construct validity is using a factor analysis method. Factor analysis is a statistical method through which the number and nature of variables which measures the test are clarified. In factor analysis, this is done by combining various variables together as we have been making a few variables as a factor. This method defines the interrelations of analysis data and decreases variables to factors for simplicity. In the performed case study in Iranian industrial organization in order to define effective factors on organization agility, on the base of conceptual model, a questionnaire was compiled. The results of questionnaires were analysed by descriptive statistics and all variables were categorized in twelve factors through exploratory factor analysis. Using confirmatory factor analysis method, exploratory factors were surveyed whose result has a close adaptation with literature of organization agility and condition of studied Iranian industrial organization.