Journal of Optimization in Industrial Engineering
Volume:3 Issue: 6, Summer and Autumn 2010

In this paper, absorbing Markov chain models are developed to determine the optimum process mean levels for both a single-stage and a serial two-stage production system in which items are inspected for conformity with their specification limits. When the value of the quality characteristic of an item falls below a lower limit, the item is scrapped. If it falls above an upper limit, the item is reworked. Otherwise, the item passes the inspection. This flow of material through the production system can be modeled in an absorbingMarkov chain characterizing the uncertainty due to scrapping and reworking. Numerical examples are provided to demonstrate the application of the proposed model.

Analytic hierarchy process (AHP) has been criticized considerably for possible rank reversal phenomenon caused by the addition or deletion of an alternative. While in many cases this is a perfectly valid phenomenon, there are also many cases where ranks should be preserved. Our findings indicate that using the geometric mean reduces the variance in ratings substantially; hence, yielding lower inconsistency in ratings. An approach is therefore proposed using the geometric mean aggregation to avoid rank reversal phenomenon. A practical example is examined using the proposed approach to demonstrate its validity and practicability in rank preservation. This paper also compares the REMBRANDT system with the proposed approach to avoid rank reversal phenomenon.

The problem we investigate deals with the optimal assignment of resources to the activities of a stochastic project network. We seek to minimize the expected cost of the project include sum of resource utilization costs and lateness costs. We assume that the work content required by the activities follows an exponential distribution. The decision variables of the model are the allocated resource quantities. We construct a continuous time Markov chain model for the activity network and use the PhaseType distribution to evaluate the project completion time. Then we use Fibonacci search over the interval of permissible allocations to the activity to seek the minimum expected cost.

Measurement systems analysis (MSA) has been applied in different aspect of industrial assessments to evaluate various types of quantitative and qualitative measures. Qualification of a measurement system depends on two important features: accuracy and precision. Since the capability of each quality system is severely related to the capability of its measurement system, the weakness of the two mentioned features can reduce the reliance on the qualitative decisions. Consequently, since in the literature fuzzy MSA is not considered as an independent study, in this paper, a fuzzy method is developed for increasing method accuracy and precision by ncountering the impreciseness of some measures of MSA. To do so, bias, capability, and gauge repeatability and reproducibility (GR&R) indices are considered as triangular fuzzy numbers. The application of the proposed method is illustrated through a case study taken from an automotive parts industry. All rights reserved.

The purpose of this research is to evaluate the effectiveness of implementation Quality Management System (ISO 9001:2000) in gas processing plants of National Iranian Gas Company using balanced scorecard approach. More specifically, we chose the balancedscorecard framework to examine five well-known gas processing plants in National Iranian Gas Company from four different perspectives in order to provide a more comprehensive view of organizational performance after the registration of ISO 9001. The methodology used in this research comprised administering a questionnaire to 360 respondents in the company in order to find out the results of using ISO 9001:2000 on the firms under study based on four BSC aspects and. The results indicated that: First, the BSC could be used successfully to measure the performance of quality systems in the gas processing plants of NIGC. Second, the findings indicated ranking of five plants based on the BSC perspective. However, the customer and learning perspectives are neglected in the target companies.

Using the mathematic techniques such as Fuzzy approach has useful outcomes for production planning in different sources.In this paper LGP1 was used to model the objectives such as: avoidance of shortage or surplus of demand, access to maximum of income, using the normal capacity of production and organizing the inventory of warehouse, within the framework of Goal constraints like balancing between demand and inventory, rate of production within every period and the constraints of threshold of inventory at the end of every month. On these lines, the goal programming is one of the best methods for analyzing the multi objective decision making in cement industry management. The most principal disadvantage of goal programming is that all the parameters of model should be defined carefully and all of the objectives and constraints should certainly be determined. For taking over on this problem we introduced the Fuzzy concept.In this research, the mathematic goal programming model in the cement industry is modeled by Fuzzy and absolute approach. This research is intended to answer this question, which one presents the optimal solution for production process planning, Fuzzy or absolute approach? The necessity information to do this research is obtained with using field methods, desk surveys, observations, factory documents, and interviews or questionnaires. In this article we use GP to formulize, AHP2 for grading and weighting and LINGO for solving. Afterwards, the data are entered in the formula modeled before and are solved using LINGO software.

This paper proposes a bi-objective model for the facility location problem under a congestion system. The idea of the model is motivated by applications of locating servers in bank automated teller machines (ATMS), communication networks, and so on. This model can be specifically considered for situations in which fixed service facilities are congested by stochastic demand within queueing framework. We formulate this model with two perspectives simultaneously: (i) customers and (ii) service provider. The objectives of the model are to minimize (i) the total expected travelling and waiting time and (ii) the average facility idle-time. This model represents a mixed-integer nonlinear programming problem which belongs to the class of NP-hard problems. In addition, to solve the model, two metaheuristic algorithms including non-dominated sorting genetic algorithms (NSGA-II) and non-dominated ranking genetic algorithms (NRGA) are proposed. Besides, to evaluate the performance of the two algorithms some numerical examples are produced and analyzed with some metrics to determine which algorithm works better.

This paper studies the multi-stage supply chain system (MSSCM) controlled by the kanban mechanism. In the kanban system, decision making is based on the number of kanbans as well as batch sizes. A kanban mechanism is employed to assist in linking different production processes in a supply chain system in order to implement the scope of just-in-time (JIT) philosophy. For a MSSCM, a mixed-integer nonlinear programming (MINLP) problem is formulated from the perspective of JIT delivery policy where a kanban may reflect to a transporter. Since the adopted model is of MINLP type and solving it by branch and bound (B&B) takes time, a metaheuristic is presented.This metaheuristic is an electromagnetic algorithm (EA). The EA is compared against an existing algorithm and also B&B results to evaluate the proposed metaheuristic. Extensive experiments and statistical analyses demonstrate that our proposed EM is more efficientthan B&B with regard to the objective functions considered in this paper.