INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)
Year:2010 | Volume:21 | Issue:3|Papers Count:17

In this paper, Multi-Objective Flexible Job-Shop scheduling with Parallel Machines in Dynamic manufacturing environment (MO-FDJSPM) is investigated. Moreover considering dynamical job-shop environment (jobs arrived in non-zero time), It contains two kinds of flexibility which is effective for improving operational manufacturing systems. The non-flexibility leads to scheduling program which have problems like useless loading machines, bottleneck machines, decreasing desirability sources and a poor function in just in time delivery. Regarding to the flexibility in manufacturing systems, a job could be processed not only in several stations (operational flexibility) but also on several parallel machines in each station (flexibility of parallel machines) which both of them are considered in this paper. In the recent researches about FJSPM and FDJSPM, the single objective models were assessed. Whereas in competitive conditions, decision-makers encountered with simultaneous multi-objective problems that a number of them could be completely conflict with each other. In this research, the objectives are makespan, mean flow time and mean tardiness. These objectives are adaptable to the concept of just-in-time and supply chain management. Since the problem is NP-hard, an improved Genetic Algorithm is proposed. Proposed GA compare with Genetic Programming (GP) and GA, the result demonstrate inherence proposed GA. The control parameters in proposed GA are dynamic and changed through the algorithm that leads to reducing the probability of early convergence and local optimum. The mean results for three flexibility levels show that there is 4.9%, 5.33% & 4.6% improvement in proposed GA compared with previous results.

In this paper, a review of Multiple objective Flowshop Scheduling Problems is introduced. Different version of the problem are classified according to different multi objective optimization approaches, flowshop assumptions, solving methods, and also objective functions terms. This paper can be considered as a complete and valuable reference for researchers in the field of scheduling, especially multiple objective ones.

In this paper, the system reliability in the design stage and before production is improved. The proposed algorithm is based on the fact that the chosen structure should be practical in reality. First, possible structures for the system of interest are designed then the optimum structure is chosen for production. The proposed approach to select the optimal alternative is to determine the effective criteria for selecting process, then using analytic network process as a robust and useful technique for multi-attribute decision making.

In many applications the quality of a process or product is best characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. Profile monitoring is used to understand and to check the stability of this relationship over time. Therefore checking the capability of a profile seems essential. Process capability indices have become very popular in assessing the capability of manufacturing processes in practice during the past decade. In this paper, a method is proposed for determining the capability of a linear profile by means of calculating the capability indices for intercept and slope, independently. The intercept measures are explanatory variables that can be treated the same as single quantities. Therefore, the problem is reduced to evaluating the process capability for slope of the profile. Since the overall slope is a linear regression on a series of subsequent observations, the mutual gradients provide a set of variables for process capability study. After checking the normality and control status of the slope variables, PCRs can be evaluated for sample data.

The Capacitated Clustering Problem (CCP) is a classical location problem with various applications in data mining. In the capacitated clustering problem, a set of n entities is to be partitioned into p disjoint clusters, such that the total dissimilarity within each cluster is minimized subject to constraints on maximum cluster capacity. Dissimilarity of a cluster is the sum of the dissimilarities between each entity that belongs to the cluster and the median associated with the cluster. In this paper two solution methods proposed for the problem. First method is a simulation annealing algorithm which uses different neighborhood structures randomly. The second method is a genetic algorithm approach which strengthened by a heuristic local search method. Computational results of test samples from literature demonstrate the robustness and efficiency of the proposed solution methods. This confirms that the proposed algorithm provides high quality solutions in reasonable time.

In this paper the delivery times in the delivery chains is modeled and monitored statistically. The model is developed based on a double warning control chart using Lorenzen and Vance cost function and it is solved by using the Genetic Algorithm approach. The model is tested using the TNT post services data in the United States of America.

Dynamic route guidance system is an important section of intelligent transportation systems. The main core of this system is computation of shortest path based on the real-time information. In this research the formulation of dynamic guidance of vehicle has been presented based on characteristics of intelligent transportation systems and using the general level of service criteria which include such variables as travel time, fuel consumption objective consideration of socio-economic reality and more completely description of traffic condition. Because of time-varying flows on traffic networks, the routing algorithm based on artificial intelligence techniques emphasized on a hybrid genetic algorithm to solve the presented model is developed. the important results of this paper is the ability of designing algorithm in better trip planning, routing decisions in urban transportation network include the robust routing strategy in adjustment with dynamic traffic condition and presentation of several route alternative to reduce travel cost. Finally the experimental simulation results which verify the feasibility and validity of this algorithm are shown.

Haulage in open-pit mines allocates half of operational costs. Improvement in this system has significant effects in decrease of costs and increase of efficiency. Productivity in mines is a mixture of machinery efficiency and implementation of production strategies. In this paper after classifying dispatching models in open-pit mines, a new mathematical model will be introduced, to execute at the beginning and also during the working shift. Execution of the model before shift start creates a general vision of production parameters and grade control according to production strategies. Also execution of model during the shift with real-time information tries to keep the results near the standard deviation of initial results. This model can be used to develop heuristic models and truck dispatching simulation softwares.

With the existence of ambiguity in the financial processes of projects, fuzzy concepts are being implemented into the foundation and essence of the current article. Authors have employed chance constrained programming and simulated annealing as appropriate tools for determining the net present worth value of several projects. At the end, the most economical project was chosen. In this article, the investment expenses and annual net financial processes are considered fuzzy taking the credibility definition into consideration. The proposed model is then solved by simulated annealing program and to check its credibility we have compared the results of our model with the results obtained by the Genetic Algorithm as well as the Branch and Bound routine. The results indicate that for small sized problems, Genetic Algorithm has worked relatively with less error amounts and produces better results. All developed algorithms such as simulated annealing and fuzzy simulation are coded in MATLAB R2009a environment and on the personal computer with266GHZ.