نشریه بین المللی مهندسی صنایع و مدیریت تولید
سال بیست و سوم شماره 2 (شهریور 1391)

Multiple attribute decision making models which select the best alternative out of all possible alternatives are used in different environments such as community, economy, management, army, etc. In recent years most of the performed researches in multiple attribute decision making’s field have been focused on single period evaluations; while the value of attributes may not be fixing during all periods. Hence this research presents how to use a MADM model, for example TOPSIS model, in case of multi period. In this research, for describing the relations between periods, regression equations are used. The proposed model is also implemented in textile industry as a case study.

This paper proposes a novel, multi-objective integer programming model for an open-shop scheduling problem (OSSP). Three objectives are to minimize the makespan, total job tardiness and earliness, and total jobs setup cost. Due the complexity to solve such a hard problem, we develop a meta-heuristic algorithm based on multiobjective scatter search (MOSS), and a number of test problems are solved by this proposed algorithm. Finally, to prove its efficiency, the related results are compared with the results obtained by the wellknown multi-objective evolutionary algorithm, called NSGA-II. The results confirm the efficiency and the effectiveness of our proposed MOSS to provide good solutions, especially for medium and largesized problems.

This article aims to evaluate the relative importance of product design requirements in Quality Function Deployment (QFD) environment taking customer needs and producers constraints. Considering the matrix of QFD, we can take the customers needs into consideration only. However, we present a technique that can take into account the production constraints in the product design process as well as the customer needs. In this regard, the DEA technique which is a linear based model is proposed here. First, we discuss about the literature review on the context of QFD and DEA, and after explaning the combination process of these techniques a case study is represented. The tile industry case study proposed here takes into consideration the customer needs, their relative importance, constraints against the producers, and design requirements in the specified field. We evaluate not only the relationship between the customer needs and design requirements but we evaluate the relationship between the design requirements and producers constraints. Finally, by constructing the model using DEA and the regarding constraints we calculate the relative importance of design requirements

In the real world clustering problems, it is often encountered to perform cluster analysis on data sets with mixed numeric and categorical values. However, most existing clustering algorithms are only efficient for the numeric data rather than the mixed data set. In addition, traditional methods, for example, the K-means algorithm, usually ask the user to provide the number of clusters. In this paper, we propose a new method to cluster mixed data and automatically evolve the number of clusters as well as clustering of data set. In the proposed method, Davies-Bouldin Index is used as fitness function and we use the genetic algorithm to optimize fitness function. Also, we use a more accurate distance measure for calculating the distance between categorical values. The performance of this algorithm has been studied on real world and simulated data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach

A heuristic method based on simulation and design of experiments for performance improvement of assembly line is presented in this paper. In the proposed method, all effective factors on the performance of an assembly line are specified using the expert's point of view and then a 2k factorial design is used for the selection of the most effective factors. Also a factorial design is applied for assessment of interaction among important factors and comparison of different levels. A simulation method is employed for evaluating the performance of proposed alternatives instead of using real case. A straightforward assembly line analysis is presented and exemplified in this work by extracting and analyzing effective factors of an auto light company. Number of workstations, operators and speed of machines are chosen as the main factors. Next to the identification of different levels using factorial design, the method showed significant improvement on the assembly line of auto light company.

Uncertainty is an inseparable part of supplying chain management, which could be resulted from the customer’s demand fluctuation, change of production capacity or lack of reliability of the raw materials suppliers and the contractors. to act properly in response of the said matters, the safety stock could be used.In the present article, considering the existing problems and conditions of Ghataat Mehvari khorasan Company brought in this text, a fuzzy double objective model has been developed based on the total cost and the customer service level to set the safety stock in the supply chain of the mentioned company. Also,existing limitations in the said unit including production capacity,batch size for ordering raw materials and financial limitation for purchasing raw materials are considered for the model. the fuzzy reliability of suppliers and contractors has been specified by a fuzzy expert system with no presumption for demand distribution, and to control and correct all the anticipation deviation from the fact,the rolling horizon technique is used and finally safety stock has been set in such way that the minimum total cost and the customer service level have reached a designed level.

The Robot Motion Planning (RMP) problem deals with finding a collision-free start-to-goal path for a robot navigating among workspace obstacles. Such a problem is also encountered in path planning of intelligent vehicles and Automatic Guided Vehicles (AGVs). In terms of kinematic constraints, the RMP problem can be categorized into two groups of Holonomic and Nonholonomic problems. In the first group the robot can move freely from a point to any other point in the free space, while in the second one the robot’s movement is restricted to a subset of moves, as the constraints of a car for moving sideways. This paper proposes a solution to the RMP problem for car-like robots by the Fast Marching Method (FMM), which is a numerical technique for solving the Eikonal nonlinear partial differential equation. At first a smooth collision-free path is generated without considering the nonholonomic constraints, and then it is adjusted to accommodate the kinematic constraints using the Virtual Obstacles concept, which is a novel contribution. The presented method is fast and exact and finds the optimal path. Comparisons against another nonholonomic graph-search-based method showed the advantage of the new method over it in terms of path length and runtime.

Customer classification using k-means algorithm for optimizing the transportation plans is one of the most interesting subjects in the Customer Relationship Management context. In this paper, the real-world data and information for a spare-parts distribution company (ISACO) during the past 36 months has been investigated and these figures have been evaluated using k-means tool developed for spare-part demand similarity function in different regions of the country. Similarity function for customer behavior in different regions has been defined. Based on this function and with help of k-means algorithm, customers have been grouped and similar customers were put in the same groups. Customer similarity function has been developed through 5 steps and has been defined individually based on each factors of Euclidean distance, customer's order time and bulk value of the order. Then, these three factors have been combined and DCB function has been defined. In the final step, different weights have been allocated to different years and seasons and BCD function has been improved. The grouping process has been improved for three functions of Euclidean distance, DCB and BCD. This process was executed using the R software and the improved BCD function was recognized as the optimum grouping function. Then, using DMT model, customer behavior has been analyzed at each part and the proper distribution policies have been defined. Results indicate a significant cost reduction (32%) in spare-parts distribution costs for ISACO

Project scheduling is one of the main fundamentals in project management so the related progressive methods which consider most of the project limitations would be valuable. The GERT (Graphical Evaluation and Review Technique) is a network analysis method which has unique capabilities in comparison with other conventional scheduling methods. On the other hand and, there is no definite conformity between the scheduling and implementation of projects mostly in the reality. So a project always accompanied by uncertainty. In this research, for decreasing the constraint’s consequences, caued by ambiguity and indecisiveness, fuzzy logic and fuzzy control system has been recommended. For this purpose, the mean, variance, value and number of loops in the GERT network and also environmental effective risks has been considered as fuzzy variables. Thus, by using capabilities of GERT network and with emphasis on fuzzy logic and fuzzy system, a fuzzy control system has been designedfor the first time. In accordance with the results obtained from 15 experiments which was done among different medium-sized projects, it could be assert that, by using the designed control system, scheduling the project in a more logically and precisely way would be achievable