INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)
Year:2013 | Volume:24 | Issue:3 |Papers Count:20

This paper deals with robot move sequencing and scheduling in two and three machine robotic cells. The machines in the cell are CNC and because of having flexibility feature, they can perform all necessary operations to manufacture parts. The manufactured parts is identical. In the most past researches in this field, flow shop layout has been considered. In this paper we ignore this layout, so a new cycle for robot move sequences is considered. Also the machine layout differs with past studies and the robot has rotational move. We determine the cycle time of proposed cycle, then it is proved that proposed cycle time for two and three machine robotic cell is smaller than existing feasible cycle time that leads to maximize output rate in that cells.

Consider a supply chain that consists of one vendor and multiple retailers. We assume that the vendor procures product from an external source and sells it with the same wholesale price to the retailers. Demands in retailers’market are decreasing function of the price and vendor makes use a vendor managed inventory strategy. In this paper we propose model of supply chain in centralized and decentralized state. Supply chain performance in centralized state can be analyzed by mathematic optimization model while for analyzing supply chain performance in decentralized state use Stackelberg game. Finally, numerical analyses about the proposed models are done and performance of supply chain in centralized and decentralized state is compared.

As knowledge is power, information is power in supply chains. It (information) provides the decision maker the power to get ahead of the competition, the power to run a business smoothly and efficiently, and the power to succeed in an ever more complex environment. Information plays a key role in the management of the supply chain. But how the different combination of information sharing based on Information flow can affects the performance is not yet understood. In this study, information sharing scenarios is designed to analyze supply chain performance through a simulation model. We identify the most efficient scenario and estimate the each efficiency of information-sharing scenarios.Results show that two-way information sharing acts much more efficient than other scenarios and Demand information sharing is more efficient than Supply information sharing. In the end, using the factorial experiment design, the impact of demand process parameters on the profit of information sharing was explored, and it showed that the correlation coefficient between the demand in this period and prior periods, have the most positive impact on the profit from the two-way information sharing. Vice versa, the variance of error term in the demand process has the most negative impact on the profit.

Robust design is a new and innovative approach for product quality improvement with the lowest cost which sustains the profitability and competitiveness of manufacturing and service organizations in today’s competitive markets. In the nearly 25 years of its existence, interesting innovative based solutions have been proposed to solve the problems in this area. Recent years have witnessed a growing interest of research in the study of the robustness on the time line. In this area, one often needs to consider multiple quality characteristics. However, little work has been done to address this critical issue. The purpose of this article is to solve this problem with the aid of mathematical modeling of quality characteristic via desirability function. It is expected that the obtained results can satisfy the designers’ requirements to a large extent.

Identifying risk factors and selecting the suppliers based on these factors appears to be one of the main challenges for manufacturers. As a result, in this paper a distinctive framework for evaluating suppliers based on these risk factors is proposed and solved by multi-criteria decision making (MCDM) techniques and two-level data envelopment analysis (DEA) approach. The measures have been identified by reviewing the literature and the assistance of the experts in this area. Consequently, they have been categorized by Factor Analysis (FA). The measures in each group are then respectively weighted based on the experts’ evaluation of the measures and by Analytical Hierarchy Process (AHP) technique. In the next step, the two-level Data Envelopment Analysis (DEA) was applied to rank 12 presumptive suppliers of a manufacturer according to risk measures in two levels. The advantage of applying two-level DEA would be the direct inclusion of risk sub-factors in the computations. This technique benefits from more accurate results, whereas in the other evaluation approaches, in case of many factors and sub-factors, some might be integrated in to others or omitted.

Job shop scheduling deals with distributions between jobs in order to find a schedule with minimum possible time. To formulate job shop scheduling problems, various factors such as the activity processing time and due date for delivering jobs are often ambiguously known to the analyst. In these situations, the use of fuzzy parameters and multi-objective goals based on fuzzy knowledge seems necessary. Since this problem is non-polynomial, we propose a method based on extremal optimization in order to select and modify less valuable but more probable activities. This method leads to decrease the number of less valuable activities and to increase the number of activities with the same value. In such cases, any partial change in scheduling may produce big changes. For instance, it leads to escape from local optima and move toward global one. Lots of simulations on and experimental data, demonstrate proper convergence rate and good results in comparison with other methods. The correctness of given results and proposed algorithm have been proven by means of axiom of convergence, statistical t-test t and possibility error (F).

Real option is a promising approach for valuation of projects especially under uncertainty conditions. However many researchers believe that the results of this approach cannot be completely relied, because real option overestimates value of inherent flexibilities in projects. The main reason for overestimation of the flexibilities is to apply valuation procedures of financial options in real options while these two options have some differences. In this paper, we apply Asian real option approach for valuation of projects in order to become more realistic. Asian option is a kind of financial option that its value, despite of ordinary options, depends on price variation of underlying asset during a period of time. This study utilizes binomial bushy tree for valuation of Asian option. Our proposed approach has been applied in a real project. As expected, the Asian real option estimates the value of flexibilities inherent in the project less than the plain vanilla option.

In this paper, dynamic PERT networks with finite capacity of concurrent projects are expressed in the framework of networks of queues. In this investigation, it is assumed that the system capacity is finite and new projects are generated according to a Poisson process. There is only one server in every service station and the discipline of queue is FCFS (Fist Come, First Served). Each activity is performed independently in its corresponding service station with exponential distribution. For calculating the mean project completion time in dynamic PERT network with finite capacity, we first convert the network of queues into a stochastic network. Then, by constructing a proper finite-state continuous-time Markov model, a system of differential equations is created to solve and find the manufacturing lead time distribution for any particular work, analytically. Finally, by using Little’s Theorem the mean project completion time is obtained.

In recent years the complexity of projects, competitive business environment and organization resource constraints has resulted in attention to project management in achieving project objectives. Consequently employers looking to increase quality, reduce execution time and costs that are the main objective of the projects. It is therefore essential project managers with appropriate resource allocation and decision making process in order to meet the above objectives. Since most previous work has been done in terms of certainty and unrealistic conditions, there is a serious split in the decisions made by the project managers in the field of project management knowledge. Thus, one of the most prominent features of this study is to consider the conditions of uncertainty which is closer to the real world. In the present study, a fuzzy mathematical model is suggested for a network of activities which each includes several modes of execution in order to select the best mode among the possible modes and trade-off their criteria that will result to achieve the aforesaid purposes. To do this, a None-dominated Sorting Genetic Algorithm (NSGA-II) was designed to solve the suggested model. Then, a case study was conducted on the oil industry and the performance of the suggested model and the algorithm was examined. Furthermore, the level of attention paid by the project manager to the qualitative comments of the experts is shown using Fuzzy Alpha-cut Method. Testing the validity of such computational results indicates that when a=1, then the answers are oriented mostly toward the project contractual conditions which are the same conditions as expected by project manager. The more we are approaching from certainty conditions toward a=0 and indeed the uncertainty conditions, the worse conditions the answers resulted by solving the algorithm will have in terms of time, cost and quality. It is due to the fact that the project manager is taking the uncertainty conditions and risks into consideration by paying attention to the experts’ comments that will threat and worsen the general situation of the project.

Performance appraisal is a process that will be used by firms to evaluate their employees’ efficiency and productivity in order to make different decision such as determination of salary, reward, training needs and etc. A basic drawback of previous methods of performance evaluation is their biased results due to influence of rater’s personal motives and subjective. In this research two new methods have been developed that will resolve the problem. The proposed methods are applicable for the jobs that quantitative standards can be defined for employees working. As a case study, proposed methods were implemented in one of the HESA’s laboratories. For evaluating the long-term performance of total system, statistical analysis based on steady state simulation was used. The results indicate significant and positive effects of the new methods.