دانلود مقاله ISI انگلیسی شماره 18975
عنوان فارسی مقاله

شبیه سازی سیستم های وسایل نقلیه خودکار هدایت شونده (AGV) بر اساس فلسفه دریافت به موقع (JIT) در یک محیط تولید کارگاهی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
18975 2007 13 صفحه PDF سفارش دهید 6190 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Simulation of automated guided vehicle (AGV) systems based on just-in-time (JIT) philosophy in a job-shop environment
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Simulation Modelling Practice and Theory, Volume 15, Issue 3, March 2007, Pages 272–284

کلمات کلیدی
سیستم های وسایل نقلیه خودکار هدایت شونده - شبیه سازی - جریان دو جهته - طراحی تجربی
پیش نمایش مقاله
پیش نمایش مقاله شبیه سازی سیستم های وسایل نقلیه خودکار هدایت شونده (AGV) بر اساس فلسفه دریافت به موقع (JIT)  در یک محیط تولید کارگاهی

چکیده انگلیسی

Automated Guided Vehicle (AGV) systems have been frequently used as material handling equipment in manufacturing systems since the last two decades. The use of AGV systems has taken attention of practitioners and researchers. Although there are numerous studies concerning with AGV systems in literature, a few of them deal with the adaptation of these systems into JIT systems. Moreover, the facility layouts considered in those studies have flow-shop environment. In this paper, a simulation model of a hypothetical system which has a job shop environment and which is based on JIT philosophy was developed. In addition, a dispatching algorithm for vehicles moving through stations was presented in order to improve transportation efficiency. In given layout, multiple vehicles can move and their bi-directional flow is allowed. After the model had been established, it was mentioned how to perform simulation output analysis. The factors which may be important for the system were determined by output analysis. An experiment plan was prepared by taking into account these factors. In this plan, two levels were selected for each factor and an experimental design was conducted. The effect of each factor on each performance measure and the interaction of these factors were examined.

مقدمه انگلیسی

Conventional approaches used in modeling manufacturing systems can be divided into two types: Analytical models and simulation models. Both techniques have advantages and disadvantages. Analytical technique defines the system as mathematical equations and it can optimize the system. However, since modeling a system as mathematical equations requires a lot of assumptions about the system and the more assumptions are considered the more the system becomes unreal. On the other hand, simulation technique may not give optimum solutions. It investigates a system’s long-term behavior. In spite of the fact that generating a solution about a system is more time-consuming in simulation technique, it is more convenient in modeling complex systems than analytical technique. In modeling AGV systems, because there are a lot of variables and parameters, simulation technique is extremely useful in modeling these systems. According to Banks et al. [1], Free-path transporters can move through a system without concern for delays caused by other vehicles. Guided transporters move along a fixed path and may contend with each other for space along that path. Especially, if there are multiple vehicles traveling through a system and bi-directional paths are available, vehicle routing problem emerges. Vehicle routing is known as a difficult problem in literature. To solve this problem, different policies should be developed. Also in achieving an acceptable solution to this problem, the structure of considered system plays a crucial role. According to Kimura and Terada [7], multi-stage production processes can be classified into two types: push systems and pull systems. Most of the American and European production systems employ push systems whereas the Japanese JIT systems employ pull systems. As pointed out in Baykoc and Erol [2] the main difference between the two systems is that in the pull system, the material is routed from the preceding stage to the succeeding stage according to the consumption rate of succeeding stage. Although there are numerous studies examining and investigating behaviors of AGV systems, a few of them investigate the relationship between AGV systems and JIT production philosophy. At those studies, facility layouts of systems have a flow-shop environment. Such a job-shop environment, the factors which may be important were determined. This paper aims to examine how these factors affect performance measures specified in the job-shop environment. To accomplish this, experimental design, one of the statistical procedures, was applied. By the aid of experimental design, main and interaction effects among factors and the statistical significances of these effects were clearly considered. In other words, it was desired to show that how this system reacted under different factor settings. In simulation model of considered system, demand arrivals are stochastic and, processing time of each job on each station is deterministic. The effects of four factors on the system performance measures, which are time in system (flow-time), time between consecutive jobs, throughput rate and number of jobs waiting on AGV queue, are tested. Subsequent section considers some simulation studies related to AGV systems in literature.

نتیجه گیری انگلیسی

In this essay, the behavior of an AGV system which is based on JIT concept has been discussed via simulation. In spite of the fact that there are simulation studies concerning with JIT production system, this study differentiates at one point from past researches. The most contribution of this study is to show the applicability of JIT system in job-shop environment. Also, the study suggests the AGV dispatching algorithm (MP) to improve transportation efficiency. Finally, the study provides a detailed experimental design features and exact analysis on simulation results. Higher performance may be obtained from JIT production system by this study. From ANOVA tables, there is a statistical evident that number of vehicle, number of kanban and arrival rate of demand affect the time in system of each job. As the number of vehicle increases, flow-time of each job decreases. Also when the number of kanban increases, flow-time of each job strikingly increases. As demand rate decreases, time in system of each job also decreases. Besides, two-way interactions are detected on time in system (between number of vehicle and number of kanban, between number of vehicle and arrival rate of demand). Finally, three-way interaction occurred on time in system (among number of vehicle, number of kanban and arrival rate of demand). On the other hand, as number of vehicle increases from two to three, vehicle queue length extremely decreases. Another main effect on vehicle queue length is number of kanban. This factor is found statistically significant. Only two-way interaction on vehicle queue length is found between the number of vehicle and the number of kanban. On output rate, number of vehicle and arrival rate of demand have an effect, as number of vehicle increases, number of finished jobs increases. Same things can be declared for arrival rate of demand. Only two-way interaction is shown between number of vehicle and arrival rate of demand. On inter departure time of jobs, only number of jobs plays a crucial role. According to the results, number of vehicle plays a crucial role on all performance measures. On the other hand, vehicle dispatching rule does not affect any performance measure. This study can be extended in several points. Machine failures are ignored in this study. Naturally, it cannot be seen how the system respond machine failures. Considering machine failures make the system more realistic. Thus, various policies can be developed. In future researches, machine failures may be regarded. Secondly, it is mentioned that a dispatching algorithm (MP) was proposed to improve transportation efficiency. However, the performance of this algorithm was not presented exactly. If this algorithm is thought as a factor, precise effect of it can be observed. Lastly, it was stated that in experimental design two levels were selected for each factor. A regression equation (metamodel), which is a function of factors, can be suggested by increasing the levels of factors. Thus, the function constituted is able to yield the values of performance measures. As a result computer time, on which simulation runs must be performed, will be saved.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.