روش های ژنتیکی برای زمان بندی وظایف نگهداری و تعمیرات پیشگیرانه در یک خط تولید تک محصولی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22254||2001||12 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 74, Issues 1–3, December 2001, Pages 135–146
The present article deals with optimising the schedule of maintenance tasks of all the machines in a single product manufacturing production line. This study was made in the context of one machine assigned to one operator. This operator intervenes to change tools during a stoppage. Our goal is to increase the overall through-put of the line. We firstly formalised the problem and showed the difficulty of its analytical resolution. Then, we presented the software environment that enables the resolution of this problem: it is made up of a simulator of the production line and an optimiser using the genetic algorithms. Our approach to the scheduling of maintenance tasks was validated upon an actual production line of car engines. We focused our study on the setting of parameters of a genetic algorithm. We proceeded with a systematic approach inspired by the Taguchi method to find the best combination of levels for each studied parameter and performed a statistical confirmation of the results. Finally we validated the genetic approach as against naive optimisation.
The domain of this study is the manufacturing production systems. More precisely, the line concerned is a single product mass production line. This line, as in Fig. 1, of a succession of machines alternating with buffers of pieces.Each machine unit in this line is characterised as follows. • The machines perform multiple operations, the succession of which is a line of type “transfer”. As the line is balanced, the machines have equal duration cycles. • The preventive maintenance tasks on these machines are realised by the operator. Each task occupies a certain amount of the operator's time. The number of maintenance tasks varies according to the machine. There are two sorts of preventive maintenance operations: • a tool change, necessitating a machine stoppage; • a control, which does not necessitate a machine stoppage. This work has been done in the following framework. Hypothesis 1. The assignment of operators to the machine is restricted to the case of one machine assigned to one operator: an operator is always present on each machine of the line. Hypothesis 2. The changing of tools is more critical than the controls; since, through the stoppage of the machine, it has a negative influence on the number of pieces produced. In a normal situation, the controls are done in “masked time”, i.e. without stopping the machine. Consequently these controls are not considered in this study. The subject of this article is the scheduling of preventive maintenance tasks, namely the changing of tools on all the machines in the line. Anderson et al.  have presented a review of scheduling in production machines or in computers. They are academic scheduling problems. Here in this article, we aim to address the industrial problem defined in Section 2.
نتیجه گیری انگلیسی
The method proposed offers a theoretical interest, verified by simulation. The gain obtained is considerable, given the price of the pieces produced. Indeed, the industrialists of the sector concerned admit that a solution might be implemented if the resulting gain exceeded 1%. A feasibility study is currently under way with our industrial partner to assess the practicality of this approach on an actual production line. The problems are the material constraints derived from the anticipated changes of tools. The consequences of breakdowns on improvement is also under evaluation. The pursuit of this study will be oriented towards the following points: Firstly, assessing the cost of each solution in order to choose the most profitable one. The production gains must be compared with the extra cost entailed by the premature change of tools. A multicriteria analysis may be useful to connect the several optimisation objectives: throughput maximisation, cost minimisation. Secondly, there is the question of assigning the operators to the machines. One must study the general case of multiple operators assigned to a set of machines. Both their location in relation to the machines and their level of training must be taken into account. The use of constraints system programming could be helpful. At last, it must be noticed that the approach presented in the case of a single product manufacturing production line can easily be extended to a multiple products line.