تعداد ماشین آلات، قاعده اولویت، و تعیین تاریخ مقرر در سیستم های تولید انعطاف پذیر با استفاده از شبکه های عصبی مصنوعی

When there is a production system with excess capacity, i.e. more capacity than the demand for the foreseeable future, upper management might consider utilizing only a portion of the available capacity by decreasing the number of workers or halting production on some of the machines/production lines, etc. while preserving the flexibility of the production system to satisfy demand spikes. To achieve this flexibility, upper management might be willing to attain some pre-determined/desired performance values in a production system having identical parallel machines in each work center. In this study, we propose a framework that utilizes parallel neural networks to make decisions on the availability of resources, due date assignments for incoming orders, and dispatching rules for scheduling. This framework is applied to a flexible manufacturing system with work centers having parallel identical machines. The artificial neural networks were able to satisfactorily capture the underlying relationship between the design and control parameters of a manufacturing system and the resulting performance targets.

In the global competitive market, several factors might affect the management of demand and capacity. For example, seasonality in demand, a new competitor in the global market, or an economic or political crisis might force underutilization of the available capacity. In any of these cases, upper management might not have the luxury of running production at capacity for a long period of time. One alternative might be to reduce short-run operational capacity by shutting certain production lines/work stations/machines while preserving the flexibility to satisfy demand spikes. This could lead to opportunities for larger orders if the incoming orders are satisfied while achieving some critical and conflicting objectives such as faster delivery speed, greater reliability, higher customer satisfaction, and minimum cost. To achieve these objectives, management might need a decision-support tool that will provide the optimal resource structure and scheduling policy. For a given resource structure and scheduling policy, computing the performance of a production system is straightforward. However, to employ ‘what-if’ analysis requires simulation. If the goal is to maintain certain performance measures at predetermined levels to accommodate the unexpected demand, then a what-if approach, which might require extensive simulation, may not be feasible at the operational level. Therefore, an intelligent decision system is necessary to support management's operational decisions in the short run.

In this paper, we have utilized artificial neural networks to model a highly nonlinear system, a multi-objective nonlinear optimization problem in a manufacturing system having identical parallel machines in each work center, in which the goal is to obtain a solution that is as close as possible to management goals. Management can utilize the proposed framework to effectively manage the capacity of the production plant while achieving certain performance measures. This framework will be beneficial especially in environments where a trained workforce is abundant, or workers can be trained in a short amount of time to perform the required operations. Results from the case study indicate that the artificial neural networks were able to satisfactorily capture the underlying relationship between the design and control parameters of a manufacturing system and the resulting performance targets. Note that the framework presented in this paper not only applies to systems having excess capacity due to low demand but also to systems, in which there is enough demand that makes the capacity a bottleneck. However, in this case, determination of the number of machines might become irrelevant. The framework can then be utilized to find the priority rule and due date determination coefficient in order to have a solution as close as possible to the targeted ones.