دینامیک محصولات و سیستم های کار عمل خودگردان در تولید و مونتاژ

Autonomous production is characterized by local and autonomous decision making of intelligent logistic objects such as work systems that adjust production rates and parts that decide which products they “want” to become and which orders they will fill. It is important to understand and have confidence in dynamic interactions of these objects and their resulting performance. In this paper the dynamic interaction of autonomous products and work systems is investigated using a hybrid simulation model and a control-theoretic model. Results obtained using both models show that these dynamic interactions can be well behaved and predictable. Through linearized models of continuous input flows at nominal rates, tools of control theory are shown to build confidence in complex system dynamic behavior of interacting autonomous logistics objects when decision-making logic is modeled in a way that makes control-theoretic analyses tractable.

It is becoming increasingly difficult to ignore the fact that the complexity and the dynamics of modern production facilities and their connecting supply chains have a major impact on the performance of manufacturing enterprises. It has been shown that dynamic complexity drivers significantly affect the performance of manufacturing plants [1] and that supply chain complexity, including technology and information processing, significantly affects delivery performance [1] and [2]. Questions have been raised regarding the ability of present production planning and control methods to handle this challenge with their centralized control approach [3], [4] and [5]. In recent years, researchers have proposed a paradigm shift from centralized to de-centralized control approaches as a way to cope with this complexity [3]. Concepts of de-centralized control, called autonomous control and characterized by decentralized decision-making in heterarchical systems [4], aspire to provide logistic objects with decision capabilities. The most significant difference between heterarchical and hierarchical systems is that objects can operate independently from each other and have equal rights to access resources [6]. With the highest level of autonomous control, master–slave relationships are eliminated, global information sharing is avoided, and intelligent objects make decisions based on local information and a minimal amount of information obtained from other objects [7]. The underlying hypothesis is as follows: by enabling logistic objects to make decisions on their own, the level of autonomous control rises and the overall achievement of logistic objectives can be increased [4]. These objectives can include short delivery times, high due date reliability, low capital tie-up costs, and desired capacity utilization [8]. Fig. 1 illustrates the hypothesized relationship between degree of autonomous control within a production system and achievement of logistic targets. It is assumed that autonomous control can use the production system's inherent and so far unused flexibility potentials to find better trade-offs between conflicting targets of production logistics, as compared to traditional, hierarchical planning schemes, in particular under variable environmental conditions. Today's production systems operate on a relatively low level of autonomous control. By increasing the level of autonomous control, the remaining logistic potential can be developed and a higher logistic target achievement can be attained. The behavior of these heterarchical systems however, depends strongly on the local decision making logic that is implemented [9]. Unfortunately, without global information, de-centralized decision-making can converge to local rather than global optima. Therefore, it is important to understand the dynamic behavior of autonomous production in order to avoid possible undesired characteristics, such as deadlock situations or the mutual amplification of system adjustments leading to unfavorable system states that diminish performance and to gain confidence in adoption and implementation of the concept.Various concepts have been investigated for autonomous control in production including decision-making based on Internet routing protocols [11] and biological examples such as ants [12] and bees [13]. Levels of autonomy in production logistics have been characterized, including requirements for decision alternatives within production processes [14]. Control-theoretic concepts have been developed for local regulation of work-in-process (WIP) [15] and lead-time [16] by autonomous work systems. The purpose of this work was to study and characterize the dynamic interaction of autonomous products and autonomous work systems. This is of interest because different kinds of logistic objects can pursue individual and possibly conflicting objectives. Products, for example, can try to navigate through production in the fastest possible way by choosing work systems with short waiting queues [13], while work systems can try to maintain ideal WIP [15] because WIP determines capital employed and influences both capacity utilization and throughput times [8]. During production, autonomous products and work systems make a multitude of decisions; hence, as individual logistic objects pursue object-specific goals, their resulting interacting dynamic behavior is of essential importance. First, the concept of autonomous product manufacturing using autonomous work systems is introduced. Simulation results are then used to illustrate dynamic behavior and show proof of concept. After that, a control-theoretic model is presented that permits quantitative characterization of the interacting dynamic behaviors of the logistic objects. Fundamental dynamic properties are derived that are difficult to obtain from simulations and verify the reliability of the interactions and the desirability of the resulting performance. Finally, the results of the simulation studies and control theoretic analyses are compared, and conclusions are drawn regarding the efficacy of pairing autonomous products with autonomous work systems in production, as well as the utility of control-theoretic analysis in this domain.

The dynamic interactions between autonomous logistic objects, specifically autonomous products and autonomous work systems, were studied using both hybrid simulation and control-theoretic analysis. Several scenarios were used to illustrate the dynamics of these interactions in both modeling approaches. Individual products chose the work system with lowest WIP for processing, and individual work systems adjusted their capacity with the goal of maintaining their WIP at a planned level. Decisions made by products therefore affected work system WIP, which was also affected by capacity adjustments made by work systems. Both hybrid simulation and control-theoretic analysis showed that the decision rules used resulted in modeled production results that were dynamically well behaved and reasonable, including effective reactions to disturbances. To make control-theoretic analysis tractable, the decision-making logic used by the autonomous products was approximated by equations modeling fractional work flows to work systems. These equations then were linearized given the nominal rate of work input and nominal WIP in the work systems. This allowed fundamental dynamic properties of the interactions between logistic objects to be predicted and assessed. Responses predicted by hybrid simulation and control-theoretic analyses were similar for the one product variant, two work system example studied; differences were primarily due to neglecting the discrete nature of individual products in the control-theoretic model. The dominating characteristic times of response calculated using control-theoretic analysis were found to be favorable, constant for a range of operating conditions, and in agreement with simulation results. The control-theoretic models provided significant insight into dynamic behavior of the interacting autonomous logistic objects. The fundamental dynamic properties that were derived are difficult to obtain using simulations. They augment the simulations, building confidence in the reliability of the designed interactions and the desirability of resulting performance. The hybrid simulation also was used to study a more complex example that included multiple product variants, multiple work systems, multiple processing steps, and processing flexibility. Also for this example, the interactions between autonomous products and autonomous work systems were well-behaved and reasonable in response to the modeled disturbances. These results provide evidence that paring of autonomous products and autonomous work systems can result in effective autonomous production that is both efficient with respect to the attainment of logistic targets as well as robust against disturbances. Additional research is needed to more fully understand and characterize the dynamic behavior of production systems in which the logistics of product variants and customer orders are handled by autonomous products that share more information, incorporate more complex decision rules, and interact with autonomous machines and work systems that incorporate more complex forms of production regulation and decision making.