On this basis, two important structures, i.e., the control structure of autonomous objects and the structure of cloud computing, must be described and their adjustabilities have to be defined. Among several potential objects for autonomy in logistics and production operations, pallets (fixtures, bins, etc.) as the closest transport means to single products in assembly shop-floors are selected to be explored for autonomous control. The control structure of such objects in a complex environment of material flows can be chrono- logically classified into seven formats as following.
First, the simple conventional control structure with each single pallet following the preplanned schedule, done by a central controller in an offline manner. In this structure, the center is responsible for controlling the accurate execution of the plan. Here, unseen events and rescheduling are time-consuming and proble- matic occasions which is not suitable for mass-customization with real-time flexibility, see Figure 1.
Figure 1: Conventional, hierarchical, and central scheduling and control
Second, the interconnectivity of engaged objects, i.e., pallets and machines, causes a better control structure by means of the central controller. In this case, the center has real-time information about the condition of stations and pallets. Once an unseen event happens in the pre-scheduled operations or in the state of the machines, it can be centrally controlled and rescheduled. Through control and monitoring the required rescheduling takes place regar- ding the real-time state of machines, see Figure 2.
Autonomous Control in Fully Modular Production Systems with Contribution of Cloud Computing
Production Engineering Planning and Control of Production Systems 37 Figure 2: Real-time monitoring of conditions, prompt scheduling
with hierarchy and centralized control
Third, realization of decentralized control done by each single pallet, based on bilateral negotiations. In this type, the initial schedule of each pallet for operations can be rendered by itself or be given from a center. But the final real-time schedule (control) is done thanks to a fully interconnected structure among pallets and machines. Derived from the existing knowledge inside each object or given from a center, a primary operations’ sequence is generated. However, bilateral negotiations in real-time between the service-provider (machines) and service-consumer (pallets) cause subjective rescheduling between the conflicting plans, although being coordinated by single machines. In this type, there is no need to distribute the knowledge or control means to distributed pallets, but the fixed machines can take on this responsibility; so that less technological impediments occur. Indeed, the mission of the central controller is distributed to the stations, so that each decides on the feasible schedule regarding its current state and communicating pallets, see Figure 3.
Figure 3: Real-time agent-based negotiation between service-provider and service-consumer
Fourth, totally autonomous plan and control just by pallets, based on own knowledge and without any consideration of others’ inten- tions. This type reflects fully self-organized objects with learning and, thereupon, real-time decision-making capabilities in a totally heterarchical and decentralized circumstance. Distributed proces-
sors with fairly huge computation capability between pallets are the technical disadvantage of this model, while real-time awareness from counterparts is missing as well. The only resource for decision- making in this case is the self-knowledge derived from learning, see Figure 4.
Figure 4: Self-organized objects with learning, real-time decision-making under heterarchy and decentralization
Fifth, self-organized objects with learning and negotiation capa- bility for real-time decision-making. Here, the autonomous pallets have the possibility to negotiate with other autonomous objects, so that a common consensus about unique resources can be achieved. Despite positive awareness of the others’ intentions, realization of this model requires a very precise regulation in the negotiations, see Figure 5.
Figure 5: Self-organized objects with learning and negotiation for real-time decision-making
Sixth, a nonconventional central controller for distributed and autonomous objects. In this structure, the problem of realizing decentralized control with autonomous objects can be solved via cloud computing. The model of the central controller is kept from conventional systems, but the roles of objects and center are diffe- rent; the center is a cloud. Here, the major computational loads are allocated to the cloud as a central computational resource, while
each distributed autonomous object has just the minimum requi- rement for processors (e.g., digital tag) to transact with the center. Autonomous objects are fully self-organized in this model, but the advantages of a conventional central controller can be achieved as well. The central cloud has the information of all connected objects and can be used not only as a computational resource, but also as a common platform for coordinating heterogeneous targets. See Figure 6.
Figure 6: Autonomous objects with suggestion-making and negotiation (coordination) via cloud central controller
Seventh, realization of decentralized control done by each single pallet, based on clusters and constructive recommendations. This model is twofold; it facilitates and can be facilitated by modular systems. While every autonomous unit makes its own decision about how to proceed in the complex system to achieve its temporal objective, some federations seem beneficial to reduce diverse performances for a common transient target of interrelated objects. Meanwhile, making constructive suggestions by auto- nomous objects about their upcoming processes contributes to the reduction of complexity in controlling and handling material flows. Thanks to full interconnectivity, autonomous objects with similar procedures and goals, instead of competing ones, collaborate with each other; by means of building temporal clusters. For instance, pallets with similar semi-finished products and comparable orders can contribute to a common cluster and render an optimal sche- dule for the clusters’ members. Local experience of each unit in the cluster and purposeful exchange of information between objects reduces the decision- making time and increases productivity. Con- sequently, autonomous units in the swarm of modules have to be connected to their stakeholders. Here, the role of the cloud is quite positive in a model, where modules and autonomous units can collaborate with each other for building clusters and consensuses, see Figure 7.
Figure 7: Realization of virtual cooperation clusters regarding common temporal resources