Autonomous control of production networks using a pheromone approach

Optimizing manufacturing systems consists in generating large-quantity outputs to fulfill customers demands. But naturally machines may fail and the production process is either slowed down or completely interrupted. In order to keep production running, we are interested in assigning repair crews to currently broken-down machines. But due to the limited repair capacity and the dynamics involved in the production process, we propose a scheduling problem based on ordinary differential equations for the description of buffer levels and the actually available processing capacity. We discuss properties of the model and present a solution approach leading to a mixed-integer programming model.

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“…where ui(t) τi ≈ Bf (t), see details in [1,14]. In summary, we consider the following ordinary differential equation for the buffer levels:…”

Section: Model Descriptionmentioning

confidence: 99%

Production systems with limited repair capacity

Göttlich

Herty²,

Ringhofer

et al. 2012

Optimization

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“…Equation (1) represents an approximate relaxation model where the actual depletion of the inventory in finite time is replaced by an exponential decay to zero.…”

Section: The Model 21 the Supplier Modelmentioning

confidence: 99%

“…The inventory model used here essentially corresponds to replacing the inventory by a processor with a very short cycle time ε, and then using a version of the model for a production unit developed in [2] and [10]. Similar simple inventory models have been used in [1] and [3] in a different context.…”

Section: The Model 21 the Supplier Modelmentioning

confidence: 99%

“…it cannot process more than µ∆t items in the infinitesimal time interval ∆t. We use a generalization of the model employed in [1] to incorporate this additional limitation. The inclusion of payments in the model allows for the definition of capital flows through the network and, consequently, for the occurrence of bankruptcies if the capitalization of a node in the network falls below a certain threshold.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of order policies in supply networks

Göttlich

Herty

Ringhofer

2010

European Journal of Operational Research

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The purpose of this paper is to develop a model which allows for the study and optimization of arbitrarily complex supply networks, including order policies and money flows. We propose a mathematical description that captures the dynamic behavior of the system by a coupled system of ordinary differential delay equations. The underlying optimization problem is solved using discretization techniques yielding a mixed-integer programming problem.

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“…Therefore, research is needed into a mathematical description of the possibly complex behaviour of autonomous assembly systems [10]. Mathematical closed-form solutions such as controltheoretic models [23], general flow models [6], [78], Markov chain models and queuing models are good candidates [10], [115]. One of the first approaches for dynamic reconfiguration of manufacturing systems is the Biological Manufacturing System (BMS) concept ( Figure 7).…”

Section: Layout Designmentioning

confidence: 99%

Autonomous Processes in Assembly Systems

2007

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Today's assembly systems have to be flexible to adapt quickly to an increasing number and variety of products and changing demand volume. To manage these dynamics, flexible, reconfigurable, and autonomous assembly systems were proposed and partly realised in the last two decades. The flexibility and adaptability is realised by clustering the assembly system into subsystems and modules which get a certain degree of autonomy and control themselves in a decentralised way. This keynote paper will present the general principles of autonomy and the proposed concepts, methods and technologies to realise autonomous processes in assembly systems. Different approaches for design and autonomous operation of assembly will be explained and future trends towards fully autonomous components of an assembly system as well as autonomous parts and products will be discussed.

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Autonomous control of production networks using a pheromone approach

Cited by 85 publications

References 12 publications

Production systems with limited repair capacity

Production systems with limited repair capacity

Optimization of order policies in supply networks

Autonomous Processes in Assembly Systems

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