Vittaldas V. Prabhu

Nonlinear dynamics in distributed arrival time control of heterarchical manufacturing systems

Heterarchical control architectures with fully distributed control have been developed in order to improve responsiveness and effectiveness of manufacturing shop-floor control systems. The dynamics of these highly distributed systems have been difficult to predict particularly when control is based on heuristics. In this paper a dynamical model is developed for a single machine processing an arbitrary number of parts. The structure of the system, which requires queuing of parts when they arrive at a machine, leads to nonlinearities such as dead-zone and discontinuities. A continuous arrival time controller of the integrating type is used that results in a system that can be modeled using nonlinear differential equations that can be solved using a method due to Filippov (1960, 1988). This enables prediction of trajectories of part arrival times and derivation of closed form expressions for steady-state values. The analytical model for the dynamics is validated and the dynamic response of the system is illustrated using numerical simulation.

Performance of real-time distributed arrival time control in heterarchical manufacturing systems

A highly distributed feedback control algorithm for autonomous part entities in heterarchical manufacturing systems is presented in this paper. A difference equation-based model is developed to analyze the discrete time dynamics of the resulting nonlinear control system. Control parameters are found analytically that guarantee that the system is bounded under disturbances. The dynamic response to: (i) changes in due dates; and (ii) the bulk arrival of parts is presented. The ability of the system to exponentially reduce the mean and variance of due-date deviation in the single machine case makes it an attractive option for real-time control of just-in-time production.

Modeling green factory physics — An analytical approach

There is a need for energy-aware models of manufacturing systems that link the physics of energy consumption at the individual machine-level to the energy consumption at the factory-level. Such energy-aware models would enable analysis of green factory designs, especially for evaluating alternatives during early design stages. This paper proposes to leverage existing analytical models based on queuing theory to include energy control for waste reduction. Specifically we propose analytical models for single server and serial production lines by extending the basic M/M/1 model with energy control policy for managing idle time power consumption. These analytical models can be readily used to estimate reduction in energy waste for different production and power parameters. Simulation experiments are used to test the robustness of the analytical models by using exponential, normal, hyper-exponential and hypo-exponential distributions. Results show that the energy consumption estimated by the analytical models differ less than 10%, indicating that the proposed models are reasonably robust.

Stable fault adaptation in distributed control of heterarchical manufacturing job shops

In this paper, a control theoretic model is developed for analyzing the dynamics of distributed cooperative control systems for manufacturing job shops with multiple processing steps with parallel dissimilar machines in which parts control their release times autonomously. The model allows an arbitrary number of part types to be produced using an arbitrary number of machines with an arbitrary number of alternate routings. Conditions for global stability of the resulting distributed control system with nonlinearities are shown using results from Lyapunov stability theory. System stability is found to be robust to a variety of faults and disturbances that may be encountered in a manufacturing environment as long they are bounded in the mean. Feedback enables implicit adaptation to faults in real time, which allows the flexibility in the systems to be fully utilized to compensate for faults and disturbances. Numerical simulation experiments are used to illustrate the global stability and the distributed fault adaptation capability of the system without requiring explicit notification or compensation to conditions such as machine deterioration, multiple machine failures, and network communication delays. Simulation results for job shops with 2000 parts are also presented to illustrate the scalability of the approach.

Integrated process control and condition-based maintenance scheduler for distributed manufacturing control systems

Competitive enterprises resort to decentralization and distributed structures to manage and control complexity. Integrated decision-making within these autonomous controllers can enhance the performance of these systems. In this paper, it is hypothesized that a significant improvement in performance can be realized by taking process control decisions in conjunction with condition monitoring and machine maintenance decisions in a distributed environment. An architecture is proposed for unifying process control and condition-based maintenance scheduling. Sensory information indicative of the current condition of the machine is also provided to autonomous controllers to enable estimation of the remaining useful lifetime of the machine. Estimates of the remaining lifetime of the machine and the scheduled maintenance time are then used to adjust the set points for the operating parameters and modify the maintenance schedules for the maintenance crew, respectively. The proposed architecture is presented and exemplified using a case study of laser-based free-forming process. The results indicate that the architecture can adjust the operating parameters and develop new maintenance schedules based on the changes of the machine condition.

Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach

Abstract Heterarchical FMS control architectures localize decisional capabilities in each entity, resulting in highly reactive, low complexity control architectures. Unfortunately, these architectures present myopic behavior since decisional entities have limited visibility of other decisional entities’ behavior and the alignment of an entity’s decision with the system’s global objective. In this paper, we propose a semi-heterarchical architecture in which a supervisor tackles different kinds of myopic decisions using simulation–optimization mechanisms and the current conditions of a flexible manufacturing system (FMS). The supervisor uses simulation results to calculate local and global performances and to evolve the solutions proposed by the optimization mechanisms. The approach proposed was configured to control a real assembly cell with highly heterarchical approaches. The completion time variance was used as the performance measure for myopic behavior reduction. The simulation results showed that the semi-heterarchical architecture can reduce myopic behavior whereby it strikes a balance between the ability to react to disturbances and maintaining low complexity, thus making it suitable for production control.

Energy-aware manufacturing operations

This editorial introduces the special issue on energy-aware manufacturing operations in the International Journal of Production Research. The 12 papers in this special issue were selected because of their high quality and also because they deal with topics related to energy-aware manufacturing operations. Three broad challenges are collectively addressed by the papers in this special issue: energy-efficiency vs. manufacturing-system effectiveness in optimisation; the volatility in energy availability, supply and cost; modelling energy consumption in varying scales and across different sub-systems. Previous global discussions about the state of the art in energy-aware manufacturing operations are provided, as well as exploratory guidelines for future research in this area.

A vector space model for variance reduction in single machine scheduling

Reducing the variance of part completion times about promised due dates is an important element of Just-In-Time production because it reduces the work-in-process inventory and tardiness simultaneously. Scheduling models and algorithms are developed to minimize the Mean Squared Deviation (MSD) of completion times about due dates on a single machine. A generic model is developed in real vector space for understanding the structural relationship between the optimal schedule and the location of the due dates. Geometric insights gained from this vector space model are used to relate the shortest and longest processing time sequences to the level of difficulty of the MSD optimization problem. The vector space model is used to develop dominance conditions for a branch and bound algorithm and to analytically synthesize parameters for a continuous variable feedback control algorithm for distributed scheduling. The control algorithm lends itself to massively parallel / distributed computation and is found to produce near optimal solutions efficiently, which makes it more scalable and practical compared to the branch and bound algorithm. Computational experiments with both approaches are presented.

Modelling and analysis of energy footprint of manufacturing systems

Increasing the energy efficiency of manufacturing plants will reduce the production costs and environmental impact. In order to analyse and improve the energy efficiency of manufacturing plants, however, we need models to evaluate the energy footprints of the plants. A key challenge of estimating plant-level footprints is that systemic methods of connecting information on the product, machine and plant levels are not available. Thus, we propose methods to parameterise product-level elements and to model machine-level factors based on those elements. From the machine-level models, the proposed approach performs simulation experiments and provides the energy footprints in closed-form equations for the plant level. We also suggest that the resulting model can be combined with probabilistic techniques to benchmark the energy efficiency of plants at the industry level. In a case study, we demonstrate how to apply the proposed methods to estimate the energy footprint of a hypothetical plant. The procedures introduced here enable manufacturers to evaluate the energy consumption of their facilities at early stages of manufacturing, and provide tools to assess the energy efficiency of their plant by comparison with peers.

Sustainability in Manufacturing Operations Scheduling: Stakes, Approaches and Trends

In this paper it is explained how manufacturing operations scheduling can contribute to sustainability. For that purpose, the relevant stakes are first presented. Sustainable manufacturing operations are then characterized. Different forms of sustainability in manufacturing operations scheduling are pointed out and some illustrative contributions are positioned.