* Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

Toyota production system : beyond large-scale production

Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided.

Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey

In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling.

The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.

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A survey of dynamic scheduling in manufacturing systems

http://akturk.bilkent.edu.tr/ie573/aytug.pdf

Executing production schedules in the face of uncertainties: a review and some future directions

In cloud computing, cloud providers can offer cloud consumers two provisioning plans for computing resources, namely reservation and on-demand plans. In general, cost of utilizing computing resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total resource provisioning cost. However, the best advance reservation of resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' resource prices. To address this problem, an optimal cloud resource provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing resources for being used in multiple provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of resource provisioning in cloud computing environments.

Optimization of Resource Provisioning Cost in Cloud Computing

CABINS: a framework of knowledge acquisition and iterative revision for schedule improvement and reactive repair

In order to solve a real-time scheduling problem, a computationally intensive search-based optimization method is not practical, but the efficient dispatching rule that is well-customized for the specific problem at hand can be an effective problem solving method. A dispatching rule is scheduling heuristics that decide the sequence of operations to be executed at each resource in the scheduling problem. However, developing a customized dispatching rule for specific scheduling problems is an arduous task even for domain experts or researchers in the scheduling problem. In this research, the author views scheduling problems as multi-agent problem solving and proposes an approach for synthesizing the dispatching rule by means of Genetic Programming (GP). In the preliminary experiments, the author got the results showing that GP-based multi-agent dispatching scheduler outperformed the well-known dispatching rules.

Job-shop scheduling with genetic programming

This paper describes a human gait animation system with a precise neuromusculoskeletal model and evolutionary computation. The neuromusculoskeletal model incorporates 14 rigid bodies, 19 degrees of freedom, 60 muscular models, 16 pairs of the neural oscillators, and other neuronal systems. By changing the search parameters and the evaluative criteria of the evolutionary search process, we demonstrate various locomotive patterns, such as normal gait, pathological gait, running and ape-like walking. The proposed simulation system takes not only kinematic data but also in vivo dynamic data such as energy consumption information into consideration, so that the resultant locomotion patterns are natural and valid from a biomechanical point of view. Hence the simulation system can also be used for finding a biologically appropriate physical model to realize a desired gait by simultaneously modifying the body dynamics parameters with the neuronal parameters. This capability creates a novel application of human gait simulation systems, such as rehabilitation tool design and consultation for physically handicapped people. Copyright © 2003 John Wiley & Sons, Ltd.

Human gait simulation with a neuromusculoskeletal model and evolutionary computation

A new integrated architecture for distributed planning and scheduling is proposed that exploits constraints for problem decomposition and coordination. The goal is to develop an efficient method to solve densely constrained planning/scheduling problems in a distributed manner without sacrificing solution quality. A prototype system (CAMPS) was implemented, in which a set of intelligent agents try to coordinate their actions for ‘satisfying’ planning/scheduling results by handling several intra- and inter-agent constraints. The repair-based methodology for distributed planning/scheduling is described, together with the constraint-based mechanism of dynamic coalition formation among agents.

CAMPS: a constraint-based architecturefor multiagent planning and scheduling

Adding knowledge to a knowledge-based system is not monotonically bene cial. We discuss and experimentally validate this observation in the context of CABINS, a system that learns control knowledge for iterative repair in ill-structured optimization problems. In CABINS, situation-dependent user's decisions that guide the repair process are captured in cases together with contextual problem information. During iterative revision in CABINS, cases are exploited for both selection of repair actions and evaluation of repair results. In this paper, we experimentally demonstrated that unltered learned knowledge can degrade problem solving performance. We developed and experimentally evaluated the e ectiveness of a set of knowledge ltering strategies that are designed to increase problem solving e ciency of the intractable iterative optimization process without sacri cing solution quality. These knowledge ltering strategies utilize progressive case base retrievals and failure information to (1) validate the e ectiveness of selected repair actions and (2) give-up further repair if the likelihood of success is low. The ltering strategies were experimentally evaluated in the context of job-shop scheduling, a well known ill-structured problem.

/pdf/improving-system-performance-in-case-based-iterative-2uyx1wbxos.pdf

Kazuo Miyashita

Papers

CABINS: a framework of knowledge acquisition and iterative revision for schedule improvement and reactive repair

Job-shop scheduling with genetic programming

Human gait simulation with a neuromusculoskeletal model and evolutionary computation

CAMPS: a constraint-based architecturefor multiagent planning and scheduling

Improving system performance in case-based iterative optimization through knowledge filtering