Abstract

Estimating the cycle time of each job over event streams in intelligent manufacturing is critical. These streams include many long-lasting events which have certain durations. The temporal relationships among those interval-based events are often complex. Meanwhile, network latencies and machine failures in intelligent manufacturing may cause events to be out-of-order. This topic has rarely been discussed because most existing methods do not consider both interval-based and out-of-order events. In this work, we analyze the preliminaries of event temporal semantics. A tree-plan model of interval-based out-of-order events is proposed. A hybrid solution is correspondingly introduced. Extensive experimental studies demonstrate the efficiency of our approach.

Highlights

  • In recent years, estimating the cycle time of each job over event streams in intelligent manufacturing applications has received a lot of attention from production control, soft computing, and operations management researchers because of its critical role in the competitiveness of intelligent manufacturing [1]

  • 2) We propose a model of interval-based out-of-order events that includes the logical and physical expression of these events

  • 4) We develop a hybrid solution to solve interval-based out-of-order event processing that can switch from one level of output accuracy to another in real time

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Summary

Introduction

In recent years, estimating the cycle time of each job over event streams in intelligent manufacturing applications has received a lot of attention from production control, soft computing, and operations management researchers because of its critical role in the competitiveness of intelligent manufacturing [1]. These event streams are sequences of interval-based events that are temporally ordered or out-of-order.

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