A Group Replacement Decision Support System Based on Internet of Things

Liu Liu

doi:10.3390/math7090810

Abstract

This paper combines computer-based monitoring technologies and Internet of things (IoT) technology to develop IoT condition-based group replacement decision support system for a production/service system with numerous parallel independent operating servers. This proposed IoT conditioned-based group replacement decision support system first develops the discounted cost model for a service/production system with numerous independent working servers. The original discounted cost model is further revised into an equivalent model to stimulate the proof procedure by applying the uniformization approach. Several significant theoretical properties are proved and many numerical examples are conducted for two kinds of group replacement policies, respectively. The first class of group replacement policy is developed and proved theoretically that there is a threshold of amount of customers existed to activate the group replacement depending on various amount of operating servers; numerical examples conducted in this study can also illustrate the above theoretical outcomes already derived for the first class of group replacement policy. Besides, for the second class of group replacement policy, the results of numerical examples definitely demonstrate that there is a threshold of the amount of operating servers needed to start the group replacement according to distinct amount of customers in the system. This proposed IoT condition-based group replacement decision support system derives the structure and detailed procedure flow to actually conduct the group replacement operations for many practical service or production systems.

Highlights

To keep production/service system manipulating normally to run production line successfully or offer suitable services for customers is very significant in such customer-oriented business world.On the contrary, the delay of production flow or the loss of customers would have occurred and caused a lot of cost
Several significant theoretical properties are proven and many numerical examples are conducted for two kinds of group replacement policies, respectively
Several numerical examples were programmed in Maple 10 (Version 10, Maplesoft, Waterloo, ON, Canada) and were run on Asus

Summary

Introduction

To keep production/service system manipulating normally to run production line successfully or offer suitable services for customers is very significant in such customer-oriented business world.On the contrary, the delay of production flow or the loss of customers would have occurred and caused a lot of cost. With regard to single-unit systems, different replacement policies including age-replacement [1,2,3,4,5,6,7,8,9,10], preventive replacement [11,12,13,14], failure limit replacement, and block replacement [5] are proposed, combined with minimal repair [5], unplanned replacement, warranty policy [5,13,14], reliability criteria and other options depending on different conditions. Chien [3] establishes a completely renewable age-replacement policy combined with a proportional warranty policy. Lim et al [4] presents age replacement policy depending on imperfect repair with random

Objectives

Results

Conclusion