Robust‐optimum multi‐attribute age‐based replacement policy

Abstract

Purpose -A common model in the age-based replacement policy is based on the cost attribute and assumes that the model parameters are known. In practice, the model parameters are estimated from limited historical data, which brings uncertainty into the model. Moreover, minimizing the cost is not the only goal of the maintenance activity. From the decision maker's point of view, the multi-attributes and the uncertainty of the age-based replacement policy are two important aspects to take into consideration in the decision-making process. The purpose of this paper is to propose an approach for a robust-optimum multi-attribute age-based replacement policy. Design/methodology/approach -The proposed approach is based on a combination of the multiattribute age-based replacement policy and robust design problem philosophy. A case study is provided for illustrating the application of the proposed method. Findings -It is found that the proposed approach can determine the interval time for preventive replacement that provides a robust and optimum solution for a multi-attribute age-based replacement policy. Practical implications -The proposed approach can be used by the decision maker in determining a robust-optimum interval time for preventive replacement of multi-attribute age-based replacement, a time interval which is not only optimum, but also robust. Originality/value -This paper presents an approach that simultaneously considers the multi-attributes and the uncertainty in the age-based replacement policy which is, to date, not available.

The Economic Management of Physical Assets: The Practical Case of an Urban Passenger Transport Company in Portugal

“…[44] present a new approach to economic models for determining the most appropriate time for replacing equipment in services, which permits the evaluation of the life cycle of the equipment by the managers. Several studies consider the adoption of reliability parameters and maintenance costs to help evaluate more rational replacement decisions [44,45].…”

“…Regarding predictive maintenance versus equipment replacement, and specifically oil analysis, some mathematical models and concepts have been used [12][13][14]22,24,41,44]. On the other hand, many studies have considered reliability parameters and maintenance costs to help evaluate more rational replacement decisions [13,14,22,24,44,45]. It should, however, be noted that there are other tools that may contribute to the development of a new vehicle replacement optimization model, such as fuzzy logic (Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1.…”

Reserve Fleet Indexed to Exogenous Cost Variables

“…In the road transport sector, the efficient use of assets is linked to a well-structured policy of fleet evaluation and replacement. Some cases of fleet replacement applied to urban buses are reported in Keles and Hartman (2004), Khasnabis et al (2002), Jin and Kite-Powell (2000), Scarf and Bouamra (1999), Leung and Cheng (2000), Beichelt (2001), Zohrul Kabir (1996, Wijaya et al (2012), Raposo et al (2014). Campos et al (2010) present a proposal for a generic model of a stochastic process based on neural networks.…”

Untitled

“…In companies of road transport sector, the efficient use of assets is linked to a well-structured fleet assessment and replacement policy. Some cases of fleet replacement applied to the city bus segment are reported in ( [13], [28], [39], [47], [84], [88], [89], [91], [92], [93], [94], [95], [96], [97], [98], [99], [111] and [118]). [13] proposes a policy for optimal replacement intervals for programming technical systems based only on the maintenance cost parameter: a system is replaced by a new one as soon as the maintenance cost within a replacement cycle reaches or exceeds one certain level.…”

“…A numerical solution is proposed and illustrated by [99], using data from a given fleet, they considered a two-cycle replacement model, with decision variables based on the age of replacement of the current fleet, where the size of the new fleet is considered, the optimal values for the decision variables can be found by minimizing the total cost discounted per unit of time or the value of the equivalent income. Many studies consider reliability parameters and maintenance costs to help assess more rational replacement decisions, for example: ( [88], [89], [91], [92], [93], [94], [95], [96], [97], [98], [99], [111] and [118]).…”