Abstract
This article presents a model of performance analysis for a truck fleet system in an openpit mine, considering special characteristics of haul fleets. In these systems, the expected availability of each piece of equipment and its operating capacity are the fundamental variables to construct a global fleet performance function. Our analytical algorithm considers heterogeneous fleets with known individual characteristics of transport capacity and failure and repair behavior. The results converge to a new indicator denominated “Equivalent Availability” (EA), which arises from the need to evaluate the capacity of the truck fleet to operate at a lower payload than required using different combinations of equipment to achieve an availability goal. EA is a key indicator to determine the productive capacity of a process, and for selecting equipment and their combinations to achieve production objectives. To exemplify the potentialities of the EA, a case study is implemented in a Chilean copper truck fleet mining process.
Highlights
In industrial processes, greater flexibility means better productivity, efficiency, and general results [12]
We present a new methodology for evaluating the availability and production level of complex systems, which represents load-sharing configurations with overcapacity and flexible work levels
We develop this methodology for systems that operate at less loads than required, which has not been previously addressed. We applied this methodology to a case study that makes understanding and application easier and presents significance, since it allows systems dimensioning, including the equivalent availability (EA) Index as a key variable
Summary
Greater flexibility means better productivity, efficiency, and general results [12] In this context, modelling systems in dynamic conditions have great importance in productive processes modelling, especially in those with multi-products, multi-configurations, and fleets [11, 24, 30] A system is considered dynamic when its characteristics and logical or capacity configuration change over time [9]. A small improvement in transportation costs or efficiency percentage can produce meaningful savings The latter motivates the development of analytical proposals that are capable of defining and quantifying the performance of a fleet, without losing sight of its complex characteristics. Jenab and Dhillon [14] employ a Markov chain to analyse the availability and reliability for K of n reversible multi-state systems for identical and independent component conditions. Alternatives for solving the problem of dynamic systems reliability calculation [26] and their respective restrictions are described as follows: 1. Markov chains: Common with a maximum number of analysed equipment as a restriction
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Eksploatacja i Niezawodność – Maintenance and Reliability
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
