Maintenance optimisation modelling to inform engineering capability investment
Lead Research Organisation:
University of Southampton
Department Name: Southampton Business School
Abstract
With highly complex naval ships having a lifespan in excess of 25 years, the through-life cost of maintaining a modern fleet represents a substantial burden on the customer future budget allocation. Maintenance optimisation and the ability to feed experience back in to the design management loop are imperative in ensuring the competitiveness and future enhancement of the Fleet Service business.
The research will address the following objectives:
1. To develop a systems view of the current process and information flows. Methodologies might include soft systems methodology, causal mapping and system dynamics.
2. Review the current state of the art in engineering maintenance of ships
3. Build mathematical models to optimise the maintenance process. Methodologies might include mathematical programming, data envelopment analysis, search heuristics, discrete event simulation, multiple-criteria decision analysis and analytic hierarchy process.
4. Build a simulation or system dynamics model of the system incorporating the optimised processes to evaluate impact and improvement over current practice.
The research will address the following objectives:
1. To develop a systems view of the current process and information flows. Methodologies might include soft systems methodology, causal mapping and system dynamics.
2. Review the current state of the art in engineering maintenance of ships
3. Build mathematical models to optimise the maintenance process. Methodologies might include mathematical programming, data envelopment analysis, search heuristics, discrete event simulation, multiple-criteria decision analysis and analytic hierarchy process.
4. Build a simulation or system dynamics model of the system incorporating the optimised processes to evaluate impact and improvement over current practice.
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/P510646/1 | 01/10/2016 | 30/09/2021 | |||
| 1928376 | Studentship | EP/P510646/1 | 01/10/2017 | 30/09/2021 | Marios Kremantzis |
| Description | Data Envelopment Analysis (DEA) is a well-established approach for evaluating the performance of Decision-Making Units (DMUs). Conventional DEA uses single-stage models that ignore the processes inside a DMU. More recent research aims to address this by using 2-stage DEA models to help detect sources of inefficiency. A typical 2-stage DEA structure is serial, whereby the first stage produces intermediate measures which form the inputs to the second stage. This paper puts the spotlight on the 2-stage structure proposed by Yu and Shi (2014) with additional inputs in the second stage and part of intermediate measures as final outputs, which we will henceforth refer to as a "generalised" 2-stage DEA structure. We employ additive-based efficiency measurement and decomposition to formulate a CCR self-evaluation DEA model. The study also contributes to the literature by proposing a Goal Programming-Multiple Criteria DEA (GP-MCDEA) cross-efficiency model as an alternative secondary goal to deal with the non-unique optimal multipliers of the afore-mentioned model. Additionally, the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is applied to delineate the final weights assigned to the individual cross-efficiencies to obtain the ultimate cross-efficiency scores; this alternatively solves the aggregation problem in cross-efficiency. Finally, a novel relational Nash bargaining game common-multipliers DEA model is introduced for a fairer assessment of the DMUs, both in the entire system and its constituent parts. An application illustrates the applicability of the proposed models, as well as that the proposed GP-MCDEA and Nash efficiency models help to enhance the weighting scheme and the discriminatory power in this generalised 2-stage DEA system. |
| Exploitation Route | The outcomes of this funding might be proved useful in conditions under which a performance measurement, evaluation, selection and ranking of a number of decision-making units is necessary. |
| Sectors | Aerospace, Defence and Marine,Retail,Transport,Other |