A New Multi Echelon Repair Network Model with Multiple Upstream Locations for Level of Repair Analysis Problem

Level of repair analysis (LORA) determines (1) the best decision during a malfunction of each product component; (2) the location in the repair network to perform the decision and (3) the quantity of required resources in each facility. Capital goods have long life cycles and their total life cycle costs are extremely high. LORA, which can be done repeatedly during the life cycle of the product, both at design and product support phase, plays an important role in minimising the total life cycle costs of capital goods. It is mostly applied to systems that operate in different geographical areas and deployed in different regions, which include different subsystems with special technology and expertise, and have a complex product structure. In this study, we propose a new mathematical model to the LORA problem, which is more comprehensive and flexible than the other pure LORA models in the literature. The proposed model uses the multiple upstream approach that allows the transfer of the components from a location in the lower echelon to the predefined locations in the upper echelon and determines the material movement paths between each facility, defining the facilities’ locations in the repair network. The performance of the proposed model is tested on benchmark instances and the results are compared with the single upstream model. Computational experiments show that the proposed model is more effective than the single upstream model and reduces the total life cycle costs by 4.85% on average, which is an enormous cost saving when total life cycle costs of capital goods are considered.

A Multi-Objective Mathematical Model for Level of Repair Analysis with Lead Times and Multi-Transportation Modes

In the event of failure of the product, level of repair analysis (LORA) is used to determine (1) whether the defective component should be discarded or repaired and (2) where this repair is made. In the literature, these repair operations are made with the aim of minimizing the total life cycle cost of the product. In this paper, we develop a multi-objective decision model that minimizes both the repair time (affected by lead times) and the repair costs. Our proposed model also considers the movement of the defective components to be performed by multiple transportation modes such as highway, railway, and airway. We use the epsilon constraint method to generate the Pareto frontier and analyze the trade-off between total repair costs and total repair time. We demonstrate the approach on an example problem.

Joint Optimization Method of Spare Parts Stocks and Level of Repair Analysis Considering the Multiple Failure Modes

For the repair level and spare parts stocking problems, generally METRIC type methods and Level of repair analysis (LORA) are used separately. Since LORA does not consider the availability of capital goods, solving LORA and spare parts stocking problems sequentially may lead to suboptimal solutions. On these considerations, this study presents a joint optimization method to minimize the service logistics cost under the constraints of system availability. Maintenance capability factor and maintenance decisions are introduced into the joint optimization model to express the influence of multiple failure modes on repair level and spare parts stocking. Thus, we establish the bridge relationship between LORA and METRIC models. The joint optimization model is solved by an improved iterative algorithm, and a typical fleet system is taken as an example to verify the correctness and effectiveness of the model and the algorithm. Compared with the optimization of spare parts inventory and maintenance level independently, the joint optimization method could effectively reduce the service logistic system cost.

Simulation-based joint optimization of fleet system modularity and level of repair decisions considering different failure rates of components

PurposeModularization and level of repair analysis for fleet system influences every phase of the system life cycle. Modular based fleet system design raises new issues since the maintenance/repair services introduces further requirements than traditional product engineering. The decision of modular system and level of repair plays an important role to reduce the Life Cycle Costs (LCC) of fleet maintenance system. The concept of modularity has been extended to services in maintenance for the varieties of fleet systems such as wind turbines, gas turbines, advance machine tools and aircrafts etc. System modularity allows the designers to use of different design alternatives and ease of fault diagnosis, repair and services. The purpose of this paper to develop a joint optimization approach for optimal selection of modular design and level of repair decisions. Usually these two decisions are taken separately.Design/methodology/approachIn the proposed joint approach, level of repair analysis is used to obtain the optimal modular design decisions with reduced life cycle cost. In the existing research, the effect of system modularity on the level of repair decisions is investigated. The simulation-based approach is used to solve this joint problem. Which is rarely seen in the existing literature. A genetic algorithm-based simulation is used to investigate the joint problem. The proposed approach also evaluates all the possible configurations of modular design to justify the integrated effect of modularity and maintenance decisions, that is Level of Repair (LOR).FindingsThis paper highlights interactive effect of system modularity and level of repair decisions for the system operated in multi-echelon maintenance network. A comparative study is provided on effect of system modularity and level of repair decisions considering the time dependent failure rate and constant failure rate of the system components. A simulation based joint approach is used to solve this problem. The results obtained from the investigation are shown that modularity plays an important role to allocate modularity and level of repair decisions for the fleet system. The novelty of this research work is to identify the role of modularization to optimizing the level of repair decisions. The models, that is time-dependent failure rate and constant failure rate presented in this study provides more practical approach to deal the modularity and level of repair analysis.Research limitations/implicationsThe proposed joint approach illustrates using a numerical case of a mechanical system operated at fleet level. More modular structure in terms of number of modules in the machine may be presented for an industrial case. Additionally, the joint approach can also be extended for the any other consumer product and system. But, the prime motive of the paper is to highlights the importance of the modular design while selecting the level of repair decisions.Originality/valueThis is the first work which consider the joint optimization of modular design and level of repair analysis to the best of authors knowledge. Present paper is a more practical approach for identifying the modular design and level of repair decisions for the system operated at fleet level.