Ayse Sena Eruguz

A survey of maintenance and service logistics management

A classification of maintenance and service logistics literature for capital goods.The key characteristic of the maritime sector is used as a guideline.State-of-the-art, lessons learned, and research directions for each characteristic.A big part of this survey is relevant also for other capital-intensive industries.A research agenda is suggested. Maintenance and service logistics support are required to ensure high availability and reliability for capital goods and typically represent a significant part of operating costs in capital-intensive industries. In this paper, we present a classification of the maintenance and service logistics literature considering the key characteristics of a particular sector as a guideline, i.e., the maritime sector. We discuss the applicability and the shortcomings of existing works and highlight the lessons learned from a maritime sector perspective. Finally, we identify the potential future research directions and suggest a research agenda. Most of the maritime sector characteristics presented in this paper are also valid for other capital-intensive industries. Therefore, a big part of this survey is relevant and functional for industries such as aircraft/aerospace, defense, and automotive.

Optimising reorder intervals and order-up-to levels in guaranteed service supply chains

We consider the problem of determining the optimal reorder intervals R and order-up-to levels S in a multi-echelon supply chain system where all echelons are assumed to have fixed ordering costs and to operate with a (R, S) policy with stationary nested power-of-two reorder intervals. By using the guaranteed service approach to model the multi-echelon system facing a stochastic demand, we formulate the problem as a deterministic optimisation model in order to simultaneously determine the optimal R and S parameters as well as the guaranteed service times. The model is a non-linear integer programming (NLIP) problem with a non-convex and non-concave objective function including rational and square root terms. Then, we propose a sequential optimisation procedure (SOP) to obtain near-optimal solutions with reasonable computational time. The numerical study demonstrates that for a general acyclic multi-echelon system with randomly generated parameters, the SOP is able to obtain near-optimal solutions of about 0.46% optimality gap in average in a few seconds. Moreover, we propose an improved direct approach using a global optimiser, bounding the decision variables in the NLIP model and considering the SOP solution as an initial solution. Numerical examples illustrate that this reduces significantly the computational time.

A Review of the Guaranteed-Service Model for Multi-Echelon Inventory Systems

Guaranteed-Service Model (GSM) approach for the safety stock placement problem aims at finding the optimal placement of safety stocks in a multi-echelon inventory system under bounded demand and guaranteed service times. Although this model has been present for more than fifty years, the last decade witnessed a growing interest. Recent works include new versions of the problem with relaxed assumptions and efficient solution methods for different supply chain network topologies. The purpose of this paper is to review and summarize the literature on the GSM and to suggest future research directions.

Cycle-Service-Level in Guaranteed-Service supply chains

The Guaranteed-Service Model (GSM) aims at determining the optimal placement and amount of safety stocks in a guaranteed-service supply chain. In the GSM, safety stocks are used to protect against demand variability up to specified demand bounds. The model does not address what happens when demand exceeds these bounds while in practice, companies might take extraordinary measures to handle the excess demand. For guaranteed-service supply chains, assessing the service level that results from carrying safety stocks is crucial to estimate the real frequency of extraordinary measures to be deployed. In the GSM literature, demand bounds are usually defined in terms of Cycle-Service-Level (CSL). Simulation studies we carried for realworld supply chains show that the effectively observed CSL at final customer stages may be less than the one used to define the demand bounds and the absolute gap may be up to 4.76%.

Survey in Operations Research and Management ScienceA survey of maintenance and service logistics management: Classification and research agenda from a maritime sector perspective

A classification of maintenance and service logistics literature for capital goods.The key characteristic of the maritime sector is used as a guideline.State-of-the-art, lessons learned, and research directions for each characteristic.A big part of this survey is relevant also for other capital-intensive industries.A research agenda is suggested. Maintenance and service logistics support are required to ensure high availability and reliability for capital goods and typically represent a significant part of operating costs in capital-intensive industries. In this paper, we present a classification of the maintenance and service logistics literature considering the key characteristics of a particular sector as a guideline, i.e., the maritime sector. We discuss the applicability and the shortcomings of existing works and highlight the lessons learned from a maritime sector perspective. Finally, we identify the potential future research directions and suggest a research agenda. Most of the maritime sector characteristics presented in this paper are also valid for other capital-intensive industries. Therefore, a big part of this survey is relevant and functional for industries such as aircraft/aerospace, defense, and automotive.

Integrated maintenance and spare part optimization for moving assets

ABSTRACT We consider an integrated maintenance and spare part optimization problem for a single critical component of a moving asset for which the degradation level is observable. Degradation is modeled as a function of the current operating mode, mostly dictated by the actual location of the moving asset. The spare part is stocked at the home base that the moving asset eventually visits. Alternatively, the spare part can be stocked on-board the moving asset to prevent costly expedited deliveries. The costs associated with spare part deliveries and part replacements depend on the operating mode. Our objective is to minimize the expected total discounted cost of spare part deliveries, part replacements, and inventory holding over an infinite planning horizon. We formulate the problem as a Markov decision process and characterize the structure of the optimal policy, which is shown to be a bi-threshold policy in each operating mode. Our numerical experiments show that the cost savings obtained by the integrated optimization of spare part inventory and part replacement decisions are significant. We also demonstrate the value of the integrated approach in a case study from the maritime sector.