Maintenance and spare parts management are interrelated and the literature shows the significance of optimizing them jointly. Simulation is an efficient tool in modeling such a complex and stochastic problem. In this paper, we optimize preventive maintenance and spare provision policy under continuous review in a non-identical multi-component manufacturing system through a combined discrete event and continuous simulation model coupled with an optimization engine. The study shows that production dynamics and labor availability have a significant impact on maintenance performance. Optimization results of Simulated Annealing, Hill Climb and Random solutions are compared. The experiments show that Simulated annealing achieved the best results although the computation time was relatively high. Investigating multi-objective optimization might provide interesting results as well as more flexibility to the decision maker. INTRODUCTIONMaintenance plays a vital role in sustaining and improving asset availability, which in turn affects the total output production of the system. A large and growing body of literature has investigated the optimization of maintenance in manufacturing systems. Dekker (1996) conducted a comprehensive review of maintenance optimization models and applications. It is interesting to note that simulation optimization was not mentioned in his research, rather, the focus was on mathematical modeling only. As the complexity of maintenance systems has increased, the limitation of mathematical modeling became apparent as many maintenance policies are not analytically traceable (Garg and Deshmukh 2006, Nicolai andDekker 2008). In a recent review, Sharma and Yadava (2011) discussed the published maintenance optimization models. They observed that the use of simulation has been an emerging trend which changed the maintenance view. This is mainly because simulation allows experimentation and better understanding of complex systems (Sebastian 2006). Simulation based optimization (Rogers 2002) is an approach that consists of two main elements: a simulation model and an optimization engine. As shown in Figure 1, the optimization engine provides the simulation model with the decision variables. The simulation runs and provides the optimization engine with the objective function. Depending on the optimization algorithm the optimization engine will conduct an analysis and provide the simulation model with a new set of variables seeking to improve the objective function. This cycle will continue until a condition is met. For example a specific number of evaluations without improvement in the objective function. 1109 978-1-4799-3950-3/13/$31.00 ©2013 IEEE

Purpose – Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues. Design/methodology/approach – This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring. Findings – This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective. Practical implications – The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs. Originality/value – A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

Purpose – The demand for contracts on assets availability has increased. Recently published papers show that the use of asset health monitoring technologies is being encouraged to improve the asset performance. This is based on reason rather than analysis. This paper aims to understand and assess the effect of different types of business processes for maintenance resource levels on the behaviour of the maintenance operations and asset availability located at different customer locations using different asset monitoring levels. Design/methodology/approach – A discrete event simulation (DES) model was developed to mimic complex maintenance operations with different monitoring levels (reactive, diagnostics, and prognostics). The model was created to understand and assess the influence of resources (labour and spare parts) on a particular maintenance operation. The model was created to represent different levels of asset monitoring to be applied in a case study. Subsequently, different levels of spare parts (ranging from deficient inventory to a plentiful spares inventory) and labour were applied to show the effects of those resources on the asset availability. Findings – This research has found that the DES was able to discern different processes for asset monitoring levels in complex maintenance operations. It also provided numerical evidence about applying such asset monitoring levels and proved that the higher asset monitoring level does not always guarantee higher asset availability. Practical implications – The developed model is a unique model that can provide the decision makers of maintenance operations with numerical evidence to select an appropriate asset monitoring level based on their particular maintenance operations. Originality/value – A novel DES model was developed to support maintenance operations decision makers in selecting the appropriate asset monitoring level for their particular operations. This unique approach provides numerical evidence rather than reasoning, and also proves that the higher asset monitoring level does not always guarantee higher asset availability.

In this research, the aim is to find the most appropriate inventory management logic and set of rules along with the optimal decision values that will minimize the bullwhip effect in a supply chain, taking the beer game supply chain as a reference model. In order to achieve this, a simulation model of the beer game supply chain is developed along with an ordering strategy based on the Economic Order Quantity with additional rules, such as no backorder policy, vendor-managed inventory, and taking into consideration route deliveries, all of which are implemented in the ordering algorithm. In the literature, there is extensive research conducted on the causes of the bullwhip effect and in the presence of certain inventory management policies. However, these terms are rarely combined with simulation modeling to provide satisfactory proven results. In this article, our proposed ordering algorithm avoids the bullwhip effect to a very large extent. The results show that approximately half the cost is incurred compared to recent studies with the same settings.

The increasing number of cancer patients, coupled with the development and use of new, efficient medications, has increased the demand for cancer service care. Outpatient chemotherapy clinics (OCCs) are complex facilities, due to the large variability in treatment durations resulting from different cancer types and chemotherapy protocols under limited resources. In this paper we address the operations planning problem encountered by OCCs, of optimally assigning the first days of treatment for a set of new patients in tandem with the presence of existing patients. We propose a two-phase approach to optimise the start days of a set of new patients. In the first stage, a system dynamics (SD) simulation model, adapted from the literature, is used to find the critical days for the new patients -i.e., the upper bounds by which patients must begin treatment. The optimal start days are then determined using a new mixed-integer programming (MIP) model. The results show that the optimal start days can be effectively prioritised and evaluated using simulation and mathematical programming.