Agent-Based Decision Support in Maintenance Service Operations Per Hilletofth1*, Lauri Lättilä2, Sandor Ujvari3, and Olli-Pekka Hilmola4 1* Corresponding author, Logistics Research Centre, School of Technology and Society, University of Skövde, SE-541 28 Skövde, Sweden, E-mail: [email protected] 2 Lappeenranta University of Technology, Kouvola Unit, Finland, Prikaatintie 9, FIN- 45100 Kouvola, Finland, E-mail: [email protected] 3 Logistics Research Centre, School of Technology and Society, University of Skövde, SE-541 28 Skövde, Sweden, E-mail: [email protected] 4 Lappeenranta University of Technology, Kouvola Unit, Finland, Prikaatintie 9, FIN- 45100 Kouvola, Finland, E-mail: [email protected] Abstract In this research an agent-based decision support system for service related maintenance has been developed, the maintenance planning is complex including corrective and preventive tasks of several non-associated plants. This type of problem is well suited for modeling and implementation using Agent-Based Modeling and Simulation (ABMS). The simulation model enables decision-makers to iteratively set parameters, run simulations and evaluate results. Research shows that this approach can improve the understanding of the problem domain and also generate a basis for decision-making. Keywords: Service operations, Decision support, Agent-based modeling and simulation Introduction Outsourcing of maintenance involves the use of external companies, referred to as Maintenance Service Providers (MSP); MSPs can encompass the entire maintenance function or select activities. Provided services can either be corrective, preventive or conditional (Garg and Deshmukh, 2006). MSPs are subject to a rapidly changing business environment strongly influenced by the Forrester Effect of non-stock production (Akkermans and Vos 2003), and research shown that information is vital in avoiding up- and down swings in resource needs. Additionally, operations need to be well balanced in terms of utilization rate of personnel (hours billed divided by hours worked) versus service rate towards customers. Another important topic is to keep a balance between maintenance cost and the up-time of the customers’ production systems. Reality is that MSPs also serve other customers in various geographical locations, and these can have long-term contracts for service levels. One way to improve decision-making is to generate business intelligence by fusing large amounts of data from various sources (Information Fusion, IF). The purpose of IF is typically to extract relevant information from several sources with known certainty to make better decisions than if fusion was not used. It can be defined as “the study of efficient methods for automatically or semi-automatically transforming information from different sources and different points in time into a representation that provides Proceedings of the 16th International Annual EurOMA Conference (Gothenburg, Sweden) effective support for human or automated decision-making” (Boström et al., 2007). One method to realize IF in complex industrial environments, which normally is not highlighted as an IF method, is Agent-Based Modeling and Simulation (ABMS). It is related to IF in the way that information from different sources are collected and fused in a synergistic manner into a situation image that provides effective support for human decision-making. Empirical studies have shown that managers aided by agent-based simulations can benefit in several ways (Nilsson and Darley, 2006). In this research work an agent-based simulation model of service related maintenance has been developed. The simulation model is inspired by an actual case company, e.g. some stochasticity estimates are gained from the case company, but additional data has also been used. Empirical data was collected during a three year period (2006-2008) from various sources including databases, interviews, observations, and internal documents. In the simulation model, the service order fulfilment process is managed by a set of agents that are responsible for one or more activity. It comprise a complex service network (more than 25 customer factories), which is modeled using one common type of industrial machine (CNC) to be served. Provided maintenance service was categorized as either corrective or planned maintenance; the resource expertise needed was categorized into two classes as well, mechanical and electrical. Whole model was built with Anylogic software. The overall purpose of this simulation model is to increase the understanding of the problem domain and to form a basis for better decision-making (i.e. constitute a decision support system), thus improving performance in MSP environment. The remainder of this paper is structured as follows: In Section 2, the concept of agent-based decision support is discussed through existing literature. Thereafter, in Section 3 the research environment is presented. In Section 4, the simulation model is discussed while some initial simulation results are presented in Section 5. In final Section 6, research is concluded and further research avenues are proposed. Agent-Based Decision Support ABMS represents a new paradigm in modeling and simulation, especially suited for complex and dynamic systems distributed in time and space (Lim and Zhang, 2003). It implies that the real (observed) system of interest is modeled in form of a set of interacting agents within a certain environment (i.e. as an agent system) and implemented in simulation software, resulting in an agent-based simulation model/application (Figure 1). An agent system consists of a couple of individual agents with specified relationships to one another within a certain environment. The agents are presumed to be acting in what they perceive as their own interests, such as economic benefit (i.e. they have individual missions), and their knowledge regarding the entire system (i.e. other agents and environment) is limited (Macal and North, 2006). Still, the most important feature in an agent system is the agents’ ability to collaborate, coordinate and interact with each other as well as with the environment to achieve common goals. By sharing information, knowledge, and tasks among the agents in the system, collective intelligence may emerge that can not be derived from the internal mechanism of an individual agent. Furthermore, the ability to coordinate makes it possible for agents to coordinate their actions among themselves, i.e. taking the effect of another agent’s actions into account when making a decision about what to do. The term Multi-Agent System (MAS) is commonly used for agent systems including several interacting and collaborating agents. 2 Figure 1 – The process of agent-based modeling and simulation Agent-based simulation can be used to simulate the actions and interactions of individual agents in an agent system to evaluate the agents’ effects on the system as a whole as well as to evaluate the system in general. This implies that an agent-based simulation model can be used as a decision support system (Figure 2). The simulation model consists of the interacting agents and some performance and risk indicators. The utilized data in the simulation model can be collected from databases, observations, interviews or documents in the real system. Decision-makers, can set parameters in the simulation model, run the simulation and evaluate the results. Based on the retrieved information/knowledge they can make decisions regarding how to handle the real system. They could also continually alter different parameters and simulate again to evaluate different management alternative. This implies that an agent-based decision support system fuses information from different sources in a synergistic manner into a situation image that provides effective support for human decision-making. Therefore, it could be regarded as an IF method. Figure 2 – Agent-based decision support system Nilsson and Darley (2006) conclude in their empirical study that decision-makers aided by agent-based simulations can benefit in several ways: (1) help them acquire an increased understanding of the impact of unscheduled factors (e.g. breakdowns, accidents, demands change), (2) guide decision-makers’ instinct, since interactive agents generate an emergent pattern, which can be explained and understood and therefore beneficial for the improvement of decision-making in companies and (3) help decision-makers to find where the highest leverage is to be gained among improvement alternatives. Even if the interest in implementing agent models in various types of business is increasing (e.g. Albino et al., 2007; Chen et al., 2008; Hilletofth, 2007; Shen et al., 2007; Wang et al., 2007), it is currently a quite vague concept. Based on literature reviews Davidsson et al. (2005) and Cantamessa (1997) conclude that very few field 3 experiments and developed systems have been reported in the academic literature. Davidsson et al. (2005) reviewed the maturity of agent approaches presented in the literature and used the following four main levels: (1) conceptual proposal, (2) simulation experiment, (3) field experiment, and (4) deployed system. In their sample of 56 journal articles published between 1992 and 2005, it was only identified one level 4 and three level 3 research works. A more recent literature review confirms that this situation still exists (Hilletofth et al., 2009); only one manuscript from 33 journal articles published during 2000-2008 included empirically verified