After the repair is done, a decision (deterministic) needs to be made whether the equipment is already due for regular maintenance (every 90 days) no maintenance is the default. The equipment is automatically replaced when its age is such that it has experienced at least a certain number (4 in the case of Table 1) of consecutive maintenance periods (90 days). One choice (probabilistic) may be to repair or replace the equipment when an SC arrives at a site where the equipment failed repair is the default. In Figure 3, places of decision (equivalent to “if” statements) are represented by three lozenge-shaped symbols. The transition from one state to the next can be triggered by (i) a state change rate (e.g., failure rate) (ii) a timeout (e.g., time to finish maintenance, replacement, or repair) or delay or (iii) a message between agents (e.g., SC arrived for repair, SC arrived for maintenance). The equipment at all borehole sites is assumed to be initially working (green). When each borehole unit operates as planned, some water-related revenue stream is generated that benefits the population who depend on the borehole sites for livelihood and socio-economic development.įigure 3 shows the statechart used to map the lifecycle of each borehole site equipment which can go through five possible states over time: working (green), failed (red), replaced or repaired (yellow), or under maintenance (blue). Each site involves a variety of equipment used for pumping (e.g., water pumps), storage (e.g., tanks), filtration (e.g., water filtration units), distribution (e.g., pipes, canals), and wastewater collection and treatment facilities, etc. The location of each site is defined by its latitude and longitude GIS coordinates. The borehole units at each site serve the water needs of people in that area. The case study considered here involves multiple water borehole sites distributed over a given geographic area. The dynamic considered herein was adapted from that used in the Field Service AB model, which can be found in the Big Book of Simulation Modeling by Borshchev (pp. The purpose of this paper is to explore the applicability of the AB and SD methods to model a specific case study of managing water field services in a remote region (Afar regional state) of Ethiopia. Finally, the last step is to carry out various sensitivity analyses, recommend possible intervention scenarios, and decide on the pros and cons of their implementation. Once simplified and abstracted, the next stage is to select appropriate modeling tools jointly with realistic input data to simulate and reproduce the dynamics of the issues of interest. ![]() This is followed by developing a clear understanding of the issues and their dynamics, being able to simplify these issues, and accessing databases. This cannot be done blindly, and a methodology must be followed, starting with getting acquainted with the context and scale of the landscape in which the problems unfold. Computer simulations can be used instead to decide where to intervene in these systems while accounting for their evolution over time (i.e., their dynamics). Many problems residing at the crossroads between socio-economic, natural, and infrastructure systems are complex and cannot be addressed with simple analytical tools. ![]() Their differences aside, both methods offer policymakers the opportunity to make strategic, tactical, and logistical decisions supported by integrated computational models. The SD method cannot capture the service crew dynamics explicitly and can only model the average state of the equipment at the borehole sites. The strength of the AB method resides in its ability to capture in a disaggregated way the mobility of the individual service crews and the performance of the equipment (working, repaired, replaced, or maintained) at each borehole site. Two software modeling tools (AnyLogic and STELLA) are used to explore the benefits and limitations of the AB and SD methods to simulate the dynamic being considered. The crews respond to specific operation and maintenance requests. The water utility employs several service crews initially stationed at a single central location. The equipment at all borehole sites is managed by a single water utility that has adopted specific repair, replacement, and maintenance rules and policies. Water borehole sites are distributed over an area and serve the water needs of a population. This paper explores the applicability of the agent-based (AB) and system dynamics (SD) methods to model a case study of the management of water field services.
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