Manning and Automation Model for Naval Ship Analysis and Optimization

The manning of a ship is a major driver of total ownership cost. The Government Accounting Office (GAO) states that “the cost of the ship’s crew is the largest expense incurred over the ship’s lifetime” [1]. This cost is largely determined by decisions made during concept design, which may include significant new support costs ashore. Consequently, reliable manpower estimates and related design decisions should be made early in the design process, preferably in concept design. The ship concept exploration process developed at Virginia Tech uses a MultiObjective Genetic Optimization to search the design space for feasible and non-dominated ship concepts based on cost, risk and effectiveness. This requires assessment of thousands of designs without human intervention. The total ship design problem must be set up before actually running the optimization. If manning is to be included in this process, manning estimate tools must be run seamlessly as part of the overall ship synthesis and optimization. This paper describes a method of implementing a manning task network analysis tool (ISMAT, Integrated Simulation Manning Analysis Tool) in an overall ship synthesis program and design optimization. The inputs to the analysis are ship systems (propulsion, combat systems, communication, etc), maintenance strategy, and level of automation. The output of the manning model is the number of crew required to accomplish a given mission for a particular selection of systems, maintenance and automation. Task network analysis programs are ideal for this problem. They can manage the probabilistic nature of a military mission and equipment maintenance, and can be used to simplify the problem by breaking down the complex functions and tasks of a ship’s crew. The program builds large and complex functions from small related tasks. This simplifies the calculation of personnel and time utilization, and allows a more flexible scheme for building complex mission scenarios. ISMAT is run in a preoptimization step to build a response surface model (RSM) for calculating required manning as a function of systems, maintenance and automation. The RSM is added to the ship synthesis model to calculate required manning. A concept exploration case study using this model is performed for an Air Superiority Cruiser, CG(X). The performance of the manning model in this case study is assessed and recommendations are made for future work. MOTIVATION & INTRODUCTION In a report to Congress on the effects of performing manpower estimates early in the design process, the GAO stated, “when applied to ships early in their development and throughout their design, human systems (analysis) has the potential to substantially reduce requirements for personnel, leading to significant cost savings” [1]. There are a number of options available to ship designers to reduce ship manning requirements. These options include automation, changing maintenance philosophies, improving system reliabilities, revising crew training and others. All of these options have the possibility to reduce crew size but cost (including shore-based cost), reliability, worklife issues, and effectiveness cannot be sacrificed or ignored. Manning analyses are traditionally done by hand, one ship class at a time, late in the design process. Design optimization requires a hands-off manpower calculation early in the design process that can calculate manning levels for different levels of automation, maintenance strategies and ship system configurations. Concept design is traditionally an “ad hoc” process. Selection of design concepts for assessment is guided primarily by experience, design lanes, rules-of-thumb, and imagination. Communication and coordination between design disciplines (hull form, structures, resistance, manning, etc.) require significant designer involvement and effort. Concept studies continue until resources or time runs out. In concept exploration, many (millions) of feasible designs may exist in the design space. An efficient and robust method to search the design space for optimal concepts is essential. This cannot be done by hand, one design at a time. Multi-objective optimization methods provide a solution to this problem [2-4].