A good police officer manpower supply plan helps departments arrange more precisely manpower supply for each work shift to meet the daily demand. However, currently the police officer manpower supply plans for police departments/divisions in Taiwan are typically generated with decision maker experiences, which are neither efficient nor effective. It is difficult to control the planning quality, usually resulting in inadequate manpower supplies. In addition, currently each station of a police division for most departments in Taiwan generally performs the manpower supply planning independently, without considering mutual support between stations. Hence, how to build the police officer manpower supply strategies under scarce human resources, and to develop routine and anticipated manpower supply models, are very important issues for police divisions/departments to efficiently use their manpower. In this study, two routine deterministic-demand and two routine stochastic-demand police officer manpower supply planning models for the short-term operation are developed from a system optimization point of view, considering different manpower supply strategies and real practices for a police division. In addition, an anticipated deterministic-demand and an anticipated stochastic-demand police officer manpower supply planning models are developed by considering different manpower supply strategies and real practices for a police division. Because the content of this study is large, the study is divided into a three-year project.In the first year, we will adopt the mathematical programming method to develop two routine deterministic-demand police officer manpower supply models, a basic model and a mutual support model, by considering different supply strategies and related operating constraints, with the objective of minimizing total manpower supply-hours, given that the daily demand for routine duties is known and fixed, to determine the work shifts and the associated daily manpower supplies. Since both models’ problem sizes are medium, we will first try to utilize the CPLEX, a mathematical programming solver, to solve the deterministic models. If it is inefficient to solve the two models, we will develop heuristics according to the problem characteristics. In the second year, we will develop two routine stochastic-demand police officer manpower supply planning models by modifying the fixed demands in the corresponding deterministic-demand models. We will adopt the problem decomposition technique and the method for solving deterministic-demand models to develop heuristics to efficiently solve the two stochastic models. In the third year, focusing on large-scale events we will consider the demand constraints and related supply constraints to construct an anticipated deterministic-demand police officer manpower supply model, and then to develop an anticipated stochastic-demand manpower supply planning model by modifying the fixed demand parameters in the deterministic-demand model. CPLEX will first be used to solve the two models. If it is not efficient, then we will develop a problem-oriented heuristic to efficiently the two models. Finally, beside the contribution to the academics, these models with the solution methods are expected to be useful for police departments/divisions to generate optimal manpower supply plans, so as to reduce the shortage of police officers and improve the performance in managing the police officer human resources.