Resource planning at urban scale has received significant scrutiny in recent years. What has been paid less attention is whether existing tools such as optimization models can play a role in helping with this planning. The reason for the historical lack of interest is argued as due to both insufficient computing capability and practical modelling difficulties. The proposition put forward here is that optimization software can work with large distributed systems associated with urban resources. Resources considered include inter alia capital, employees, and technologies. To help support the contention, a mixed integer linear program and a nonlinear program have been formulated and tested. RESCOM and LAPM are optimization models developed in this research to make decisions on urban scale at the concept stage of design. The focus is on residential energy retrofits and workforce allocation for technology deployment respectively. Solutions are described to the problems encountered. Obstacles include use of available data, modelling interventions in a computationally efficient manner, and modelling thermal energy requirements as dependent on both temperature and thermal transmittance. Real world case studies in the London Borough of Haringey demonstrate the functionalities of the models. It is proposed that it is the structure rather than the objective function's value which should be analysed. Three methods are employed to test the resilience of solution structures as follows: i) scenario analysis, ii) generation of near-optimal solution alternatives, and iii) uncertainty analysis. The detailed analysis of the results is an important - but often omitted - step yielding interesting findings. Yet the insights must be considered with respect to the scope and assumptions within the models. Notwithstanding, RESCOM and LAPM carefully combine modern developments in the literature with practical real-world constraints to provide novel contributions to existing knowledge. The hypothesis put forward as a result of the research described in this work is that optimization software can be successfully employed for modelling resources planning at the large scale.