Hariga, MoncerAs’ad, RamiAlbinali, Hamad A.Rahman2025-07-082025-07-082025-0435.232-2025.17https://hdl.handle.net/11073/26192A Master of Science thesis in Engineering Systems Management by Hamad A. Rahman Albinali entitled, “Queuing-Based Optimization of EV Charging Stations: A Case Study of Manama City, Bahrain”, submitted in April 2025. Thesis advisor is Dr. Moncer Hariga and thesis co-advisor is Dr. Rami As'ad. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). Embargo expires July 08, 2026.The transition to electric mobility necessitates proper strategic planning of charging infrastructure to ensure efficiency, accessibility, and user satisfaction. Today, Electric vehicle (EV) charging stations (CSs) must be strategically located to maximize user satisfaction, enhance accessibility, and balance costs for both charging station owners (CSOs) and electric vehicle users (EVUs). However, most existing mathematical models overlook real-world service constraints, often assuming unlimited waiting capacity at CSs. Another often-overlooked aspect in planning EV charging infrastructure is the blocking cost incurred when EVUs arrive at a station that has neither available charging slots nor waiting space. This situation can lead to inefficient and suboptimal infrastructure planning decisions. Additionally, research on the Charging Station Location Problem (CSLP) remains limited in the Middle East, where countries such as the Kingdom of Bahrain (KoB) face significant barriers to EV adoption, such as inadequate charging infrastructure, range anxiety, and policy uncertainty. To address these gaps, this study develops an optimization model for CS deployment considering CSOs and EVUs costs while explicitly accounting for a queuing behavior having a finite queue length. The problem is formulated as a highly complex Mixed-Integer Nonlinear Programming (MINLP) model. To effectively solve this problem, we propose an iterative solution procedure that initially optimizes CSs without considering queuing aspects, including only CSO related and access costs in the objective function. The resulting relaxed problem is formulated as a Mixed Integer Linear Programming (MILP) model and solved through CPLEX optimizer. Subsequently, the results of this MILP model are iteratively refined to incorporate queuing effects, and the process is terminated once no further cost improvements are attained. A case study of Manama City, the capital of the KoB, was conducted to test the model’s ability in identifying the optimal locations for establishing CSs. The results offer practical insights for policy makers and industry stakeholders to develop sustainable and effective EV charging networks in the KoB.en-USElectric Vehicle (EV)Charging Station Location Problem (CSLP)Markovian Queuing TheoryMixed-Integer Nonlinear Programming (MINLP)Charging Station OwnerElectric Vehicle UserThesis