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Manufacturing industries are facing a rapidly changing market environment characterized by product competitiveness, short product life cycles, and increased product varieties. This scenario has given rise to the demand for improved capacity planning efficiency while maintaining their flexibilities. One important aspect of capacity planning is machine loading, which is known for its complexity encompassing various types of flexibility aspects that pertain to part selection and operation assignment along with constraint. The main objective of flexible manufacturing system (FMS) is to balance the productivity and flexibility of the production shop floor. From the literature, researchers have proposed many methods and approaches to attain a balance in exploring (global improvement) and exploiting (local improvement). We propose a constraint-chromosome genetic algorithm to solve this problem, which aims at mapping the right chromosome representation to the domain problem as well as helps avoid getting trapped in local minima. The objective functions are to minimize the system unbalance and increase throughput while satisfying the technological constraints. The performance of the proposed algorithm is tested on 10 sample problems available in the FMS literature and compared with existing solution methods Based on the results, the overall combined objective function increased by 3.60% from the previous best result.