In the context of Industry 4.0, the design of efficient Material Handling Systems (MHS) plays a critical role in optimizing industrial operations and enhancing productivity. The integration of advanced technologies, automation, and complex systems has revolutionized industrial processes, emphasizing the need for defining coherent MHS alternatives prior to the deployment of a specific solution. This paper presents an integrated MHS alternative generation approach that combines MHE allocation, fleet sizing, and key performance indicator (KPI)-based evaluation to enhance MHS. The proposed approach deploys a Constraint Satisfaction Problem (CSP) framework, which defines the conditions that must be satisfied by the values assigned to the MHE allocation and fleet sizing variables. The constraints capture the physical and operational characteristics of MHE and MHS, ensuring that the generated alternatives adhere to the specific requirements and limitations of the system. Once a set of feasible MHS alternatives complying with various constraints is established, further assessments can be carried out to gauge the dynamic behavior of these systems using methods like Discrete Event Simulation. The paper emphasizes the importance of incorporating a broader range of data categories, such as layout constraints, product characteristics, and Industry 4.0 challenges, to enhance the effectiveness and accuracy of the MHS design process. To evaluate the generated alternatives, a set of KPIs is proposed, encompassing operational, economic, environmental, safety, and security aspects. These KPIs enable decision-makers to assess alternative performance across multiple metrics, aiding in the identification of relevant solutions aligned with company objectives. The incorporation of KPIs provides a pre-evaluation of MHS alternatives, streamlining subsequent simulation and multicriteria decision-making processes. This research acknowledges the importance of alternative generation as an integral part of the broader MHS design problem.