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.

在工业 4.0 的背景下，高效物料搬运系统 (MHS) 的设计在优化工业运营和提高生产率方面发挥着关键作用。先进技术、自动化和复杂系统的集成彻底改变了工业流程，强调需要在部署特定解决方案之前定义一致的 MHS 替代方案。本文提出了一种集成的 MHS 替代发电方法，该方法结合了 MHE 分配、机队规模和基于关键绩效指标(KPI) 的评估来增强 MHS。所提出的方法部署了约束满足问题 (CSP) 框架，该框架定义了分配给 MHE 分配和车队规模变量的值必须满足的条件。这些约束捕获了 MHE 和 MHS 的物理和操作特性，确保生成的替代方案符合系统的特定要求和限制。一旦建立了一组符合各种约束的可行的 MHS 替代方案，就可以使用离散事件模拟等方法进行进一步的评估，以衡量这些系统的动态行为。该论文强调了纳入更广泛的数据类别（例如布局约束、产品特性和工业 4.0 挑战）的重要性，以提高 MHS 设计流程的有效性和准确性。为了评估生成的替代方案，提出了一组 KPI，涵盖运营、经济、环境、安全和安保方面。这些 KPI 使决策者能够跨多个指标评估替代绩效，帮助识别与公司目标相符的相关解决方案。KPI 的纳入提供了 MHS 替代方案的预评估，简化了后续模拟和多标准决策流程。这项研究承认替代发电作为更广泛的 MHS 设计问题的一个组成部分的重要性。