Globalization, the growth of the world population, urbanization and the growth of the volume of the flow of goods have generated numerous problems in city logistics (CL). The opportunity to solve them is found in various fields by defining and implementing initiatives, concepts, measures, modern technologies and scenarios. The efficiency of the solution largely depends on the efficiency of logistics centers, which is one of the key subsystems of CL. The requirements for the reliable delivery of goods to customers in urban areas are conditioned by the efficiency their order fulfillment in logistics centers. Therefore, optimizing material handling (MH) time and costs aimed at reducing delivery errors, minimizing damage to goods and increasing customer service efficiency is directly conditioned by the automation of MH in logistics centers. Accordingly, this paper aims to rank and select smart MH solutions in logistics centers where deliveries are prepared for the supply of the city area. This paper proposes four smart solutions for a real company, and fourteen criteria are selected for the evaluation. A new hybrid Multi-Criteria Decision-Making model that combines the Fuzzy Analytic Hierarchy Process method, used to determine the criteria weights, and the Fuzzy COmprehensive distance-Based RAnking (FCOBRA) method, used to rank the alternatives, is proposed. The application of the model shows that the best alternative is the implementation of an autonomous forklift, which can greatly automate logistics activities and reduce the rate of delivery errors. The main contributions of this research are the definition of smart solutions, a framework for their evaluation and a new model for their ranking.

The logistics network is considered the provider of logistics activities in supply chains. The fluctuating requirements of customers and the logistics network’s complex structure are only a few of the factors that cause challenges to its management. Industrial facilities are particularly vulnerable to challenges because material handling operations dominate in addition to manufacturing activities. Disruptions at industrial plants are disseminated through the logistics network, affecting all supply chain participants. As a result, reducing material handling time and costs to decrease material losses, pollution, and productivity is vital to their business. Due to their distinctive properties and significant share in finished goods, bulk materials are particularly vulnerable to issues during manufacturing. Accordingly, this study aims to rank and select technologies for handling bulk materials in an industrial plant where the production of construction materials is performed. This paper proposes four alternative solutions for the observed case study, and nine criteria were selected for the evaluation. A new hybrid multi-criteria decision-making model is proposed. The model combines Fuzzy Step-Wise Weight Assessment Ratio Analysis (SWARA), used to determine the weight of criteria, and the Axial-Distance-Based Aggregated Measurement (ADAM) method, used to rank alternative solutions. The model results indicate that the pneumatic conveyor is the best ranked alternative that significantly increases productivity, reduces losses, and improves working conditions. The key contributions of this study are its analysis of the efficiency of the technologies proposed for bulk material handling and the development and implementation of a model framework for the ranking of these technologies.

The food supply chain (FSC) faces significant challenges, including the short shelf life of products, stringent food safety standards, and the growing demand for online ordering. These challenges underscore the need for a resilient and sustainable FSC, particularly in the confectionery industry, which is further burdened by the demand for innovative and healthier products. The aim of this paper is to optimize material handling activities in warehouse operations within the confectionery industry by ranking and selecting adequate material handling equipment (MHE). This paper proposes a novel hybrid multi-criteria decision-making model that integrates the Simple Aggregation of Preferences Expressed by Ordinal Vectors Group Decision Making (SAPEVO-M), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy COmprehensive distance-Based Ranking (FCOBRA) methods. The model was applied to a real-world case study involving four alternative solutions and twelve defined evaluation criteria. The application of the model identified the implementation of an Automated Guided Vehicle system (AGVs) as the optimal alternative, offering substantial automation of logistics activities and addressing identified company challenges. The engagement of AGVs is estimated to reduce operational costs by 20%, improve warehouse operation efficiency by 30%, and decrease CO₂ emissions by 25%. The contribution of this paper lies in the development of a methodological framework for evaluating and selecting MHE, as well as in highlighting the importance of optimizing material handling processes in the confectionery industry.