Storage technology selection is a very important design decision that greatly affects the future performance of a warehouse; for example, it greatly affects its costs. In making this decision, the designer is faced with a complex issue. It is necessary to select the appropriate option from a wider set of available technologies, taking into account numerous influencing factors. In design practice, solving this problem is primarily based on the experience of designers and the recommendations of manufacturers of these technologies. In the academic literature, this problem has not been properly posed and solved, so there are no papers that comprehensively address this complex design problem. The main goal of this paper is to fill that gap. The presented approach consists of two basic stages. In the first stage, starting from the definition of the project task, potential technologies are generated and critical factors are considered, in order to arrive at a set of acceptable technologies. In the second stage, these technologies are ranked, and a basis for decision making is created. This stage is based on multi-criteria decision making: the Fuzzy Analytic Hierarchy Process (FAHP) method is used to determine the weights of the criteria, and the Weighted Aggregated Sum Product Assessment (WASPAS) method is used to obtain the rankings. The application of the defined approach is tested on real assignments (distribution warehouse, production warehouse, and holding warehouse) and is proven to be applicable to solving these types of problems. The results obtained for the three tested examples prove the suitability of the application of the proposed approach in terms of the aspects of both the quality of the solution and the speed of obtaining it. Considering the practical application of the suggested and filling the recognized literature gap, evident contributions are achieved.

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.

The concept of crowd logistics has been present in practice for several decades, while it has gained importance in the literature in recent years. Crowd delivery is a widely accepted concept, while crowd storage is still nowhere near its potential. To the best of the authors’ knowledge, there are no scientific papers dealing with crowd storage in the literature. Crowd storage is one of the categories of crowd logistics services, which involves renting free space on the basis of an agreement, usually between individuals, using specialized online platforms, which are also markets for crowd storage services. Since the concept has not been researched in the literature so far, this paper focuses on several aspects. A unique methodological approach that includes three phases is proposed in this paper. In the first phase, research regarding crowd storage services in the world is carried out. The second phase involves research regarding the crowd storage services in Serbia. The third phase, at the same time the most complex, in the first step defines user requirements and the required resources for the development of crowd storage platforms based on the survey results. In the second step, the weights of the criteria are determined by applying the QFD method. In the third step, the WASPAS method is used for the software solution selection. Furthermore, the results of the research show that crowd storage services in Serbia are still not experiencing expansion. Finally, based on the results of the survey, and by applying the QFD method, user requirements are determined, as well as the resources needed. The crowd storage platform is the alternative with the highest value after the evaluation. Bearing in mind the aforementioned, the justification for the introduction of the specialized online crowd storage platform is confirmed. The concept itself creates numerous positive effects of sustainability: human, social, economic, and environmental.

In order to increase the efficiency of the order-picking process, warehouses are forced to find ways to adopt to constantly intensifying changes in the assortment and quantities of stored products. Accordingly, we present a methodology that deals with such a problem at a tactical level by defining the optimal size and an allocation of products within the order-picking area of the most typical order-picking setting. The methodology combined two methods, dynamic programming and simulation modelling, with the aim of taking advantages of their positive features. In that sense, the optimal allocation of products for different sizes of the order-picking zone were obtained by the dynamic programming approach. Afterwards, the influence of a demand’s seasonality and variations were treated by the simulation model, so that the more realistic performances of the system were captured for the optimal allocation of products. The methodology was tested on the retailer data with significant week seasonality. The obtained results confirmed the practical applicability of the methodology in real systems, while the sensitivity analysis of results showed that special attention and effort should be given to the determination of costs related to the engagement of order-pikers, storage equipment and unit replenishment during a planning period.

The warehouse is an important factor in the manufacturing process. At present, there are constant demands for the more efficient and effective operation of warehouse systems. For this reason, the rationalization of existing warehouses...

Introduction/purpose: Intralogistics involves the organization, control, realization, and optimization of material and information flows within a technologically integrated system. The selection and application of appropriate material handling equipment (MHE) play a crucial role in shaping intralogistics systems. This paper aims to define guidelines for selecting MHE based on scientific research. The methodology implemented to achieve this goal comprises several fundamental steps. Methods: The initial step involves analyzing three typical groups of tasks in (intra)logistics systems: transshipment, internal transport, and production activities. The analysis focuses on identifying and describing key task parameters, such as the type of unit load, quantity, flow intensity, and the location and timing of task initiation and completion. Next, there is a step devoted to the analysis of relevant scientific research. The literature is searched according to specific criteria, such as publication year, keywords, and citation count, to form a database of relevant papers. The analysis focuses on identifying and linking the parameters of practical tasks with those described in the literature and on the characteristics of the MHE used to facilitate the selection of appropriate MHE for these tasks. Results: Following this, in the third step, the observed task is classified according to standard task types found in the literature. Conclusion: Finally, in the last/fourth step, regression analysis is applied to define the guidelines for MHE selection based on the frequency of use of specific MHE groups for certain task groups as described in the literature.