The planning of charging infrastructure is crucial to developing electric vehicles. Planning for charging stations requires considering several variables, including building costs, charging demand, and coverage levels. It might be advantageous to use a multi-objective optimization method based on the NSGA-II. We need to address the current problems in choosing the location of electric vehicle charging stations. Firstly, urban land use is divided into five functional areas, and the TF-IDF algorithm is applied to the division of functional areas. A combined clustering algorithm is proposed to cluster POIs in functional areas into several clusters and determine the cluster centers as charging demand points. We Analyze charging practices and travel patterns of electric car users, fit the charging likelihood of various functional regions, and calculate the charging demand of each charging demand point in the study area. Introduce the NSGA-II algorithm and consider the charging station's progressive coverage to fit the actual area covered by the charging station.Taking the maximization of system benefits and the maximization of the minimum coverage level as the optimization objectives to carry out multi-objective optimization. Finally, we take the charging station planning in the urban area of Hohhot as an example and provide different site selection planning schemes. The planning schemes for different numbers of charging stations are analyzed to obtain a charging station planning scheme that takes into account both objectives.

Cellular manufacturing is considered as a lean technique of producing similar parts using sells or groups of team members, workstations, or equipment to facilitate operations by removing setup and unnecessary cost components among various operations. Cell formation and layout planning are the most components of the cellular manufacturing. This paper presents a dynamic method to minimize different costs including the total cost of movements within and between cells and exceptional parts. In this study, the Hierarchical Genetic Algorithm (HGA) is used for solving the resulted model and the results are compared with genetic algorithm. The results have indicated that the proposed method could reach optimal solutions for some small and medium sized problems in reasonable amount of time.

Manufacturing facilities are systems that require adequate designing, maintenance and reservations for improvement in the future. Layouts need to be effectively designed to reduce operating to the minimum. Computer simulation is a process of investigating and analyzing the behavior of production processes for effective decision-making using computers to generate solutions that will positively impact short, and long-term planning of the Plants and save costs of real-life implementation. This study investigated a 500Kg capacity shea nut processing plant using FlexSim©. The findings from the initial model were not effective and experienced bottlenecks in workstations (Roaster and Milling) sections, poor cycle and lead times coupled with manual labor, Plant efficiency was 35.7%. However, the Improvement Layout Model was able to address these bottlenecks, the results showed the Plant efficiency increased to 83.3%, shorter lead and cycle times, improved machine utilization, and throughput capacity of the Plant. The results were an indication of conformance to the layout design developed to aid in enhancing the traditional shea nut processing that is largely dominated by traditional processing practices.

These days, one of primary concerns for residence of big cities is associated with the access on shopping centers. People prefer to live in places, which are close to their works, shopping centers and schools. Local governments also prefer to find the most appropriate places in an attempt to reduce unnecessary travels and traffic jams. In this paper, we present an empirical investigation to determine the most important factors influencing facility location for fruit and vegetable market in city of Tehran, Iran. The study has implemented two methods of analytical hierarchy process (AHP) and factor analysis for the investigation. The implementation of AHP has considered two main criteria including Geographic location of market and market, amenities and existing infrastructures. In terms of Geographic location of market, cost saving and public infrastructure are found to be the most important factors while in terms of market based factors, Space and availability of parking are the most important factors. We also use factor analysis and the results of our survey have indicated that Space and Ease of access were two most important factors, which must be considered for facility location.

The Quadratic Assignment Problem (QAP) is still considered to be one of the most difficult and widely used models in combinatorial optimization. The layout of healthcare facilities has been its most significant application area since the 1970s, representing a crucial field of study for increasing operational efficiency, patient safety, and staff flow. The QAP context has been continually altered and supplemented to cover the particular intricacies of the healthcare sector. After Elshafei's groundbreaking paper in 1977, the QAP framework was reinvented and extended to the point where it gained acceptance in the healthcare facility location planning area. This review offers a synthesis of the existing literature from 1977 to 2025 and classifies the research into ten different methodological streams: Exact Solution Methods, Classical Heuristics, Metaheuristics, Hybrid Approaches, Robust Optimization, Fuzzy QAP, Stochastic Programming, Multi-Objective QAP, Special Structure Exploitation, and Parallel & Dis-tributed Computing. The critical assessment of the transition of solution procedures and how the techniques for handling uncertainty have been developed shows how the research has progressed from modeling with one deterministic objective to a sophisticated data-driven approach where multiple objectives are characterized as well as the inherent uncertainties of the system. The analysis indicates the integration and hybridization trend—in the case of algorithms, objectives, and data sources is quite strong—pointing out the future lines of research in areas such as real-time adaptive layouts, deep learning integration, and pandemic-responsive design.

A good facility layout plays an important role on increasing the profitability of a production unit. A good location needs to meet different criteria such as the distance between the plants and the places to reach raw materials, customers, etc. In this paper, we proposed a multi criteria decision making problem to locate a suitable dairy plant. We assume that all factors influencing the plant involves uncertainty and proposed fuzzy numbers to handle the uncertainty associated with all input parameters. We apply the method for a real-world case study of dairy production unit and analyze the results of our proposed model.