## 1 Introduction  
Increasing competition, coupled with advancing computing technologies and the advent of decentralization in the supply chain, has led to the attainment of a shorter product life cycle, reducing production costs and responding to customer demands with greater flexibility. Thus, manufacturing units are now leaning toward a distributed manufacturing environment far from the traditional approach of promptly manufacturing products [1]. This involves multiple processes consisting of classification of manufacturing units, assignment of tasks as per product category on the basis of requirements, and information exchange within various units of an enterprise and between firms. All these together represent parameters of a compound scenario needed to be refined. In this paper, the implications of the proposed classification and optimization-/simulation-based integrated approach for the considered system is presented. Managing supplier relationships and estimating the level of risk involved with various categories of suppliers, their capabilities, core services, constraints, target industries, and customers are some of the parameters for classification and selection. Dealing with selection of suppliers on such a large scale becomes tedious; thus, it is required to subsume advanced techniques for supplier classification. This research incorporates text mining based on supervised machine-learning models as one of the approaches for a better resolution of the aforementioned supplier classification problem. Text mining is the operation through which information from unstructured text documents is extracted by devising non-trivial patterns and trends through statistical pattern learning. It contains various steps, viz., pre-processing, structuring of the input text data, extracting patterns, and classification of information from various sources into a pre-defined genre [2]. Irrespective of any kind of manufacturing system, the prominent functions are process planning and scheduling. The former includes raw materials, semi-finished products, machine tools, and process information. The latter includes the allocation of resources over a set of constraints for manufacturing various entities [3]. The delay of time between planning and execution phase demands the modification of process plans. In other words, it is estimated that 20-30 percent of the already-generated process plans has to be modified in a given life cycle as a result of sequential processing of planning and scheduling in the existing systems [4]. This study seeks to address the following questions:  
* How can supervised machine-learning models be employed to find an efficient method for supplier classification, categorizing suppliers based on specific tasks? * What type of mathematical model can be developed considering the optimization of various conflicting objectives such as completion time, energy consumption, interoperability, machine utilization rate, service utilization, and utility? * In what way can evolutionary algorithms be utilized to optimize scheduling and planning? * What are the benefits of the proposed approach on the considered problem, and how would these effects influence the manufacturing system in a real-time environment? * How can the effectiveness of the proposed Hybridized evolutionary (HMFO) algorithm be validated? In this paper, the context of the problem considered is a distributed manufacturing environment where different enterprises are geographically distributed, in which coordination and collaboration among such enterprises for mutual exchange of information without any significant loss of data represent a challenge. Motivated by the nature of the problem and the factors considered above, this study, in a distributed network manufacturing system, pursues the classification, coordination, and communication to optimize scheduling and planning.