## 2 Literature Review
A Disruptive Innovation-Like Algorithm (DILA) is presented to minimize the tardiness for the job and to obtain the optimal schedule for one machine by varying setup times at irregular interval of time [18]. A novel way for formalizing datasets and concepts utilized for ontology were embedded in the product itself and hence made it interoperable in NMS. Furthermore, a two-level nested solution algorithm was implemented by developing a hybrid adaptive genetic algorithm (HAGA) to achieve optimal process plans for multiple jobs in NMS. The feasibility of the approach is investigated through numerical experiments [19]. The Binary Spring Search Algorithm (BSSA) based on simulation of Hookes law is employed to solve various optimization problems. Moreover, the results obtained by BSSA are compared with other standard binary algorithms, namely the grasshopper mechanism, bat algorithm, etc. [20]. The mobile-agent-based system, introduced for IPPS in NMS to prove the consistency of the proposed model comparison, was made with the Controlled Elitist Non-dominated sorting GA [21]. The effectiveness of the integration of production planning and scheduling approach is compared and proved with conventional sequential scheduling approach. An evolutionary algorithm-based GA approach was adopted for the scheduling of integrated manufacturing and distribution systems [22]. A two-loop algorithm consists of the longest processing-time rule-based tabu search, which is proposed to obtain the optimal schedule in IPPS by minimizing the total cost for manufacturing and maintenance of machines arranged in series in case of NMS [23]. A hybrid dynamic-DNA assisted an evolutionary algorithm proposed to solve N-person non-co-operative game in context to produce various optimal schedule for several jobs in a NMS [24]. A Chaotic Particle Swarm optimization (C-PSO) algorithm for IPPS problem in network manufacturing and compared it with other benchmark algorithms like Genetic Algorithm (GA), Simulated Annealing (SA), and hybrid algorithm to prove its superiority [25]. A Hybrid Particle Swarm Optimization (H-PSO) was described for IPPS and delivery route planning. This was implemented utilizing multi-purpose machines to minimize the cost and earliness and tardiness of the jobs [26]. A logic-based Benders decomposition (LBBD) algorithm mainly separates the decision variables into two sub-categories, i.e., master problem deals with the process plan and sub-problem deals with the sequencing has been emphasized that can solve the IPPS problem for finding an exact solution [27]. A MILP formulation was proposed to minimize the storage cost and workforce cost in the airline industry. This problem mainly deals with the maintenance and repair operations allocation, and it is an NP-hard problem solved by evolutionary algorithms [28]. A combination of algorithms, namely H-PSO and GA with special operators, has been presented to deal with the existed uncertainty in IPPS problem. Later, standard problems are considered to validate the effectiveness of the presented hybrid approach [29]. To overcome the trapping of solutions to a local optimum while solving the multi-modal functions by using several heuristic algorithms, a Comprehensive Learning Particle Swarm Optimizer (CLPSO) has been proposed that combines the advantage of Local Search (LS) strategy along with the excellent global search ability of Particle Swarm Optimizer (PSO). In this work, several multi-modal benchmark functions such as CEC2013 are tested for determining the effectiveness of CLPSO-LS algorithm [30]. A dynamic scheduling method based on event-triggered dynamic task scheduling (EDS) proposed to get the optimal services times for cloud manufacturing systems. A case study of numerical control machines has been considered to prove the effectiveness of the proposed methodology. [31]. Furthermore, generic mediator architecture for effective coordination and task planning in a Distributed Manufacturing Environment (DME) were developed [32].