Investigating The Performance Of Metaheuristics To Optimize The Revenue Of Semiconductor Supply Chain

Investigating the performance of metaheuristics to optimise the revenue of the semiconductor supply chain is the final year project that focuses on to identify the supply chain shortage problem related to semiconductors and to compare among the 20 metaheuristics algorithm optimization programmes capable of optimising the semiconductor supply chain. Thorough planning is required at many levels, from the initial layout design through the building of the facility itself. It is imperative that the correct planning decisions are made from the beginning and that the operating policies in existing and proposed factories maximise the product output without sacrificing product quality or factory reliability if the increasing consumer demands for greater product complexity and diversity at lower cost are to be met profitably. On a typical wafer, there are between 300 and 500 manufacturing steps, and the product cycle time is frequently more than a month. In addition, machines are divided into serial and batch types based on the quantity of items being processed at the same time. Wafer lots would have to wait longer for batch operations due to batch size transformations. It is difficult to analyse the production planning with all of these aspects in consideration. As the rivalry expands, semiconductor manufacturers must be nimble and flexible in order to stay in business. Changes in the product mix make the system even more complicated. An enormous number of distinct products must be processed by machines in a semiconductor manufacturing facility (fab), leading to resource sharing issues and the potential for a lengthy line. Due to their simplicity, flexibility, vast number of accessible methods, and ability to avoid local optimums throughout the computing process, metaheuristics are becoming increasingly popular for addressing optimization issues. The algorithms used to tackle optimization issues in several scientific and technological sectors do not require major modifications to be successful. Optimisation is possible for every objective function, regardless of whether or not it is continuous or differentiable. Due to the random nature of metaheuristic approaches, there is a high probability that global optimal solutions will be discovered. A predetermined optimization goal can be achieved by quantifying the quality measures, also known as fitness functions, objective functions, or goodness levels. These measurements aim to maximise desired qualities while decreasing undesirable ones. Small fitness values are often indicative of close-to-optimal control-variable values, as shown in this case. Below is the comparison of the fitness value among 20 metaheuristics algorithms run through Python programming