Because each component of an integrated manufacturing system of die casting process requires managing the big data. Of course, die casting industry has faced these challenges very close. The most impressive part is the big amounts of data which can be analyzed for production improvements.

Machines used in production systems exchange very big amounts of process data with one another. Moreover, component-driven production is supported. In Industrial Revolution 4.0 applications the production is increasingly digitized. Additionally, the designed assembly line has a structure that includes pneumatic and mechanical systems that can ensure the assembly of 3400 products per hour in total. The design of the subjected furniture fitting has been re-engineered for these purposes and finally at least 800 N strength capacity is achieved. In this study, an automation cell, with high productivity, that assembles three separate parts into one crucial furniture fitting which will meet customer expectations has been established on an industrial scale. In the global furniture manufacturing industry, customers expect to be able to disassemble their end-products and require durable connecting fittings. Moreover, at least fifteen workers were required to assemble 1400 products per hour at maximum capacity. The strength capacity of the existing fasteners produced within the company Mesan, was around 500 N on average by manual assembling. It is almost an obligation to use furniture fittings consisting of at least three parts in demountable wooden panel systems in the furniture manufacturing industry. The paper also proposes a Decision Support System, addresses application issues, and concludes with further studies. The algorithm terminates after a given number of iterations. An initial solution is obtained using randomly generated cutting patterns for the mathematical model. The first step is a heuristic algorithm that produces a cutting pattern at every iteration, which is then fed into a novel mathematical model to determine an optimal solution. The approach to solving the problem has two steps. This study aims to develop a production plan that minimizes the waste of cutting steel rods of different lengths and diameters in required lengths.

It is solved using column generation techniques. The one-dimensional cutting stock problem (CSP) is an NP-hard problem, including discrete demands and capacitated planning objectives. It is applied in a manufacturing setting where rolls of steel rods of different lengths are cut according to customer requirements. This study solves a one-dimensional cutting stock problem with multiple stock lengths. Thus, the main contribution of this study is to include machine eligibility to the conventional job scheduling problem and to use WOA while solving the corresponding problem. Although, in some studies in the literature, WOA is used to solve job shop scheduling problems, there is not a study which uses WOA as a solution methodology for parallel machine job scheduling problem with machine eligibility consideration to the best of our knowledge.

Some toy instances of different sizes are created and the results obtained by using CPLEX and WOA are compared. Then, WOA which is a current meta-heuristic, used to solve the same problem. This problem is initially solved by using Optimization Programming Language namely CPLEX Studio IDE 20.1.0. The objective of this study is to find the best possible job schedule while minimizing the make-span (i.e., the length of time elapsed from the beginning of first job to the end of the last job.) of the system. Whale Optimization Algorithm (WOA) is one of these meta-heuristic methods which is recently developed.

Meta-heuristics are widely used methods in OR literature.

Definition of mass production