Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Modelling and Solving Mixed-model Parallel Two-sided Assembly Line Problems

The global competitive environment and the growing demand for personalised products have increased the interest of companies in producing similar product models on the same assembly line. Companies are forced to make significant structural changes to rapidly respond to diversified demands and convert their existing single-model lines into mixed-model lines in order to avoid unnecessary new line construction cost for each new product model. Mixed-model assembly lines play a key role in increasing productivity without compromising quality for manufacturing enterprises. The literature is extensive on assembling small-sized products in an intermixed sequence and assembling large-sized products in large volumes on single-model lines. However, a mixed-model parallel two-sided line system, where two or more similar products or similar models of a large-sized product are assembled on each of the parallel two-sided lines in an intermixed sequence, has not been of interest to academia so far. Moreover, taking model sequencing problem into consideration on a mixed-model parallel two-sided line system is a novel research topic in this domain. Within this context, the problem of simultaneous balancing and sequencing of mixed-model parallel two-sided lines is defined and described using illustrative examples for the first time in the literature. The mathematical model of the problem is also developed to exhibit the main characteristics of the problem and to explore the logic underlying the algorithms developed. The benefits of utilising multi-line stations between two adjacent lines are discussed and numerical examples are provided. An agent-based ant colony optimisation algorithm (called ABACO) is developed to obtain a generic solution that conforms to any model sequence and it is enhanced step-by-step to increase the quality of the solutions obtained. Then, the algorithm is modified with the integration of a model sequencing procedure (where the modified version is called ABACO/S) to balance lines by tracking the product model changes on each workstation in a complex production environment where each of the parallel lines may a have different cycle time. Finally, a genetic algorithm based model sequencing mechanism is integrated to the algorithm to increase the robustness of the obtained solutions. Computational tests are performed using test cases to observe the performances of the developed algorithms. Statistical tests are conducted through obtained results and test results establish that balancing mixed-model parallel two-sided lines together has a significant effect on the sought performance measures (a weighted summation of line length and the number of workstations) in comparison with balancing those lines separately. Another important finding of the research is that considering model sequencing problem along with the line balancing problem helps algorithm find better line balances with better performance measures. The results also indicate that the developed ABACO and ABACO/S algorithms outperform other test heuristics commonly used in the literature in solving various line balancing problems; and integrating a genetic algorithm based model sequencing mechanism into ABACO/S helps the algorithm find better solutions with less amount of computational effort

Enhancing the bees algorithm using the traplining metaphor

This work aims to improve the performance of the Bees Algorithm (BA), particularly in terms of simplicity, accuracy, and convergence. Three improvements were made in this study as a result of bees’ traplining behaviour. The first improvement was the parameter reduction of the Bees Algorithm. This strategy recruits and assigns worker bees to exploit and explore all patches. Both searching processes are assigned using the Triangular Distribution Random Number Generator. The most promising patches have more workers and are subject to more exploitation than the less productive patches. This technique reduced the original parameters into two parameters. The results show that the Bi-BA is just as efficient as the basic BA, although it has fewer parameters. Following that, another improvement was proposed to increase the diversification performance of the Combinatorial Bees Algorithm (CBA). The technique employs a novel constructive heuristic that considers the distance and the turning angle of the bees’ flight. When foraging for honey, bees generally avoid making a sharp turn. By including this turning angle as the second consideration, it can control CBA’s initial solution diversity. Third, the CBA is strengthened to enable an intensification strategy that avoids falling into a local optima trap. The approach is based on the behaviour of bees when confronted with threats. They will keep away from re-visiting those flowers during the next bout for reasons like predators, rivals, or honey run out. The approach will remove temporarily threatened flowers from the whole tour, eliminating the sharp turn, and reintroduces them again to the habitual tour’s nearest edge. The technique could effectively achieve an equilibrium between exploration and exploitation mechanisms. The results show that the strategy is very competitive compared to other population-based nature-inspired algorithms. Finally, the enhanced Bees Algorithms are demonstrated on two real-world engineering problems, namely, Printed Circuit Board insertion sequencing and vehicles routing problem

Key performance indicators for sustainable manufacturing evaluation in automotive companies

The automotive industry is regarded as one of the most important and strategic industry in manufacturing sector. It is the largest manufacturing enterprise in the world and one of the most resource intensive industries of all major industrial system. However, its products and processes are a significant source of environmental impact. Thus, there is a need to evaluate sustainable manufacturing performance in this industry. This paper proposes a set of initial key performance indicators (KPIs) for sustainable manufacturing evaluation believed to be appropriate to automotive companies, consisting of three factors divided into nine dimensions and a total of 41 sub-dimensions. A survey will be conducted to confirm the adaptability of the initial KPIs with the industry practices. Future research will focus on developing an evaluation tool to assess sustainable manufacturing performance in automotive companies