Fantahun Melaku Defersha

Additive Manufacturing (AM) is considered an innovative technology to fabricate goods with green characteristics. In comparison to conventional manufacturing (CM) approaches, AM technologies have shown impressive results in enhancing sustainability in production systems. Various research has been conducted to assess the environmental impacts of AM based on the well-known life cycle assessment (LCA) framework. However, this approach requires intensive domain knowledge to build the environmental impact model and interpret the impacts of input variances. This knowledge barrier may cause delays and challenges in the selection of the optimal design and process parameters for additively manufactured parts in the product design and planning stages due to the iterative design-evaluation process. As such, the research community demands an automated LCA tool for supporting AM toward elevated sustainability. To achieve this ambitious goal, this paper particularly investigates the fundamental question – “What are the key influential parameters that pose an impact on the environmental sustainability of AM?”. A methodological framework for identifying the key influential parameters for AM is proposed. The framework was demonstrated by taking the fused filament fabrication (FFF) process as an example. Based on instantiation, LCA of over 200 AM instances, and correlation analysis, the key influential parameters are identified. Finally, a dataset with the identified features could be constructed. This dataset is expected to establish a common base for scale-up with joint efforts from the AM community.

Traveling salesman, linear ordering, quadratic assignment, and flow shop scheduling are typical examples of permutation-based combinatorial optimization problems with real-life applications. These problems naturally represent solutions as an ordered permutation of objects. However, as the number of objects increases, finding optimal permutations is extremely difficult when using exact optimization methods. In those circumstances, approximate algorithms such as metaheuristics are a plausible way of finding acceptable solutions within a reasonable computational time. In this paper, we present a technique for clustering and discriminating ordered permutations with potential applications in developing neural network-guided metaheuristics to solve this class of problems. In this endeavor, we developed two different techniques to convert ordered permutations to binary-vectors and considered Adaptive Resonate Theory (ART) neural networks for clustering the resulting binary vectors. The proposed binary conversion techniques and two neural networks (ART-1 and Improved ART-1) are examined under various performance indicators. Numerical examples show that one of the binary conversion methods provides better results than the other, and Improved ART-1 is superior to ART-1. Additionally, we apply the proposed clustering and discriminating technique to develop a neural-network-guided Genetic Algorithm (GA) to solve a flow-shop scheduling problem. The investigation shows that the neural network-guided GA outperforms pure GA.

The work in this paper is motivated by a recently published article in which the authors developed an efficient two-stage genetic algorithm for a comprehensive model of a flexible job-shop scheduling problem (FJSP). In this paper, we extend the application of the algorithm to solve a lot streaming problem in FJSP while at the same time expanding the model to incorporate multiple objectives. The objective function terms included in our current work are the minimization of the (1) makespan, (2) maximum sublot flowtime, (3) total sublot flow time, (4) maximum job flowtime, (5) total job flow time, (6) maximum sublot finish-time separation, (7) total sublot finish-time separation, (8) maximum machine load, (9) total machine load, and (10) maximum machine load difference. Numerical examples are presented to illustrate the greater need for multi-objective optimization in larger problems, the interaction of the various objective function terms, and their relevance in providing better solution quality. The ability of the two-stage genetic algorithm to jointly optimize all the objective function terms is also investigated. The results show that the algorithm can generate initial solutions that are highly improved in all of the objective function terms. It also outperforms the regular genetic algorithm in convergence speed and final solution quality in solving the multi-objective FJSP lot streaming. We also demonstrate that high-performance parallel computation can further improve the performance of the two-stage genetic algorithm. Nevertheless, the sequential two-stage genetic algorithm with a single CPU outperforms the parallel regular genetic algorithm that uses many CPUs, asserting the superiority of the two-stage genetic algorithm in solving the proposed multi-objective FJSP lot streaming.

The high price of petroleum, overconsumption of plastic products, recent climate change regulations, the lack of landfill spaces in addition to the ever-growing population are considered the driving forces for introducing sustainable biodegradable solutions for greener environment. Due to the harmful impact of petroleum waste plastics on human health, environment and ecosystems, societies have been moving towards the adoption of biodegradable natural based polymers whose conversion and consumption are environmentally friendly. Therefore, biodegradable biobased polymers such as poly(lactic acid) (PLA) and polyhydroxyalkanoates (PHAs) have gained a significant amount of attention in recent years. Nonetheless, some of the vital limitations to the broader use of these biopolymers are that they are less flexible and have less impact resistance when compared to petroleum-based plastics (e.g., polypropylene (PP), high-density polyethylene (HDPE) and polystyrene (PS)). Recent advances have shown that with appropriate modification methods—plasticizers and fillers, polymer blends and nanocomposites, such limitations of both polymers can be overcome. This work is meant to widen the applicability of both polymers by reviewing the available materials on these methods and their impacts with a focus on the mechanical properties. This literature investigation leads to the conclusion that both PLA and PHAs show strong candidacy in expanding their utilizations to potentially substitute petroleum-based plastics in various applications, including but not limited to, food, active packaging, surgical implants, dental, drug delivery, biomedical as well as antistatic and flame retardants applications.