Research Paper on Industrial and Systems Engineering

Introduction

Industrial and systems engineering is an engineering branch that deals explicitly with the optimization of systems, organizations and complex processes. Industrial and systems engineers, therefore, involve themselves in finding ways through which they can ensure elimination of wastes in the processes of production (Akkaya et al. 1000). The engineers in the field of industrial and systems engineering, devise systems that are efficient for integration of machines, materials, information, energy, and workers in making a particular product or providing a service. In the past, human beings have been faced with challenges that are increasingly complex and therefore had to think holistically and systematically to come up with solutions that would successfully respond to the problems. Besides, the emergence of new technology such as the autonomous systems is likely to redefine industrial and systems engineering. Such a system will at one point find itself in a situation whereby it has to choose whether to abide by the law or not. One puzzle that is likely to emerge is that even after these machines have been enabled to distinguish between what is ethically right and wrong, can one be certain they will choose the former alternative? Conversely, Generalists have used the responses in the development of generic principles as well as practices that replicate success. Some of the laws developed have made significant contributions towards the evolution of industrial and systems engineering discipline.

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Complexity of Systems

Technology alone is not enough to solve the very many grand challenges that human beings are faced with today. Industrial systems engineering acts as a high-value engineering discipline with an aim of ensuring delivery of changes that are transformational or new and advanced approaches anytime they are needed. The term industrial systems engineering is used in most cases to indicate an area that is broad and full of relevant activities including complexity and systems science deliberately steering clear of a definition that is precise (Akkaya et al. 1000). The Grand challenge in the industrial and systems engineering field lies in looking into the complexity of systems, their organization, behavior, and emergence. The utilization of various tools like visualization and modeling to have an understanding of their complexity, document their evolution and access any information relevant to their existence is also a significant problem in the field of ISE.

Autonomous Systems

The establishment of an indicative industrial and systems engineering grand challenge aims at inspiring an agenda of research and also at bridging the existing gap between industrial applications and academic knowledge. The features of complex systems in ISE have to be explored thoughtfully then understood through a series of grand challenges that are easily recognizable. Breaking through the various challenges in the ISE field is a significant milestone, especially in technological and knowledge advancement (Loughborough University 2). Research that has been on-going in the Industrial and Systems Engineering has successfully resulted in the drafting of seven striking Challenges. The latter, nevertheless, comprises of two basic types of complex systems noted; the autonomous and the heterogeneous. Heterogeneous systems are formed by multiple interconnections of systems with humans being part of the loops. Autonomous systems, on the other hand, do not depend on any form of human interaction. The most prominent of all problems in industrial and systems engineering is autonomous systems.

The above-mentioned systems have been created with an aim of replacing systems that are human-based on so many aspects. The benefits associated with these autonomous systems are many and vary in different issues like infrastructure requirements, cost-effectiveness and also in their abilities to perform without feelings or emotions (Loughborough University 2). There is, however, a need to ascertain the responsibility of autonomous systems' actions. Their problem lies in ensuring that their interaction with each other is geared towards their individual and collective gains; especially as they advance and evolve into systems that are ultra-scalable. True autonomous systems rarely rely on any human operators and thus build and incorporate various disciplines like distributed computing, artificial intelligence, software engineering, sociology, economics, systems that are object-oriented and organizational science.

Currently, the autonomous systems and multi-agent systems are receiving a lot of attention and they represent a large area of both development and research. The challenge is, nevertheless, primarily concerned with autonomous systems that are ultra-scalable since their existence is possible from a single autonomous system to a vast civilization of various autonomous systems co-operatively working together. The function and form of individuals in the civilization bracket do not have to be the same. Instead, they can be diversified according to the needs. Autonomous agents, as well as multi-agent systems, therefore, require ways of designing, analyzing and forming more complex systems that are effective (Schneider et al. 185). The construction and design of autonomous systems that are ultra-scalable should be done in a way such that they are in a state such that they can be reconfigured automatically.

The automatic reconfiguration will ensure tackling of new and advanced tasks and evolvement with technology. In the ultra-scalable autonomous systems, each part must be fully aware of what surrounds it (its environment) and thus cooperatively undertake its assigned tasks with the near systems (Schneider et al. 190). The ultimate goal for such systems is always to ensure intelligent responses to events just like humans would do. Designing and construction of an autonomous system whose architecture is ultra-scalable is not an easy thing to go by. The objective of this scenario is to ensure that the autonomous ultra-scalable system can reconfigure itself and tackle advanced and new tasks while at the same time evolving with changes in its tasking or the enabling technology. The design also needs to ensure that the smaller autonomous systems are fully aware of their environment and that they can cooperatively undertake their assigned tasks with the agents and systems that neighbor them.

Industrial and Systems Engineering environment designs that permit or allow testing and evaluation of ultra-scalable autonomous systems is also a challenge. The definitive goal for the systems is to ensure they respond intelligently to situations and events with outcomes that are better or similar to those exhibited or achieved by systems that are human based. New theories and tools, therefore, have to be introduced, for instance, autonomous models of cognition, to aim at reducing any form of human intervention (Schneider et al. 200). Another challenge that falls within autonomous systems is defining their systems' security, privacy, and trust. To overcome this challenge, a system paradigm that is entirely autonomous should be established. New tools and methodologies of systems engineering should also be identified and incorporated. Ethics, legal and social acceptability of autonomous systems cannot quickly come by unless mechanisms and confidence are developed to arrive at a stage where human beings will willingly handover total authority to the autonomous systems.

The development of autonomous systems is expected to meet the expectations of its users. This implies that for a system to be fully autonomous, it is expected to conform to ethically binding principles in order to it to function without the intervention of human beings (Williams, Andrew & Paul 66). The same systems are expected to contribute significantly to the progress of human nature, especially in special functions involving the military forces, since they will ensure that they are not exposed to any danger. In such a case, it is bound to save many lives. Its vulnerability can, however, be distinguished by establishing whether the system will be in a position to distinguish between the enemy and ally. It is feared that once it malfunctions, it may lead to detrimental consequences. According to the proponents of these systems, "Autonomous systems do not tire," meaning that they are in a position to substantiate this human weakness whenever they are required to (Williams, Andrew & Paul 66).

Currently, there is no autonomous system that is capable of distinguishing between civilians and combatants (Anderson et al. 71). This is a major ethical consideration that has been made regarding the systems. Besides, there is a likelihood that they can be compromised by hackers just like any other gadgets. In the event that this occurs, it is expected that they may end up posing a danger to human life. Despite how well configured these weapons may be, there is no chance that they will be in a position to reproduce the same kind of moral reasoning that ordinary human beings have, "they do not possess emotions or feel an attachment to their comrades" (Anderson et al. 76). There is, however, a popular belief among the proponents of these systems indicating that they are to be reviewed prior to being used as a state`s arsenal (Williams, Andrew & Paul 74).

In attaining social and legal acceptability, the autonomous systems and machines will have to develop self-healing mechanisms especially in the events of failure, whether intermittent or complete. A system's ability to assume full accountability for its actions and mistakes will also need to be investigated. Designing of system architectures that fully support autonomous agents has been a challenge in realizing the full independence of autonomous systems (Schneider et al. 200). Additionally, the integration of an autonomous system environment where there is the coexistence of human controlled systems has also been challenging. Methods of ensuring autonomous systems fail in a manner that is non-detrimental have, however, been developed. Coexistence with human-controlled systems has been enhanced through the development of new context awareness on the basis of self and as well as on the entire autonomous community. Self-configuration and quick adaptation to situations that are continuously changing have also been highly significant.

In any endeavor that attempts prediction of future needs, staying within the comfort zone is very easy, and one can also easily be confined to thinking about only short and medium-term needs. Over the recent years, systems' growth has been witnessed from just a single user to the inclusion of multi-user systems that are geographically distributed. Around the world, high bandwidth networks' advent has greatly and further enabled data connectivity between software and hardware systems (Lamnabhi-Lagarrigue et al. 30). Regardless of this, some of these connections are unregulated and could lead to possible security breaches. As the systems' scales grow, their failure chances also increase, therefore putting vast amounts of data at risk. The design of ultra-scalable heterogeneous systems is, therefore, a challenge to industrial and systems engineering.

Most times, systems that are large-scale present significant challenges based on architecture, privacy, and security of data. Systems that are ultra-scale thus need to be open so that they can evolve into more complex and more significant systems and also include that rapidly changing technology. The architecture of ultra-scale systems must, hence, be in a position to support hundreds of nodes having the level of integrity and security that is required (Lamnabhi-Lagarrigue et al. 40). The internet is a perfect example of such systems as it...