Ko and Nof’s (2012) study explored the basic problems that might occur in collaborative production system to prevent the effective outcome. According to the authors, task processing is effective in customer-focused and concurrent technology if there is a practical collaboration between multiple participants. The researchers further argue that despite the cooperation, the systems might encounter some challenges in the environment that require decisions that cannot be managed by simple Coordination Protocols (CPs). Based on the situation, the investigation is focused on finding solutions by proposing a framework protocols design that can enable the system to solve such complex issues. They have cited other studies to support their arguments and provide a clear view of how collaborative production can be properly improved through well-designed protocols to enhance task administration. The article summary will explore the feasibility and efficiency of the design framework of different Task Administration Protocols (TAPs).
The Problem Addressed
The key research work problem is how to design a protocol framework that can easily minimize possible issues due to collaboration amongst participants. According to the authors, the task administration process might face complex challenges that simple CPs cannot resolve in a collaborative system. Therefore, to make the platform effective and reliable, they are forced to perform different experiments to determine designs that can easily work efficiently in an environment that involves several tasks.
The challenge addressed by the authors is significant in the dynamic systems that involve different processes. Understanding possible issues in a manufacturing environment that might is essential because it enables an individual to formulate the appropriate measures to solve and improve the situation. Furthermore, the course gives deep insight into how the management may configure the system full of different participants to maximize the potential and achieve the intended goals. The problem allows me to comprehend technology’s role in the manufacturing environment. Moreover, it allows me to explore different design framework protocols that can help in advancing the control process for effective collaboration in my project.
Background and Known Practice
Based on the research work, the current production systems experience several factors that cause unpredictable changes. It, therefore, becomes a challenge for the CPs to manage the tasks assigned to them following the nature of the systems (Ko & Nof, 2012). According to the authors, CPs are designed to facilitate effective management through controlling a defined dependence on the available tasks. The CPs follow the framework of Contract Net Protocol (CNP), a system whereby the assignments are announced to the agents, and the one with the best bid secures it. The approach is more of a decentralized platform used to provide an adequate allocation of resources in a distributed organization. Despite the technique’s efficacy, the authors indicated that CPs are not capable of handling issues involving complex decisions (Gulzar et al., 2018). This aspect renders CPs less effective in enhancing a collaborative production system.
According to the article, the complex problems include adapting the requirements of the tasks to constraints and performing urgent task-based on the order of priority. These decisions cannot be executed by the CPs, thus prompting the formation of a framework that can facilitate the management of technical issues. Effective protocols should be aligned with a Collaborative Control theory (CCT) (Moghaddam & Nof, 2018). Ko and Nof’s (2012) study defines the protocol which makes timely and reliable decisions as TAPs. They can enable the organization’s performance to be improved to enhance the attainment of the set goals.
New Methods and Results
The research study involves finding solutions to the collaborative problems that CPs system could not manage to correct. According to their research, the authors investigated the TAPs designs suitable for control of collective production (Ko & Nof, 2012). The writers developed framework protocols to address problems associated with collaboration amongst multiple systems. They group the task administration into three broad categories: initialization, allocation, and task monitoring.
Initialization of Task-based on Priority
The authors stated that tasks are distributed into different monetary units that contain varied information, capabilities, and objectives in the initialization phase. When CPs are used, the priority is evaluated according to the management’s decisions, the price of the task, or the time limit. Based on this approach, the priority is determined by the monetary value assigned to the job. This makes it difficult to alter the priority in the event of emerging issues such as the arrival of tasks with better money value.
Market-Based Resource Allocation
In this approach, the CPs operate as per the CNP framework, whereby the market is decentralized into contractors and bidders. The focus of the technique is to combine both the auction-based and market-based approaches to solve the allocation of resources. According to the research study, the authors stated that the CNP framework is more effective in computation and communication than centralized methods making it easier to overcome the problems.
The authors stated that the tasks should be monitored frequently to detect any possible errors. The approach is essential in allowing the removal or correction of resource failure to lower the degradation of performance of the tasks. The authors proposed using an effective time-out protocol with a proper threshold. The precaution allows the system to reduce the resource that can tamper with the overall process.
The article provides critical steps in designing the TAPs protocols. According to the paper, TAPs enable the handling of collaboration problems between task and resource agents in a multi-agent platform (Ko & Nof, 2012). Based on the research paper, various sub-protocols are executed for varied contexts of task administrations. The authors stated that TAPs have the potential to handle three sub-protocols whereby each manages respective administration context to facilitate the ability to handle complex issues during the administration of the tasks. The TAPs design consists of three key components: Task Requirement Analysis Protocol (TRAP), which examines the task requirements. Shared Resource Allocation Protocol (SRAP) determines the best resource for the work, and lastly, Synchronization and Time-Out Protocol (STOP) timed out the served help.
Strengths and Limitations of the Article
The strengths of the article include the use broader perspective to provide deep insight into the research problem. It also cited other peoples’ studies to enrich the content of the investigation. The paper addresses the key issue, thus making users understand its purpose. Its main weakness is the use of complex diagrams in the form of a flow chart that can easily confuse the reader.
The research work has enabled me to have understood how problems associated with collaborative production systems can be managed through modification of the system. Based on the investigations, I can effectively formulate a protocol to minimize challenges that management encounters due to poor decisions of the framework. The investigation has provided good insight to enable organizations to enhance collaboration amongst different participants. The steps of designing the TAPs framework should be elaborated further for an easy understanding of the procedure involved.
In conclusion, the article “Design and application of task administration protocols for collaborative production and service systems” has outlined the key process by which an organization can overcome the problems associated with a collaborative production system. Authors have clearly described designing protocols that can help an organization manage task administration that requires urgent intervention. The TAPs system effectively enables managers to meet the set goals due to limited failures.
Multi-agent System Optimisation in Factories of the Future
The rapid improvement of technologies, including robotics, Internet of Things IoT, Internet of Services (IoS), and cyber argument collaborations, has improved the operations of warehouses. However, like many other technological advancements, it has its own challenges. One of these issues is increased costs in multi-robot automation operations. To overcome these obstacles and make warehouse systems reach their full potential studies have to be done to improve the systems. Dusadeerungsikul et al. (2021) look at how to further improve the warehouse system structure by introducing a new collaborative workflow protocol for cyber collaborative warehouses. The study proves that Cyber Collaborative Warehouse (C2W) operations can be significantly improved by focusing on multi-robot task allocation and scheduling. Dusadeerungsikul et al. (2021) suggest the use of a multi-system optimization model called collaboration planning requirement protocol for HUB-CI (CRP-H) to improve the efficiency and lower operation costs in warehouses. This paper summarizes the article titled ” multi-agent system optimization in factories of the future: cyber collaborative warehouse study” that the CRP-H protocol can deliver total operation cost, makespan, and total weighted completion time.
The Problem Addressed and the Significance
The recent improvements in smart warehouse automation systems have been of great help to managers, suppliers, and distributors. He et al. (2018) prove that the single robot policies designed for decision-making have helped managers of warehouses automate routine decisions with much ease. Nevertheless, fast improvements and technological changes have challenged emerging, future factories, warehouses, and service systems (Dusadeerungsikul et al., 2021). One of the biggest challenges that the warehouses face is a limited amount of study on managing the great inflow of data generated by the IoT and the IoS devices. This complication has necessitated the shift from single robot decision-making to multi-task allocation and scheduling. However, the literature and scope of study of multi-agent systems remain limited, and thus the changes in technology have made the single robot warehouse house systems less effective.
The authors used a model to show how cyber collaborative warehouses are faced with a multi-robot task allocation problem. They found a problem that when multiple robots performed a task, the total operational costs were significantly higher than when performed by an individual robot. The problem addressed by the paper is important to the main operations in a warehouse. Currently, most warehouses aim to package, store and retrieve packages flexibly and cost-effectively. Without a proper understanding of how multiple robotics should work, the performance of warehouses in the ever-changing technological landscape may be limited. Understanding how to manage the vast amount of information fed to the C2W, an issue addressed by the authors, would streamline the main operations of a warehouse.
Background and Known Practice
The new collaboration requirement planning protocol for HUB-CI (CRP-H) is an improvement of a previous study called “collaboration requirement planning protocol for HUB-CI in factories of the future.” According to Dusadeerungsikul et al. (2021), the C2W has three main agents; human operations, robots, and warehouse shelves. Huang et al. (2020) say the role of human operators is to input information into the systems using a spatial interface called VIPO. According to Dusadeerungsikul et al. (2019), apart from providing interphase to input the data, the VIPO also acts as an intelligence agent and helps fill the missing information. The paper illustrates that this input is processed by the HUB-CI, utilizing CRP-H, and the plan is then distributed to the warehouse shelves. The robots and the warehouse shelves then schedule and store packages.
New Methods and Results
The HUB-CI role is to maintain CRP-H, which has two modules. The first is the optimizer CRP-1that is used to assign a package to a robot or a robot team (Dusadeerungsikul et al., 2021). The goal of the CRP-1 is to reduce the costs of operation by assigning tasks to the most suitable agent. The optimizer assigns two types of tasks, the collaborative and non-collaborative assignments, but it does not give the sequence in which they are allocated. Allocating the sequencing of the tasks given is the role of the harmonizer (CRP-II), the second module. The harmonizer minimizes the makespan and the total weighted completion time by sequencing the tasks. The harmonizer faces computation burdens that must be lowered by an algorithm called collaborative robotics scheduling (CRS).
The researchers conducted three stimulation experiments in MATLAB, the first on system performance in the normal condition, the second to show where the UR happens during operation, and lastly to show critical missing information. The first experiment shows a reduced total average cost and completion time compared to the control experiment. At significance level 0.05, two sample standard t-tests (p<0.0001) confirm that the CRP-H significantly outperforms the baseline both in terms of total operating cost and total weighted completion time (Dusadeerungsikul et al., 2021). In experiment two, the results show the performance of the CRP-H when compared with unplanned operations to be lower than the baseline in both average operational cost and the weighted completion time. The human interventions in the last experiment also show reduced average operational cost and weighted completion time compared to the baseline. This statement proves that CRP-H improves the overall cost and time of completing tasks in C2W.
Strengths and Limitations of the Article
This article possesses several strengths, but it also has its fair share of limitations. The biggest obvious strength while going through the article is that the argument is explicit. The authors went a long way to ensure no vagueness or ambiguity in the paper by conducting and giving real-time examples and case studies. The general outline of the paper provides clear definition of terms making even a non-expert reader be able to understand the paper. The study has conducted extensive previous research on the topic, and the results from its experiments are conclusive. The paper is well illustrated with theoretical concepts backed up with concrete figures. The article has very few weaknesses, and it should be considered a very good article.
The study results show that the new planning protocol for HUB-CI (CRP-H) improves warehouse performance by collaborating with different C2W agents. Stakeholders involved in the warehouse, especially the automation 5.0, can benefit greatly by reading and applying the CRP-H in C2W. The analysis provided used the study to help them deal with complex tasks in a warehouse setting. Users should be able to use the information to know when to use one, two, or multiple robots in a warehouse setting. This article has enhanced my understanding of automation in a warehouse setting. I now know that organizations can save time and money without additional investment by using a collaborative warehouse strategy.
Dusadeerungsikul, P. O., He, X., Sreeram, M., & Nof, S. Y. (2021). Multi-agent system optimization in factories of the future: Cyber collaborative warehouse study. International Journal of Production Research, 1–15. Web.
Dusadeerungsikul, P. O., Sreeram, M., He, X., Nair, A., Ramani, K., Quinn, A. J., & Nof, S. Y. (2019). Collaboration requirement planning protocol for HUB-CI in factories of the future. Procedia Manufacturing, 39, 218–225. Web.
Gulzar, M. M., Rizvi, S. T. H., Javed, M. Y., Munir, U., & Asif, H. (2018). Multi-agent cooperative control consensus: A comparative review. Electronics, 7(2), 22. Web.
He, Z., Aggarwal, V., & Nof, S. Y. (2018). Differentiated service policy in smart warehouse automation. International Journal of Production Research, 56(22), 6956–6970.
Huang, G., Rao, P. S., Wu, M.-H., Qian, X., Nof, S. Y., Ramani, K., & Quinn, A. J. (2020). Vipo. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
Ko, H. S., & Nof, S. Y. (2012). Design and application of task administration protocols for collaborative production and service systems. International Journal of Production Economics, 135(1), 177-189.
Moghaddam, M., & Nof, S. Y. (2018). Collaborative service-component integration in cloud manufacturing. International Journal of Production Research, 56(1-2), 677-691.