Role Engine Implementation for a Continuous and Collaborative Multi-Robot System

In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multi-robot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian Process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment's skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach's feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.Comment: 10 pages, 18 figures, summited in IEEE Transactions on Systems, Man and Cybernetics(T-SMC

An Efficient Outpatient Scheduling Approach

Outpatient scheduling is considered as a complex problem. Efficient solutions to this problem are required by many health care facilities. Our previous work in Role-Based Collaboration (RBC) has shown that the group role assignment problems can be solved efficiently. Making connections between these two kinds of problems is meaningful. This paper proposes an efficient approach to outpatient scheduling by specifying a bidding method and converting it to a group role assignment problem. The proposed approach is validated by conducting simulations and experiments with randomly generated patient requests for available time slots. The major contribution of this paper is an efficient outpatient scheduling approach making automatic outpatient scheduling practical. The exciting result is due to the consideration of outpatient scheduling as a collaborative activity and the creation of a qualification matrix in order to apply the group role assignment algorithm. Note to practitioners -As the “Age Wave” approaches, health care facilities are becoming relatively scarce worldwide compared with what are demanded. The varying availability, requirements, and preferences of both facilities and outpatients make the problem of scheduling outpatient appointments on health care facilities extremely challenging. Traditional manually operated scheduling systems based on phone calls are out of date although they are still widely used due to lack of new effective scheduling systems. To solve such a problem requires an efficient Web-based system to schedule the appointments instantly in order to make full use of those expensive and critical facilities. It is able to optimize concerned performance objectives in a clinical environment. The proposed approach provides a technical foundation for efficient Web-based scheduling systems, which can be applied directly to not only outpatient scheduling in the health care sector, but also in some other real-world scheduling applications

Improving group role assignment problem by incremental assignment algorithm

The Assignment Problem is a basic combinatorial optimization problem. In a weighted bipartite graph, the Assignment Problem is to find a largest sum of weights matching. The Hungarian method is a well-known algorithm which is combinatorial optimization. Adding a new row and a new column to a weighted bipartite graph is called the Incremental Assignment Problem (IAP). The maximum weighted matching (the optimal solution) of the weighted bipartite graph has been given. The algorithm of the Incremental Assignment Problem utilizes the given optimal solution (the maximum weighted matching) and the dual variables to solve the matrix after extended bipartite graph. This thesis proposes an improvement of the Incremental Assignment Algorithm (IAA), named the Improved Incremental Assignment Algorithm. The improved algorithm will save the operation time and operation space to find the optimal solution (the maximum weighted matching) of the bipartite graph. We also present the definition of the Incremental Group Role Assignment Problem that based on the Group Role Assignment Problem (GRAP) and Incremental Assignment Problem (IAP). A solution has been designed to solve it by using the Improved Incremental Assignment Algorithm (IIAA). In this thesis, simulation results are presented. We utilize the tests to compare the algorithm of the Incremental Assignment Problem and the Improved Incremental Assignment Algorithm (IIAA) to show the advantages of IIAA.Master of Science (MSc) in Computational Science

The 0 -1 multiple knapsack problem

In operation research, the Multiple Knapsack Problem (MKP) is classified as a combinatorial optimization problem. It is a particular case of the Generalized Assignment Problem. The MKP has been applied to many applications in naval as well as financial management. There are several methods to solve the Knapsack Problem (KP) and Multiple Knapsack Problem (MKP); in particular the Bound and Bound Algorithm (B&B). The bound and bound method is a modification of the Branch and Bound Algorithm which is defined as a particular tree-search technique for the integer linear programming. It has been used to obtain an optimal solution. In this research, we provide a new approach called the Adapted Transportation Algorithm (ATA) to solve the KP and MKP. The solution results of these methods are presented in this thesis. The Adapted Transportation Algorithm is applied to solve the Multiple Knapsack Problem where the unit profit of the items is dependent on the knapsack. In addition, we will show the link between the Multiple Knapsack Problem (MKP) and the multiple Assignment Problem (MAP). These results open a new field of research in order to solve KP and MKP by using the algorithms developed in transportation.Master of Science (MSc) in Computational Scienc

Using group role assignment to solve Dynamic Vehicle Routing Problem

The Dynamic Vehicle Routing Problem (DVRP) is a more complex problem than the traditional Vehicle Routing Problem (VRP) in the combinatorial optimization of operations research. With more degrees of freedom, DVRP introduces new challenges while judging the merit of a given route plan. This thesis utilized the time slice strategy to solve dynamic and deterministic routing problems. Based on Group Role Assignment (GRA) and two different routing methods (Modified Insertion heuristic routing and Modified Composite Pairing Or-opt routing), a new ridesharing system has been designed to provide services in the real world. Simulation results are presented in this thesis. A qualitative comparison has been made to outline the advantages and performance of our solution framework. From the numerical results, the proposed method has a great potential to put into operation in the real world and provides a new transit option for the public.Master of Science (MSc) in Computational Scienc