After nearly 30 years of development, service robot technology has made important achievements in the interdisciplinary aspects of machinery, information, materials, control, medicine, etc. These robot types have different shapes, and mainly in some are shaped based on application. Till today various structure are proposed which for the better analysis’s need to have the mathematical equation that can model the structure and later the behaviour of them after implementing the controlling strategy. The current paper discusses the various shape and applications of all available service robots and briefly summarizes the research progress of key points such as robot dynamics, robot types, and different dynamic models of the differential types of service robots. The current review study can be helpful as an initial node for all researchers in this topic and help them to have the better simulation and analyses. Besides the current research shows some application that can specify the service robot model over the application.

/pdf/review-on-the-service-robot-mathematical-model-1z5cwwit.pdf

Review On: The Service Robot Mathematical Model

In this paper we study the scheduling of a given set of jobs on several identical parallel machines tended by a common server. Each job must be processed on one of the machines. Prior to processing, the server has to set up the relevant machine. The objective is to schedule the jobs so as to minimize the total weighted job completion times. We provide an approximation algorithm to tackle this intractable problem and analyze the worst-case performance of the algorithm for the general, as well as a special, case of the problem.

Approximation algorithm for parallel machine scheduling with a common server

An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages

Digital-PV: A digital twin-based platform for autonomous aerial monitoring of large-scale photovoltaic power plants

In this paper, an improved DE algorithm called double population self-adaptive differential evolution (DPSADE) algorithm is used to solve the hybrid flow shop load balancing scheduling problem (HFS-LBSP) which combines a novel double population cooperative evolution mechanism with a special self-adaptive parameter adjusting strategy. A mathematical model in which the weighted summation of the processing time load balancing cost and the total parallel machine waiting time is set as load balancing comprehensive evaluation index is formulated for this problem. In the last, The comparison between DPSADE and DE, SADE demostrates the effectiveness of DPSADE in solving HFS-LBSP.

/pdf/an-effective-de-algorithm-for-hybrid-flow-shop-load-vdba1vvk67.pdf

An effective DE algorithm for hybrid flow shop load balancing scheduling problem

Digital twin application for reinforcement learning based optimal scheduling and reliability management enhancement of systems

This paper considers the practical manufacturing environment of the hybrid flow shop (HFS) with non-identical machines in parallel. In order to significantly enhance the performance level of manufacturing, maintaining load balancing among parallel machines is very important. The aim of this paper is to minimize makespan with load balancing in a non-identical parallel machine environment by using hybrid genetic algorithm (HGA). In the HGA, the neighborhood search-based method is used together with genetic algorithm as local optimization method to balance the exploration and exploitation abilities. The representation of chromosome used in this paper is composed of two layers: allocation layer and sequencing layer, which can be encode and decoded easily. In generating initial population, a special constraint of load balancing between parallel machines is used to reduce the number of individuals. And particular crossover operation is used, which generates multiple offspring at a time, so that the efficiency of the algorithm can be well improved. At last, the proposed algorithm is tested on a benchmark, and numerical example shows good result.

A Hybrid Genetic Algorithm for Hybrid Flow Shop Scheduling with Load Balancing

This paper discusses the structure of wheel mobile robot, dynamics model and control model are developed. The method is tested by computer simulation, which is used to control the wheel mobile robot to follow straightaway and circle path, and numerical results show good result.

Dynamics Model and Control of Path Following of Wheel Mobile Robot

This paper addresses the problem of scheduling jobs on two identical parallel machines with a single server such that the makespan is minimized. Each job can be processed by either one of the two parallel machines, but before processing, a setup operation must be done by a single server. Two mixed integer programming models for the considered scheduling problem are developed using time indexed variable and sequence indexed variable respectively. To evaluate the performance of the two models, CPLEX is used to test instances generated randomly. The results show that the sequence indexed model outperforms on the number of variables and computational time, which can be used to solve the instance of 20 jobs.

Mixed Integer Programming Models for Two Identical Parallel Machines with a Single Server

Open shop scheduling problem is a typical NP problem with wide engineering background. It is of importance with respect of theory and application. In this paper, a mixed integer programming model was established with the objective to minimize the makespan based on the characteristics of the open shop, and a evolution genetic algorithm(EGA) was proposed. The representation of chromosome used in this paper was composed of two layers: operation layer and machine layer, which can be encoded and decoded easily. In generating initial population, DS/LTRP heuristic was used in order to improve the quality of the population. And particular crossover operation was proposed, which generated multiple offspring at a time, so that the efficiency of the algorithm can be well improved. At last, the proposed algorithm was tested on taillard benchmark, and numerical example showed good result.

Research on Open Shop Scheduling Based on Genetic Algorithm

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