Scheduling Of Mobile Robots Research Articles

Genetic Algorithm for Solving Mobile Robot Scheduling Problem in Flexible Manufacturing Environment

The scheduling genuinely a complex process, aimed at optimizing operational activities in pursuit of one or more objectives by leveraging production data which may include previous schedules. The scheduling problem in Flexible Manufacturing System (FMS) is commonly categorized as Nondeterministic Polynomial (NP)-hard combinatorial optimization problems and it remains as an endure problem to industrial practitioners and researchers. As part of real production scheduling, once one task is finished processing on a machine, transportation equipment such as mobile robot transports the completed task to the next machine. The problem of scheduling mobile robot in FMS pertains to the task allocation process for the robots, considering the transportation costs and the time spent to complete all operations. In recent years, Genetic Algorithm (GA) has been a remarkably effective search algorithm for solving a wide range of scheduling problems in a manner that achieves near-optimal solutions. This paper presents the metaheuristic techniques, specifically genetic algorithm, to address the NP-hard scheduling problem of two identical mobile robots in Job-Shop FMS environment. The algorithm is developed with the aim of finding feasible solutions to the integrated problem by minimizing the amount of time it takes to finish all tasks, commonly referred to as makespan. The performance of GA is evaluated with some numerical experiments which is executed via Matlab software. The scheduling results shows that the developed GA able to obtained the near-optimal solution of minimal makespan and converge within a short period of time.

Adaptive Mobile Robot Scheduling in Multiproduct Flexible Manufacturing Systems Using Reinforcement Learning

Abstract The integration of mobile robots in material handling in flexible manufacturing systems (FMS) is made possible by the recent advancements in Industry 4.0 and industrial artificial intelligence. However, effectively scheduling these robots in real-time remains a challenge due to the constantly changing, complex, and uncertain nature of the shop floor environment. Therefore, this paper studies the robot scheduling problem for a multiproduct FMS using a mobile robot for loading/unloading parts among machines and buffers. The problem is formulated as a Markov Decision Process, and the Q-learning algorithm is used to find an optimal policy for the robot's movements in handling different product types. The performance of the system is evaluated using a reward function based on permanent production loss and the cost of demand dissatisfaction. The proposed approach is validated through a numerical case study that compares the proposed policy to a similar policy with different reward function and the first-come-first-served policy, showing a significant improvement in production throughput of approximately 23%.

Research on the Scheduling of Mobile Robots in Mixed-Model Assembly Lines Considering Workstation Satisfaction and Energy Consumption

With the intensification of the global energy crisis, the production costs of manufacturing companies have increased significantly. To reduce the production energy consumption and costs in mixed-model assembly lines while improving efficiency and workstation satisfaction, novel line-integrated supermarkets and mobile robots are introduced. Considering the split delivery caused by workstation satisfaction and the mobile robot’s energy limitation, a multiobjective mathematical model of mobile robot scheduling in a mixed-model assembly line with a fuzzy time window is presented with the goal of maximizing workstation satisfaction while minimizing energy consumption. On this basis, according to the problem’s characteristics, a nondominated sorting genetic algorithm II with variable neighborhood search (VNSGA-II) is developed that constructs the initial solution using a heuristic method, improves crossover operation, and performs neighborhood search using three operators: exchange, insertion, and 2-opt to improve the solution’s quality. Finally, two numerical experiments are used to validate the model and algorithm. The results demonstrate that: (1) The scheduling model for mobile robots in a mixed-model assembly line that allows for spilt delivery and uses a normal fuzzy membership function to characterize workstation satisfaction is more in line with production practice. (2) The VNSGA-II algorithm can quickly establish a reasonable scheduling scheme for mobile robots in a mixed-model assembly line, and provide managers with a basis for making scientific decisions. Compared to MOPSO and NSGA-II, workstation satisfaction has improved by 0.91% and 1.12%, respectively, and mobile robots’ energy consumption has decreased by 12.53% and 13.66%, respectively.

A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA–SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.

Scheduling of mobile robots for transportation and manufacturing tasks

Current manufacturing systems have a tendency to become more and more flexible to adapt to the needs of product diversification. Such a system, e.g. flexible manufacturing system, consists of a number of automatic machines, material handling devices such as automated guided vehicles (AGVs) or mobile robots, and a central control computer. Mobile robots, as well as AGVs, can move around in their working space to transport components among machines. The main novelty of this work is that mobile robots can also execute various value-added tasks without human intervention thanks to their manipulation arms which is not possible for AGVs. This new characteristic certainly makes the problem more complex and computationally expensive. To utilize these manufacturing systems in an efficient manner, it is necessary to schedule transportation of product components by mobile robots and to schedule processing of products on machines possibly by mobile robots. Consequently, the key innovation of this work lies in consideration of three interrelated sub-problems which must be solved. They include computing the sequence of operations on machines, the robot assignment for transportation, and the robot assignment for processing. To achieve this goal, a computationally efficient hybrid heuristic method combining genetic algorithm and tabu search is developed to solve the problem considering makespan minimization. A mixed-integer programming (MIP) model is formulated. Another combined method using results of the hybrid heuristic is proposed to speed up the solving of MIP model. The quality of hybrid heuristic’s solutions is compared and evaluated by using those of the MIP model as reference points.

Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study

The typical production system in carton box production companies is cell-production. These cells normally benefit from a mobile robot which serves the machines according to a given schedule. One of the main problems of such companies is finding the order of robot moves in a way that the time required for completing all jobs is minimized. In the studied case in this research, each cell contains three machines of which, two or three of them might be activated for production process depending on the product type. These machines are equipped with a one-capacitated input and output buffer. Considering the fact that the machines are capable of performing any operation, the assignments of the jobs to them may have several alternatives. The one-capacitated buffers make the robot scheduling more complex as they act as extra stations to be served by the robot (contribute to exponential increase in job assignments permutation). This study aims to deal with this complexity and provide a decision-making toolbox for business owners to determine and employ the best robot moving schedule according to the characteristics of the problem. The mentioned approach significantly contributes to decision-maker’s effective time management and results in adopting a better production scheme for each production cycle. In line with this prospect, this research proposes a sequential part production matrix (SPPM) to determine feasible robot move strategies through which the best scheduling scheme is introduced for different problem configurations. Additionally, a metaheuristic algorithm is proposed to determine the best robot move strategy for cases with more active machines in a cell as manual determination of the robot move strategies becomes exhaustive in such cases.

Makespan minimisation for pre-emptive scheduling of mobile robots in FMS environment

Modern manufacturing models demand customisation and service orientation. The generations of mobile robots will hence have to cope with more complex tasks and rapidly adapt to new situations. With the high level of flexibility, the mobile robots are able to not only transport materials as conventional transporting robots, but also perform manufacturing tasks at some certain machines using their mounted manipulation arms. This paper considers the problem of simultaneous scheduling of machines and such mobile robots in a flexible manufacturing system. The pre-emption occurs when manufacturing tasks are interrupted to allow the mobile robots to carry out the transport. The performance criterion is to minimise the makespan. A genetic-algorithm-based heuristic is developed to find near-optimal solutions. To evaluate the performance of the proposed heuristic, a mixed-integer programming model is also presented. A numerical example and computational experiments are conducted to demonstrate the effectiveness of the proposed approaches.

Service Oriented Architecture for Dynamic Scheduling of Mobile Robots for Material Supply

This paper presents the design and prototype implementation of a service based control system responsible for the material supply operations in assembly lines. Mobile Assistant Units (MAUs) are responsible for the transportation of the consumables from the warehouses to the production stations. A service oriented, web based software has been developed for the monitoring of the shop floor status and the parts’ supply dynamic scheduling, based on time and inventory. The proposed system has been applied to a case from the automotive sector demonstrating the ease of deployment and efficiency in the co-ordination of the mobile units’ operation.

Navigation and Scheduling of Mobile Robots in Uncertain Environment

Navigation and Scheduling of Mobile Robots in Uncertain Environment

Hierarchical Scheduling of Mobile Robots in Production-Transportation Supply Chains

In this paper we propose a decomposition approach that hierarchically integrates the batching and local search heuristics in manufacturing scheduling. The problem comprises two main interrelated stages embedded in any production-transportation supply chain, namely (i) scheduling processing of raw materials and robot's transportation operation within each individual cell, and (ii) scheduling of transportation and distribution of batches of semi-finished products between cells. Several efficient heuristic algorithms are proposed. This work has been motivated by a real-life problem of production planning for a CIM system for manufacturing of multi-component products served by robots.