scholarly journals A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Building Blocks ◽  
Allocation Problem ◽  
Accurate Analysis ◽  
Allocation Problems ◽  
Scheduling Method ◽  
Project Scheduling Problem ◽  
Block Based ◽  
Better Than

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

Research of Flexible Dynamic Scheduling Problem Based on Genetic Algorithm

2009 ◽  
Vol 16-19 ◽  
pp. 743-747
Author(s):  
Yu Wu ◽  
Xin Cun Zhuang ◽  
Cong Xin Li
Keyword(s):  
Genetic Algorithm ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Dynamic Scheduling ◽  
Scheduling Problem ◽  
Static Scheduling ◽  
Coding Method ◽  
Scheduling Method ◽  
Distribution Principle ◽  
Working Procedure

Solve the flexible dynamic scheduling problem by using “dynamic management & static scheduling” method. Aim at the property of flexible Manufacturing systems, the dynamic scheduling methods are analyzed and a coding method based on working procedure is improved in this paper. Thus it can be efficiently solve the problem of multiple working routes selection under the active distribution principle. On the other hand, the self-adaptive gene is provided and the parameters of the genetic algorithm are defined. In such a solution, the scheduling is confirmed to be simple and efficient.

scholarly journals Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1251
Author(s):  
Sabyasachi Mondal ◽  
Antonios Tsourdos
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Minimum Cost ◽  
Allocation Problem ◽  
Two Dimensional ◽  
Tabular Form ◽  
Comparison Study ◽  
New Variety ◽  
Quantum Genetic Algorithm ◽  
Better Than

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.

Dynamic Scheduling Method for Steelmaking-Continuous Casting

2010 ◽  
Vol 44-47 ◽  
pp. 2162-2167 ◽  
Author(s):  
Dong Feng He ◽  
Ai Jun Xu ◽  
Gang Yu ◽  
Nai Yuan Tian
Keyword(s):  
Genetic Algorithm ◽  
Continuous Casting ◽  
Dynamic Scheduling ◽  
Utilization Rate ◽  
Production Data ◽  
High Quality ◽  
Static Scheduling ◽  
Scheduling Method ◽  
Scheduling Plan ◽  
Generation Period

A method of dynamic scheduling of steelmaking-continuous casting is proposed, which includes static scheduling based on genetic algorithm and dynamic scheduling based on scheduling rules, mathematical model and complete rescheduling utilizing genetic algorithm. The simulation with eight hours’ production data in S steel plant showed that the method could draw quickly a high quality and performable dynamic scheduling plan according to random production disturbance. The average utilization rate of converter could reach 95% and the generation period of initial scheduling is less than 3 minutes. The max dynamic scheduling adjustment period did not exceed 1 minute.

scholarly journals Multi-Criteria Decision-Making Methods to Address Water Allocation Problems: A Systematic Review

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 125
Author(s):  
Sintayehu Legesse Gebre ◽  
Dirk Cattrysse ◽  
Jos Van Orshoven
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Water Allocation ◽  
Water Shortage ◽  
Allocation Problem ◽  
Future Research ◽  
Multi Criteria Decision Making ◽  
Nsga Ii ◽  
Allocation Problems ◽  
Water Problems

The water allocation problem is complex and requires a combination of regulations, policies, and mechanisms to support water management to minimize the risk of shortage among competing users. This paper compiles the application of multi-criteria decision-making (MCDM) related to water allocation. In this regard, this paper aims to identify and to discern the pattern, distribution of study regions, water problem classifications, and decision techniques application for a specific water allocation problem. We applied a systematic literature review study from 2000 to 2019 by using four literature databases (Web of Science, Scopus, Science Direct, and Google Scholar). From 109 papers, 49 publications have been identified and information extracted. This study reveals that in the past two decades the application of MCDM in the area of water allocation has increased particularly after 2014. Around 65% and 12% of study papers were conducted in Asia and Europe, respectively. Water shortage, water use management, and water quality were consecutively the most top-ranked discussed water problems. NSGA II (non-dominated sorting genetic algorithm), GA (genetic algorithm), and LP (linear programming) are the more often applied decision methods to solve water allocation problems. The key findings of this study provide guidelines for future research studies.

scholarly journals A SYSTEMATIC COLLECTION OF NATURAL INTERACTIONS FOR IMMERSIVE MODELING FROM BUILDING BLOCKS

2021 ◽  
Vol 1 ◽  
pp. 283-292
Author(s):  
Jakob Harlan ◽  
Benjamin Schleich ◽  
Sandro Wartzack
Keyword(s):  
User Interfaces ◽  
Building Blocks ◽  
Hand Tracking ◽  
Spatial Concepts ◽  
Natural User Interfaces ◽  
Modeling Systems ◽  
Block Based ◽  
Natural Interactions ◽  
Tracking Technology ◽  
Systematic Collection

AbstractThe increased availability of affordable virtual reality hardware in the last years boosted research and development of such systems for many fields of application. While extended reality systems are well established for visualization of product data, immersive authoring tools that can create and modify that data are yet to see widespread productive use. Making use of building blocks, we see the possibility that such tools allow quick expression of spatial concepts, even for non-expert users. Optical hand-tracking technology allows the implementation of this immersive modeling using natural user interfaces. Here the users manipulated the virtual objects with their bare hands. In this work, we present a systematic collection of natural interactions suited for immersive building-block-based modeling systems. The interactions are conceptually described and categorized by the task they fulfil.

scholarly journals Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

scholarly journals Optimization of spectrum sensing in cognitive radio using genetic algorithm

2012 ◽  
Vol 25 (3) ◽  
pp. 235-243 ◽  
Author(s):  
Rashmi Deka ◽  
Soma Chakraborty ◽  
Sekhar Roy
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Rapid Development ◽  
Probability Of Detection ◽  
Radio System ◽  
Probability Of False Alarm ◽  
Space Allocation ◽  
Wireless Environment ◽  
Better Than

Spectrum availability is becoming scarce due to the rise of number of users and rapid development in wireless environment. Cognitive radio (CR) is an intelligent radio system which uses its in-built technology to use the vacant spectrum holes for the use of another service provider. In this paper, genetic algorithm (GA) is used for the best possible space allocation to cognitive radio in the spectrum available. For spectrum reuse, two criteria have to be fulfilled - 1) probability of detection has to be maximized, and 2) probability of false alarm should be minimized. It is found that with the help of genetic algorithm the optimized result is better than without using genetic algorithm. It is necessary that the secondary user should vacate the spectrum in use when licensed users are demanding and detecting the primary users accurately by the cognitive radio. Here, bit error rate (BER) is minimized for better spectrum sensing purpose using GA.

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