354 research outputs found
Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems
Recovering the timetable after a delay is essential to the smooth and efficient operation
of the railways for both passengers and railway operators. Most current
railway rescheduling research concentrates on static problems where all delays are
known about in advance. However, due to the unpredictable nature of the railway
system, it is possible that further unforeseen incidents could occur while the trains
are running to the new rescheduled timetable. This will change the problem, making
it a dynamic problem that changes over time. The aim of this work is to investigate
the application of ant colony optimisation (ACO) to dynamic and dynamic multiobjective
railway rescheduling problems. ACO is a promising approach for dynamic
combinatorial optimisation problems as its inbuilt mechanisms allow it to adapt to
the new environment while retaining potentially useful information from the previous
environment. In addition, ACO is able to handle multi-objective problems by
the addition of multiple colonies and/or multiple pheromone and heuristic matrices.
The contributions of this work are the development of a junction simulator to
model unique dynamic and multi-objective railway rescheduling problems and an
investigation into the application of ACO algorithms to solve those problems. A
further contribution is the development of a unique two-colony ACO framework to
solve the separate problems of platform reallocation and train resequencing at a UK
railway station in dynamic delay scenarios.
Results showed that ACO can be e
ectively applied to the rescheduling of trains
in both dynamic and dynamic multi-objective rescheduling problems. In the dynamic
junction rescheduling problem ACO outperformed First Come First Served
(FCFS), while in the dynamic multi-objective rescheduling problem ACO outperformed
FCFS and Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stateof-
the-art multi-objective algorithm. When considering platform reallocation and
rescheduling in dynamic environments, ACO outperformed Variable Neighbourhood
Search (VNS), Tabu Search (TS) and running with no rescheduling algorithm. These
results suggest that ACO shows promise for the rescheduling of trains in both dynamic
and dynamic multi-objective environments.Engineering and Physical Sciences Research Council (EPSRC
Task allocation and motion coordination of multiple autonomous vehicles - with application in automated container terminals
University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis focuses on developing an approach to solve the complex problem of task allocation and motion coordination simultaneously for a large fleet of autonomous vehicles in highly constrained operational environments. The multi-vehicle task allocation and motion coordination problem consists of allocating different tasks to different autonomous vehicles and intelligently coordinating motions of the vehicles without human interaction. The motion coordination itself comprises two sub-problems: path planning and collision / deadlock avoidance. Although a number of research studies have attempted to solve one or two aspects of this problem, it is rare to note that many have attempted to solve the task allocation, path planning and collision avoidance simultaneously. Therefore, it cannot be conclusively said that, optimal or near-optimal solutions generated based on one aspect of the problem will be optimal or near optimal results for the whole problem. It is advisable to solve the problem as one complete problem rather than decomposing it. This thesis intends to solve the complex task allocation, path planning and collision avoidance problem simultaneously.
A Simultaneous Task Allocation and Motion Coordination (STAMC) approach is developed to solve the multi-vehicle task allocation and motion coordination problem in a concurrent manner. Further, a novel algorithm called Simultaneous Path and Motion Planning (SiPaMoP) is proposed for collision free motion coordination. The main objective of this algorithm is to generate collision free paths for autonomous vehicles, once they are assigned with tasks in a conventional path topology of a material handling environment. The Dijkstra and A * shortest path search algorithms are utilised in the proposed Simultaneous Path and Motion Planning algorithm.
The multi-vehicle task allocation and motion coordination problem is first studied in a static environment where all the tasks, vehicles and operating environment information are assumed to be known. The multi-vehicle task allocation and motion coordination problem in a dynamic environment, where tasks, vehicles and operating environment change with time is then investigated. Furthermore, issues like vehicle breakdowns, which are common in real world situations, are considered. The computational cost of solving the multi-vehicle STAMC problem is also addressed by proposing a distributed computational architecture and implementing that architecture in a cluster computing system. Finally, the proposed algorithms are tested in a case study in an automated container terminal environment with a large fleet of autonomous straddle carriers.
Since the multi-vehicle task allocation and motion coordination is an NP-hard problem, it is almost impossible to find out the optimal solutions within a reasonable time frame. Therefore, this research focuses on investigating the appropriateness of heuristic and evolutionary algorithms for solving the STAMC problem. The Simulated Annealing algorithm, Ant Colony and Auction algorithms have been investigated. Commonly used dispatching rules such as first come first served, and closest task first have also been applied for comparison. Simulation tests of the proposed approach is conducted based on information from the Fishermen Island's container terminal of Patrick Corporation (Pty.) Ltd in Queensland, Australia where a large fleet of autonomous straddle carriers operate. The results shows that the proposed meta-heuristic techniques based simultaneous task allocation and motion coordination approach can effectively solve the complex multi-vehicle task allocation and motion coordination problem and it is capable of generating near optimal results within an acceptable time frame
Hybrid Meta-heuristic Algorithms for Static and Dynamic Job Scheduling in Grid Computing
The term ’grid computing’ is used to describe an infrastructure that connects geographically
distributed computers and heterogeneous platforms owned by multiple organizations
allowing their computational power, storage capabilities and other resources to be selected
and shared. Allocating jobs to computational grid resources in an efficient manner is one
of the main challenges facing any grid computing system; this allocation is called job
scheduling in grid computing. This thesis studies the application of hybrid meta-heuristics
to the job scheduling problem in grid computing, which is recognized as being one of
the most important and challenging issues in grid computing environments. Similar to
job scheduling in traditional computing systems, this allocation is known to be an NPhard
problem. Meta-heuristic approaches such as the Genetic Algorithm (GA), Variable
Neighbourhood Search (VNS) and Ant Colony Optimisation (ACO) have all proven their
effectiveness in solving different scheduling problems. However, hybridising two or more
meta-heuristics shows better performance than applying a stand-alone approach. The new
high level meta-heuristic will inherit the best features of the hybridised algorithms, increasing
the chances of skipping away from local minima, and hence enhancing the overall
performance. In this thesis, the application of VNS for the job scheduling problem in grid
computing is introduced. Four new neighbourhood structures, together with a modified
local search, are proposed. The proposed VNS is hybridised using two meta-heuristic
methods, namely GA and ACO, in loosely and strongly coupled fashions, yielding four
new sequential hybrid meta-heuristic algorithms for the problem of static and dynamic
single-objective independent batch job scheduling in grid computing. For the static version
of the problem, several experiments were carried out to analyse the performance of the
proposed schedulers in terms of minimising the makespan using well known benchmarks.
The experiments show that the proposed schedulers achieved impressive results compared
to other traditional, heuristic and meta-heuristic approaches selected from the bibliography.
To model the dynamic version of the problem, a simple simulator, which uses
the rescheduling technique, is designed and new problem instances are generated, by
using a well-known methodology, to evaluate the performance of the proposed hybrid
schedulers. The experimental results show that the use of rescheduling provides significant
improvements in terms of the makespan compared to other non-rescheduling approaches
Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation
Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly
Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly
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HEDCOS: High Efficiency Dynamic Combinatorial Optimization System using Ant Colony Optimization algorithm
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDynamic combinatorial optimization is gaining popularity among industrial practitioners due to the ever-increasing scale of their optimization problems and efforts to solve them to remain competitive. Larger optimization problems are not only more computationally intense to optimize but also have more uncertainty within problem inputs. If some aspects of the problem are subject to dynamic change, it becomes a Dynamic Optimization Problem (DOP).
In this thesis, a High Efficiency Dynamic Combinatorial Optimization System is built to solve challenging DOPs with high-quality solutions. The system is created using Ant Colony Optimization (ACO) baseline algorithm with three novel developments.
First, introduced an extension method for ACO algorithm called Dynamic Impact. Dynamic Impact is designed to improve convergence and solution quality by solving challenging optimization problems with a non-linear relationship between resource consumption and fitness. This proposed method is tested against the real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem and the theoretical benchmark Multidimensional Knapsack Problem (MKP).
Second, a non-stochastic dataset generation method was introduced to solve the dynamic optimization research replicability problem. This method uses a static benchmark dataset as a starting point and source of entropy to generate a sequence of dynamic states. Then using this method, 1405 Dynamic Multidimensional Knapsack Problem (DMKP) benchmark datasets were generated and published using famous static MKP benchmark instances as the initial state.
Third, introduced a nature-inspired discrete dynamic optimization strategy for ACO by modelling real-world ants’ symbiotic relationship with aphids. ACO with Aphids strategy is designed to solve discrete domain DOPs with event-triggered discrete dynamism. The strategy improved inter-state convergence by allowing better solution recovery after dynamic environment changes. Aphids mediate the information from previous dynamic optimization states to maximize initial results performance and minimize the impact on convergence speed. This strategy is tested for DMKP and against identical ACO implementations using Full-Restart and Pheromone-Sharing strategies, with all other variables isolated.
Overall, Dynamic Impact and ACO with Aphids developments are compounding. Using Dynamic Impact on single objective optimization of MMPPFO, the fitness value was improved by 33.2% over the ACO algorithm without Dynamic Impact. MKP benchmark instances of low complexity have been solved to a 100% success rate even when a high degree of solution sparseness is observed, and large complexity instances have shown the average gap improved by 4.26 times. ACO with Aphids has also demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2% for a total compounded dynamic optimization performance improvement of 6.02 times. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5% for a total compounded dynamic optimization performance improvement of 8.99 times
Energy Efficient Manufacturing Scheduling: A Systematic Literature Review
The social context in relation to energy policies, energy supply, and
sustainability concerns as well as advances in more energy-efficient
technologies is driving a need for a change in the manufacturing sector. The
main purpose of this work is to provide a research framework for
energy-efficient scheduling (EES) which is a very active research area with
more than 500 papers published in the last 10 years. The reason for this
interest is mostly due to the economic and environmental impact of considering
energy in production scheduling. In this paper, we present a systematic
literature review of recent papers in this area, provide a classification of
the problems studied, and present an overview of the main aspects and
methodologies considered as well as open research challenges
Achieving cost competitiveness with an agent based integrated process planning and production scheduling system
From a manufacturing perspective, the efficiency of manufacturing operations (such as process planning and production scheduling) are the key element for enhancing manufacturing competence. Process planning and production scheduling functions have been traditionally treated as two separate activities, and have resulted in a range of inefficiencies. These include infeasible process plans, non-available/overloaded resources, high production costs, long production lead times, and so on. Above all, it is unlikely that the dynamic changes can be efficiently dealt with. Despite much research has been conducted to integrate process planning and production scheduling to generate optimised solutions to improve manufacturing efficiency, there is still a gap to achieve the competence required for the current global competitive market. In this research, the concept of multi-agent system (MAS) is adopted as a means to address the aforementioned gap. A MAS consists of a collection of intelligent autonomous agents able to solve complex problems. These agents possess their individual objectives and interact with each other to fulfil the global goal. This paper describes a novel use of an autonomous agent system to facilitate the integration of process planning and production scheduling functions to cope with unpredictable demands, in terms of uncertainties in product mix and demand pattern. The novelty lies with the currency-based iterative agent bidding mechanism to allow process planning and production scheduling options to be evaluated simultaneously, so as to search for an optimised, cost-effective solution. This agent based system aims to achieve manufacturing competence by means of enhancing the flexibility and agility of manufacturing enterprises
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