SELF-ASSEMBLED METAL ATOM CHAINS ON GRAPHENE NANORIBBONS

Electronic and magnetic properties of alkali and alkaline-earth metal doped graphene nanoribbons (GNRs) are studied by the pseudopotential density functional method. Strong site dependence is observed in metal adsorption on GNRs, and the adsorbed metal atoms are found to spontaneously form atomic chains in a particular form of GNRs. Such doped GNRs exhibit intriguing magnetic properties such as hysteresis and spin compensation as metal atoms switch from one edge to another at alternating gate voltages. Our study shows that the metal atoms can be used as reagents that can identify the edge atomic structures of GNRs and also as gate-driven spin valves that control the spin current in GNRs.X1170sciescopu

ELECTRONIC PROPERTY OF NA-DOPED EPITAXIAL GRAPHENES ON SIC

The electronic property of epitaxial graphenes with Na adsorption or intercalation is studied with the use of pseudopotential density functional method. It is found that the charge transfer and the Na binding energy show strong coverage dependence. Calculated energetics shows that Na prefers the intercalation between the buffer and top graphene layers to the adsorption on top graphene layer. The buffer layer is inert to Na adsorption on top graphene layer but it is charged when Na atoms are intercalated. This indicates that the conduction of epitaxial graphenes can be affected significantly by Na intercalation.X1119sciescopu

Performance optimisation of mobile robots in dynamic environments

This paper presents a robotic simulation system, that combines task allocation and motion planning of multiple mobile robots, for performance optimisation in dynamic environments. While task allocation assigns jobs to robots, motion planning generates routes for robots to execute the assigned jobs. Task allocation and motion planning together play a pivotal role in optimisation of robot team performance. These two issues become more challenging when there are often operational uncertainties in dynamic environments. We address these issues by proposing an auction-based closed-loop module for task allocation and a bio-inspired intelligent module for motion planning to optimise robot team performance in dynamic environments. The task allocation module is characterised by a closed-loop bid adjustment mechanism to improve the bid accuracy even in light of stochastic disturbances. The motion planning module is bio-inspired intelligent in that it features detection of imminent neighbours and responsiveness of virtual force navigation in dynamic traffic conditions. Simulations show that the proposed system is a practical tool to optimise the operations by a team of robots in dynamic environments. © 2012 IEEE.published_or_final_versionThe IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS 2012), Tianjin, China, 2-4 July 2012. In Proceedings of IEEE VECIMS, 2012, p. 54-5

Stochastic Lot Sizing for Shareholder Wealth Maximisation under Carbon Footprint Management

Fulltext in http://www.jiii.org/index.php?m=content&c=index&a=show&catid=41&id=141There is a growing consensus that human beings must cut greenhouse gas emissions to mitigate global warming and the resultant impacts on the environment. However, production optimisation has rarely taken this issue into consideration, often leading to environmentally unsustainable operation decisions. This paper presents a lot sizing batch optimisation model for a stochastic make-to-order production environment under the carbon emission trading mechanism—currently the most effective market-based carbon emission controlling system, with an aim to maximise the long-term sustainable interests of corporate owners, well-known as the shareholder wealth. To more closely reflect the real-world manufacturing environment, the proposed model adopts general distributions, instead of unrealistic theoretical assumptions, for random variables. We apply the model to investigate the impacts of the carbon emission trading mechanism on shareholder wealth, and test its hedging capability against a series of risk factors. The analytical results provide insights into production optimisation with carbon footprint management.International Conference on Industrial Engineering and Applications (ICIEA 2014), Sydney, Australia, 29-30 May 2014. In Journal of Industrial and Intelligent Information, 2015, v. 3 n. 1, p. 1-

A decomposition based algorithm for flexible flow shop scheduling with machine breakdown

Research on flow shop scheduling generally ignores uncertainties in real-world production because of the inherent difficulties of the problem. Scheduling problems with stochastic machine breakdown are difficult to solve optimally by a single approach. This paper considers makespan optimization of a flexible flow shop (FFS) scheduling problem with machine breakdown. It proposes a novel decomposition based approach (DBA) to decompose a problem into several sub-problems which can be solved more easily, while the neighbouring K-means clustering algorithm is employed to group the machines of an FFS into a few clusters. A back propagation network (BPN) is then adopted to assign either the shortest processing time (SPT) or the genetic algorithm (GA) to each cluster to solve the sub-problems. If two neighbouring clusters are allocated with the same approach, they are subsequently merged. After machine grouping and approach assignment, an overall schedule is generated by integrating the solutions to the sub-problems. Computation results reveal that the proposed approach is superior to SPT and GA alone for FFS scheduling with machine breakdown. © 2009 IEEE.published_or_final_versionThe IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2009), Hong Kong, 11-13 May 2009. In Proceedings of CIMSA, 2009, p. 134-13

Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

Flexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. Although some methods have been developed to address such problems, they remain inherently difficult to solve by any single approach. This paper presents a novel decomposition-based approach (DBA), which combines both the shortest processing time (SPT) and the genetic algorithm (GA), to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times. In the proposed DBA, a neighbouring K-means clustering algorithm is developed to firstly group the machines of an FFS into an appropriate number of machine clusters, based on their stochastic nature. Two optimal back propagation networks (BPN), corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either SPT or GA to each machine cluster for sub-schedule generation. Finally, an overall schedule is generated by integrating the sub-schedules of machine clusters. Computation results show that the DBA outperforms SPT and GA alone for FFS scheduling with stochastic processing times. © 2012 Elsevier Ltd. All rights reserved.postprin

Lot Sizing Optimisation for Stochastic Make-to-order Manufacturing

Lot sizing is pivotal to batch manufacturing, especially in stochastic environments. Although progress has been made in the field for some operational objectives, the optimised results are often rendered unrealistic because few studies have considered the overall business goal and the economic environment where businesses operate. This paper examines a stochastic lot sizing optimisation model for make-to-order manufacturing with a focus on the overall business goal—the maximisation of shareholder wealth. In addition to the economic objective, the effect of the economic environment is also incorporated into this model. Numerical experiments validate the importance of considering such economic and financial constraints and objectives, especially for firms with relatively high setup costs or being sensitive to lead times. The proposed model can assist the management in gaining insight into potential challenges and opportunities pertinent to the shareholder wealth.published_or_final_versio

A virtual dual-level reconfigurable additive manufacturing system for digital object fabrication

This paper proposes a virtual dual-level reconfigurable additive manufacturing system (DRAMS) for simulation and verification of deposition strategies in digital fabrication of product prototypes. The DRAMS is aimed to improve additive manufacturing (AM) processes with the concept of system reconfiguration. It consists of adaptable support and manipulation modules for deposition of fabrication materials. Topologies are investigated to determine the structures of these modules, and methods are developed to evaluate and optimize the system configuration. Simulations show that the DRAMS can not only handle prototypes of different sizes and fabrication materials, but also increase the process speed. The DRAMS offers an effective tool for simulation, verification and optimization of deposition strategies under different system configurations to improve process performance.postprintThe 21st Annual International Solid Freeform Fabrication (SFF) Symposium: An Additive Manufacturing Conference, Austin, TX., 9-11 August 2010. In Proceedings of the 21st International SFF Symposium, 2010, p. 266-27

A dynamic priority-based approach to concurrent toolpath planning for multi-material layered manufacturing

This paper presents an approach to concurrent toolpath planning for multi-material layered manufacturing (MMLM) to improve the fabrication efficiency of relatively complex prototypes. The approach is based on decoupled motion planning for multiple moving objects, in which the toolpaths of a set of tools are independently planned and then coordinated to deposit materials concurrently. Relative tool positions are monitored and potential tool collisions detected at a predefined rate. When a potential collision between a pair of tools is detected, a dynamic priority scheme is applied to assign motion priorities of tools. The traverse speeds of tools along the x-axis are compared, and a higher priority is assigned to the tool at a higher traverse speed. A tool with a higher priority continues to deposit material along its original path, while the one with a lower priority gives way by pausing at a suitable point until the potential collision is eliminated. Moreover, the deposition speeds of tools can be adjusted to suit different material properties and fabrication requirements. The proposed approach has been incorporated in a multi-material virtual prototyping (MMVP) system. Digital fabrication of prototypes shows that it can substantially shorten the fabrication time of relatively complex multi-material objects. The approach can be adapted for process control of MMLM when appropriate hardware becomes available. It is expected to benefit various applications, such as advanced product manufacturing and biomedical fabrication. © 2010 Elsevier Ltd. All rights reserved.postprin

A decomposition-based approach to flexible flow shop scheduling under stochastic setup times

Research on production scheduling under uncertainty has recently received much attention. This paper presents a novel decomposition-based approach (DBA) to flexible flow shop (FFS) scheduling under stochastic setup times. In comparison with traditional methods using a single approach, the proposed DBA combines and takes advantage of two different approaches, namely the Genetic Algorithm (GA) and the Shortest Processing Time Algorithm (SPT), to deal with uncertainty. A neighbouring K-means clustering algorithm is developed to firstly decompose an FFS into an appropriate number of machine clusters. A back propagation network (BPN) is then adopted to assign either GA or SPT to generate a sub-schedule for each machine cluster. Finally, an overall schedule is generated by integrating the sub-schedules of the machine clusters. Computation results reveal that the DBA is superior to SPT and GA alone for FFS scheduling under stochastic setup times. © 2010 IEEE.published_or_final_versionThe 5th IEEE International Conference on Intelligent Systems (IS 2010), London, UK., 7-9 July 2010. In Proceedings of the 5th IS, 2010, p. 55-6