Metabolic flux simulation of microbial systems based on optimal planning algorithms

The genomic scale metabolic networks of the microorganisms can be constructed based on their genome se-quences,functional annotations,and biochemical reactions,reflecting almost all of the metabolic functions.Mathematical simulations of metabolic fluxes could make these functions be visualized,thereby providing guidance for rational engineering design and experimental operations.This review summarized recently devel-oped flux simulation algorithms of microbial systems.For the single microbial systems,the optimal planning algorithm has low complexity because there is no interaction between microorganisms,and it can quickly simulate the stable metabolic states through the pseudo-steady hypothesis.Besides,the experimental conditions of single microbial systems are easier to reach or close to the optimal states of simulation,compared with pol-ymicrobial systems.The polymicrobial culture systems could outcompete the single microbial systems as they could relieve metabolic pressure through metabolic division,resource exchange,and complex substrate co-utilization.Besides,they provide varieties of intracellular production environments,which render them the po-tential to achieve efficient bioproduct synthesis.However,due to the quasi-steady hypothesis that restricts the simulation of the dynamic processes of microbial interactions and the algorithm complexity,there are few re-searches on simulation algorithms of polymicrobial metabolic fluxes.Therefore,this review also analyzed and combed the microbial interactions based on the commonly used hypothesis of maximizing growth rates,and studied the strategies of coupling interactions with optimal planning simulations for metabolism.Finally,this review provided new insights into the genomic scale metabolic flux simulations of polymicrobial systems.

NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

A novel approach for collision-free path planning of a multiple degree-of-freedom （DOF） articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point （VNP）, a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

RESEARCH ON JUMPING SEQUENCE PLANNING ISSUES OF HOPPING ROBOTS

The wheeled or crawled robots often suffer from big obstacles or ditches, so a hopping robot needs to fit the tough landform in the field environments. In order to jump over obstacles rapidly, a jumping sequence must be generated based on the landform information from sensors or user input. The planning method for planar mobile robots is compared with that of hopping robots. Several factors can change the planning result. Adjusting these coefficients, a heuristic searching algorithm for the jumping sequence is developed on a simplified landform. Calculational result indicates that the algorithm can achieve safety and efficient control sequences for a desired goal.

DYNAMIC ADVANCED PLANNING AND SCHEDULING WITH FROZEN INTERVAL FOR NEW ORDERS

A dynamic advanced planning and scheduling （DAPS） problem is addressed where new orders arrive on a continuous basis. A periodic policy with frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule at each rescheduling point for both original orders and new orders that both production idle time and penalties on tardiness and earliness of orders are minimized. The proposed methodology is tested on a small example to illustrate the effect of the frozen interval. The results indicate that the suggested approach can improve the schedule stability while retaining efficiency.

PRECESION： progressive recovery and restoration planning of interdependent services in enterprise data centers

The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Under the demand of strategic air traffic flow management and the concept of trajectory based operations（TBO）,the network-wide 4D flight trajectories planning（N4DFTP） problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories（4DTs）（3D position and time） for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategiclevel conflict management is developed in this paper.Specifically,a bi-objective N4 DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm（MOMMA） that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4 DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.

Methodology and Algorithms for Planning Cost-Effective High Survivability SDH Networks

This paper presents a methodology for planning new SDH target transmission network in China,which is of,in structure into four levels: the inter province backbone network,the intra province backbone networks,local networks,and subscriber access networks.At meantime,in order to obtain high degree of network survivability,topology configure of protection and reconfiguration for each level network is also discussed. In order to help the planner in computer aid design and analysis,a modified integrated self healing network design algorithms ( The integration of Capacity Assignment Optimization Algoritms, R D, SSR and DSH ) which support this planning methodology is presented and discussed.The computer simulation results are also given to show the practical application of this methodology and benefits of this algorithm.The results show that this algorithm is very effective for designing a higher degree of self healing network with least spare capacity.

A Smooth and Efficient Gait Planning for Humanoids Based on Human ZMP

Based on the ZMP(zero moment point)trajectory and the walking data of human,a new method is proposed to improve the robot walking smoothness as well as to save energy.Firstly,a measurement system is designed to measure the data of humans including the ZMP trajectory and the waist trajectory.Secondly,a new gait planning method which includes presetting the allowable ZMP region is proposed through analyzing human data.Thirdly,the new planning method is applied to the multi-link model based gait planning method.Finally,the feasibility of the proposed method is verified by simulation and experiments.