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Using Mpi Portable Parallel Programming With The Message Passing Interface

It is complex and difficult to perform the vehicle scheduling of urban bus lines, which is important to reduce the operational cost and improve the quality of public transportation services. One has to assign vehicles to cover a set of trips contained in a timetable while minimizing multiple objectives that may conflict with each other. Existing approaches combine these objectives in a weighted fashion to form a single objective and then use a single-objective optimization approach to solve it. However, they can only produce one solution, and it is not easy to assign a proper weight for each objective to obtain a superior solution that can balance different objectives. In this paper, a methodology is presented to create a set of Pareto solutions for this problem. First, a set of candidate vehicle blocks is generated. Then, multiple block subsets are selected from this candidate set by an improved multiobjective genetic algorithm combined with a departure-time adjustment procedure to obtain multiple Pareto solutions. To encode a solution, we propose a coding scheme that has a relatively short coding length and low decoding complexity. This approach is applied to a real-world vehicle scheduling problem of a bus line in Nanjing, China. Experiments show that this approach is able to quickly produce satisfactory Pareto solutions that outperform the actually used experience-based solution.

Vehicle Scheduling of an Urban Bus Line via an Improved Multiobjective Genetic Algorithm

This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the central plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm.

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Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

This study investigates a new biobjective lane- reservation problem, which is to exclusively reserve lanes from an existing transportation network for special transport tasks with given deadlines. The objectives are to minimize the total negative impact on normal traffic due to the reduction of available lanes for general-purpose vehicles and to maximize the robustness of the lane-reservation solution against the uncertainty in link travel times. We first define the robustness for the lane- reservation problem and formulate a biobjective mixed-integer linear program. Then, we develop an improved exact $\varepsilon$ -constraint and a cut-and-solve combined method to generate its Pareto front. Computational results for an instance based on a real network topology and 220 randomly generated instances with up to 150 nodes, 600 arcs, and 50 tasks demonstrate that the proposed method is able to find the Pareto front and that the proposed cut-and-solve method is more efficient than the direct use of optimization software CPLEX.

An Improved Exact $\varepsilon$ -Constraint and Cut-and-Solve Combined Method for Biobjective Robust Lane Reservation

Multi-disciplinary design optimisation MDO is one of critical methodologies to the implementation of enterprise systems ES. MDO requiring the analysis of fluid dynamics raises a special challenge due to its extremely intensive computation. The rapid development of computational fluid dynamic CFD technique has caused a rise of its applications in various fields. Especially for the exterior designs of vehicles, CFD has become one of the three main design tools comparable to analytical approaches and wind tunnel experiments. CFD-based design optimisation is an effective way to achieve the desired performance under the given constraints. However, due to the complexity of CFD, integrating with CFD analysis in an intelligent optimisation algorithm is not straightforward. It is a challenge to solve a CFD-based design problem, which is usually with high dimensions, and multiple objectives and constraints. It is desirable to have an integrated architecture for CFD-based design optimisation. However, our review on existing works has found that very few researchers have studied on the assistive tools to facilitate CFD-based design optimisation. In the paper, a multi-layer architecture and a general procedure are proposed to integrate different CFD toolsets with intelligent optimisation algorithms, parallel computing technique and other techniques for efficient computation. In the proposed architecture, the integration is performed either at the code level or data level to fully utilise the capabilities of different assistive tools. Two intelligent algorithms are developed and embedded with parallel computing. These algorithms, together with the supportive architecture, lay a solid foundation for various applications of CFD-based design optimisation. To illustrate the effectiveness of the proposed architecture and algorithms, the case studies on aerodynamic shape design of a hypersonic cruising vehicle are provided, and the result has shown that the proposed architecture and developed algorithms have performed successfully and efficiently in dealing with the design optimisation with over 200 design variables.

A supportive architecture for CFD-based design optimisation

The invention provides a wing profile optimal design method of a parallel difference evolutionary algorithm based on open computing language (Open CL) The wing profile optimal design method is used for wing profile design A standard wing profile function and a profile function are selected, the profile function serves as a design variable, an optimization objective function is determined, steps of the difference evolutionary algorithm are divided into different stages according to processed data, and all stages are packaged in different cores for operating based on Open CL In population updating, mutation operators are used for generating test vectors and crossover operators are used for generating descendants; individuals in a population are restored to the wing profile shape and tested whether to meet geometric constraint; computational fluid dynamics (CFD) analysis is used for obtaining fitness of the individuals and seeking the optimum individual, and whether the optimum individual meets performance constraint is tested; and finally iteration is finished and the optimum result is copied back into a host internal storage The wing profile optimal design method achieves parallel processing of a wing profile design process, performs sufficient search in effective space, shortens design period, achieves cross-platform wing profile optimal design, and improves design efficiency

Wing profile optimal design method of parallel difference evolutionary algorithm based on open computing language (Open CL)

A Fast Calculation and Rendering Method for Infrared Characteristics of Hypersonic Vehicle

The invention discloses an aircraft aerodynamic configuration optimum design method based on the improved parallel DE algorithm. The method is used for achieving aircraft aerodynamic configuration optimum design. According to the method, different DE algorithm patterns are integrated in a pattern pool, and therefore the DE pattern selection range is expanded; besides, a dynamic parameter pattern is newly adopted, and more effective research on a large area of unknown solution spaces can be achieved through the pattern. In the iteration process, test vectors are generated through mutation operation and interlace operation; individual codes in a species group are reduced to corresponding aircraft configurations, the CFD computational analysis process is introduced to an individual fitness evaluation stage and serves as a basis for selection on a new generation of individuals, therefore, the design result analysis process and the parameter modification process can be effectively combined, and the design cycle is shortened; offspring groups are generated through selection operation. In view of the characteristic that the computing amount in the aircraft aerodynamic configuration design process is large, the parallel computing mechanism is introduced into the method, and therefore the method greatly shortens algorithm running time and improves design efficiency.

Guanghong Gong

Papers

A supportive architecture for CFD-based design optimisation

Wing profile optimal design method of parallel difference evolutionary algorithm based on open computing language (Open CL)

A Fast Calculation and Rendering Method for Infrared Characteristics of Hypersonic Vehicle

Aircraft aerodynamic configuration optimum design method based on improved parallel DE algorithm