Application of Dynamic Programming Algorithm Based on Model Predictive Control in Hybrid Electric Vehicle Control Strategy

A good hybrid vehicle control strategy cannot only meet the power requirements of the vehicle,but also effectively save fuel and reduce emissions.In this paper,the construction of model predictive control in hybrid electric vehicle is proposed.The solving process and the use of reference trajectory are discussed for the application of MPC based on dynamic programming algorithm.The simulation of hybrid electric vehicle is carried out under a specific working condition.The simulation results show that the control strategy can effectively reduce fuel consumption when the torque of engine and motor is reasonably distributed,and the effectiveness of the control strategy is verified.

Two-Machine Hybrid Flow-Shop Problems in Shared Manufacturing

In the“shared manufacturing”environment,based on fairness,shared manufacturing platforms often require manufacturing service enterprises to arrange production according to the principle of“order first,finish first”which leads to a series of scheduling problems with fixed processing sequences.In this paper,two two-machine hybrid flow-shop problems with fixed processing sequences are studied.Each job has two tasks.The first task is flexible,which can be processed on either of the two machines,and the second task must be processed on the second machine after the first task is completed.We consider two objective functions:to minimize the makespan and tominimize the total weighted completion time.First,we show the problem for any one of the two objectives is ordinary NP-hard by polynomial-time Turing Reduction.Then,using the Continuous ProcessingModule(CPM),we design a dynamic programming algorithm for each case and calculate the time complexity of each algorithm.Finally,numerical experiments are used to analyze the effect of dynamic programming algorithms in practical operations.Comparative experiments show that these dynamic programming algorithms have comprehensive advantages over the branch and bound algorithm(a classical exact algorithm)and the discrete harmony search algorithm(a high-performance heuristic algorithm).

A Multi-Customer Supply Chain Scheduling with Subcontracting Option on a Single Machine

This paper studies the cost problem caused by the activity of the work-piece in the supply chain. The objective function is to find an optimal ordering that minimizes the total cost of production, transportation and subcontracting. This paper presents a dynamic programming algorithm for the corresponding sorting problem, and finally demonstrates the feasibility of the algorithm through an example.

Optimization of Numerical Control Program and Machining Simulation Based on VERICUT

In the machining process of large-scale complex curved surface,workers will encounter problems such as empty stroke of tool,collision interference,and overcut or undercut of the workpieces.This paper presents a method for generating the optimized tool path,compiling and checking the numerical control(NC)program.Taking the bogie frame as an example,the tool paths of all machining surface are optimized by the dynamic programming algorithm,Creo software is utilized to compile the optimized computerized numerical control(CNC)machining program,and VERICUT software is employed to simulate the machining process,optimize the amount of cutting and inspect the machining quality.The method saves the machining time,guarantees the correctness of NC program,and the overall machining efficiency is improved.The method lays a good theoretical and practical foundation for integration of the similar platform.

The Theoretical and Experimental Analysis of the Maximal Information Coefficient Approximate Algorithm

In the era of big data,correlation analysis is significant because it can quickly detect the correlation between factors.And then,it has been received much attention.Due to the good properties of generality and equitability of the maximal information coefficient(MIC),MIC is a hotspot in the research of correlation analysis.However,if the original approximate algorithm of MIC is directly applied into mining correlations in big data,the computation time is very long.Then the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n2.4 when parameters are default.And the experiments show that the large number of candidate partitions of random relationships results in long computation time.The analysis is a good preparation for the next step work of designing new fast algorithms.

Island partition of the distribution system with distributed generation

In this paper, a novel optimum island partition model based on Tree Knapsack Problem (TKP) is presented for the distribution system integrated with distributed generation (DG), and a Depth-first Dynamic Programming Algorithm (DPA) is used to solve this model. With the considerations of the load priority, controlled/uncontrolled loads, and the constraints of power balance, voltage and equipment capacity, the model can meet the practical engineering requirements very well. The island partition problem of the distribution system integrated with multiple DGs is first decomposed into multiple TKPs, each of which is solved by DPA respectively. Then, the initial optimum island partition scheme is gained through an island combination procedure, and the final island partition scheme is obtained after feasibility checking and adjustment. Since the algorithm proposed owns the advantages of strong theoretical foundation and low computational complexity, it can find the approximate optimal solution within a limited time. The results of examples demonstrate the validity of the new model and algorithm.

Batch Scheduling with Deteriorating Jobs to Minimize the Total Completion Time

We consider bounded parallel-batch scheduling with proportional-linear deteriorating jobs and the objective to minimize the total completion time.We give some properties of optimal schedules for the problem and present for it a dynamic programming algorithm running in O(b^(2)m^(2)2^(m))time,where b is the size of a batch and m is the number of distinct deterioration rates.

Portfolio Optimization with Uncertain Exit Time in Infinite-Time Horizon

In this paper, we study infinite-period mean-variance formulations for portfolio selections with an uncertain exit time. We employ the convergence control method together with the dynamic programming algorithm to derive analytical expressions for the optimal portfolio policy and the mean-variance efficient frontier under certain conditions. We illustrate these results by an numerical example.