This paper investigates an optimal bank angle generation method for two types of Mars entry guidance problems based on a predetermined bank angle structure. The Mars robotic mission, which is the first type of problem...
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ISBN:
(纸本)9781624106316
This paper investigates an optimal bank angle generation method for two types of Mars entry guidance problems based on a predetermined bank angle structure. The Mars robotic mission, which is the first type of problem, aims for the maximum terminal altitude at the end of the entry phase to secure enough time to deaccelerate an entry vehicle. The other type of problem, the human Mars mission, requires the minimum terminal velocity or energy to reduce the total amount of fuel consumed during the following powered descent phase. Several papers regarding the Mars optimal entry guidance have demonstrated that the optimal bank angle profile can be captured as a bang-bang structure, which is also the expected solution for the minimum terminal velocity problem based on the optimal control theory. By utilizing a few parameters, the bang-bang structure can easily be shaped;consequently, a parameter optimization approach turns the optimal control problem of the Mars entry guidance into a parameter searching problem, which is relatively simple to solve. Therefore, in this paper, a technique for formulating the two entry guidance problems into parameter optimization problems are proposed. To demonstrate the performance of the proposed guidance method, numerical simulations of two representative Mars entry examples are conducted. Simulation results verify that the parameter optimization approach generates the optimal entry trajectory, either maximizing or minimizing the target cost while satisfying terminal conditions and path constraints. Furthermore, the parameter optimization approach is shown to be superior in computational efficiency over a commercial nonlinear programming solver.
Speed profiles play an important impact on the operational energy consumption of electric buses (EBs). In this study, an eco-driving method is proposed for a connected and automated EB serving on the bus rapid transit...
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Speed profiles play an important impact on the operational energy consumption of electric buses (EBs). In this study, an eco-driving method is proposed for a connected and automated EB serving on the bus rapid transit (BRT) route. Considering the effect of speed and road load conditions on motor efficiency in estimating energy consumption, a nonlinear programming model is proposed to optimize the energy-saving speed profile. Then the sparrow search algorithm is used for the solution to ensure that the computing time is less than the predetermined time gap. Finally, a real BRT route is taken as an example to carry out comparative and sensitivity analyses. Results indicate that: (i) the average motor efficiency can be improved by 2.11% after taking into account the effect of speed and road load conditions on motor efficiency in estimating energy consumption;(ii) the number of driving stages with uniform speed on each segment is recommended as 2 for balancing the computation time and solution quality;(iii) planning the energy-saving speed profile while regulating the signal timing at intersections improves the operational efficiency and exploit the energy-saving potential of the CAEB;(iv) as bus operation punctuality constraints become tighter, there is an increase in the operational efficiency, but a decline in the energy-saving potential of the CAEB.
Firms have to determine the right features and prices for their new products as they introduce new product generations to the market. We consider the problem of determining the features of a new product that a monopol...
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Firms have to determine the right features and prices for their new products as they introduce new product generations to the market. We consider the problem of determining the features of a new product that a monopolist will introduce into a market that contains an existing product as well as setting the prices of the existing and the new products. The firm also decides on offering only the new product or both the existing and the new products. We explicitly capture the effects of unique features, which are specific to one of the two products, and common features which are shared between the new and the existing product on these decisions. The problem is formulated as a nonlinear-mixed-integer program with general cost, demand, and price functions. For the case of linear cost, demand, and price, the nonlinear-mixed-integer program is converted to a nonlinear program and solved analytically. Based on this solution, the optimal prices for both products and the optimal unique features for the new product are derived in closed form, a linear-time algorithm is presented to determine the optimal common features, and the optimality conditions of keeping the existing product in the market are characterized. We show that the selection of the unique features, but not the common ones, is based on the difference between a feature's contribution to the product's demand and its cost adjusted by the price sensitivity in the linear case. Moreover, we find that the firm, if it wants to avoid demand cannibalization, should remove the existing product from the market rather than offer two products with mainly unique features. Capturing the effects of unique and common features directly allows firms to decide on the best rollover strategy and determine the right features and prices
Earthworks in construction projects are performed in the early stages in construction and in most construction projects, earthworks are considered very costly. Project managers find difficulties in finishing solutions...
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Earthworks in construction projects are performed in the early stages in construction and in most construction projects, earthworks are considered very costly. Project managers find difficulties in finishing solutions to minimize the time & cost of earthworks in construction projects. To assist project managers in decision making, this paper will introduce a simulation-based model to optimize the earthworks machines based on their productivity rates. Previous attempts to solve this issue resulted in finding the optimal number of machines that needs to be allocated, without taking into consideration the fluctuation of productivity rates of the machines during the execution stage;as well as previous models were applied during the planning phase of the project only. This paper will use a Multi-Objective Optimization Model that will minimize the duration and cost of the earthworks process by using multi-objective genetic algorithms that can be applied during both the planning and execution phase.
Any abnormal situation of the hub port may bring adverse effects such as time waste and cost increase to the shipping company. This paper solves the hub-and-spoke shipping network transportation optimization problem i...
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ISBN:
(纸本)9781665476614
Any abnormal situation of the hub port may bring adverse effects such as time waste and cost increase to the shipping company. This paper solves the hub-and-spoke shipping network transportation optimization problem in the case of partial failure and complete failure of hub ports. Partial failure means that the transshipment demand of the hub port exceeds its capacity, resulting in congestion at the hub port. Complete failure means that the hub port cannot continue to provide services for some reason. The purpose of this paper is to optimize the hub-and-spoke shipping network system to minimize the cost loss of shipping companies in the event of hub port failure. This is a mixed-integer nonlinear programming problem. The simulation example proves that the model in this paper can effectively reduce the loss of cargo flow due to the failure of the hub port and significantly improve the reliability of the shipping network.
Purpose The effect of customers' forward-looking behavior on firms' profit has been highlighted by many researchers and practitioners. This study aims to develop a mathematical model for new generation product...
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Purpose The effect of customers' forward-looking behavior on firms' profit has been highlighted by many researchers and practitioners. This study aims to develop a mathematical model for new generation products to analyze the optimal pricing and advertising policies in the presence of homogeneous forward-looking customers. A firm that produces and sells a new generation product was considered. This firm aimed to determine the optimal pricing and advertising expenditure by maximizing the total profit. Design/methodology/approach The demand was presented as a diffusion model inspired by the Bass diffusion model. This paper used Pontryagin's maximum principle to analyze the proposed model. The presented model was implemented in some numerical examples by proposing a heuristic solution method. Numerical examples confirmed the theoretical results. Findings This paper found a threshold on the optimal advertising policy depends on customers' forward-looking behavior, advertising coefficient (both direct and word-of-mouth advertising) and discount rate. The funding showed that the optimal pricing path of the first generation was monotonically decreasing or increasing and, then, decreasing. Results revealed that, by increasing the customers' forward-looking behavior, the firm should reduce the price and advertising expenditure. Also, the price was shown to be negatively affected by the discount rate and word-of-mouth advertising. The profitability will improve if the firm spends more budget on advertising by increasing the discount rate and advertising effectiveness. Further, when the word-of-mouth advertising effect is high, the firm should increase the advertising expenditure first and, then, decrease it. Originality/value Nowadays, forward-looking customers' anticipation for releasing a new generation can harm the firms' profit. In this regard, this research analyzed optimal pricing and advertising policies for a new generation product in a market populated by homogeneous
Different from most existing algorithms that explore the integration of information from RGB and thermal (RGB-T) hierarchical features, we propose a novel adaptive learning of modal information from the decision-level...
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Different from most existing algorithms that explore the integration of information from RGB and thermal (RGB-T) hierarchical features, we propose a novel adaptive learning of modal information from the decision-level perspective to achieve efficient and robust tracking. In our paradigm, the relative reliability between different modalities is mined by maximizing the peak-to-sidelobe ratio (PSR) model. Synchronously, the learned reliability can also be used to guide the correct update of the target template for each modality. Experiments on widely used large-scale benchmarks demonstrate that our method achieves competitive performance against other state-of-the-art trackers while enabling real-time tracking.
Aiming at the problem that it is difficult for an orbital photovoltaic panel cleaning robot to span a large distance between photovoltaic panels, a method of designing and optimizing a non-coplanar orbit based on Bezi...
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Aiming at the problem that it is difficult for an orbital photovoltaic panel cleaning robot to span a large distance between photovoltaic panels, a method of designing and optimizing a non-coplanar orbit based on Bezier curves is proposed. Firstly, the robot's motion law is analyzed to obtain trajectory data for a single work cycle. Then, Bezier curves are utilized for trajectory design to ensure a smooth transition during the spanning motion phase. Thirdly, with the average value of the minimum distance between the Bezier curve and the point set data of the spanning motion phase as the optimization objective function, the nonlinear planning based on the SQP algorithm was adopted for the optimization of the upper and lower trajectories. Finally, the results of the case calculations indicate that the standard deviation of the optimized upper and lower trajectories was reduced by 35.63% and 40.57%, respectively. Additionally, the ADAMS simulation validation demonstrates that the trajectory errors of the four wheels decreased by a maximum of 8.79 mm, 23.78 mm, 10.11 mm, and 14.97 mm, respectively, thereby confirming the effectiveness of the trajectory optimization.
In this paper we study the worst-case complexity of an inexact augmented Lagrangian method for nonconvex constrained problems. Assuming that the penalty parameters are bounded we prove a complexity bound of O(vertical...
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In this paper we study the worst-case complexity of an inexact augmented Lagrangian method for nonconvex constrained problems. Assuming that the penalty parameters are bounded we prove a complexity bound of O(vertical bar log(epsilon)vertical bar) outer iterations for the referred algorithm to generate an epsilon-approximate KKT point for epsilon is an element of (0, 1). When the penalty parameters are unbounded we prove an outer iteration complexity bound of O(epsilon(-2/(alpha-1))), where alpha > 1 controls the rate of increase of the penalty parameters. For linearly constrained problems these bounds yield to evaluation complexity bounds of O(vertical bar log(epsilon)vertical bar(2)epsilon(-)(2)) and O(epsilon(-(2(2+)(alpha)/)(alpha-1+2))), respectively, when appropriate first-order methods (p = 1) are used to approximately solve the unconstrained subproblems at each iteration. In the case of problems having only linear equality constraints the latter bounds are improved to O(vertical bar log(epsilon)vertical bar(2)epsilon(-)((p+1)/p)) and O(epsilon(-(4/)(alpha-1+p+1/p))), respectively, when appropriate p-order methods (p >= 2) are used as inner solvers.
A general parametric nonlinear mathematical programming problem with an operator equality constraint and a finite number of functional inequality constraints is considered in a Hilbert space. Elements of a minimizing ...
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A general parametric nonlinear mathematical programming problem with an operator equality constraint and a finite number of functional inequality constraints is considered in a Hilbert space. Elements of a minimizing sequence for this problem are formally constructed from elements of minimizing sequences for its augmented Lagrangian with values of dual variables chosen by applying the Tikhonov stabilization method in the course of solving the corresponding modified dual problem. A sequential Kuhn-Tucker theorem in nondifferential form is proved in terms of minimizing sequences and augmented Lagrangians. The theorem is stable with respect to errors in the initial data and provides a necessary and sufficient condition on the elements of a minimizing sequence. It is shown that the structure of the augmented Lagrangian is a direct consequence of the generalized differentiability properties of the value function in the problem. The proof is based on a "nonlinear" version of the dual regularization method, which is substantiated in this paper. An example is given illustrating that the formal construction of a minimizing sequence is unstable without regularizing the solution of the modified dual problem.
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