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Abstract In this paper, we develop strategies for the problem of space station on-orbit activity re-planning. Considering the effects of emergencies, a preprocessed strategy is firstly proposed to reconstruct conflicting missions. Furthermore, a greedy algorithm with interval-based strategies is used to re-plan the activities of the reconstructed missions by utilizing mission priority and preplanned activity schedule intervals. Taking into account the propagation of complicated constraints, a time backtrack iteration strategy is adopted to solve constraint conflicts. We demonstrate the proposed approach with a notional operation scenario of the Chinese future space station. The results indicate that our method can successfully re-plan space station on-orbit activities when emergencies occur, all constraint conflicts can be solved, and mission accomplishment rate is increased, in comparison with a method that does not include re-planning strategies. Highlights • A domain model is developed to describe the space station operation concepts. • A preprocessing strategy is used in the model reconfiguration of conflicting mission. • A greedy algorithm with resolving strategies is efficient for re-planning problem. • A time backtrack iteration strategy can successfully solve constraint conflicts. [ABSTRACT FROM AUTHOR]

Space station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers’ preferences. [ABSTRACT FROM AUTHOR]

This article studies the optimization of space station short-term mission planning (STMP) problems. The domain knowledge including description and the concept definitions of the STMP problem are presented, an STMP constraint satisfaction model is developed, and then an iterative conflict-repair method with the resolving strategies is proposed to satisfy complicated constraints. A genetic algorithm (GA) is adopted to optimize the STMP problem. The proposed approach is evaluated using a test case with 15 missions, 13 devices and three astronauts. The results show that the established STMP constraint satisfaction model is effective, and the iterative conflict-repair method can make the plan satisfy all constraints considered and can effectively improve the optimization performance of the GA. [ABSTRACT FROM AUTHOR]

This study extends a previously proposed single-objective optimization formulation of space station logistics strategies to multi-objective optimization. The four-objective model seeks to maximize the mean utilization capacity index, total utilization capacity index, logistics robustness index and flight independency index, aiming to improve both the utilization benefit and the operational robustness of a space station operational scenario. Physical programming is employed to convert the four-objective optimization problem into a single-objective problem. A genetic algorithm is proposed to solve the resulting physical programming-based optimization problem. Moreover, the non-dominated sorting genetic algorithm-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the physical programming-based solution. The proposed approach is demonstrated with a notional one-year scenario of China's future space station. It is shown that the designer-preferred compromise solution improving both the utilization benefit and the operational robustness is successfully obtained. [ABSTRACT FROM AUTHOR]

Abstract: During a space station operation scenario, there are complicated interaction relationships and restrictions between different types of operation missions, including on-orbit experiment arrangements, crew rotation, selection of flying orbit, and the cargo vehicle resupplying strategies. The space station overall mission planning, considering all the operation missions concurrently and comprising the launch window constraints of visiting vehicles, is focused on obtaining appropriate operation arrangements, improving program utilisation capability, and minimising lifecycle operation cost. A hierarchical, three-level, decomposed framework is proposed to model the overall mission planning problem, in which the first-level problem deals with the top-level mission scenario parameters, the second-level problem deals with the vehicle visiting strategy, and the third-level problem deals with the flying orbital missions. In accordance with the hierarchical problem framework, the planning approaches for each level problem are analysed and developed. Furthermore, the integrated planning procedure for the overall planning problem is formulated. The results prove that planning different operation missions concurrently is helpful in satisfying the complicated constraints, thus improving program benefit and decreasing operation cost, and it is essential and more practical to consider launch window constraints in the planning model. [Copyright &y& Elsevier]

Abstract: A space station orbit design mission is characterized by a long-duration and multi-step decision process. First, the long-duration design process is divided into multiple planning periods, each of which consists of five basic flight segments. Second, each planning period is modeled as a multi-step decision process, and the orbital altitude strategies of different flight segments have interaction effects on each other. Third, a dynamic programming method is used to optimize the total propellant consumption of a planning period while considering interaction effects. The step cost of each decision segment is the propellant for orbital-decay maintenance or lifting altitude, and is calculated by approximate analytical equations and combining a shooting iteration method. The proposed approach is demonstrated for a typical orbit design problem of a space station. The results show that the proposed approach can effectively optimize the design of altitude strategies, and can save considerable propellant consumption for the space station than previous public studies. [Copyright &y& Elsevier]

This paper develops a problem formulation corresponding to the long-term overall operation planning for large-scale space-station missions that need to be executed in 3–5 years. The space station overall operation planning consists of the on-orbit activity allocation and its supporting logistics design. A two-level optimization approach is proposed to solve this complicated problem hierarchically by using a multi-objective evolutionary algorithm. The first-level problem is to optimize the overall allocation scheme of on-orbit activities to achieve the activity distribution equilibrium on logistics demand, resource requirement, and activity characteristics. The second-level problem is to optimize the launch time and loading scheme of visiting vehicles under multiple constraints to meet the on-board supply demand, and a pretreatment strategy is proposed based on the space-station logistics domain knowledge to resolve constraints a priori. Numerical results indicate that the proposed formulation for space-station overall operation planning is effective, and the proposed pretreatment approach can reduce the difficulty of satisfying multiple constraints in optimization. • The strategy that plans space-station logistics according to on-orbit activity allocation schemes is proposed. • The mathematical model of space-station on-orbit activity allocation and logistics planning is formulated. • The pretreatment strategy for logistics planning is proposed to resolve multiple constraints before optimization. [ABSTRACT FROM AUTHOR]

Overall allocation of space station on-orbit activities is the process of allocating a set of on-orbit activities to be executed on the space station in a long-term planning period into several short-term subperiods while balancing logistics demand, resource requirement and the execution priority of on-orbit activities. The basic mathematical model is firstly formulated. Then a heuristic allocation strategy is proposed to reduce optimization difficulty and a hybrid decision-making approach combining physical programming and evolutionary computation is developed to directly search a Pareto-optimal solution, and the performance of these methods in addressing the problem is compared with that of NSGA-III. Three test case with 500, 1000 and 2000 on-orbit activities respectively are simulated to demonstrate the proposed approaches. The results show that the heuristic allocation strategy is effective and performs better than only applying meta-heuristics, and the decision-making approach based on physical programming can efficiently achieve a decision-maker-preferred solution that is converged better than the Pareto frontier obtained by NSGA-III. The obtained allocation scheme satisfies all equilibrium indexes and constraints. • The mathematical model of overall allocation of space station on-orbit activities is constructed. • The heuristic allocation strategy is proposed to reduce the number of objective functions. • A hybrid decision-making approach is developed to obtain the desirable solution efficiently. [ABSTRACT FROM AUTHOR]

Different from the current studies on the impulsive rendezvous which are mainly normal propellant-optimal or time-optimal designs, the safety-optimal impulsive rendezvous with consideration of practical trajectory uncertainties is investigated in this paper. An optimization model for safety-optimal rendezvous is established based on the Clohessy-Wiltshire (C-W) equations, which employs one new, recently proposed quantitative performance index of the rendezvous trajectory safety as the objective function. A real-coded genetic algorithm is adopted to locate the optimal solution. The safety index and the optimization approach are tested by several rendezvous problems with different numbers of impulses and different approach directions. The practical application value of the safety-optimal rendezvous trajectory is demonstrated by comparing with the traditional propellant-optimal and time-optimal rendezvous trajectories. [ABSTRACT FROM AUTHOR]

Abstract: The rendezvous and docking mission is usually divided into several phases, and the mission planning is performed phase by phase. A new planning method using mixed integer nonlinear programming, which investigates single phase parameters and phase connecting parameters simultaneously, is proposed to improve the rendezvous mission’s overall performance. The design variables are composed of integers and continuous-valued numbers. The integer part consists of the parameters for station-keeping and sensor-switching, the number of maneuvers in each rendezvous phase and the number of repeating periods to start the rendezvous mission. The continuous part consists of the orbital transfer time and the station-keeping duration. The objective function is a combination of the propellant consumed, the sun angle which represents the power available, and the terminal precision of each rendezvous phase. The operational requirements for the spacecraft–ground communication, sun illumination and the sensor transition are considered. The simple genetic algorithm, which is a combination of the integer-coded and real-coded genetic algorithm, is chosen to obtain the optimal solution. A practical rendezvous mission planning problem is solved by the proposed method. The results show that the method proposed can solve the integral rendezvous mission planning problem effectively, and the solution obtained can satisfy the operational constraints and has a good overall performance. [Copyright &y& Elsevier]