A comparative analysis of algorithms for satellite operations scheduling

Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration.Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration

Investigation and modelling of large scale cratering events : Lessons learnt from experimental analysis

Initiated as part of the 2010 Spin Your Thesis campaign, a new ESA Education programme, a group from the University of Glasgow Space Advanced Research Team successfully conducted a series of impact cratering experiments under a highly accelerated reference frame. This aimed to: reproduce and define the physical conditions of large-scale cratering events onto highly porous asteroids; provide cratering response data for the validation and advancement of numerical models; and support the generation of a reliable scaling theory for cratering events. Impact cratering is a fundamental process that has shaped and continues to shape the formation and evolution of our solar system and other planetary systems. Although much is known on the impact dynamics of rocky, brittle bodies, such as asteroids, little is known on the physical response of highly porous bodies. Consequently the physical response of porous bodies can not be compared to conventional models. Therefore throughout the experiment campaign, variation into the target material’s porosity and projectile density was examined. All in-situ measurements were recorded relative to the crater’s morphological profile and ejecta distribution. This occurred under increasing levels of acceleration, thereby validating that the experiment occurred within the crater dominated gravity regime. This paper details the programmatics issues of the initiative, experiences and lessons learnt from the student perspective. From its initial proof-of-concept the Spin Your Thesis campaign provided a solid foundation from the development of an experimental idea, enabling high scientific return and personal development

Optimal power harness routing for small-scale satellites

This paper presents an approach to optimal power harness design based on a modified ant colony optimisation algorithm. The optimisation of the harness routing topology is formulated as a constrained multi-objective optimisation problem in which the main objectives are to minimise the length (and therefore the mass) of the harness. The modified ant colony optimisation algorithm automatically routes different types of wiring, creating the optimal harness layout. During the optimisation the length, mass and bundleness of the cables are computed and used as cost functions. The optimisation algorithm works incrementally on a finite set of waypoints, forming a tree, by adding and evaluating one branch at a time, utilising a set of heuristics using the cable length and cable bundling as criteria to select the optimal path. Constraints are introduced as forbidden waypoints through which digital agents (hereafter called ants) cannot travel. The new algorithm developed will be applied to the design of the harness of a small satellite, with results highlighting the capabilities and potentialities of the code

Optimal dynamic operations scheduling for small-scale satellites

A satellite's operations schedule is crafted based on each subsystem/payload operational needs, while taking into account the available resources on-board. A number of operating modes are carefully designed, each one with a different operations plan that can serve emergency cases, reduced functionality cases, the nominal case, the end of mission case and so on. During the mission span, should any operations planning amendments arise, a new schedule needs to be manually developed and uplinked to the satellite during a communications' window. The current operations planning techniques over a reduced number of solutions while approaching operations scheduling in a rigid manner. Given the complexity of a satellite as a system as well as the numerous restrictions and uncertainties imposed by both environmental and technical parameters, optimising the operations scheduling in an automated fashion can over a flexible approach while enhancing the mission robustness. In this paper we present Opt-OS (Optimised Operations Scheduler), a tool loosely based on the Ant Colony System algorithm, which can solve the Dynamic Operations Scheduling Problem (DOSP). The DOSP is treated as a single-objective multiple constraint discrete optimisation problem, where the objective is to maximise the useful operation time per subsystem on-board while respecting a set of constraints such as the feasible operation timeslot per payload or maintaining the power consumption below a specific threshold. Given basic mission inputs such as the Keplerian elements of the satellite's orbit, its launch date as well as the individual subsystems' power consumption and useful operation periods, Opt-OS outputs the optimal ON/OFF state per subsystem per orbital time step, keeping each subsystem's useful operation time to a maximum while ensuring that constraints such as the power availability threshold are never violated. Opt-OS can provide the flexibility needed for designing an optimal operations schedule on the spot throughout any mission phase as well as the ability to automatically schedule operations in case of emergency. Furthermore, Opt-OS can be used in conjunction with multi-objective optimisation tools for performing full system optimisation. Based on the optimal operations schedule, subsystem design parameters are being optimised in order to achieve the maximal usage of the satellite while keeping its mass minimal

Adalimumab or Cyclosporine as Monotherapy and in Combination in Severe Psoriatic Arthritis: Results from a Prospective 12-month Nonrandomized Unblinded Clinical Trial

Objective. To assess the efficacy and safety of adalimumab or cyclosporine (CYC) as monotherapy or combination therapy for patients with active psoriatic arthritis (PsA), despite methotrexate (MTX) therapy. Methods. A prospective 12-month, nonrandomized, unblinded clinical trial of 57, 58, and 55 patients who received CYC (2.5-3.75 mg/kg/day), adalimumab (40 mg every other week), or combination, respectively. Lowering of concomitant nonsteroidal antiinflammatory drugs (NSAID) and corticosteroids and reductions of adalimumab and/or CYC doses in responding patients were not restricted. Results. Mean numbers of tender/swollen joints at baseline were 9.7/6.7 in CYC-treated, 13.0/7.8 in adalimumab-treated, and 14.5/9.4 in combination-treated patients, indicating lesser disease severity of patients assigned to the first group. The Psoriatic Arthritis Response Criteria at 12 months were met by 65% of CYC-treated (p = 0.0003 in favor of combination treatment), 85% of adalimumab-treated (p = 0.15 vs combination treatment), and 95% of combination-treated patients, while the American College of Rheumatology-50 response rates were 36%, 69%, and 87%, respectively (p <0.0001 and p = 0.03 in favor of combination treatment). A significantly greater mean improvement in Health Assessment Questionnaire Disability Index was achieved by combination treatment (-1.11) vs CYC (-0.41) or adalimumab alone (-0.85). Combination therapy significantly improved Psoriasis Area and Severity Index-SO response rates beyond adalimumab, but not beyond the effect of CYC monotherapy. Doses of NSAID and corticosteroids were reduced in combination-treated patients; CYC doses and frequency of adalimumab injections were also reduced in 51% and 10% of them, respectively. No new safety signals were observed. Conclusion. The combination of adalimumab and CYC is safe and seemed to produce major improvement in both clinical and serological variables in patients with severely active PsA and inadequate response to MTX. (First Release Sept 1 2011; J Rheumatol 2011;38:2466-.74; doi:10.3899/jrheum.110242