Determining Optimum Planting Schedule Using Diet Optimization and Advanced Crop Scheduling Models

Published Materials
Source: OAI


In this paper, optimum crop planting schedule that would minimize the equivalent system mass (ESM) of a bioregenerative advanced life support system (ALSS) is determined using an advanced crop scheduling model in conjunction with a diet optimization model. Mixed-integer linear programming (MILP) models are developed to determine crop scheduling and optimum diet for the crewmembers. Given the activity schedule of the crewmembers, the diet optimization module constructs a diet cycle of 20-30 days that would meet the necessary nutritional requirements observing a predetermined diet variety. In doing so the diet optimization tries to minimize the overall system ESM. Necessary biomass amounts calculated by this model are fed into the crop scheduling model as the demands. Given these demands and growth parameters for these crops, the crop scheduling model determines the best planting schedule that will optimize the system behavior, i.e., the one that would minimize ESM. Description:11 pages

1 Read
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this study, the relationship among crewmembers and crops water consumption and production, water treatment capacity, water storage tank capacity, and water supply required from Earth is explored. Two mission durations and two scenarios are studied to investigate the impact of mission duration and crops addition, respectively, on water subsystem cost. Results show that increase in water treatment capacity can effectively reduce water storage tank equivalent system mass (ESM) to a certain extent. Mission duration affects water storage tank ESM unless water removal technology and in situ resource utilization (ISRU) are added into the crewmembers-only and crewmembers-and-crops scenarios, respectively, in addition to the water treatment process at optimal capacity. Although the results are system specific, it demonstrates that trade study analysis can be performed to evaluate the trade off of water treatment technologies, water removal technologies, and ISRU technologies against water storage tank and supply using ESM.
    Habitation 08/2008; 11(4):173-183. DOI:10.3727/154296608785908642
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This article is the second part of a two-part research study of a theoretical water-treatment system based on the NASA Baseline Values and Assumptions Document (BVAD) [7]. It focuses on the decision-making model created to choose the "best" policy to be applied to the water-treatment system based on hourly system conditions. Due to the resemblance between the behavior of this system and the Markovian process, this system is constructed based on the Markovian model. The water system consists of two subsystems: a hygiene-water subsystem that supplies water for laundry, urinal flush, dish wash, oral hygiene and shower; and a potable-water subsystem that supplies water for drinking and food rehydration. In order to assess the conditions of the water system, various aspects of the system, such as hourly and accumulated water deficiency, and amount of clean water available for use, are captured on an hourly basis. A baseline policy and policies derived from it are tested to find the best policy for the system to operate under the most economical conditions while providing enough clean water for crew consumption. The best policy is obtained through various mathematical modeling techniques. Outcomes are compared against a system that uses the baseline policy. Results show that an intuitively "good" policy may not always be the best policy for the system. The system performance is measured in terms of a reward value, which is assigned based on the system conditions.
    Habitation 05/2009; 12(1):27-32. DOI:10.3727/154296610X12686999887166
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a simulation-based optimization approach is proposed to study design of life-support systems, i.e., systems that provide basic life-support elements such as potable water and oxygen, for manned space missions. The method integrates deterministic mathematical programming models, which set tactical control parameters, stochastic discrete-event simulation, which accounts for the uncertainty, and a time-series data-mining approach, which calculates strategic set points for tactical control. Total cost distributions of the system, probabilistic capacities of the technologies in the system and the basic life-support element amounts that are needed to support crew life and activities for the duration of a given mission are determined using the proposed framework. The methodology is demonstrated using four different technology levels for a mission scenario at which a crew of six spends 600 days on the Martian surface.
    Acta Astronautica 08/2009; 65(3-4-65):330-346. DOI:10.1016/j.actaastro.2009.02.017 · 1.12 Impact Factor
Show more