The synthetic aperture radar (SAR) technology enables satellites to efficiently acquire high quality images of the Earth surface. This generates significant communication traffic from the satellite to the ground stations, and, thus, image downlinking often becomes the bottleneck in the efficiency of the whole system. In this paper we address the downlink scheduling problem for Canada's Earth observing SAR satellite, RADARSAT-2. Being an applied problem, downlink scheduling is characterised with a number of constraints that make it difficult not only to optimise the schedule but even to produce a feasible solution. We propose a fast schedule generation procedure that abstracts the problem specific constraints and provides a simple interface to optimisation algorithms. By comparing empirically several standard meta-heuristics applied to the problem, we select the most suitable one and show that it is clearly superior to the approach currently in use.
This paper investigates an image acquisition scheduling problem for a Canadian surveillance-of-space satellite named Sapphire that takes images of deep space Earth-orbiting objects. For a set of resident space objects (RSOs) that needs to be imaged within the time horizon of one day, the Sapphire image acquisition scheduling (SIAS) problem is to find a schedule that maximizes the “Figure of Merit” of all the scheduled RSO images. To address the problem, we propose an effective GRASP heuristic that alternates between a randomized greedy constructive procedure and a local search procedure. Experimental comparisons with the currently used greedy algorithm are presented to demonstrate the merit of the proposed algorithm in handling the SIAS problem.
We consider the problem of routing samples taken from patients to laboratories for testing. These samples are taken from patients housed in hospitals, and are sent to laboratories in other hospitals for testing. The hospitals are distributed in a geographical area, such as a city. Each sample has a deadline, and all samples have to be transported within their deadlines. We have a fixed number of vehicles as well as an unlimited number of taxis available to transport the samples. The objective is to minimize a linear function of the total distance travelled by the vehicles and the taxis. We provide a mathematical programming formulation for the problem using the multi-commodity network flow model, and solve the formulation using CPLEX, a general-purpose MIP solver. We also provide a computational study to evaluate the solution procedure.
353Rafiey A., Sokol V., Krishnamurti R., Mitrovic Minic S., Punnen A. and Teja Malladi K.. A Network Model for the Hospital Routing Problem.
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