Krishna Teja Malladi

Satellite downlink scheduling problem: A case study

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.

Optimization of operational level transportation planning in forestry: a review

ABSTRACT Transportation of forest products accounts as a major contributor to the total operational costs; hence, its optimization has become an important aspect in supply chain planning. Transportation optimization at the operational level includes decisions related to product flow, storage, pre-processing, and routing and scheduling of vehicles. The decisions and constraints in the model depend on the type of product that is transported. Earlier review articles on forest transportation optimization focused only on log transportation, while in this review paper, products such as logs, biomass, pulp and furniture are considered and their similarities and differences are highlighted. Most of the previous studies focused on optimizing the total cost of transportation, while environmental aspects of truck routing and scheduling in forestry were not considered. Uncertainties in parameters such as supply and demand quantities and transportation time were not explored fully in the models. In addition to storage and truck routing and scheduling, considering pre-processing (e.g. sorting, grinding, blending, bucking) decisions at forest sites, satellite yards and the mills in the models could be done in future studies. It is important that aspects related to truck configuration, type and capacity be considered in the models as there is limited accessibility of large trucks such as large chip vans to forest sites. Management practices such as just-in-time production and vendor-managed inventory systems could be considered in forest supply chain planning. Using big data and business analytics techniques are other new trends that could improve decision-making related to logistics and transportation planning in forestry.

The Satellite Downlink Scheduling Problem: A Case Study of RADARSAT-2

Mission planning operations of Earth observing satellites involve acquisition of images and downlinking (downloading) the acquired images of prescribed areas of the Earth to one or more ground stations. Efficient scheduling of image acquisition and image downlinking plays a vital role in successful satellite mission planning. The image acquisition and downlinking operations are often interlinked and solved using heuristic algorithms that take advantage of the flexibility allowed within such integrated systems. In this chapter, we study the mission planning operations of Canada’s Earth observing synthetic aperture radar (SAR) satellite, RADARSAT-2.

Clustered maximum weight clique problem

This paper examines the Clustered Maximum Weight Clique Problem which is derived from the Satellite Image Acquisition Scheduling Problem.Two variants of the problem are introduced.Matheuristic algorithm, which exploits the power of commercial mixed-integer programming solvers, is developed.Extensive computational experiments are conducted on clustered adaptations of DIMACS and BHOSLIB benchmark instances for the Maximum Clique Problem. We introduce the Clustered Maximum Weight Clique Problem (CCP), a generalization of the Maximum Weight Clique Problem, that models an image acquisition scheduling problem for a satellite constellation. The solution of CCP represents satellite schedules that satisfy customer requests for satellite imagery. Each request has a priority, an area of interest, and a time window. Often, the area of interest is too large to be imaged by one satellite pass and it has to be divided into several smaller images. Each image has one or more opportunities for an acquisition by a satellite.The problem is modeled by a clustered weighted graph. A graph node represents one opportunity for an image acquisition by one satellite. A graph edge indicates that either two opportunities are not in conflict can both be in a schedule, or two opportunities are not acquiring the same image. Each graph node has a weight that represents the area size of the image. The graph nodes are partitioned into clusters each of which encompasses all the opportunities of one customer request. The priority of the request is captured by the cluster weight. The time window of the request restricts the number of opportunities.The CCP deals with finding a clique of a maximum weight where the weight combines the node weights and the cluster weights. More precisely, the cluster weight is multiplied by the contribution of the sum of the weights of the clique nodes. The contribution is either a linear function or a piece-wise linear function, where the latter is meant to favour finalizing an already partially served customer request.The paper presents several mathematical programming formulations of the CCP and proposes matheuristic solution approaches. The computational study is performed on the clustered adaptations of the DIMACS and BHOSLIB benchmark instances for the Maximum Weight Clique Problem. The achieved results are encouraging.

Satellite Constellation Image Acquisition Problem: A Case Study

This chapter deals with the image acquisition scheduling of Earth observing satellites that revolve around the Earth in specific orbits and take images of prescribed areas requested by the clients. Often a satellite cannot acquire the images of a requested area in a single pass and it is necessary to divide the area into multiple strips each of which can be acquired in one satellite pass. Each satellite might have several image acquisition opportunities for each strip as the satellites can take images using different incidence angles. Then the Satellite Image Acquisition Scheduling Problem (SIASP) is to select the opportunities to acquire as many images as possible, without repetition, within a planning horizon while considering the image priorities and energy constraints. The proposed SIASP model employs a piecewise linear objective function to favor completion of an image acquisition request over partial acquisition of many requests. Extensive experimental study has been carried out using realistic randomly generated instances based on the forecasted statistics provided by MDA, Richmond, Canada. These experiments are intended as a preliminary investigation of the image acquisition scheduling for the Canadian RADARSAT Constellation Mission (RCM), a constellation of three satellites to be launched in 2018.

A Network Model for the Hospital Routing 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.