4,692 research outputs found
AN OPTIMIZATION OF CUSTOMER BANDWIDTH ASSIGNMENT WITH GROUND SEGMENT CONSIDERATION FOR INVESTMENT EFFICIENCY USING GENETIC ALGORITHM
Investment of communication satellite consists of two segment, i.e space segment and ground segment. Currently, the investments are not managed efficiently due to lack of strategy in assigning the customer bandwidth. In fact, an optimization of the customer bandwidth assignment can save telecommunication investment resources, especially transponder bandwidth, transponder power, and ground segment. Many works have been performed to optimize both transponder power and transponder bandwidth investment. However, there is no effort has been devoted to optimize ground segment resources. As result, the optimization is not comprehensive. Indeed, identification on the ground segment resource is still performed manually that causes increasing overhed of investments. This study addresses above the issues by proposing new schemes to solve the problems. It introduces a comprehensive method for customer bandwidth assignment and automation of ground segment identification, especially Up Converter and Down Converter. The proposed schemes for customer bandwidth assignment and automation ground segment investment use Genetic Algorithm with modification in objective function. Financial efficient matrix is used for evaluating the performance of the proposed schemes. The experimental result shows that the proposed schemes have a good performance when number of individuals is 1000, number of generation is 10, probability of crossover is 0.8, probability of mutation is 0.5, mutation startegy is using unifrom strategy, and parent selection method is Roulette Wheel. The amount of saving in terms of ground segment investment is over than USD 500,000. It is achieved when all the existing carriers are simultaneously reorganized or migrated from existing satellites to new satellite
Dynamic approach to solve the daily drayage problem with travel time uncertainty
The intermodal transport chain can become more e cient by means of a good organization of
drayage movements. Drayage in intermodal container terminals involves the pick up and delivery
of containers at customer locations, and the main objective is normally the assignment
of transportation tasks to the di erent vehicles, often with the presence of time windows. This
scheduling has traditionally been done once a day and, under these conditions, any unexpected
event could cause timetable delays. We propose to use the real-time knowledge about vehicle
position to solve this problem, which permanently allows the planner to reassign tasks in case
the problem conditions change. This exact knowledge of the position of the vehicles is possible
using a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show
that this additional data can be used to dynamically improve the solution
Two-echelon freight transport optimisation: unifying concepts via a systematic review
Multi-echelon distribution schemes are one of the most common strategies adopted by the transport companies in an aim of cost reduction, but their identification in scientific literature is not always easy due to a lack of unification. This paper presents the main concepts of two-echelon distribution via a systematic review, in the specific a meta-narrative analysis, in order to identify and unify the main concepts, issues and methods that can be helpful for scientists and transport practitioners. The problem of system cost optimisation in two-echelon freight transport systems is defined. Moreover, the main variants are synthetically presented and discussed. Finally, future research directions are proposed.location-routing problems, multi-echelon distribution, cross-docking, combinatorial optimisation, systematic review.
Order acceptance and scheduling in a single-machine environment: exact and heuristic algorithms.
In this paper, we develop exact and heuristic algorithms for the order acceptance and scheduling problem in a single-machine environment. We consider the case where a pool consisting of firm planned orders as well as potential orders is available from which an over-demanded company can select. The capacity available for processing the accepted orders is limited and orders are characterized by known processing times, delivery dates, revenues and the weight representing a penalty per unit-time delay beyond the delivery date promised to the customer. We prove the non-approximability of the problem and give two linear formulations that we solve with CPLEX. We devise two exact branch-and-bound procedures able to solve problem instances of practical dimensions. For the solution of large instances, we propose six heuristics. We provide a comparison and comments on the efficiency and quality of the results obtained using both the exact and heuristic algorithms, including the solution of the linear formulations using CPLEX.Order acceptance; Scheduling; Single machine; Branch-and-bound; Heuristics; Firm planned orders;
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.Comment: 23 page
A evolutionary algorithm for dynamically optimisation of drayage operations
Proper planning of drayage operations is fundamental in the quest for the economic viability of intermodal freight transport. The work we present here is a dynamic optimization model which uses real-time knowledge of the fleet's position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks
Dynamic optimisation of urban intermodal freight transport with random transit times, flexible tasks and time windows
Es una ponencia de The Sixth International Conference on City Logistics, en Puerto Vallarta, México http://toc.proceedings.com/18996webtoc.pdfAn improvement on drayage operations is necessary for intermodal freight transport to become competitive. When drayage takes place in cities or urban centres transit times are usually random, as a consequence finding the optimal fleet schedule is very difficult, and this schedule can even change during the day. The work we present here is a dynamic optimisation model which uses real-time knowledge of the fleet’s position, permanently enabling the planner to reallocate tasks as the problem conditions change. Stochastic trip times are considered, both in the completion of each task and between tasks. Tasks can also be flexible or well-defined. We describe the algorithm in detail for a test problem and then apply it to a set of random drayage problems of different size and characteristics, obtaining significant cost reductions with respect to initial estimates.Junta de AndalucÃa SR0197/200
Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Operations with Uncertain Demand
Humanitarian logistics service providers have two major responsibilities
immediately after a disaster: locating trapped people and routing aid to them.
These difficult operations are further hindered by failures in the
transportation and telecommunications networks, which are often rendered
unusable by the disaster at hand. In this work, we propose two-echelon vehicle
routing frameworks for performing these operations using aerial uncrewed
autonomous vehicles (UAVs or drones) to address the issues associated with
these failures. In our proposed frameworks, we assume that ground vehicles
cannot reach the trapped population directly, but they can only transport
drones from a depot to some intermediate locations. The drones launched from
these locations serve to both identify demands for medical and other aids
(e.g., epi-pens, medical supplies, dry food, water) and make deliveries to
satisfy them. Specifically, we present two decision frameworks, in which the
resulting optimization problem is formulated as a two-echelon vehicle routing
problem. The first framework addresses the problem in two stages: providing
telecommunications capabilities in the first stage and satisfying the resulting
demands in the second. To that end, two types of drones are considered. Hotspot
drones have the capability of providing cell phone and internet reception, and
hence are used to capture demands. Delivery drones are subsequently employed to
satisfy the observed demand. The second framework, on the other hand, addresses
the problem as a stochastic emergency aid delivery problem, which uses a
two-stage robust optimization model to handle demand uncertainty. To solve the
resulting models, we propose efficient and novel solution approaches
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