2,442 research outputs found
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Fuzzy Cognitive Map based Prediction Tool for Schedule Overrun
The main aim of any software development organizations is to finish the project within acceptable or customary schedule and budget Software schedule overrun is one of a question that needs more concentration Schedule overrun may affect the whole project success like cost quality and increases risks Schedule overrun can be reason of project failure In today s competitive world controlling the schedule slippage of software project development is a challenging task Effective handling of schedule is an essential need for any software project organization The main tasks for software development estimation are determining the effort cost and schedule of developing the project under consideration Underestimation of project done knowingly just to win contract results into loses and also the poor quality project So precise schedule prediction leads to efficient control of time and budget during software development In this paper we developed a new technique for the prediction of schedule overrun This paper also presents the comparison with other algorithms of schedule estimation and Tool developed by us and at last proved that Fuzzy cognitive map based prediction tool gives more accurate results than other training algorithm
Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects
While monolithic satellite missions still pose significant advantages in terms of accuracy and
operations, novel distributed architectures are promising improved flexibility, responsiveness,
and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite
networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance
satellites are becoming feasible and advantageous alternatives requiring the adoption
of new operation paradigms that enhance their autonomy. While autonomy is a notion that
is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic
in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations
for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy
is also presented as a necessary feature to bring new distributed Earth observation functions
(which require coordination and collaboration mechanisms) and to allow for novel structural
functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission
Planning and Scheduling (MPS) frameworks are then presented as a key component to implement
autonomous operations in satellite missions. An exhaustive knowledge classification explores the
design aspects of MPS for DSS, and conceptually groups them into: components and organizational
paradigms; problem modeling and representation; optimization techniques and metaheuristics;
execution and runtime characteristics and the notions of tasks, resources, and constraints.
This paper concludes by proposing future strands of work devoted to study the trade-offs of
autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that
consider some of the limitations of small spacecraft technologies.Postprint (author's final draft
Dynamic vehicle routing with time windows in theory and practice
The vehicle routing problem is a classical combinatorial optimization
problem. This work is about a variant of the vehicle routing problem
with dynamically changing orders and time windows. In real-world
applications often the demands change during operation time. New orders
occur and others are canceled. In this case new schedules need to be
generated on-the-fly. Online optimization algorithms for dynamical
vehicle routing address this problem but so far they do not consider
time windows. Moreover, to match the scenarios found in real-world
problems adaptations of benchmarks are required. In this paper, a
practical problem is modeled based on the procedure of daily routing of a
delivery company. New orders by customers are introduced dynamically
during the working day and need to be integrated into the schedule. A
multiple ant colony algorithm combined with powerful local search
procedures is proposed to solve the dynamic vehicle routing problem with
time windows. The performance is tested on a new benchmark based on
simulations of a working day. The problems are taken from Solomon’s
benchmarks but a certain percentage of the orders are only revealed to
the algorithm during operation time. Different versions of the MACS
algorithm are tested and a high performing variant is identified.
Finally, the algorithm is tested in situ: In a field study, the
algorithm schedules a fleet of cars for a surveillance company. We
compare the performance of the algorithm to that of the procedure used
by the company and we summarize insights gained from the implementation
of the real-world study. The results show that the multiple ant colony
algorithm can get a much better solution on the academic benchmark
problem and also can be integrated in a real-world environment
A New Heuristic for Scheduling Optimization of Non-Repetitive Construction Projects under Constrained Resources
In Sudan, the clients, contractors and consultants (stakeholders) suffer from the
elongation of project completion time, especially in the case of limited resources. This problem
results in the conflict among them, and hence leads to project delay that consequently influences
the overall project cost. To solve this problem, data from ten construction projects executed in
Khartoum state and other towns was collected, simulated and analyzed. Primavera software
program was used as a simulator tool and sixteen selected heuristics were applied to the ten
projects. Statistical and operational research tools combined with the existing heuristics, while
considering best common practices in construction industry, were used. Lindo software, as a
decision making tool, is then used to find the optimum solution, i.e., finding the minimum time
to complete the project under limited resources. The results were then evaluated and, hence,
concluded that the optimum solution of the extra needed time at its minimum possible rate (to
complete the project under limited resources) was achieved as a result of implementing the
heuristic of “minimum late start time”. This new “selected” heuristic optimizes the scheduling
time of non-repetitive projects while considering the availability of limited resources
Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion
International audienc
Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms
Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown
Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks
Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique
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