Abstract: Wireless channels are characterized by more serious
bursty and location-dependent errors. Many packet scheduling
algorithms have been proposed for wireless networks to guarantee
fairness and delay bounds. However, most existing schemes do not
consider the difference of traffic natures among packet flows. This
will cause the delay-weight coupling problem. In particular, serious
queuing delays may be incurred for real-time flows. In this paper, it
is proposed a scheduling algorithm that takes traffic types of flows
into consideration when scheduling packets and also it is provided
scheduling flexibility by trading off video quality to meet the
playback deadline.
Abstract: The wireless link can be unreliable in realistic wireless
sensor networks (WSNs). Energy efficient and reliable data
forwarding is important because each node has limited resources.
Therefore, we must suggest an optimal solution that considers using
the information of the node-s characteristics. Previous routing
protocols were unsuited to realistic asymmetric WSNs. In this paper,
we propose a Protocol that considers Both sides of Link-quality and
Energy (PBLE), an optimal routing protocol that balances modified
link-quality, distance and energy. Additionally, we propose a node
scheduling method. PBLE achieves a longer lifetime than previous
routing protocols and is more energy-efficient. PBLE uses energy,
local information and both sides of PRR in a 1-hop distance. We
explain how to send data packets to the destination node using the
node's information. Simulation shows PBLE improves delivery rate
and network lifetime compared to previous schemes. Moreover, we
show the improvement in various WSN environments.
Abstract: This paper will discuss about an active power generator scheduling method in order to increase the limit level of steady state systems. Some power generator optimization methods such as Langrange, PLN (Indonesian electricity company) Operation, and the proposed Z-Thevenin-based method will be studied and compared in respect of their steady state aspects. A method proposed in this paper is built upon the thevenin equivalent impedance values between each load respected to each generator. The steady state stability index obtained with the REI DIMO method. This research will review the 500kV-Jawa-Bali interconnection system. The simulation results show that the proposed method has the highest limit level of steady state stability compared to other optimization techniques such as Lagrange, and PLN operation. Thus, the proposed method can be used to create the steady state stability limit of the system especially in the peak load condition.
Abstract: Fair share is one of the scheduling objectives supported on many production systems. However, fair share has been shown to cause performance problems for some users, especially the users with difficult jobs. This work is focusing on extending goaloriented parallel computer job scheduling policies to cover the fair share objective. Goal-oriented parallel computer job scheduling policies have been shown to achieve good scheduling performances when conflicting objectives are required. Goal-oriented policies achieve such good performance by using anytime combinatorial search techniques to find a good compromised schedule within a time limit. The experimental results show that the proposed goal-oriented parallel computer job scheduling policy (namely Tradeofffs( Tw:avgX)) achieves good scheduling performances and also provides good fair share performance.
Abstract: This paper presents the result of three senior capstone
projects at the Department of Computer Engineering, Prince of
Songkla University, Thailand. These projects focus on developing an
examination management system for the Faculty of Engineering in
order to manage the examination both the examination room
assignments and the examination proctor assignments in each room.
The current version of the software is a web-based application. The
developed software allows the examination proctors to select their
scheduled time online while each subject is assigned to each available
examination room according to its type and the room capacity. The
developed system is evaluated using real data by prospective users of
the system. Several suggestions for further improvements are given
by the testers. Even though the features of the developed software are
not superior, the developing process can be a case study for a projectbased
teaching style. Furthermore, the process of developing this
software can show several issues in developing an educational
support application.
Abstract: Scheduling algorithm is a key technology in satellite
switching system with input-buffer. In this paper, a new scheduling
algorithm and its realization are proposed. Based on Crossbar
switching fabric, the algorithm adopts serial scheduling strategy and
adjusts the output port arbitrating strategy for the better equity of every
port. Consequently, it increases the matching probability. The
algorithm can greatly reduce the scheduling delay and cell loss rate.
The analysis and simulation results by OPNET show that the proposed
algorithm has the better performance than others in average delay and
cell loss rate, and has the equivalent complexity. On the basis of these
results, the hardware realization and simulation based on FPGA are
completed, which validate the feasibility of the new scheduling
algorithm.
Abstract: This paper presents a modified version of the
maximum urgency first scheduling algorithm. The maximum
urgency algorithm combines the advantages of fixed and dynamic
scheduling to provide the dynamically changing systems with
flexible scheduling. This algorithm, however, has a major
shortcoming due to its scheduling mechanism which may cause a
critical task to fail. The modified maximum urgency first scheduling
algorithm resolves the mentioned problem. In this paper, we propose
two possible implementations for this algorithm by using either
earliest deadline first or modified least laxity first algorithms for
calculating the dynamic priorities. These two approaches are
compared together by simulating the two algorithms. The earliest
deadline first algorithm as the preferred implementation is then
recommended. Afterwards, we make a comparison between our
proposed algorithm and maximum urgency first algorithm using
simulation and results are presented. It is shown that modified
maximum urgency first is superior to maximum urgency first, since it
usually has less task preemption and hence, less related overhead. It
also leads to less failed non-critical tasks in overloaded situations.
Abstract: This paper proposes a power-controlled scheduling scheme for devices using a directional antenna in smart home. In the case of the home network using directional antenna, devices can concurrently transmit data in the same frequency band. Accordingly, the throughput increases compared to that of devices using omni-directional antenna in proportional to the number of concurrent transmissions. Also, the number of concurrent transmissions depends on the beamwidth of antenna, the number of devices operating in the network , transmission power, interference and so on. In particular, the less transmission power is used, the more concurrent transmissions occur due to small transmission range. In this paper, we considered sub-optimal scheduling scheme for throughput maximization and power consumption minimization. In the scheme, each device is equipped with a directional antenna. Various beamwidths, path loss components, and antenna radiation efficiencies are considered. Numerical results show that the proposed schemes outperform the scheduling scheme using directional antennas without power control.
Abstract: Maize and Indian mustard are significant crops in
semi-arid climate zones of India. Improved water management
requires precise scheduling of irrigation, which in turn requires an
accurate computation of daily crop evapotranspiration (ETc). Daily
crop evapotranspiration comes as a product of reference
evapotranspiration (ET0) and the growth stage specific crop
coefficients modified for daily variation. The first objective of
present study is to develop crop coefficients Kc for Maize and Indian
mustard. The estimated values of Kc for maize at the four crop
growth stages (initial, development, mid-season, and late season) are
0.55, 1.08, 1.25, and 0.75, respectively, and for Indian mustard the Kc
values at the four growth stages are 0.3, 0.6, 1.12, and 0.35,
respectively. The second objective of the study is to compute daily
crop evapotranspiration from ET0 and crop coefficients. Average
daily ETc of maize varied from about 2.5 mm/d in the early growing
period to > 6.5 mm/d at mid season. The peak ETc of maize is 8.3
mm/d and it occurred 64 days after sowing at the reproductive growth
stage when leaf area index was 4.54. In the case of Indian mustard,
average ETc is 1 mm/d at the initial stage, >1.8 mm/d at mid season
and achieves a peak value of 2.12 mm/d on 56 days after sowing.
Improved schedules of irrigation have been simulated based on daily
crop evapo-transpiration and field measured data. Simulation shows a
close match between modeled and field moisture status prevalent
during crop season.
Abstract: Resource-constrained project scheduling is an NPhard
optimisation problem. There are many different heuristic
strategies how to shift activities in time when resource requirements
exceed their available amounts. These strategies are frequently based
on priorities of activities. In this paper, we assume that a suitable
heuristic has been chosen to decide which activities should be
performed immediately and which should be postponed and
investigate the resource-constrained project scheduling problem
(RCPSP) from the implementation point of view. We propose an
efficient routine that, instead of shifting the activities, extends their
duration. It makes it possible to break down their duration into active
and sleeping subintervals. Then we can apply the classical Critical
Path Method that needs only polynomial running time. This
algorithm can simply be adapted for multiproject scheduling with
limited resources.
Abstract: Higher productivity and less cost in the ship
manufacturing process are required to maintain the international
competitiveness of morden manufacturing industries. In shipbuilding,
however, the Engineering To Order (ETO) production method and
production process is very difficult. Thus, designs change frequently.
In accordance with production, planning should be set up according
to scene changes. Therefore, fixed production planning is very
difficult. Thus, a scheduler must first make sketchy plans, then
change the plans based on the work progress and modifications.
Thus, data sharing in a shipbuilding block assembly shop is very
important. In this paper, we proposed to scheduling method
applicable to the shipbuilding industry and decision making support
system through web based visualization system.
Abstract: The Aggregate Production Plan (APP) is a schedule of
the organization-s overall operations over a planning horizon to
satisfy demand while minimizing costs. It is the baseline for any
further planning and formulating the master production scheduling,
resources, capacity and raw material planning. This paper presents a
methodology to model the Aggregate Production Planning problem,
which is combinatorial in nature, when optimized with Genetic
Algorithms. This is done considering a multitude of constraints of
contradictory nature and the optimization criterion – overall cost,
made up of costs with production, work force, inventory, and
subcontracting. A case study of substantial size, used to develop the
model, is presented, along with the genetic operators.
Abstract: The broadcast problem including the plan design is
considered. The data are inserted and numbered at predefined order
into customized size relations. The server ability to create a full,
regular Broadcast Plan (RBP) with single and multiple channels after
some data transformations is examined. The Regular Geometric
Algorithm (RGA) prepares a RBP and enables the users to catch their
items avoiding energy waste of their devices. Moreover, the
Grouping Dimensioning Algorithm (GDA) based on integrated
relations can guarantee the discrimination of services with a
minimum number of channels. This last property among the selfmonitoring,
self-organizing, can be offered by servers today
providing also channel availability and less energy consumption by
using smaller number of channels. Simulation results are provided.
Abstract: This paper introduces a framework based on the collaboration of multi agent and hyper-heuristics to find a solution of the real single machine production problem. There are many techniques used to solve this problem. Each of it has its own advantages and disadvantages. By the collaboration of multi agent system and hyper-heuristics, we can get more optimal solution. The hyper-heuristics approach operates on a search space of heuristics rather than directly on a search space of solutions. The proposed framework consists of some agents, i.e. problem agent, trainer agent, algorithm agent (GPHH, GAHH, and SAHH), optimizer agent, and solver agent. Some low level heuristics used in this paper are MRT, SPT, LPT, EDD, LDD, and MON
Abstract: The job shop scheduling problem (JSSP) is well known as one of the most difficult combinatorial optimization problems. This paper presents a hybrid genetic algorithm for the JSSP with the objective of minimizing makespan. The efficiency of the genetic algorithm is enhanced by integrating it with a local search method. The chromosome representation of the problem is based on operations. Schedules are constructed using a procedure that generates full active schedules. In each generation, a local search heuristic based on Nowicki and Smutnicki-s neighborhood is applied to improve the solutions. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.
Abstract: In any distributed systems, process scheduling plays a
vital role in determining the efficiency of the system. Process scheduling algorithms are used to ensure that the components of the
system would be able to maximize its utilization and able to complete all the processes assigned in a specified period of time.
This paper focuses on the development of comparative simulator for distributed process scheduling algorithms. The objectives of the works that have been carried out include the development of the
comparative simulator, as well as to implement a comparative study
between three distributed process scheduling algorithms; senderinitiated,
receiver-initiated and hybrid sender-receiver-initiated
algorithms. The comparative study was done based on the Average Waiting Time (AWT) and Average Turnaround Time (ATT) of the
processes involved. The simulation results show that the performance of the algorithms depends on the number of nodes in the system.
Abstract: Grid computing is a group of clusters connected over
high-speed networks that involves coordinating and sharing
computational power, data storage and network resources operating
across dynamic and geographically dispersed locations. Resource
management and job scheduling are critical tasks in grid computing.
Resource selection becomes challenging due to heterogeneity and
dynamic availability of resources. Job scheduling is a NP-complete
problem and different heuristics may be used to reach an optimal or
near optimal solution. This paper proposes a model for resource and
job scheduling in dynamic grid environment. The main focus is to
maximize the resource utilization and minimize processing time of
jobs. Grid resource selection strategy is based on Max Heap Tree
(MHT) that best suits for large scale application and root node of
MHT is selected for job submission. Job grouping concept is used to
maximize resource utilization for scheduling of jobs in grid
computing. Proposed resource selection model and job grouping
concept are used to enhance scalability, robustness, efficiency and
load balancing ability of the grid.
Abstract: Project managers are the ultimate responsible for the
overall characteristics of a project, i.e. they should deliver the project
on time with minimum cost and with maximum quality. It is vital for
any manager to decide a trade-off between these conflicting
objectives and they will be benefited of any scientific decision
support tool. Our work will try to determine optimal solutions (rather
than a single optimal solution) from which the project manager will
select his desirable choice to run the project. In this paper, the
problem in project scheduling notated as
(1,T|cpm,disc,mu|curve:quality,time,cost) will be studied. The
problem is multi-objective and the purpose is finding the Pareto
optimal front of time, cost and quality of a project
(curve:quality,time,cost), whose activities belong to a start to finish
activity relationship network (cpm) and they can be done in different
possible modes (mu) which are non-continuous or discrete (disc), and
each mode has a different cost, time and quality . The project is
constrained to a non-renewable resource i.e. money (1,T). Because
the problem is NP-Hard, to solve the problem, a meta-heuristic is
developed based on a version of genetic algorithm specially adapted
to solve multi-objective problems namely FastPGA. A sample project
with 30 activities is generated and then solved by the proposed
method.
Abstract: One of the main research directions in CAD/CAM
machining area is the reducing of machining time.
The feedrate scheduling is one of the advanced techniques that
allows keeping constant the uncut chip area and as sequel to keep
constant the main cutting force. They are two main ways for feedrate
optimization. The first consists in the cutting force monitoring, which
presumes to use complex equipment for the force measurement and
after this, to set the feedrate regarding the cutting force variation. The
second way is to optimize the feedrate by keeping constant the
material removal rate regarding the cutting conditions.
In this paper there is proposed a new approach using an extended
database that replaces the system model.
The feedrate scheduling is determined based on the identification
of the reconfigurable machine tool, and the feed value determination
regarding the uncut chip section area, the contact length between tool
and blank and also regarding the geometrical roughness.
The first stage consists in the blank and tool monitoring for the
determination of actual profiles. The next stage is the determination
of programmed tool path that allows obtaining the piece target
profile.
The graphic representation environment models the tool and blank
regions and, after this, the tool model is positioned regarding the
blank model according to the programmed tool path. For each of
these positions the geometrical roughness value, the uncut chip area
and the contact length between tool and blank are calculated. Each of
these parameters are compared with the admissible values and
according to the result the feed value is established.
We can consider that this approach has the following advantages:
in case of complex cutting processes the prediction of cutting force is
possible; there is considered the real cutting profile which has
deviations from the theoretical profile; the blank-tool contact length
limitation is possible; it is possible to correct the programmed tool
path so that the target profile can be obtained.
Applying this method, there are obtained data sets which allow the
feedrate scheduling so that the uncut chip area is constant and, as a
result, the cutting force is constant, which allows to use more
efficiently the machine tool and to obtain the reduction of machining
time.
Abstract: Scheduling for the flexible job shop is very important
in both fields of production management and combinatorial
optimization. However, it quit difficult to achieve an optimal solution
to this problem with traditional optimization approaches owing to the
high computational complexity. The combining of several
optimization criteria induces additional complexity and new
problems. In this paper, a Pareto approach to solve the multi
objective flexible job shop scheduling problems is proposed. The
objectives considered are to minimize the overall completion time
(makespan) and total weighted tardiness (TWT). An effective
simulated annealing algorithm based on the proposed approach is
presented to solve multi objective flexible job shop scheduling
problem. An external memory of non-dominated solutions is
considered to save and update the non-dominated solutions during
the solution process. Numerical examples are used to evaluate and
study the performance of the proposed algorithm. The proposed
algorithm can be applied easily in real factory conditions and for
large size problems. It should thus be useful to both practitioners and
researchers.