Abstract: In this study, we are interested in the economic lot
scheduling problem (ELSP) that considers manufacturing of the
serviceable products and remanufacturing of the reworked products. In
this paper, we formulate a mathematical model for the ELSP with
reworks using the basic period approach. In order to solve this
problem, we propose a search algorithm to find the cyclic multiplier ki
of each product that can be cyclically produced for every ki basic
periods. This research also uses two heuristics to search for the optimal
production sequence of all lots and the optimal time length of the basic
period so as to minimize the average total cost. This research uses a
numerical example to show the effectiveness of our approach.
Abstract: This paper presents one comprehensive modelling approach for maintenance scheduling problem of thermal power units in competitive market. This problem is formulated as a 0/1 mixedinteger linear programming model. Model incorporates long-term bilateral contracts with defined profiles of power and price, and weekly forecasted market prices for market auction. The effectiveness of the proposed model is demonstrated through case study with detailed discussion.
Abstract: Avionics software is safe-critical embedded software
and its architecture is evolving from traditional federated architectures
to Integrated Modular Avionics (IMA) to improve resource usability.
ARINC 653 (Avionics Application Standard Software Interface) is a
software specification for space and time partitioning in Safety-critical
avionics Real-time operating systems. Arinc653 uses two-level
scheduling strategies, but current modeling tools only apply to simple
problems of Arinc653 two-level scheduling, which only contain time
property. In avionics industry, we are always manually allocating
tasks and calculating the timing table of a real-time system to ensure
it-s running as we design. In this paper we represent an automatically
generating strategy which applies to the two scheduling problems with
dependent constraints in Arinc653 partition run-time environment. It
provides the functionality of automatic generation from the task and partition models to scheduling policy through allocating the tasks to the partitions while following the constraints, and then we design a simulating mechanism to check whether our policy is schedulable or
not
Abstract: Process planning and production scheduling play
important roles in manufacturing systems. In this paper a multiobjective
mixed integer linear programming model is presented for
the integrated planning and scheduling of multi-product. The aim is
to find a set of high-quality trade-off solutions. This is a
combinatorial optimization problem with substantially large solution
space, suggesting that it is highly difficult to find the best solutions
with the exact search method. To account for it, a PSO-based
algorithm is proposed by fully utilizing the capability of the
exploration search and fast convergence. To fit the continuous PSO
in the discrete modeled problem, a solution representation is used in
the algorithm. The numerical experiments have been performed to
demonstrate the effectiveness of the proposed algorithm.
Abstract: Unlike the best effort service provided by the internet
today, next-generation wireless networks will support real-time
applications. This paper proposes an adaptive early packet discard
(AEPD) policy to improve the performance of the real time TCP
traffic over ATM networks and avoid the fragmentation problem.
Three main aspects are incorporated in the proposed policy. First,
providing quality-of-service (QoS) guaranteed for real-time
applications by implementing a priority scheduling. Second,
resolving the partially corrupted packets problem by differentiating
the buffered cells of one packet from another. Third, adapting a
threshold dynamically using Fuzzy logic based on the traffic
behavior to maintain a high throughput under a variety of load
conditions. The simulation is run for two priority classes of the input
traffic: real time and non-real time classes. Simulation results show
that the proposed AEPD policy improves throughput and fairness
over that using static threshold under the same traffic conditions.
Abstract: The economic and stable operation was affected
seriously by coal quality disturbance for power plants. Based on model
analysis, influence of the disturbance can be considered as gain change
of control system. Power capability coefficient of coal was
constructed to inhibit it. Accuracy of the coefficient was verified by
operating data. Then coal quality disturbance free system based on
gain scheduling was designed for coordinated control system.
Simulation showed that, the strategy improved control quality
obviously, and inhibited the coal quality disturbance.
Abstract: This paper addresses a current problem that occurs among Thai internet service providers with regard to bandwidth network quality management. The IPSTAR department of Telecom Organization of Thailand public company (TOT); the largest internet service provider in Thailand, is the case study to analyze the problem that exists. The Internet bandwidth network quality management (iBWQM) framework is mainly applied to the problem that has been found. Bandwidth management policy (BMP) and quality of service (QoS) are two antecedents of iBWQM. This paper investigates internet user behavior, marketing demand and network operation views in order to determine bandwidth management policy (e.g. quota management, scheduling and malicious management). The congestion of bandwidth is also analyzed to enhance quality of service (QoS). Moreover, the iBWQM framework is able to improve the quality of service and increase bandwidth utilization, minimize complaint rate concerns to slow speed, and provide network planning guidelines through Thai Internet services providers.
Abstract: In this paper the multi-mode resource-constrained project scheduling problem with discounted cash flows is considered. Minimizing the makespan and maximization the net present value (NPV) are the two common objectives that have been investigated in the literature. We apply one evolutionary algorithm named multiobjective particle swarm optimization (MOPSO) to find Pareto front solutions. We used standard sets of instances from the project scheduling problem library (PSPLIB). The results are computationally compared respect to different metrics taken from the literature on evolutionary multi-objective optimization.
Abstract: Improving the reactive power and voltage profile of a
distribution substation is investigated in this paper. The purpose is to
properly determination of the shunt capacitors on/off status and
suitable tap changer (TC) position of a substation transformer. In
addition, the limitation of secondary bus voltage, the maximum
allowable number of switching operation in a day for on load tap
changer and on/off status of capacitors are taken into account. To
achieve these goals, an artificial neural network (ANN) is designed to
provide preliminary scheduling. Input of ANN is active and reactive
powers of transformer and its primary and secondary bus voltages.
The output of ANN is capacitors on/off status and TC position. The
preliminary schedule is further refined by fuzzy dynamic
programming in order to reach the final schedule. The operation of
proposed method in Q/V improving is compared with the results
obtained by operator operation in a distribution substation.
Abstract: Normally business changes are made in order to
change a level of activity in some way, whether it is sales, cash flow,
productivity, or product portfolio. When attempts are made to make
such changes, too often the business reverts to the old levels of
activity as soon as management attention is diverted. Risk
management is a field of growing interest to project managers as well
as in general business and organizational management. There are
several approaches used to manage risk in projects and this paper is a
brief outline of some that you might encounter, with an indication of
their strengths and weaknesses.
Abstract: Given a parallel program to be executed on a heterogeneous
computing system, the overall execution time of the program
is determined by a schedule. In this paper, we analyze the worst-case
performance of the list scheduling algorithm for scheduling tasks
of a parallel program in a mixed-machine heterogeneous computing
system such that the total execution time of the program is minimized.
We prove tight lower and upper bounds for the worst-case
performance ratio of the list scheduling algorithm. We also examine
the average-case performance of the list scheduling algorithm. Our
experimental data reveal that the average-case performance of the list
scheduling algorithm is much better than the worst-case performance
and is very close to optimal, except for large systems with large
heterogeneity. Thus, the list scheduling algorithm is very useful in
real applications.
Abstract: Simulation is a very powerful method used for highperformance
and high-quality design in distributed system, and now
maybe the only one, considering the heterogeneity, complexity and
cost of distributed systems. In Grid environments, foe example, it is
hard and even impossible to perform scheduler performance
evaluation in a repeatable and controllable manner as resources and
users are distributed across multiple organizations with their own
policies. In addition, Grid test-beds are limited and creating an
adequately-sized test-bed is expensive and time consuming.
Scalability, reliability and fault-tolerance become important
requirements for distributed systems in order to support distributed
computation. A distributed system with such characteristics is called
dependable. Large environments, like Cloud, offer unique
advantages, such as low cost, dependability and satisfy QoS for all
users. Resource management in large environments address
performant scheduling algorithm guided by QoS constrains. This
paper presents the performance evaluation of scheduling heuristics
guided by different optimization criteria. The algorithms for
distributed scheduling are analyzed in order to satisfy users
constrains considering in the same time independent capabilities of
resources. This analysis acts like a profiling step for algorithm
calibration. The performance evaluation is based on simulation. The
simulator is MONARC, a powerful tool for large scale distributed
systems simulation. The novelty of this paper consists in synthetic
analysis results that offer guidelines for scheduler service
configuration and sustain the empirical-based decision. The results
could be used in decisions regarding optimizations to existing Grid
DAG Scheduling and for selecting the proper algorithm for DAG
scheduling in various actual situations.
Abstract: To provide a better understanding of fair share policies supported by current production schedulers and their impact on scheduling performance, A relative fair share policy supported in four well-known production job schedulers is evaluated in this study. The experimental results show that fair share indeed reduces heavy-demand users from dominating the system resources. However, the detailed per-user performance analysis show that some types of users may suffer unfairness under fair share, possibly due to priority mechanisms used by the current production schedulers. These users typically are not heavy-demands users but they have mixture of jobs that do not spread out.
Abstract: Restructured electricity markets may provide
opportunities for producers to exercise market power maintaining
prices in excess of competitive levels. In this paper an oligopolistic
market is presented that all Generation Companies (GenCos) bid in a
Cournot model. Genetic algorithm (GA) is applied to obtain
generation scheduling of each GenCo as well as hourly market
clearing prices (MCP). In order to consider network constraints a
multiperiod framework is presented to simulate market clearing
mechanism in which the behaviors of market participants are
modelled through piecewise block curves. A mixed integer linear
programming (MILP) is employed to solve the problem. Impacts of
market clearing process on participants- characteristic and final
market prices are presented. Consequently, a novel multi-objective
model is addressed for security constrained optimal bidding strategy
of GenCos. The capability of price-maker GenCos to alter MCP is
evaluated through introducing an effective-supply curve. In addition,
the impact of exercising market power on the variation of market
characteristics as well as GenCos scheduling is studied.
Abstract: In this paper, multi-processors job shop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan, i.e. total completion time, in which a simultanous presence of various kinds of ferons is allowed. By using the suitable hybrid of priority dispatching rules, the process of finding the best solution will be improved. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. Thus, the similar work has the same production lines. Other advantage of this algorithm is that the similar machines (not the same) can be considered. So, these machines are able to process a job with different processing and setup times. According to this capability and from this algorithm evaluation point of view, a number of test problems are solved and the associated results are analyzed. The results show a significant decrease in throughput time. It also shows that, this algorithm is able to recognize the bottleneck machine and to schedule jobs in an efficient way.
Abstract: In this paper an ant colony optimization algorithm is
developed to solve the permutation flow shop scheduling problem. In
the permutation flow shop scheduling problem which has been vastly
studied in the literature, there are a set of m machines and a set of n
jobs. All the jobs are processed on all the machines and the sequence
of jobs being processed is the same on all the machines. Here this
problem is optimized considering two criteria, makespan and total
flow time. Then the results are compared with the ones obtained by
previously developed algorithms. Finally it is visible that our
proposed approach performs best among all other algorithms in the
literature.
Abstract: In this paper, we propose APO, a new packet scheduling
scheme with Quality of Service (QoS) support for hybrid of
real and non-real time services in HSDPA networks. The APO
scheduling algorithm is based on the effective channel anticipation
model. In contrast to the traditional schemes, the proposed method is
implemented based on a cyclic non-work-conserving discipline.
Simulation results indicated that proposed scheme has good
capability to maximize the channel usage efficiency in compared to
another exist scheduling methods. Simulation results demonstrate the
effectiveness of the proposed algorithm.
Abstract: This paper presents a hybrid algorithm for solving a timetabling problem, which is commonly encountered in many universities. The problem combines both teacher assignment and course scheduling problems simultaneously, and is presented as a mathematical programming model. However, this problem becomes intractable and it is unlikely that a proven optimal solution can be obtained by an integer programming approach, especially for large problem instances. A hybrid algorithm that combines an integer programming approach, a greedy heuristic and a modified simulated annealing algorithm collaboratively is proposed to solve the problem. Several randomly generated data sets of sizes comparable to that of an institution in Indonesia are solved using the proposed algorithm. Computational results indicate that the algorithm can overcome difficulties of large problem sizes encountered in previous related works.
Abstract: A virtualized and virtual approach is presented on
academically preparing students to successfully engage at a strategic
perspective to understand those concerns and measures that are both
structured and not structured in the area of cyber security and
information assurance. The Master of Science in Cyber Security and
Information Assurance (MSCSIA) is a professional degree for those
who endeavor through technical and managerial measures to ensure
the security, confidentiality, integrity, authenticity, control,
availability and utility of the world-s computing and information
systems infrastructure. The National University Cyber Security and
Information Assurance program is offered as a Master-s degree. The
emphasis of the MSCSIA program uniquely includes hands-on
academic instruction using virtual computers. This past year, 2011,
the NU facility has become fully operational using system
architecture to provide a Virtual Education Laboratory (VEL)
accessible to both onsite and online students. The first student cohort
completed their MSCSIA training this past March 2, 2012 after
fulfilling 12 courses, for a total of 54 units of college credits. The
rapid pace scheduling of one course per month is immensely
challenging, perpetually changing, and virtually multifaceted. This
paper analyses these descriptive terms in consideration of those
globalization penetration breaches as present in today-s world of
cyber security. In addition, we present current NU practices to
mitigate risks.
Abstract: Re-entrant scheduling is an important search problem
with many constraints in the flow shop. In the literature, a number of
approaches have been investigated from exact methods to
meta-heuristics. This paper presents a genetic algorithm that encodes
the problem as multi-level chromosomes to reflect the dependent
relationship of the re-entrant possibility and resource consumption.
The novel encoding way conserves the intact information of the data
and fastens the convergence to the near optimal solutions. To test the
effectiveness of the method, it has been applied to the
resource-constrained re-entrant flow shop scheduling problem.
Computational results show that the proposed GA performs better than
the simulated annealing algorithm in the measure of the makespan