Abstract: Cloud Computing refers to applications delivered as
services over the internet, and the datacenters that provide those
services with hardware and systems software. These were earlier
referred to as Software as a Service (SaaS). Scheduling is justified by
job components (called tasks), lack of information. In fact, in a large
fraction of jobs from machine learning, bio-computing, and image
processing domains, it is possible to estimate the maximum time
required for a task in the job. This study focuses on Trust based
scheduling to improve cloud security by modifying Heterogeneous
Earliest Finish Time (HEFT) algorithm. It also proposes TR-HEFT
(Trust Reputation HEFT) which is then compared to Dynamic Load
Scheduling.
Abstract: Home Energy Management System (HEMS), which makes the residential consumers, contribute to the demand response is attracting attention in recent years. An aim of HEMS is to minimize their electricity cost by controlling the use of their appliances according to electricity price. The use of appliances in HEMS may be affected by some conditions such as external temperature and electricity price. Therefore, the user’s usage pattern of appliances should be modeled according to the external conditions, and the resultant usage pattern is related to the user’s comfortability on use of each appliances. This paper proposes a methodology to model the usage pattern based on the historical data with the copula function. Through copula function, the usage range of each appliance can be obtained and is able to satisfy the appropriate user’s comfort according to the external conditions for next day. Within the usage range, an optimal scheduling for appliances would be conducted so as to minimize an electricity cost with considering user’s comfort. Among the home appliance, electric heater (EH) is a representative appliance, which is affected by the external temperature. In this paper, an optimal scheduling algorithm for an electric heater (EH) is addressed based on the method of branch and bound. As a result, scenarios for the EH usage are obtained according to user’s comfort levels and then the residential consumer would select the best scenario. The case study shows the effects of the proposed algorithm compared with the traditional operation of the EH, and it represents impacts of the comfort level on the scheduling result.
Abstract: This paper studied the flow shop scheduling problem under machine availability constraints. The machines are subject to flexible preventive maintenance activities. The nonresumable scenario for the jobs was considered. That is, when a job is interrupted by an unavailability period of a machine it should be restarted from the beginning. The objective is to minimize the total tardiness time for the jobs and the advance/tardiness for the maintenance activities. To solve the problem, a genetic algorithm was developed and successfully tested and validated on many problem instances. The computational results showed that the new genetic algorithm outperforms another earlier proposed algorithm.
Abstract: Carefully scheduling the operations of pumps can be
resulted to significant energy savings. Schedules can be defined
either implicit, in terms of other elements of the network such as tank
levels, or explicit by specifying the time during which each pump is
on/off. In this study, two new explicit representations based on timecontrolled
triggers were analyzed, where the maximum number of
pump switches was established beforehand, and the schedule may
contain fewer switches than the maximum. The optimal operation of
pumping stations was determined using a Jumping Particle Swarm
Optimization (JPSO) algorithm to achieve the minimum energy cost.
The model integrates JPSO optimizer and EPANET hydraulic
network solver. The optimal pump operation schedule of VanZyl
water distribution system was determined using the proposed model
and compared with those from Genetic and Ant Colony algorithms.
The results indicate that the proposed model utilizing the JPSO
algorithm is a versatile management model for the operation of realworld
water distribution system.
Abstract: This paper addresses minimizing the makespan of the
distributed permutation flow shop scheduling problem. In this
problem, there are several parallel identical factories or flowshops
each with series of similar machines. Each job should be allocated to
one of the factories and all of the operations of the jobs should be
performed in the allocated factory. This problem has recently gained
attention and due to NP-Hard nature of the problem, metaheuristic
algorithms have been proposed to tackle it. Majority of the proposed
algorithms require large computational time which is the main
drawback. In this study, a general variable neighborhood search
algorithm (GVNS) is proposed where several time-saving schemes
have been incorporated into it. Also, the GVNS uses the sophisticated
method to change the shaking procedure or perturbation depending
on the progress of the incumbent solution to prevent stagnation of the
search. The performance of the proposed algorithm is compared to
the state-of-the-art algorithms based on standard benchmark
instances.
Abstract: Batch production plants provide a wide range of
scheduling problems. In pharmaceutical industries a batch process
is usually described by a recipe, consisting of an ordering of tasks
to produce the desired product. In this research work we focused
on pharmaceutical production processes requiring the culture of
a microorganism population (i.e. bacteria, yeasts or antibiotics).
Several sources of uncertainty may influence the yield of the culture
processes, including (i) low performance and quality of the cultured
microorganism population or (ii) microbial contamination. For
these reasons, robustness is a valuable property for the considered
application context. In particular, a robust schedule will not collapse
immediately when a cell of microorganisms has to be thrown away
due to a microbial contamination. Indeed, a robust schedule should
change locally in small proportions and the overall performance
measure (i.e. makespan, lateness) should change a little if at all.
In this research work we formulated a constraint programming
optimization (COP) model for the robust planning of antibiotics
production. We developed a discrete-time model with a multi-criteria
objective, ordering the different criteria and performing a
lexicographic optimization. A feasible solution of the proposed
COP model is a schedule of a given set of tasks onto available
resources. The schedule has to satisfy tasks precedence constraints,
resource capacity constraints and time constraints. In particular
time constraints model tasks duedates and resource availability
time windows constraints. To improve the schedule robustness, we
modeled the concept of (a, b) super-solutions, where (a, b) are input
parameters of the COP model. An (a, b) super-solution is one in
which if a variables (i.e. the completion times of a culture tasks)
lose their values (i.e. cultures are contaminated), the solution can be
repaired by assigning these variables values with a new values (i.e.
the completion times of a backup culture tasks) and at most b other
variables (i.e. delaying the completion of at most b other tasks).
The efficiency and applicability of the proposed model is
demonstrated by solving instances taken from a real-life
pharmaceutical company. Computational results showed that
the determined super-solutions are near-optimal.
Abstract: Workflow scheduling is an important part of cloud
computing and based on different criteria it decides cost, execution
time, and performances. A cloud workflow system is a platform
service facilitating automation of distributed applications based on
new cloud infrastructure. An aspect which differentiates cloud
workflow system from others is market-oriented business model, an
innovation which challenges conventional workflow scheduling
strategies. Time and Cost optimization algorithm for scheduling
Hybrid Clouds (TCHC) algorithm decides which resource should be
chartered from public providers is combined with a new De-De
algorithm considering that every instance of single and multiple
workflows work without deadlocks. To offset this, two new concepts
- De-De Dodging Algorithm and Priority Based Decisive Algorithm -
combine with conventional deadlock avoidance issues by proposing
one algorithm that maximizes active (not just allocated) resource use
and reduces Makespan.
Abstract: The lifetime of a wireless sensor network can be
effectively increased by using scheduling operations. Once the
sensors are randomly deployed, the task at hand is to find the largest
number of disjoint sets of sensors such that every sensor set provides
complete coverage of the target area. At any instant, only one of these
disjoint sets is switched on, while all other are switched off. This
paper proposes a heuristic search method to find the maximum
number of disjoint sets that completely cover the region. A
population of randomly initialized members is made to explore the
solution space. A set of heuristics has been applied to guide the
members to a possible solution in their neighborhood. The heuristics
escalate the convergence of the algorithm. The best solution explored
by the population is recorded and is continuously updated. The
proposed algorithm has been tested for applications which require
sensing of multiple target points, referred to as point coverage
applications. Results show that the proposed algorithm outclasses the
existing algorithms. It always finds the optimum solution, and that
too by making fewer number of fitness function evaluations than the
existing approaches.
Abstract: This paper presents a state-of-the-art survey of the
operations research models developed for internal audit planning.
Two alternative approaches have been followed in the literature for
audit planning: (1) identifying the optimal audit frequency; and (2)
determining the optimal audit resource allocation. The first approach
identifies the elapsed time between two successive audits, which can
be presented as the optimal number of audits in a given planning
horizon, or the optimal number of transactions after which an audit
should be performed. It also includes the optimal audit schedule. The
second approach determines the optimal allocation of audit frequency
among all auditable units in the firm. In our review, we discuss both
the deterministic and probabilistic models developed for audit
planning. In addition, game theory models are reviewed to find the
optimal auditing strategy based on the interactions between the
auditors and the clients.
Abstract: This research aims to develop an algorithm to
generate a schedule of multiple cranes that will maximize load
throughputs in anodizing operation. The algorithm proposed utilizes
an enumerative strategy to search for constant time between
successive loads and crane covering range over baths. The computer
program developed is able to generate a near-optimal crane schedule
within reasonable times, i.e. within 10 minutes. Its results are
compared with existing solutions from an aluminum extrusion
industry. The program can be used to generate crane schedules for
mixed products, thus allowing mixed-model line balancing to
improve overall cycle times.
Abstract: The Economic Lot Scheduling Problem (ELSP) is a
valuable mathematical model that can support decision-makers to
make scheduling decisions. The basic period approach is effective for
solving the ELSP. The assumption for applying the basic period
approach is that a product must use its maximum production rate to be
produced. However, a product can lower its production rate to reduce
the average total cost when a facility has extra idle time. The past
researches discussed how a product adjusts its production rate under
the common cycle approach. To the best of our knowledge, no studies
have addressed how a product lowers its production rate under the
basic period approach. This research is the first paper to discuss this
topic. The research develops a simple fixed rate approach that adjusts
the production rate of a product under the basic period approach to
solve the ELSP. Our numerical example shows our approach can find a
better solution than the traditional basic period approach. Our
mathematical model that applies the fixed rate approach under the
basic period approach can serve as a reference for other related
researches.
Abstract: In a practical power system, the power plants are not
located at the same distance from the center of loads and their fuel
costs are different. Also, under normal operating conditions, the
generation capacity is more than the total load demand and losses.
Thus, there are many options for scheduling generation. In an
interconnected power system, the objective is to find the real and
reactive power scheduling of each power plant in such a way as to
minimize the operating cost. This means that the generator’s real and
reactive powers are allowed to vary within certain limits so as to meet
a particular load demand with minimum fuel cost. This is called
optimal power flow problem. In this paper, Economic Load Dispatch
(ELD) of real power generation is considered. Economic Load
Dispatch (ELD) is the scheduling of generators to minimize total
operating cost of generator units subjected to equality constraint of
power balance within the minimum and maximum operating limits of
the generating units. In this paper, genetic algorithms are considered.
ELD solutions are found by solving the conventional load flow
equations while at the same time minimizing the fuel costs.
Abstract: The Scheduling and mapping of tasks on a set of
processors is considered as a critical problem in parallel and
distributed computing system. This paper deals with the problem of
dynamic scheduling on a special type of multiprocessor architecture
known as Linear Crossed Cube (LCQ) network. This proposed
multiprocessor is a hybrid network which combines the features of
both linear types of architectures as well as cube based architectures.
Two standard dynamic scheduling schemes namely Minimum
Distance Scheduling (MDS) and Two Round Scheduling (TRS)
schemes are implemented on the LCQ network. Parallel tasks are
mapped and the imbalance of load is evaluated on different set of
processors in LCQ network. The simulations results are evaluated
and effort is made by means of through analysis of the results to
obtain the best solution for the given network in term of load
imbalance left and execution time. The other performance matrices
like speedup and efficiency are also evaluated with the given
dynamic algorithms.
Abstract: Grid is an environment with millions of resources
which are dynamic and heterogeneous in nature. A computational
grid is one in which the resources are computing nodes and is meant
for applications that involves larger computations. A scheduling
algorithm is said to be efficient if and only if it performs better
resource allocation even in case of resource failure. Resource
allocation is a tedious issue since it has to consider several
requirements such as system load, processing cost and time, user’s
deadline and resource failure. This work attempts in designing a
resource allocation algorithm which is cost-effective and also targets
at load balancing, fault tolerance and user satisfaction by considering
the above requirements. The proposed Budget Constrained Load
Balancing Fault Tolerant algorithm with user satisfaction (BLBFT)
reduces the schedule makespan, schedule cost and task failure rate
and improves resource utilization. Evaluation of the proposed
BLBFT algorithm is done using Gridsim toolkit and the results are
compared with the algorithms which separately concentrates on all
these factors. The comparison results ensure that the proposed
algorithm works better than its counterparts.
Abstract: The check-in area of airport terminal is one of the
busiest sections at airports at certain periods. The passengers are
subjected to queues and delays during the check-in process. These
delays and queues are due to constraints in the capacity of service
facilities. In this project, the airport terminal is decomposed into
several check-in areas. The airport check-in scheduling problem
requires both a deterministic (integer programming) and stochastic
(simulation) approach. Integer programming formulations are
provided to minimize the total number of counters in each check-in
area under the realistic constraint that counters for one and the same
flight should be adjacent and the desired number of counters
remaining in each area should be fixed during check-in operations.
By using simulation, the airport system can be modeled to study the
effects of various parameters such as number of passengers on a
flight and check-in counter opening and closing time.
Abstract: IEEE 802.16 (WiMAX) aims to present high speed
wireless access to cover wide range coverage. The base station (BS)
and the subscriber station (SS) are the main parts of WiMAX.
WiMAX uses either Point-to-Multipoint (PMP) or mesh topologies.
In the PMP mode, the SSs connect to the BS to gain access to the
network. However, in the mesh mode, the SSs connect to each other
to gain access to the BS.
The main components of QoS management in the 802.16 standard
are the admission control, buffer management and packet scheduling.
In this paper, we use QualNet 5.0.2 to study the performance of
different scheduling schemes, such as WFQ, SCFQ, RR and SP when
the numbers of SSs increase. We find that when the number of SSs
increases, the average jitter and average end-to-end delay is increased
and the throughput is reduced.
Abstract: This paper introduces novel approaches to partitioning
and mapping in terms of model-based embedded multicore system
engineering and further discusses benefits, industrial relevance and
features in common with existing approaches. In order to assess
and evaluate results, both approaches have been applied to a real
industrial application as well as to various prototypical demonstrative
applications, that have been developed and implemented for
different purposes. Evaluations show, that such applications improve
significantly according to performance, energy efficiency, meeting
timing constraints and covering maintaining issues by using
the AMALTHEA platform and the implemented approaches.
Furthermore, the model-based design provides an open, expandable,
platform independent and scalable exchange format between
OEMs, suppliers and developers on different levels. Our proposed
mechanisms provide meaningful multicore system utilization since
load balancing by means of partitioning and mapping is effectively
performed with regard to the modeled systems including hardware,
software, operating system, scheduling, constraints, configuration and
more data.
Abstract: Job Scheduling plays an important role for efficient
utilization of grid resources available across different domains and
geographical zones. Scheduling of jobs is challenging and NPcomplete.
Evolutionary / Swarm Intelligence algorithms have been
extensively used to address the NP problem in grid scheduling.
Artificial Bee Colony (ABC) has been proposed for optimization
problems based on foraging behaviour of bees. This work proposes a
modified ABC algorithm, Cluster Heterogeneous Earliest First Min-
Min Artificial Bee Colony (CHMM-ABC), to optimally schedule
jobs for the available resources. The proposed model utilizes a novel
Heterogeneous Earliest Finish Time (HEFT) Heuristic Algorithm
along with Min-Min algorithm to identify the initial food source.
Simulation results show the performance improvement of the
proposed algorithm over other swarm intelligence techniques.
Abstract: There is decagram of strategic decisions of operations
and production/service management (POSM) within operational
research (OR) which must collate, namely: design, inventory, quality,
location, process and capacity, layout, scheduling, maintain ace, and
supply chain. This paper presents an architectural configuration
conceptual framework of a decagram of sets decisions in a form of
mathematical complete graph and abelian graph.
Mathematically, a complete graph is undirected (UDG), and
directed (DG) a relationship where every pair of vertices is
connected, collated, confluent, and holomorphic.
There has not been any study conducted which, however,
prioritizes the holomorphic sets which of POMS within OR field of
study. The study utilizes OR structured technique known as The
Analytic Hierarchy Process (AHP) analysis for organizing, sorting
and prioritizing(ranking) the sets within the decagram of POMS
according to their attribution (propensity), and provides an analysis
how the prioritization has real-world application within the 21st
century.
Abstract: This paper presents optimization of makespan for ‘n’
jobs and ‘m’ machines flexible job shop scheduling problem with
sequence dependent setup time using genetic algorithm (GA)
approach. A restart scheme has also been applied to prevent the
premature convergence. Two case studies are taken into
consideration. Results are obtained by considering crossover
probability (pc = 0.85) and mutation probability (pm = 0.15). Five
simulation runs for each case study are taken and minimum value
among them is taken as optimal makespan. Results indicate that
optimal makespan can be achieved with more than one sequence of
jobs in a production order.