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: A repairable mechanical system (as agricultural
tractor) is subject to deterioration or repeated failure and needs a
repair shops and also operator’s capability for the repair and
maintenance operations. Data are based on field visits and interviews
with 48MF 285 tractor operators from 14 villages collected in north
of Khouzestan province. The results showed that most operators were
lack the technical skill to service and repair tractors due to
insufficient training, specific education and work experience.
Inadequate repair and maintenance facilities, such as workshops,
mechanics and spare parts depots cause delays in repair work in the
survey areas. Farmers do not keep accurate service records and most
of them disregard proper maintenance and service of their tractors,
such as changing engine oil without following the manufacturer’s
recommendations. Since, Repair and maintenance facilities should be
established in village areas to guarantee timely repair in case of
breakdowns and to make spare parts available at low price. The
operators should keep service records accurately and adhere to
maintenance and service schedules according to the manufacturer’s
instructions. They should also be encouraged to do the service and
maintain their tractors properly.
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 will discuss how we optimize our physical
verification flow in our IC Design Department having various rule
decks from multiple foundries. Our ultimate goal is to achieve faster
time to tape-out and avoid schedule delay. Currently the physical
verification runtimes and memory usage have drastically increased
with the increasing number of design rules, design complexity, and
the size of the chips to be verified. To manage design violations, we
use a number of solutions to reduce the amount of violations needed
to be checked by physical verification engineers. The most important
functions in physical verifications are DRC (design rule check), LVS
(layout vs. schematic), and XRC (extraction). Since we have a
multiple number of foundries for our design tape-outs, we need a
flow that improve the overall turnaround time and ease of use of the
physical verification process. The demand for fast turnaround time is
even more critical since the physical design is the last stage before
sending the layout to the foundries.
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: Lead time is a critical measure of a supply chain's
performance. It impacts both the customer satisfactions as well as the
total cost of inventory. This paper presents the result of a study on the
analysis of the customer order lead-time for a multinational company.
In the study, the lead time was divided into three stages respectively:
order entry, order fulfillment, and order delivery. A sample of size 2,425 order lines was extracted from the
company's records to use for this study. The sample data entails
information regarding customer orders from the time of order entry
until order delivery. Data regarding the lead time of each stage for
different orders were also provided. Summary statistics on lead time
data reveals that about 30% of the orders were delivered later than the
scheduled due date. The result of the multiple linear regression
analysis technique revealed that component type, logistics parameter,
order size and the customer type have significant impacts on lead
time. Data analysis on the stages of lead time indicates that stage 2
consumed over 50% of the lead time. Pareto analysis was made to
study the reasons for the customer order delay in each stage.
Recommendation was given to resolve the problem.
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: In this study, the pedestrian simulation VISWALK
integration and application platform ant algorithms written program
made to construct a renovation engineering schedule planning mode.
The use of simulation analysis platform construction site when the user
running the simulation, after calculating the user walks in the case of
construction delays, the ant algorithm to find out the minimum delay
time schedule plan, and add volume and unit area deactivated loss of
business computing, and finally to the owners and users of two
different positions cut considerations pick out the best schedule
planning. To assess and validate its effectiveness, this study
constructed the model imported floor of a shopping mall floor
renovation engineering cases. Verify that the case can be found from
the mode of the proposed project schedule planning program can
effectively reduce the delay time and the user's walking mall loss of
business, the impact of the operation on the renovation engineering
facilities in the building to a minimum.
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: Hydrothermal liquefaction (HTL) is a technique for obtaining clean biofuel from biomass in the presence of heat and pressure in an aqueous medium which leads to a decomposition of this biomass to the formation of various products. A role of operating conditions is essential for the bio-oil and other products’ yield and also quality of the products. The effects of these parameters were investigated in regards to the composition and yield of the products. Chlorellaceae microalgae were tested under different HTL conditions to clarify suitable conditions for extracting bio-oil together with value-added co-products. Firstly, different microalgae loading rates (5-30%) were tested and found that this parameter has not much significant to product yield. Therefore, 10% microalgae loading rate was selected as a proper economical solution for conditioned schedule at 250oC and 30 min-reaction time. Next, a range of temperature (210-290oC) was applied to verify the effects of each parameter by keeping the reaction time constant at 30 min. The results showed no linkage with the increase of the reaction temperature and some reactions occurred that lead to different product yields. Moreover, some nutrients found in the aqueous product are possible to be utilized for nutrient recovery.
Abstract: The Markov decision process (MDP) based
methodology is implemented in order to establish the optimal
schedule which minimizes the cost. Formulation of MDP problem
is presented using the information about the current state of pipe,
improvement cost, failure cost and pipe deterioration model. The
objective function and detailed algorithm of dynamic programming
(DP) are modified due to the difficulty of implementing the
conventional DP approaches. The optimal schedule derived from
suggested model is compared to several policies via Monte
Carlo simulation. Validity of the solution and improvement in
computational time are proved.
Abstract: Advanced head and neck cancers are aggressive
tumours, which require aggressive treatment. Treatment efficiency is
often hindered by cancer cell repopulation during radiotherapy,
which is due to various mechanisms triggered by the loss of tumour
cells and involves both stem and differentiated cells. The aim of the
current paper is to present in silico simulations of radiotherapy
schedules on a virtual head and neck tumour grown with biologically
realistic kinetic parameters. Using the linear quadratic formalism of
cell survival after radiotherapy, altered fractionation schedules
employing various treatment breaks for normal tissue recovery are
simulated and repopulation mechanism implemented in order to
evaluate the impact of various cancer cell contribution on tumour
behaviour during irradiation. The model has shown that the timing of
treatment breaks is an important factor influencing tumour control in
rapidly proliferating tissues such as squamous cell carcinomas of the
head and neck. Furthermore, not only stem cells but also
differentiated cells, via the mechanism of abortive division, can
contribute to malignant cell repopulation during treatment.
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: Starting in 2020, an EU-wide CO2-limitation of
95 g/km is scheduled for the average of an OEMs passenger car fleet.
Taking that into consideration additional improvement measures of
the Diesel cycle are necessary in order to reduce fuel consumption
and emissions while boosting, or at the least, keeping performance
values at the same time.
The present article deals with the possibilities of an optimized
air/water charge air cooler, also called iCAC (indirect Charge Air
Cooler) for a Diesel passenger car amongst extreme-boundary
conditions. In this context, the precise objective was to show the
impact of improved intercooling with reference to the engine working
process (fuel consumption and NOx-emissions). Several extremeboundaries
- e.g. varying ambient temperatures or mountainous
routes - that will become very important in the near future regarding
RDE (Real Driving emissions) were subject of the investigation.
With the introduction of RDE in 2017 (EU6c measure), the
controversial NEDC (New European Driving Cycle) will belong to
the past and the OEMs will have to avoid harmful emissions in any
conceivable real life situation.
This is certainly going to lead to optimization-measurements at the
powertrain, which again is going to make the implementation of
iCACs, presently solely used for the premium class, more and more
attractive for compact class cars. The investigations showed a benefit
in FC between 1 and 3% for the iCAC in real world conditions.
Abstract: The objective of the Economic Dispatch(ED) Problems
of electric power generation is to schedule the committed generating
units outputs so as to meet the required load demand at minimum
operating cost while satisfying all units and system equality and
inequality constraints. This paper presents a new method of ED
problems utilizing the Max-Min Ant System Optimization.
Historically, traditional optimizations techniques have been used,
such as linear and non-linear programming, but within the past
decade the focus has shifted on the utilization of Evolutionary
Algorithms, as an example Genetic Algorithms, Simulated Annealing
and recently Ant Colony Optimization (ACO). In this paper we
introduce the Max-Min Ant System based version of the Ant System.
This algorithm encourages local searching around the best solution
found in each iteration. To show its efficiency and effectiveness, the
proposed Max-Min Ant System is applied to sample ED problems
composed of 4 generators. Comparison to conventional genetic
algorithms is presented.
Abstract: Time and cost are the main goals of the construction
project management. The first schedule developed may not be a
suitable schedule for beginning or completing the project to achieve
the target completion time at a minimum total cost. In general, there
are trade-offs between time and cost (TCT) to complete the activities
of a project. This research presents genetic algorithms (GAs) multiobjective
model for project scheduling considering different
scenarios such as least cost, least time, and target time.
Abstract: Copper being one of the major intrinsic residual
impurities in steel possesses the tendency to induce severe
microstructural distortions if not controlled within certain limits.
Hence, this paper investigates the effect of this element on the
mechanical properties of construction steel with a view to ascertain
its safe limits for effective control. The experiment entails collection
of statistically scheduled samples of hot rolled profiles with varied
copper concentrations in the range of 0.12-0.39 wt. %. From these
samples were prepared standard test specimens subjected to tensile,
impact, hardness and microstructural analyses. Results show a rather
huge compromise in mechanical properties as the specimens
demonstrated 54.3%, 74.2% and 64.9% reduction in tensile strength,
impact energy and hardness respectively as copper content increases
from 0.12 wt. % to 0.39 wt. %. The steel’s abysmal performance is
due to the severe distortion of the microstructure occasioned by the
development of incoherent complex compounds which weaken the
pearlite reinforcing phase. It is concluded that the presence of copper
above 0.22 wt. % is deleterious to construction steel performance.
Abstract: Economic Dispatch (ED) is one of the most
challenging problems of power system since it is difficult to determine
the optimum generation scheduling to meet the particular load demand
with the minimum fuel costs while all constraints are satisfied. The
objective of the Economic Dispatch Problems (EDPs) of electric
power generation is to schedule the committed generating units
outputs so as to meet the required load demand at minimum operating
cost while satisfying all units and system equality and inequality
constraints. In this paper, an efficient and practical steady-state genetic
algorithm (SSGAs) has been proposed for solving the economic
dispatch problem. The objective is to minimize the total generation
fuel cost and keep the power flows within the security limits. To
achieve that, the present work is developed to determine the optimal
location and size of capacitors in transmission power system where,
the Participation Factor Algorithm and the Steady State Genetic
Algorithm are proposed to select the best locations for the capacitors
and determine the optimal size for them.
Abstract: This research is aimed to develop the online-class
scheduling management system and improve as a complex problem
solution, this must take into consideration in various conditions and
factors. In addition to the number of courses, the number of students
and a timetable to study, the physical characteristics of each class
room and regulations used in the class scheduling must also be taken
into consideration. This system is developed to assist management in
the class scheduling for convenience and efficiency. It can provide
several instructors to schedule simultaneously. Both lecturers and
students can check and publish a timetable and other documents
associated with the system online immediately. It is developed in a
web-based application. PHP is used as a developing tool. The
database management system was MySQL. The tool that is used for
efficiency testing of the system is questionnaire. The system was
evaluated by using a Black-Box testing. The sample was composed
of 2 groups: 5 experts and 100 general users. The average and the
standard deviation of results from the experts were 3.50 and 0.67.
The average and the standard deviation of results from the general
users were 3.54 and 0.54. In summary, the results from the research
indicated that the satisfaction of users were in a good level.
Therefore, this system could be implemented in an actual workplace
and satisfy the users’ requirement effectively.