Abstract: Task Assignment and Scheduling is a challenging Operations Research problem when there is a limited number of resources and comparatively higher number of tasks. The Cost Management team at Cummins needs to assign tasks based on a deadline and must prioritize some of the tasks as per business requirements. Moreover, there is a constraint on the resources that assignment of tasks should be done based on an individual skill level, that may vary for different tasks. Another constraint is for scheduling the tasks that should be evenly distributed in terms of number of working hours, which adds further complexity to this problem. The proposed greedy approach to solve assignment and scheduling problem first assigns the task based on management priority and then by the closest deadline. This is followed by an iterative selection of an available resource with the least allocated total working hours for a task, i.e. finding the local optimal choice for each task with the goal of determining the global optimum. The greedy approach task allocation is compared with a variant of Hungarian Algorithm, and it is observed that the proposed approach gives an equal allocation of working hours among the resources. The comparative study of the proposed approach is also done with manual task allocation and it is noted that the visibility of the task timeline has increased from 2 months to 6 months. An interactive dashboard app is created for the greedy assignment and scheduling approach and the tasks with more than 2 months horizon that were waiting in a queue without a delivery date initially are now analyzed effectively by the business with expected timelines for completion.
Abstract: Web service composition combines available services
to provide new functionality. Given the number of available
services with similar functionalities and different non functional
aspects (QoS), the problem of finding a QoS-optimal web service
composition is considered as an optimization problem belonging to
NP-hard class. Thus, an optimal solution cannot be found by exact
algorithms within a reasonable time. In this paper, a meta-heuristic
bio-inspired is presented to address the QoS aware web service
composition; it is based on Elephant Herding Optimization (EHO)
algorithm, which is inspired by the herding behavior of elephant
group. EHO is characterized by a process of dividing and combining
the population to sub populations (clan); this process allows the
exchange of information between local searches to move toward
a global optimum. However, with Applying others evolutionary
algorithms the problem of early stagnancy in a local optimum
cannot be avoided. Compared with PSO, the results of experimental
evaluation show that our proposition significantly outperforms the
existing algorithm with better performance of the fitness value and a
fast convergence.
Abstract: Genetic algorithm is widely used in optimization
problems for its excellent global search capabilities and highly parallel
processing capabilities; but, it converges prematurely and has a poor
local optimization capability in actual operation. Simulated annealing
algorithm can avoid the search process falling into local optimum. A
hybrid genetic algorithm based on simulated annealing is designed by
combining the advantages of genetic algorithm and simulated
annealing algorithm. The numerical experiment represents the hybrid
genetic algorithm can be applied to solve the function optimization
problems efficiently.
Abstract: We evaluate the performance of a numerical method
for global optimization of expensive functions. The method is using a
response surface to guide the search for the global optimum. This
metamodel could be based on radial basis functions, kriging, or a
combination of different models. We discuss how to set the cyclic
parameters of the optimization method to get a balance between local
and global search. We also discuss the eventual problem with Runge
oscillations in the response surface.
Abstract: Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
Abstract: Due to uncertainty of wind velocity, wind power generators don’t have deterministic output power. Utilizing wind power generation and thermal power plants together create new concerns for operation engineers of power systems. In this paper, a model is presented to implement the uncertainty of load and generated wind power which can be utilized in power system operation planning. Stochastic behavior of parameters is simulated by generating scenarios that can be solved by deterministic method. A mixed-integer linear programming method is used for solving deterministic generation scheduling problem. The proposed approach is applied to a 12-unit test system including 10 thermal units and 2 wind farms. The results show affectivity of piecewise linear model in unit commitment problems. Also using linear programming causes a considerable reduction in calculation times and guarantees convergence to the global optimum. Neglecting the uncertainty of wind velocity causes higher cost assessment of generation scheduling.
Abstract: A key element of many distribution systems is the
routing and scheduling of vehicles servicing a set of customers. A
wide variety of exact and approximate algorithms have been
proposed for solving the vehicle routing problems (VRP). Exact
algorithms can only solve relatively small problems of VRP, which is
classified as NP-Hard. Several approximate algorithms have proven
successful in finding a feasible solution not necessarily optimum.
Although different parts of the problem are stochastic in nature; yet,
limited work relevant to the application of discrete event system
simulation has addressed the problem. Presented here is optimization
using simulation of VRP; where, a simplified problem has been
developed in the ExtendSimTM simulation environment; where,
ExtendSimTM evolutionary optimizer is used to minimize the total
transportation cost of the problem. Results obtained from the model
are very satisfactory. Further complexities of the problem are
proposed for consideration in the future.
Abstract: The aim of the current work is to present a comparison among three popular optimization methods in the inverse elastostatics problem (IESP) of flaw detection within a solid. In more details, the performance of a simulated annealing, a Hooke & Jeeves and a sequential quadratic programming algorithm was studied in the test case of one circular flaw in a plate solved by both the boundary element (BEM) and the finite element method (FEM). The proposed optimization methods use a cost function that utilizes the displacements of the static response. The methods were ranked according to the required number of iterations to converge and to their ability to locate the global optimum. Hence, a clear impression regarding the performance of the aforementioned algorithms in flaw identification problems was obtained. Furthermore, the coupling of BEM or FEM with these optimization methods was investigated in order to track differences in their performance.
Abstract: Owning to the high-speed feed rate and ultra spindle
speed have been used in modern machine tools, the tool-path
generation plays a key role in the successful application of a
High-Speed Machining (HSM) system. Because of its importance in
both high-speed machining and tool-path generation, approximating a
contour by NURBS format is a potential function in CAD/CAM/CNC
systems. It is much more convenient to represent an ellipse by
parametric form than to connect points laboriously determined in a
CNC system. A new approximating method based on optimum
processes and NURBS curves of any degree to the ellipses is presented
in this study. Such operations can be the foundation of tool-radius
compensation interpolator of NURBS curves in CNC system. All
operating processes for a CAD tool is presented and demonstrated by
practical models.
Abstract: Ant colony optimization (ACO) and its variants are
applied extensively to resolve various continuous optimization
problems. As per the various diversification and intensification
schemes of ACO for continuous function optimization, researchers
generally consider components of multidimensional state space to
generate the new search point(s). However, diversifying to a new
search space by updating only components of the multidimensional
vector may not ensure that the new point is at a significant distance
from the current solution. If a minimum distance is not ensured
during diversification, then there is always a possibility that the
search will end up with reaching only local optimum. Therefore, to
overcome such situations, a Mahalanobis distance-based
diversification with Nelder-Mead simplex-based search scheme for
each ant is proposed for the ACO strategy. A comparative
computational run results, based on nine nonlinear standard test
problems, confirms that the performance of ACO is improved
significantly with the integration of the proposed schemes in the
ACO.
Abstract: There are two common types of operational research techniques, optimisation and metaheuristic methods. The latter may be defined as a sequential process that intelligently performs the exploration and exploitation adopted by natural intelligence and strong inspiration to form several iterative searches. An aim is to effectively determine near optimal solutions in a solution space. In this work, a type of metaheuristics called Ant Colonies Optimisation, ACO, inspired by a foraging behaviour of ants was adapted to find optimal solutions of eight non-linear continuous mathematical models. Under a consideration of a solution space in a specified region on each model, sub-solutions may contain global or multiple local optimum. Moreover, the algorithm has several common parameters; number of ants, moves, and iterations, which act as the algorithm-s driver. A series of computational experiments for initialising parameters were conducted through methods of Rigid Simplex, RS, and Modified Simplex, MSM. Experimental results were analysed in terms of the best so far solutions, mean and standard deviation. Finally, they stated a recommendation of proper level settings of ACO parameters for all eight functions. These parameter settings can be applied as a guideline for future uses of ACO. This is to promote an ease of use of ACO in real industrial processes. It was found that the results obtained from MSM were pretty similar to those gained from RS. However, if these results with noise standard deviations of 1 and 3 are compared, MSM will reach optimal solutions more efficiently than RS, in terms of speed of convergence.
Abstract: Oxygen and carbon isotopes records of multi-species planktonic, benthic foraminifera and bulk carbonate sample from Central Java Indonesia demonstrate that warm sea surface temperature occurred during the Miocene. Planktonic δ18O values from this study consistently lighter (-4 to -3 ‰PDB) than previous studies that indicate sea surface temperature during Miocene in this area was warm than tropical/equatorial localities. A surprising decrease of oxygen isotopic composition was recorded at ±14 Ma where the maximum of δ18O values is -4.87 ‰PDB for Orbulina universa, -5.02 ‰PDB for Globigerinoides sacculifer and -4.30 ‰PDB for Globoquadrina dehiscens, this event we predict as Middle Miocene Optimum. Warming of sea surface temperature we interpret as related to the development of Western Pacific Warm Pool where warm water from Pacific Ocean through the Indonesian seaway appears to remain during Miocene. Our result also show increasing suddenly of oxygen isotope values of planktic, benthic and bulk carbonate sample from ± 12 Ma, the increasing cooled surface water relatively high degree with Late Miocene global cooling climate or we predict that due to closing of Indonesian Gateway.
Abstract: In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search escaping local optimum on progress to reach a global optimum. Based on the well-known idea of Genetic Algorithms, this work proposes a heuristic rule to make a mutation when the state of the network is trapped in a spurious memory. The proposal heuristic associative memory show the stored capacity does not depend on the number of stored patterns and the retrieval ability is up to ~ 1.
Abstract: This paper mainly proposes an efficient modified
particle swarm optimization (MPSO) method, to identify a slidercrank
mechanism driven by a field-oriented PM synchronous motor.
In system identification, we adopt the MPSO method to find
parameters of the slider-crank mechanism. This new algorithm is
added with “distance" term in the traditional PSO-s fitness function to
avoid converging to a local optimum. It is found that the comparisons
of numerical simulations and experimental results prove that the
MPSO identification method for the slider-crank mechanism is
feasible.