474 research outputs found
Multi-Objective Multi-mode Time-Cost Tradeoff modeling in Construction Projects Considering Productivity Improvement
In today's construction industry, poor performance often arises due to
various factors related to time, finances, and quality. These factors
frequently lead to project delays and resource losses, particularly in terms of
financial resources. This research addresses the Multimode Resource-Constrained
Project Scheduling Problem (MRCPSP), a real-world challenge that takes into
account the time value of money and project payment planning. In this context,
project activities exhibit discrete cost profiles under different execution
conditions and can be carried out in multiple ways. This paper aims to achieve
two primary objectives: minimizing the net present value of project costs and
project completion times while simultaneously improving the project's
productivity index. To accomplish this, a mathematical programming model based
on certain assumptions is proposed. Several test cases are designed, and they
are rigorously evaluated using the methodology outlined in this paper to
validate the modeling approach. Recognizing the NP-hard nature of this problem,
a multi-objective genetic algorithm capable of solving large-scale instances is
developed. Finally, the effectiveness of the proposed solution is assessed by
comparing it to the performance of the NSGA-II algorithm using well-established
efficiency metrics. Results demonstrate the superior performance of the
algorithm introduced in this study.Comment: 40 pages, 20 figures, 7 table
Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty
This study proposes an Ensemble Differential Evolution with Simula-tion-Based
Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management
(IM) under uncertainty. In this study, DE with multiple runs is combined with a
simulation-based hybridization method that includes a self-adaptive mechanism
that dynamically alters mutation and crossover rates based on the success or
failure of each iteration. Due to its adaptability, the algorithm is able to
handle the complexity and uncertainty present in IM. Utilizing Monte Carlo
Simulation (MCS), the continuous review (CR) inventory strategy is ex-amined
while accounting for stochasticity and various demand scenarios. This
simulation-based approach enables a realistic assessment of the proposed
algo-rithm's applicability in resolving the challenges faced by IM in practical
settings. The empirical findings demonstrate the potential of the proposed
method to im-prove the financial performance of IM and optimize large search
spaces. The study makes use of performance testing with the Ackley function and
Sensitivity Analysis with Perturbations to investigate how changes in variables
affect the objective value. This analysis provides valuable insights into the
behavior and robustness of the algorithm.Comment: 15 pages, 6 figures, AsiaSIM 2023 (Springer
A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview
Sustainable development has emerged as a global priority, and industries are
increasingly striving to align their operations with sustainable practices.
Parallel machine scheduling (PMS) is a critical aspect of production planning
that directly impacts resource utilization and operational efficiency. In this
paper, we investigate the application of metaheuristic optimization algorithms
to address the unrelated parallel machine scheduling problem (UPMSP) through
the lens of sustainable development goals (SDGs). The primary objective of this
study is to explore how metaheuristic optimization algorithms can contribute to
achieving sustainable development goals in the context of UPMSP. We examine a
range of metaheuristic algorithms, including genetic algorithms, particle swarm
optimization, ant colony optimization, and more, and assess their effectiveness
in optimizing the scheduling problem. The algorithms are evaluated based on
their ability to improve resource utilization, minimize energy consumption,
reduce environmental impact, and promote socially responsible production
practices. To conduct a comprehensive analysis, we consider UPMSP instances
that incorporate sustainability-related constraints and objectives
Quantum Computing Algorithms for Solving Complex Mathematical Problems
The power of quantum mechanics, that is too complex for conventional computers, can be solved by an innovative model of computing known as quantum computing. Quantum algorithms can provide exponential speedups for some types of problems, such as many difficult mathematical ones. In this paper, we review some of the most important quantum algorithms for hard mathematical problems. When factoring large numbers, Shor's algorithm is orders of magnitude faster than any other known classical algorithm. The Grover's algorithm, which searches unsorted databases much more quickly than conventional algorithms, is then discussed.  
Leo: Lagrange Elementary Optimization
Global optimization problems are frequently solved using the practical and
efficient method of evolutionary sophistication. But as the original problem
becomes more complex, so does its efficacy and expandability. Thus, the purpose
of this research is to introduce the Lagrange Elementary Optimization (Leo) as
an evolutionary method, which is self-adaptive inspired by the remarkable
accuracy of vaccinations using the albumin quotient of human blood. They
develop intelligent agents using their fitness function value after gene
crossing. These genes direct the search agents during both exploration and
exploitation. The main objective of the Leo algorithm is presented in this
paper along with the inspiration and motivation for the concept. To demonstrate
its precision, the proposed algorithm is validated against a variety of test
functions, including 19 traditional benchmark functions and the CECC06 2019
test functions. The results of Leo for 19 classic benchmark test functions are
evaluated against DA, PSO, and GA separately, and then two other recent
algorithms such as FDO and LPB are also included in the evaluation. In
addition, the Leo is tested by ten functions on CECC06 2019 with DA, WOA, SSA,
FDO, LPB, and FOX algorithms distinctly. The cumulative outcomes demonstrate
Leo's capacity to increase the starting population and move toward the global
optimum. Different standard measurements are used to verify and prove the
stability of Leo in both the exploration and exploitation phases. Moreover,
Statistical analysis supports the findings results of the proposed research.
Finally, novel applications in the real world are introduced to demonstrate the
practicality of Leo.Comment: 28 page
A formal analisys of the computational dynamics in GIGANTEC
An evolutionary algorithm formalism has been forwarded in a previous research, and implemented in the system GIGANTEC: Genetic Induction for General Analytical Non-numeric Task Evolution Compiler [Bad98][Bad99]. A dynamical model is developed to analyze the behaviour of the algorithm. The model is dependent in its analysis on classical Compilers Theory, Game Theory and Markov Chains and its convergence characteristics. The results conclude that a limiting state is reached, which is independent of the initial population and the mutation rate, but dependent on the cardinality of the alphabet of the driving L-system
Zone design of specific sizes using adaptive additively weighted voronoi diagrams
Territory or zone design processes entail partitioning a geographic space, organized as a set of areal units, into different regions or zones according to a specific set of criteria that are dependent on the application context. In most cases, the aim is to create zones of approximately equal sizes (zones with equal numbers of inhabitants, same average sales, etc.). However, some of the new applications that have emerged, particularly in the context of sustainable development policies, are aimed at defining zones of a predetermined, though not necessarily similar, size. In addition, the zones should be built around a given set of seeds. This type of partitioning has not been sufficiently researched; therefore, there are no known approaches for automated zone delimitation. This study proposes a new method based on a discrete version of the adaptive additively weighted Voronoi diagram that makes it possible to partition a two-dimensional space into zones of specific sizes, taking both the position and the weight of each seed into account. The method consists of repeatedly solving a traditional additively weighted Voronoi diagram, so that each seed?s weight is updated at every iteration. The zones are geographically connected using a metric based on the shortest path. Tests conducted on the extensive farming system of three municipalities in Castile-La Mancha (Spain) have established that the proposed heuristic procedure is valid for solving this type of partitioning problem. Nevertheless, these tests confirmed that the given seed position determines the spatial configuration the method must solve and this may have a great impact on the resulting partition
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