Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması

Mean-variance portfolio optimization model, introduced by Markowitz, provides a fundamental answer to the problem of portfolio management. This model seeks an efficient frontier with the best trade-offs between two conflicting objectives of maximizing return and minimizing risk. The problem of determining an efficient frontier is known to be NP-hard. Due to the complexity of the problem, genetic algorithms have been widely employed by a growing number of researchers to solve this problem. In this study, a literature review of genetic algorithms implementations on mean-variance portfolio optimization is examined from the recent published literature. Main specifications of the problems studied and the specifications of suggested genetic algorithms have been summarized

A novel metaheuristic for traveling salesman problem: blind mole-rat algorithm

Traveling Salesman Problem (TSP) is the problem of finding a minimum distance tour of cities beginning and ending at the same city and that each city are visited only once. As the number of cities increases, it is difficult to find an optimal solution by exact methods in a reasonable duration. Therefore, in recent five decades many heuristic solution methods inspired of nature and biology have been developed. In this paper, a new metaheuristic method inspired of the by-passing the obstacle strategy of blind mole rats living in their individual tunnel systems under the soil is designed for solving TSP. The method is called as Blind Mole-rat Algorithm. The proposed algorithm is tested on different size of symmetric TSP problems and the results are compared to the best known results. Initial test results are promising although proposed metaheuristic is not yet competitive enough among other algorithms in the literature

Simulated annealing algorithm for solving sequence-dependent disassembly line balancing problem

In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that concerns with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent time increments among disassembly tasks. Due to the high complexity of the SDDLBP, there is currently no known way to optimally solve even moderately sized instances of the problem; therefore an efficient methodology based on the simulated annealing is proposed to solve the SDDLBP. © IFAC

An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery

Along with the progress in computer hardware architecture and computational power, in order to overcome technological bottlenecks, software applications that make use of expert and intelligent systems must race against time where nanoseconds matter in the long-awaited future. This is possible with the integration of excellent solvers to software engineering methodologies that provide optimization-based decision support for planning. Since the logistics market is growing rapidly, the optimization of routing systems is of primary concern that motivates the use of vehicle routing problem (VRP) solvers as software components integrated as an optimization engine. A critical success factor of routing optimization is quality vs. response time performance. Less time-consuming and more efficient automated processes can be achieved by employing stronger solution algorithms. This study aims to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) which is a popular extension of the basic Vehicle Routing Problem arising in real world applications where pickup and delivery operations are simultaneously taken into account to satisfy the vehicle capacity constraint with the objective of total travelled distance minimization. Since the problem is known to be NP-hard, a hybrid metaheuristic algorithm based on an ant colony system (ACS) and a variable neighborhood search (VNS) is developed for its solution. VNS is a powerful optimization algorithm that provides intensive local search. However, it lacks a memory structure. This weakness can be minimized by utilizing long term memory structure of ACS and hence the overall performance of the algorithm can be boosted. In the proposed algorithm, instead of ants, VNS releases pheromones on the edges while ants provide a perturbation mechanism for the integrated algorithm using the pheromone information in order to explore search space further and jump from local optima. The performance of the proposed ACS empowered VNS algorithm is studied on well-known benchmarks test problems taken from the open literature of VRPSPD for comparison purposes. Numerical results confirm that the developed approach is robust and very efficient in terms of both solution quality and CPU time since better results provided in a shorter time on benchmark data sets is a good performance indicator. © 2016 Elsevier Lt

A tabu search algorithm for balancing a sequence-dependent disassembly line

In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that requires the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and considering sequence-dependent part removal time increments. The four objectives to be achieved are as follows: (1) minimise the number of disassembly workstations, (2) minimise the total idle time by evenly distributing it among workstations, (3) maximise the priority of removing hazardous components as early as possible in the disassembly sequence and (4) maximise the priority of removing high demand components before low demand components. A new approach based on the tabu search (TS) algorithm is proposed to solve the SDDLBP. To the best of our knowledge, this paper investigates the first application of TS algorithm to solve the SDDLBP. Two case scenarios are considered and comparisons with ant colony optimisation and river formation dynamics approaches are provided to demonstrate the effectiveness of the algorithm. © 2013 Taylor & Francis

Ant colony optimization for sequence-dependent disassembly line balancing problem

Purpose - The purpose of this paper is to introduce sequence-dependent disassembly line balancing problem (SDDLBP) to the literature and propose an efficient metaheuristic solution methodology to this NP-complete problem. Design/methodology/approach - This manuscript utilizes a well-proven metaheuristics solution methodology, namely, ant colony optimization, to address the problem. Findings - Since SDDLBP is NP-complete, finding an optimal balance becomes computationally prohibitive due to exponential growth of the solution space with the increase in the number of parts. The proposed methodology is very fast, generates (near) optimal solutions, preserves precedence requirements and is easy to implement. Practical implications - Since development of cost effective and profitable disassembly systems is an important issue in end-of-life product treatment, every step towards improving disassembly line balancing brings us closer to cost savings and compelling practicality. Originality/value - This paper introduces a new problem (SDDLBP) and an efficient solution to the literature. © Emerald Group Publishing Limited

A genetic algorithm based examination timetabling model focusing on student success for the case of the college of engineering at Pamukkale University, Turkey

This study proposes a genetic algorithm (GA) based model to generate examination schedules such that they focus on students' success in addition to satisfying the hard constraints required for feasibility. The model is based on the idea that the student success is positively related to the adequate preparation and resting time among exams. Therefore, the main objective of this study is to maximize time length among exams (i.e., paper spread) considering the difficulties of exams. Two different genetic algorithm models were developed to optimize paper spread. In the first genetic algorithm model, a high penalty approach was used to eliminate infeasible solutions throughout generations. The second genetic algorithm model controls whether or not each chromosome joining the population satisfies the hard constraints. To evaluate the models, a set of experiments have been designed and studied using the data collected from the College of Engineering in Pamukkale University

Balancing a sequencedependent disassembly line using simulated annealing algorithm

Disturbing increase in the use of virgin resources to produce new products has threatened the environment. Many countries have reacted to this situation through regulations which aim to eliminate negative impact of products on the environment shaping the concept of environmentally conscious manufacturing and product recovery (ECMPRO). The first crucial and the most time-consuming step of product recovery is disassembly. The best productivity rate is achieved via a disassembly line in an automated disassembly process. In this chapter, we consider a sequencedependent disassembly line balancing problem (SDDLBP) with multiple objectives that is concerned with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent time increments among disassembly tasks. Due to the high complexity of the SDDLBP, there is currently no known way to optimally solve even moderately sized instances of the problem. Therefore, an efficient methodology based on the simulated annealing (SA) is proposed to solve the SDDLBP. Case scenarios are considered and comparisons with ant colony optimization (ACO), particle swarm optimization (PSO), river formation dynamics (RFD), and tabu search (TS) approaches are provided to demonstrate the superior functionality of the proposed algorithm. Copyright © 2013 by Emerald Group Publishing Limited

A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem

For remanufacturing or recycling companies, a reverse supply chain is of prime importance since it facilitates in recovering parts and materials from end-of-life products. In reverse supply chains, selective separation of desired parts and materials from returned products is achieved by means of disassembly which is a process of systematic separation of an assembly into its components, subassemblies or other groupings. Due to its high productivity and suitability for automation, disassembly line is the most efficient layout for product recovery operations. A disassembly line must be balanced to optimize the use of resources (viz., labor, money and time). In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that requires the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence dependent time increments. A hybrid algorithm that combines a genetic algorithm with a variable neighborhood search method (VNSGA) is proposed to solve the SDDLBP. The performance of VNSGA was thoroughly investigated using numerous data instances that have been gathered and adapted from the disassembly and the assembly line balancing literature. Using the data instances, the performance of VNSGA was compared with the best known metaheuristic methods reported in the literature. The tests demonstrated the superiority of the proposed method among all the methods considered. © 2014, Springer Science+Business Media New York

An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization

Portfolio optimization with cardinality constraints turns out to be a mixed-integer quadratic programming problem which is proven to be NP-Complete that limits the efficiency of exact solution approaches, often because of the long-running times. Therefore, particular attention has been given to approximate approaches such as metaheuristics which do not guarantee optimality, yet may expeditiously provide near-optimal solutions. The purpose of this study is to present an efficient hybrid metaheuristic algorithm that combines critical components from continuous ant colony optimization, artificial bee colony optimization and genetic algorithms for solving cardinality constrained portfolio optimization problem. Computational results on seven publicly available benchmark problems confirm the effectiveness of the hybrid integration mechanism. Moreover, comparisons against other methods’ results in the literature reveal that the proposed solution approach is competitive with state-of-the-art algorithms. © 2020 Elsevier B.V