A GA-simheuristic for the stochastic and multi-period portfolio optimisation problem with liabilities

Nieto, Armando; Serra, Marti; Juan, Ángel A.; Bayliss, Christopher

doi:10.1080/17477778.2022.2041990

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…It integrated Monte Carlo simulation at different stages of the genetic algorithm to maximize the expectation under uncertainty. The results showed that this algorithm had high computational efficiency [17]. Deliktaş D designed a fuzzy multi-objective genetic algorithm based on cardinality constraints for decisionmaking in portfolio optimization under fuzzy parameters.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Enterprise Marketing Project Portfolios Using Fuzzy Ant Colony Optimization

Mao

2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Enterprise marketing project portfolio optimization is important for business competitiveness, but involves multiple uncertain factors. An integrated model using fuzzy rules was proposed in this paper to enhance ant colony optimization. The fuzzy ant colony algorithm effectively handled ambiguity in project costs, returns, and risks when selecting an optimal portfolio of marketing initiatives. Experiments demonstrated the algorithm efficiency in converging towards high-quality solutions. Case studies indicated the model helped boost customer loyalty and profits through tailored marketing strategies, outperforming conventional approaches. The fuzzy optimization provides an effective decision-making framework for enterprises to maximize marketing effectiveness.

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Section: Related Workmentioning

confidence: 99%

Optimizing Enterprise Marketing Project Portfolios Using Fuzzy Ant Colony Optimization

Mao

2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Computational results on benchmark instances show that the proposed approach outperforms classical metaheuristic approaches employed in the deterministic POP. Nieto et al (2022) integrated Monte Carlo simulation at different stages of a genetic algorithm for solving a multi-period POP with assets and liabilities. A series of computational experiments, Juan, Li, Ammouriova, Panadero, and Faulin including advanced evolutionary strategies, illustrated the advantages of using the proposed algorithm in financial optimization problems under uncertainty.…”

Section: Finance and Insurancementioning

confidence: 99%

Simheuristics: An Introductory Tutorial

Juan¹,

Ammouriova

et al. 2022

2022 Winter Simulation Conference (WSC)

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Both manufacturing and service industries are subject to uncertainty. Probability techniques and simulation methods allow us to model and analyze complex systems in which stochastic uncertainty is present. When the goal is to optimize the performance of these stochastic systems, simulation by itself is not enough and it needs to be hybridized with optimization methods. Since many real-life optimization problems in the aforementioned industries are NP-hard and large scale, metaheuristic optimization algorithms are required. The simheuristics concept refers to the hybridization of simulation methods and metaheuristic algorithms. This paper provides an introductory tutorial to the concept of simheuristics, showing how it has been successfully employed in solving stochastic optimization problems in many application fields, from production logistics and transportation to telecommunication and insurance. Current research trends in the area of simheuristics, such as their combination with fuzzy logic techniques and machine learning methods, are also discussed. INTRODUCTIONSince uncertainty is present in most real-life systems, simulation methods are frequently employed to analyze complex systems in industries such as manufacturing and production logistics, transportation, health care, finance, smart cities, and telecommunication. Usually, the uncertainty in these systems is modeled via probability distributions, either theoretical or empirical ones. Then, a logical model of the system is built, and computer simulation is utilized to get insight on the system performance under different scenarios, each of which represent a possible system configuration. As pointed out by some authors, the advances in computer power and simulation software have transformed simulation into a 'first-resource' method for analyzing complex systems under uncertainty (Lucas et al. 2015).Still, simulation by itself is not an optimization tool, since it does not contain a mechanism to efficiently explore the vast solution spaces that arise in combinatorial optimization problems. Therefore, if our goal is

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“…As explained in Chica et al [11], a simheuristic approach allows for the integration of simulation and metaheuristics in order to optimize systems under conditions of stochastic uncertainty. Many papers have recently employed simheuristics to solve stochastic optimization problems in different areas, as in finance [49] or transportation [32]. Rabe et al [57] describe a simheuristic framework for dealing with systems involving mild stochastic uncertainty, which is a special case of uncertainty [69].…”

Section: Introductionmentioning

confidence: 99%

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

et al. 2023

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Stochastic, as well as fuzzy uncertainty, can be found in most real‐world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision‐makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best‐known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks.

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A GA-simheuristic for the stochastic and multi-period portfolio optimisation problem with liabilities

Cited by 6 publications

References 56 publications

Optimizing Enterprise Marketing Project Portfolios Using Fuzzy Ant Colony Optimization

Optimizing Enterprise Marketing Project Portfolios Using Fuzzy Ant Colony Optimization

Simheuristics: An Introductory Tutorial

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

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