Multi-objective optimization with a Gaussian PSO algorithm

Particle Swarm Optimization es una heurística popular usada para resolver adecuada y efectivamente problemas mono-objetivo. En este artículo, presentamos una primera adaptación de esta heurística para tratar problemas multi-objetivo sin restricciones. La propuesta (llamada G-MOPSO) incorpora una actualización Gaussiana, dominancia Pareto, una política elitista, un archivo externo y un shake-mecanismo para mantener la diversidad. Para validar nuestro algoritmo, usamos cuatro funciones de prueba bien conocidas, con diferentes características. Los resultados preliminares son comparados con los valores obtenidos por un algoritmo evolutivo multi-objetivo representativo del estado del arte en el área: NSGA-II. También comparamos los resultados con los obtenidos por OMOPSO, un algoritmo multi-objetivo basado en la heurística PSO. La performance de nuestra propuesta es comparable con la de NSGA-II y supera a la de OMOPSOParticle Swarm Optimization is a popular heuristic used to solve suitably and effectively mono-objective problems. In this paper, we present an adaptation of this heuristic to treat unconstrained multi-objective problems. The proposed approach (called G-MOPSO) incorporates a Gaussian update of individuals, Pareto dominance, an elitist policy, and a shake-mechanism to maintain diversity. In order to validate our algorithm, we use four well-known test functions with different characteristics. Preliminary results are compared with respect to those obtained by a multi-objective evolutionary algorithm representative of the state-of-the-art: NSGA-II. We also compare the results with those obtained by OMOPSO, a multi-objective PSO based algorithm. The performance of our approach is comparable with the NSGA-II and outperforms the OMOPSO.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Optimización de funciones mono-objetivo con y sin restricciones, y funciones multi-objetivo a través de heurísticas bio-inspiradas

En esta presentación se describen, en forma breve, algunas de las direcciones de investigación que en la actualidad se están desarrollando dentro de la línea ”Optimización Mono y Multiobjetivo” del Laboratorio de Investigación y Desarrollo en Inteligencia Computacional (LIDIC). Uno de los objetivos de esta línea, es el estudio y desarrollo de metaheurísticas aptas para resolver problemas de optimización. En particular, el énfasis está puesto en las heurísticas de inteligencia computacional basadas en los paradigmas de inteligencia colectiva y biológicos, como ser Particle Swarm Optimization y Sistemas Inmunes Artificiales.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Optimización de funciones mono-objetivo con y sin restricciones, y funciones multi-objetivo a través de heurísticas bio-inspiradas

En esta presentación se describen, en forma breve, algunas de las direcciones de investigación que en la actualidad se están desarrollando dentro de la línea ”Optimización Mono y Multiobjetivo” del Laboratorio de Investigación y Desarrollo en Inteligencia Computacional (LIDIC). Uno de los objetivos de esta línea, es el estudio y desarrollo de metaheurísticas aptas para resolver problemas de optimización. En particular, el énfasis está puesto en las heurísticas de inteligencia computacional basadas en los paradigmas de inteligencia colectiva y biológicos, como ser Particle Swarm Optimization y Sistemas Inmunes Artificiales.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Solving hard multiobjective problems with a hybridized method

This paper presents a hybrid method to solve hard multi- objective problems. The proposed approach adopts an epsilon-constraint method which uses a Particle Swarm Optimizer to get points near of the true Pareto front. In this approach, only few points will be generated and then, new intermediate points will be calculated using an interpola- tion method, to increase the among of points in the output Pareto front. The proposed approach is validated using two difficult multiobjective test problems and the results are compared with those obtained by a multiobjective evolutionary algorithm representative of the state of the art: NSGA-II.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Solving Hard Multiobjective Problems with a Hybridized Method

This paper presents a hybrid method to solve hard multiobjective problems. The proposed approach adopts an epsilon-constraint method which uses a Particle Swarm Optimizer to get points near of the true Pareto front. In this approach, only few points will be generated and then, new intermediate points will be calculated using an interpolation method, to increase the among of points in the output Pareto front. The proposed approach is validated using two difficult multiobjective test problems and the results are compared with those obtained by a multiobjective evolutionary algorithm representative of the state of the art: NSGA-II.Facultad de Informátic

Multi-objective optimization with a Gaussian PSO algorithm

Particle Swarm Optimization es una heurística popular usada para resolver adecuada y efectivamente problemas mono-objetivo. En este artículo, presentamos una primera adaptación de esta heurística para tratar problemas multi-objetivo sin restricciones. La propuesta (llamada G-MOPSO) incorpora una actualización Gaussiana, dominancia Pareto, una política elitista, un archivo externo y un shake-mecanismo para mantener la diversidad. Para validar nuestro algoritmo, usamos cuatro funciones de prueba bien conocidas, con diferentes características. Los resultados preliminares son comparados con los valores obtenidos por un algoritmo evolutivo multi-objetivo representativo del estado del arte en el área: NSGA-II. También comparamos los resultados con los obtenidos por OMOPSO, un algoritmo multi-objetivo basado en la heurística PSO. La performance de nuestra propuesta es comparable con la de NSGA-II y supera a la de OMOPSOParticle Swarm Optimization is a popular heuristic used to solve suitably and effectively mono-objective problems. In this paper, we present an adaptation of this heuristic to treat unconstrained multi-objective problems. The proposed approach (called G-MOPSO) incorporates a Gaussian update of individuals, Pareto dominance, an elitist policy, and a shake-mechanism to maintain diversity. In order to validate our algorithm, we use four well-known test functions with different characteristics. Preliminary results are compared with respect to those obtained by a multi-objective evolutionary algorithm representative of the state-of-the-art: NSGA-II. We also compare the results with those obtained by OMOPSO, a multi-objective PSO based algorithm. The performance of our approach is comparable with the NSGA-II and outperforms the OMOPSO.Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Solving constrained optimization using a T-Cell artificial immune system

In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for solving constrained (numerical) optimization problems. The model operates on three populations: Virgins, Effectors and Memory. Each of them has a different role. Also, the model dynamically adapts the tolerance factor in order to improve the exploration capabilities of the algorithm. We also develop a new mutation operator which incorporates knowledge of the problem. We validate our proposed approach with a set of test functions taken from the specialized literature and we compare our results with respect to Stochastic Ranking (which is an approach representative of the state-of-the-art in the area) and with respect to an AIS previously proposed.En este trabajo, se presenta un nuevo modelo de Sistema Inmune Artificial (SIA), basado en el proceso que sufren las células T. El modelo propuesto se usa para resolver problemas de optimización (numéricos) restringidos. El modelo trabaja sobre tres poblaciones: Vírgenes, Efectoras y de Memoria. Cada una de ellas tiene un rol diferente. Además, el modelo adapta dinamicamente el factor de tolerancia para mejorar las capacidades de exploración del algoritmo. Se desarrolló un nuevo operador de mutación el cual incorpora conocimiento del problema. El modelo fue validado con un conjunto de funciones de prueba tomado de la literatura especializada y se compararon los resultados con respecto a Stochastic Ranking (el cual es un enfoque representativo del estado del arte en el área) y con respecto a un SIA propuesto previamente.VIII Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

An experimental and numerical investigation on strengthening the upright component of thin-walled cold-formed steel rack structures

Cold-formed steel (CFS) racking systems are widely used for storing products in warehouses. However, as commonly used structures in storage systems, thin-walled open sections are subjected to stability loss because of various buckling modes, including flexural, local, torsional and distortional. This research proposes a novel technique to increase the ultimate capacity of uprights, utilising bolts and spacers, under flexural and compressive loads. The proposed components are attached externally to the sections in certain pitches along the length. In this regard, axial tests were performed on 72 upright frames and nine single uprights with various lengths and thicknesses. Also, the impact of using reinforcing elements was evaluated by investigating the failure modes and ultimate load results. It was concluded that the reinforcement technique is able to restrain upright flanges and therefore improve the upright profiles' strength. For testing the flexural behaviour, 18 samples of three types were made, including non-reinforced sections and two types of sections reinforced along the upright length at different pitches. After that, monotonic loading was applied along both the minor and major axes of the samples. The suggested reinforcing method leads to increasing the flexural capacity of the upright sections about both the major and minor axes. Also, by using reinforcing system, the flexural performance was improved, and buckling and deformation were constrained. In addition, the reinforcement technique was evaluated by Finite Element (FE) method. Moreover, Artificial Intelligence (AI) and Machine Learning (ML) algorithms were deployed to predict the normalised ultimate load and deflection of the profiles. Following the empirical tests, the axial and flexural performance of different CFS upright profiles with various lengths, thicknesses and reinforcement spacings were simulated and examined. It was shown that the reinforcing technique improved the capacity of the samples. Consequently, the proposed reinforcements could be considered a highly effective and low-cost technique to strengthen the axial and flexural behaviour of open CFS sections considering a trade-off between performance and cost of utilising the approach

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Modelling and Optimizing Supply Chain Integrated Production Scheduling Problems

Globalization and advanced information technologies (e.g., Internet of Things) have considerably impacted supply chains (SCs) by persistently forcing original equipment manufacturers (OEMs) to switch production strategies from make-to-stock (MTS) to make-to-order (MTO) to survive in competition. Generally, an OEM follows the MTS strategy for products with steady demand. In contrast, the MTO strategy exists under a pull system with irregular demand in which the received customer orders are scheduled and launched into production. In comparison to MTS, MTO has the primary challenges of ensuring timely delivery at the lowest possible cost, satisfying the demands of high customization and guaranteeing the accessibility of raw materials throughout the production process. These challenges are increasing substantially since industrial productions are becoming more flexible, diversified, and customized. Besides, independently making the production scheduling decisions from other stages of these SCs often find sub-optimal results, creating substantial challenges to fulfilling demands timely and cost-effectively. Since adequately managing these challenges asynchronously are difficult, constructing optimization models by integrating SC decisions, such as customer requirements, supply portfolio (supplier selection and order allocation), delivery batching decisions, and inventory portfolio (inventory replenishment, consumption, and availability), with shop floor scheduling under a deterministic and dynamic environment is essential to fulfilling customer expectations at the least possible cost. These optimization models are computationally intractable. Consequently, designing algorithms to schedule or reschedule promptly is also highly challenging for these time-sensitive, operationally integrated optimization models. Thus, this thesis focuses on modelling and optimizing SC-integrated production scheduling problems, named SC scheduling problems (SCSPs). The objective of optimizing job shop scheduling problems (JSSPs) is to ensure that the requisite resources are accessible when required and that their utilization is maximally efficient. Although numerous algorithms have been devised, they can sometimes become computationally exorbitant and yield sub-optimal outcomes, rendering production systems inefficient. These could be due to a variety of causes, such as an imbalance in population quality over generations, recurrent generation and evaluation of identical schedules, and permitting an under-performing method to conduct the evolutionary process. Consequently, this study designs two methods, a sequential approach (Chapter 2) and a multi-method approach (Chapter 3), to address the aforementioned issues and to acquire competitive results in finding optimal or near-optimal solutions for JSSPs in a single objective setting. The devised algorithms for JSSPs optimize workflows for each job by accurate mapping between/among related resources, generating more optimal results than existing algorithms. Production scheduling can not be accomplished precisely without considering supply and delivery decisions and customer requirements simultaneously. Thus, a few recent studies have operationally integrated SCs to accurately predict process insights for executing, monitoring, and controlling the planned production. However, these studies are limited to simple shop-floor configurations and can provide the least flexibility to address the MTO-based SC challenges. Thus, this study formulates a bi-objective optimization model that integrates the supply portfolio into a flexible job shop scheduling environment with a customer-imposed delivery window to cost-effectively meet customized and on-time delivery requirements (Chapter 4). Compared to the job shop that is limited to sequence flexibility only, the flexible job shop has been deemed advantageous due to its capacity to provide increased scheduling flexibility (both process and sequence flexibility). To optimize the model, the performance of the multi-objective particle swarm optimization algorithm has been enhanced, with the results providing decision-makers with an increased degree of flexibility, offering a larger number of Pareto solutions, more varied and consistent frontiers, and a reasonable time for MTO-based SCs. Environmental sustainability is spotlighted for increasing environmental awareness and follow-up regulations. Consequently, the related factors strongly regulate the supply portfolio for sustainable development, which remained unexplored in the SCSP as those criteria are primarily qualitative (e.g., green production, green product design, corporate social responsibility, and waste disposal system). These absences may lead to an unacceptable supply portfolio. Thus, this study overcomes the problem by integrating VIKORSORT into the proposed solution methodology of the extended SCSP. In addition, forming delivery batches of heterogeneous customer orders is challenging, as one order can lead to another being delayed. Therefore, the previous optimization model is extended by integrating supply, manufacturing, and delivery batching decisions and concurrently optimizing them in response to heterogeneous customer requirements with time window constraints, considering both economic and environmental sustainability for the supply portfolio (Chapter 5). Since the proposed optimization model is an extension of the flexible job shop, it can be classified as a non-deterministic polynomial-time (NP)-hard problem, which cannot be solved by conventional optimization techniques, particularly in the case of larger instances. Therefore, a reinforcement learning-based hyper-heuristic (HH) has been designed, where four solution-updating heuristics are intelligently guided to deliver the best possible results compared to existing algorithms. The optimization model furnishes a set of comprehensive schedules that integrate the supply portfolio, production portfolio (work-center/machine assignment and customer orders sequencing), and batching decisions. This provides numerous meaningful managerial insights and operational flexibility prior to the execution phase. Recently, SCs have been experiencing unprecedented and massive disruptions caused by an abrupt outbreak, resulting in difficulties for OEMs to recover from disruptive demand-supply equilibrium. Hence, this study proposes a multi-portfolio (supply, production, and inventory portfolios) approach for a proactive-reactive scheme, which concerns the SCSP with complex multi-level products, simultaneously including unpredictably dynamic supply, demand, and shop floor disruptions (Chapter 6). This study considers fabrication and assembly in a multi-level product structure. To effectively address this time-sensitive model based on real-time data, a Q-learning-based multi-operator differential evolution algorithm in a HH has been designed to address disruptive events and generate a timely rescheduling plan. The numerical results and analyses demonstrate the proposed model's capability to effectively address single and multiple disruptions, thus providing significant managerial insights and ensuring SC resilience