Hybrid of ant colony optimization and flux variability analysis for improving metabolites production

Metabolic engineering has been successfully used for the production of a variety of useful compounds such as L-phenylalanine and biohydrogen that received high demand on food, pharmaceutical, fossil fuels, and energy industries. Reaction deletion is one of the strategies of in silico metabolic engineering that can alter the metabolism of microbial cells with the objective to get the desired phenotypes. However, due to the size and complexity of metabolic networks, it is difficult to determine the near-optimal set of reactions to be knocked out. The complexity of the metabolic network is also caused by the presence of competing pathway that may interrupt the high production of a desireable metabolite. Consequently, this factor leads to low Biomass-Product Coupled Yield (BPCY), production rate and growth rate. Other than that, inefficiency of existing algorithms in modelling high growth rate and production rate is another problem that should be handled and solved. Therefore, this research proposed a hybrid algorithm comprising Ant Colony Optimization and Flux Variability Analysis (ACOFVA) to identify the best reaction combination to be knocked out to improve the production of desired metabolites in microorganisms. Based on the experimental results, ACOFVA shows an increase in terms of BPCY and production rate of L-Phenylalanine in Yeast and biohydrogen in Cyanobacteria, while maintaining the optimal growth rate for the target organism. Besides, suggested reactions to be knocked out for improving the production yield of L-Phenylalanine and biohydrogen have been identified and validated through the biological database. The algorithm also shows a good performance with better production rate and BPCY of L-Phenylalanine and biohydrogen than existing results

A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen

In silico metabolic engineering has shown many successful results in genome - scale model reconstruction and modification of metabolic network by implementing reaction deletion strategies to improve microbial strain such as production yield and growth rate. While improving the metabolites production, optimization algorithm has been implemented gradually in previous studies to identify the near - optimal sets of reaction knockout to obtain the best results. However, previous works implemented other algorithms that differ than this study which faced with several issues such as premature convergence and able to only produce low production yield because of ineffective algorithm and existence of complex metabolic data. The lack of effective genome models is because of the presence thousands of reactions in the metabolic network caused complex and high dimensional data size that contains competing pathway of non - desirable product. Indeed, the suitable population size and knockout number for this new algorithm have been tested previously. This study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux variability analysis (ACOFVA) to predict near - optimal sets of reactions knockout in an effort to improve the growth rates and the production rate of L - phenylalanine and biohydrogen in Saccharomyces cerevisiae and cyanobacteria Synechocystis sp PCC6803 respectively

An Improved Central Force Optimization Algorithm for Multimodal Optimization

This paper proposes the hybrid CSM-CFO algorithm based on the simplex method (SM), clustering technique, and central force optimization (CFO) for unconstrained optimization. CSM-CFO is still a deterministic swarm intelligent algorithm, such that the complex statistical analysis of the numerical results can be omitted, and the convergence intends to produce faster and more accurate by clustering technique and good points set. When tested against benchmark functions, in low and high dimensions, the CSM-CFO algorithm has competitive performance in terms of accuracy and convergence speed compared to other evolutionary algorithms: particle swarm optimization, evolutionary program, and simulated annealing. The comparison results demonstrate that the proposed algorithm is effective and efficient

An Improved Central Force Optimization Algorithm for Multimodal Optimization

This paper proposes the hybrid CSM-CFO algorithm based on the simplex method (SM), clustering technique, and central force optimization (CFO) for unconstrained optimization. CSM-CFO is still a deterministic swarm intelligent algorithm, such that the complex statistical analysis of the numerical results can be omitted, and the convergence intends to produce faster and more accurate by clustering technique and good points set. When tested against benchmark functions, in low and high dimensions, the CSM-CFO algorithm has competitive performance in terms of accuracy and convergence speed compared to other evolutionary algorithms: particle swarm optimization, evolutionary program, and simulated annealing. The comparison results demonstrate that the proposed algorithm is effective and efficient

Aproximações heurísticas para um problema de escalonamento do tipo flexible job-shop

Mestrado em Engenharia e Gestão IndustrialEste trabalho aborda um novo tipo de problema de escalonamento que pode ser encontrado em várias aplicações do mundo-real, principalmente na indústria transformadora. Em relação à configuração do shop floor, o problema pode ser classificado como flexible job-shop, onde os trabalhos podem ter diferentes rotas ao longo dos recursos e as suas operações têm um conjunto de recursos onde podem ser realizadas. Outras características de processamento abordadas são: datas possíveis de início, restrições de precedência (entre operações de um mesmo trabalho ou entre diferentes trabalhos), capacidade dos recursos (incluindo paragens, alterações na capacidade e capacidade infinita) e tempos de setup (que podem ser dependentes ou independentes da sequência). O objetivo é minimizar o número total de trabalhos atrasados. Para resolver o novo problema de escalonamento proposto um modelo de programação linear inteira mista é apresentado e novas abordagens heurísticas são propostas. Duas heurísticas construtivas, cinco heurísticas de melhoramento e duas metaheurísticas são propostas. As heurísticas construtivas são baseadas em regras de ordenação simples, onde as principais diferenças entre elas dizem respeito às regras de ordenação utilizadas e à forma de atribuir os recursos às operações. Os métodos são designados de job-by-job (JBJ), operation-by-operation (OBO) e resource-by-resource (RBR). Dentro das heurísticas de melhoramento, a reassign e a external exchange visam alterar a atribuição dos recursos, a internal exchange e a swap pretendem alterar a sequência de operações e a reinsert-reassign é focada em mudar, simultaneamente, ambas as partes. Algumas das heurísticas propostas são usadas em metaheurísticas, nomeadamente a greedy randomized adaptive search procedure (GRASP) e a iterated local search (ILS). Para avaliar estas abordagens, é proposto um novo conjunto de instâncias adaptadas de problemas de escalonamento gerais do tipo flexible job-shop. De todos os métodos, o que apresenta os melhores resultados é o ILS-OBO obtendo melhores valores médios de gaps em tempos médios inferiores a 3 minutos.This work addresses a new type of scheduling problem which can be found in several real-world applications, mostly in manufacturing. Regarding shop floor configuration, the problem can be classified as flexible job-shop, where jobs can have different routes passing through resources and their operations have a set of eligible resources in which they can be performed. The processing characteristics addressed are release dates, precedence constraints (either between operations of the same job or between different jobs), resources capacity (including downtimes, changes in capacity, and infinite capacity), and setup times, which can be sequence-dependent or sequence-independent. The objective is to minimise the total number of tardy jobs. To tackle the newly proposed flexible job-shop scheduling problem (FJSP), a mixed integer linear programming model (MILP) is presented and new heuristic approaches are put forward. Three constructive heuristics, five improvement heuristics, and two metaheuristics are proposed. The constructive heuristics are based on simple dispatching rules, where the main differences among them concern the used dispatching rules and the way resources are assigned. The methods are named job-by-job (JBJ), operation-by-operation (OBO) and resource-by-resource (RBR). Within improvement heuristics, reassign and external exchange aim to change the resources assignment, internal exchange and swap intend changing the operations sequence, and reinsert-reassign is focused in simultaneously changing both parts. Some of the proposed heuristics are used within metaheuristic frameworks, namely greedy randomized adaptive search procedure (GRASP) and iterative local search (ILS). In order to evaluate these approaches, a new set of benchmark instances adapted from the general FJSP is proposed. Out of all methods, the one which shows the best average results is ILS-OBO obtaining the best average gap values in average times lower than 3 minutes

Sistemática para alocação, sequenciamento e balanceamento de lotes em múltiplas linhas de produção

Diante dos desafios impostos pelo sistema econômico, características dos mercados e exigências dos clientes, as empresas são forçadas a operar com lotes de produção cada vez menores, dificultando a gestão de operações e a otimização dos sistemas produtivos. Desse modo, intensifica-se nos meios corporativos e acadêmicos a busca por abordagens que possibilitem a criação de diferenciais competitivos de mercado, sendo esta a justificativa prática deste trabalho, que propõe uma sistemática integrada para alocação, sequenciamento e balanceamento de lotes em um horizonte de programação em múltiplas linhas de produção em um sistema multiproduto com operadores polivalentes. A sistemática proposta foi dividida em três fases. A primeira fase utiliza um algoritmo genético multiobjetivo com o intuito de determinar a linha de produção em que cada lote será produzido. A segunda fase é responsável pelo sequenciamento dos lotes produtivos e se apoia em uma alteração da regra Apparent Tardiness Cost (ATC). Na terceira fase utilizou-se o método Ranked Positional Weight (RPW) para balancear a distribuição das tarefas entre os operadores polivalentes de cada linha de produção, respeitando a precedência das tarefas. A sistemática foi aplicada em dados reais do segmento têxtil, aprimorando os indicadores produtivos e de entrega e conferindo maior flexibilidade ao processo frente à demanda sazonal.Faced with the challenges imposed by the economic system, characteristic of the markets and requirements of the customers, the companies are forced to operate with smaller production batches, making it difficult to manage operations and optimization of the production systems. In this way, the search for improvements that allow the creation of competitive differentials of market is intensified in the corporate and academic circles. This is the practical justification for this work, which proposes an integrated systematics for the allocation, sequencing and balancing of batches in a horizon of programming in multiple production lines in a multiproduct system with multipurpose operators. The systematic proposal was divided into three phases. The first phase uses a multiobjective genetic algorithm with intention to determine the production line in which each batch will be produced. The second phase is responsible for the sequencing of productive batches and is based on a change in the rule Apparent Tardiness Cost (ATC). In the third phase the method Ranked Positional Weight (RPW) was used to balance the distribution of the tasks between the multipurpose operators of each line of production, respecting the precedence of the tasks. The systematics was applied in real data of the textile segment, improving the productive and delivery indicators and giving greater flexibility of the process against the seasonal demand

Optimization of Aluminium Profiles Production Planning

Dissertação de Mestrado Métodos de Apoio à Decisão EmpresarialNo atual ambiente empresarial, a crescente competitividade impulsiona as empresas a implementar estratégias de otimização para assegurar ou melhorar a sua posição no mercado. Nesse sentido é crucial tomar as melhores decisões do ponto de vista do planeamento da produção. A produção de perfis de alumínio apresenta vários desafios ao responsável da produção. Este trabalho aborda um caso real de uma empresa do setor metalúrgico, cuja área de negocio é o desenvolvimento e produção de perfis de alumínio para aplicação em diversas áreas, como obras de engenharia, arquitetura e industria em geral. O objetivo é minimizar o desperdício ocorrido na produção, designado de sucata, através da minimização dos tempos de preparação, que ocorrem quando a matriz é trocada, respeitando os prazos de entrega do produto e garantindo a qualidade. Este estudo de caso centra-se num problema de sequenciamento de flow shop que envolve tempos de preparação dependentes da sequência decorrentes da necessidade de alterar as ferramentas usadas no processo de extrusão de alumínio. Um modelo de programação inteira mista é desenvolvido e implementado para responder ao desafio da empresa. O problema será formulado na linguagem de modelagem AMPL e seria usado o solver Gurobi para resolver instâncias reais extraídas dos dados providenciados pela empresa. Os resultados obtidos com o modelo desenvolvido são comparados com a regra de despacho FIFO, em termos da soma dos tempos de preparação (e consequentemente do número de trocas de matrizes), bem como o cumprimento do prazo de entrega. Além disso, embora o procedimento atual da empresa não seja conhecido, os resultados obtidos são comparados com a média de trocas de matriz obtidas a partir dos dados históricos de produção fornecidos pela empresa. Conclui-se que, utilizando as soluções obtidas com o modelo desenvolvido, a quantidade de sucata gerada durante o processo de produção _e minimizada, uma vez que, o modelo garante a minimização da soma dos tempos de preparação e minimiza o número de vezes que ocorrem trocas de matriz.In the current business world, the increasing competitiveness forces companies to adopt optimization strategies to ensure or improve their market position. It is crucial to take the best decisions from the production planning point of view. The production of aluminium pro les poses several challenges to the production manager. This work addresses a real case of a company that operates in the aluminium market, whose core business is the development and production of aluminium pro les for application in several areas, such as, engineering, architecture and industry works in general. The aim is to minimize production waste, commonly known as scrap, through minimization of setup times, that occur when the die is changed, while respecting product delivery times and maintaining quality. This case study focuses on a scheduling ow shop problem involving sequence-dependent setup times arising from the need to change the tools used in the process of aluminium extrusion. A mixed integer programming model is developed and implemented for answering the company's challenge. The problem is formulated the AMPL modeling language and the Gurobi solver is used to solve real instances extracted from data provided by the company. The results obtained with the developed model are compared to the dispatching rule FIFO, in terms the sum of the setup times (and consequently the number of exchanges of dies), as well as the ful llment of the deadline. Furthermore, although the company's current procedure is not known, the results obtained are compared with the average of die changes obtained from the historical production data provided by the company. It is observed that by using the solutions obtained with the developed model, the quantity of scrap that is generated during the production process is minimized, since the model guarantees the minimization of the sum of the setup times, therefore minimizes the number of times there are die changes.N/

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios