This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time.

A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.

A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.

A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.

A competitividade do mercado globalizado e a busca constante das empresas em se destacarem frente aos seus clientes, oferecendo produtos de valor agregado e de preço competitivo frente aos seus concorrentes, tem levado essas empresas a buscarem formas de inovação e aplicação de técnicas e métodos que agreguem aos seus sistemas de gestão e produção, oportunidades de reduzir seus custos, a fim de oferecer o melhor preço ou aumentar seu lucro. A filosofia do Sistema Lean Manufacturing ou Manufatura Enxuta, tem sido aplicada por empresas de todo o mundo e alavancado constantes melhorias nos seus processos produtivos, através da eliminação de desperdícios. Este trabalho tem como foco o levantamento dos desperdícios existentes dentro dos processos relacionados à produção, através da aplicação de uma ferramenta de avaliação desenvolvida com base nos conceitos da Manufatura Enxuta, mais precisamente o Just in Time - JIT. Essa ferramenta desenvolvida por Rawabdeh é composta por um questionário que estabelece pesos a cada tipo de relacionamento entre os tipos de desperdício e quantifica em porcentagem o impacto que cada desperdício exerce sobre o outro. A resultante é uma matriz que pode servir de direcionamento e auxílio para tomada de decisões objetivando a minimização dos custos e conseqüentemente o aumento da lucratividade. / The competitiveness of global market and the constant search of the companies stand out in front of their customers by offering value-added products and competitive price facing its competitors has led these companies to seek ways of innovation and application of techniques and methods that add to their management systems and production opportunities to reduce their costs in order to offer the best price or increase their profit. The philosophy of Lean Manufacturing System or Lean Manufacture has been implemented by companies around the world and leveraged constant improvements in their production processes by eliminating waste. This work focuses on the removal of waste in the existing processes related to production through the application of an assessment tool developed based on the concepts of Lean Manufacturing, specifically the Just In Time - JIT. This tool developed by Rawabdeh, is composed by a questionnaire that provides weights to each type of relationship between the types of waste and quantifies the percentage that each impact waste has on each other. The result is a matrix that can serve as guideline, identification of the root causes of wastes and assistance in making decisions aimed at minimizing the costs and therefore increase profitability.

Problemas de sequenciamento são muito comuns, eles existem sempre que há uma escolha sobre a ordem em que várias tarefas podem ser realizadas. Seja o negócio uma companhia aérea, um hotel, um fabricante de computadores ou uma universidade, esses problemas fazem parte do cotidiano. A aplicação das técnicas de sequenciamento permite, por exemplo, a redução dos custos e o aumento na agilidade da cadeia de suprimentos, afetando as operações no inicio e no fim da cadeia de suprimentos pelo mundo inteiro. Este trabalho parte da intenção de abordar os princípios e as técnicas de Scheduling, com a finalidade de propor um modelo de sequenciamento para a solução de um problema em sistemas produtivos do tipo job shop com n tarefas e m máquinas, considerando os tempos de setup dependentes da sequência e tendo como horizonte de planejamento o curto prazo. O objetivo é o de minimizar a perda dos tempos não produtivos. Neste contexto, a pesquisa apresenta um enfoque tanto exploratório, quanto aplicado. Pode ser considerado exploratório, uma vez que a revisão da literatura é referência central para o desenvolvimento do modelo matemático. É aplicado considerando-se o desenvolvimento do modelo e avaliação de sua aplicabilidade. Sendo assim, a partir da definição do problema e desenvolvimento do modelo por meio do uso de técnicas matemáticas e abordagens da pesquisa operacional constatou-se que as conclusões tiradas podem inferir decisões para o problema real. Sendo que, as considerações aqui feitas têm por finalidade relatar os fatos constatados nos experimentos realizados, visando contribuir com futuras pesquisas na área. / Sequencing problems are very common, they happen every time there is a choice regarding the order in which several tasks can be performed. The business can be an airline, a hotel, a computer manufacturer or a university; these issues are part of their routine. The application of the sequencing techniques allows, for example, reducing the costs and fastening the supply chain all over the world. This work has an approach to Scheduling principles and techniques, with the objective of proposing a sequencing model for the solution of a problem in productive systems such as job shop with n tasks and m machines, considering setup times dependent on the sequence and adopting a short term planning. The goal is to minimize the waste of unproductive time. In this context, the research presents an approach both exploratory and applied. It can be considered exploratory, once that the literature review is a main reference to the development of a mathematical model. It is applied when we consider the development of the model and evaluation of its applicability. Thus, from the problem definition and the model development by the use of mathematical techniques and approaches of the operational research, we found that the conclusions drawn from the model might infer decisions for a real problem. The considerations shown here aim to report the facts given in the conducted experiments, intending to contribute to future researches in the area.

Production scheduling is one of the most important issues in the planning and operation of manufacturing systems. Customers increasingly expect to receive the right product at the right price at the right time. Various problems experienced in manufacturing, for example low machine utilization and excessive work-in-process, can be attributed directly to inadequate scheduling. In this dissertation a production scheduling algorithm is developed for Optimatix, a South African-based company specializing in supply chain optimization. To address the complex requirements of the customer, the problem was modeled as a flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and production down time. The algorithm development process focused on investigating the application of both particle swarm optimization (PSO) and differential evolution (DE) to production scheduling environments characterized by multiple machines and multiple objectives. Alternative problem representations, algorithm variations and multi-objective optimization strategies were evaluated to obtain an algorithm which performs well against both existing rule-based algorithms and an existing complex flexible job shop scheduling solution strategy. Finally, the generality of the priority-based algorithm was evaluated by applying it to the scheduling of production and maintenance activities at Centurion Ice Cream and Sweets. The production environment was modeled as a multi-objective uniform parallel machine shop problem with sequence-dependent set-up times and unavailability intervals. A self-adaptive modified vector evaluated DE algorithm was developed and compared to classical PSO and DE vector evaluated algorithms. Promising results were obtained with respect to the suitability of the algorithms for solving a range of multi-objective multiple machine scheduling problems. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Industrial and Systems Engineering / unrestricted

The objective of this thesis is to introduce MES systems with their properties and relations to other automation systems. Furthermore production scheduling theory is introduced with applicable mathematical methods. For given scheduling problem is created optimization model and basic serie of simulations is accomplished. The core of an existing MES system is transformed to web non-comercial platform. All necessary changes are listed in order to integrate production scheduling subsystem to the existing MES system.

The main aim of this thesis is flow shop and job shop scheduling problem in logistics warehouses. Managing and scheduling works is currently often problem. There is no simple solution due to complexity of this problem. This problem must be resolved because of a lack efficiency of work with a higher load such as during the christmas holidays. This paper describes the methods used to solve this problem focusing mainly on the use of search algorithms, evolutionary algorithms, specifically grammar guided genetic programming. This paper describes the problem of job shop scheduling on a simple theoretical example. The implemented algorithm for solving this problem was subjected to tests inspired on data from real warehouse, as well as synthetically created tests with more jobs and a greater number of workers. Synthetic tests were generated randomly. All tests were therefore run several times and the results were averaged. In conclusion of this work are presented the results of the algorithm and the optimum parameter settings for different sizes of problems and requirements for the solution. Genetic algorithm has been extended to calculate fitness of individuals with regard to number of collisions, extended to use priority rules during run of evolution, and some parts of algorithm was parallelized.

The Covid-19 crisis has highlighted the vulnerability of access to food and the need for local and circular food supply chains in urban environments. Nowadays, Indoor Vertical Farming has been increased in large cities and started deploying Artificial Intelligence to control vegetations remotely. This thesis aims to monitor and control the vertical farm by scheduling the farming activities by solving a newly proposed Job-shop scheduling problem to enhance food productivity. The Job-shop scheduling problem is one of the best-known optimization problems as the execution of an operation may depend on the completion of another operation running at the same time. This paper presents an efficient method based on genetic algorithms developed to solve the proposed scheduling problem. To efficiently solve the problem, a determination of the assignment of operations to the processors and the order of each operation so that the execution time is minimized. An adaptive penalty function is designed so that the algorithm can search in both feasible and infeasible regions of the solution space. The results show the effectiveness of the proposed algorithm and how it can be applied for monitoring the farm remotely. / <p>The presentation was held in zoom</p>