A Threat Assessment Model under Uncertain Environment

Threat evaluation is extremely important to decision makers in many situations, such as military application and physical protection systems. In this paper, a new threat assessment model based on interval number to deal with the intrinsic uncertainty and imprecision in combat environment is proposed. Both objective and subjective factors are taken into consideration in the proposed model. For the objective factors, the genetic algorithm (GA) is used to search out an optimal interval number representing all the attribute values of each object. In addition, for the subjective factors, the interval Analytic Hierarchy Process (AHP) is adopted to determine each object’s threat weight according to the experience of commanders/experts. Then a discounting method is proposed to integrate the objective and subjective factors. At last, the ideal of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to obtain the threat ranking of all the objects. A real application is used to illustrate the effectiveness of the proposed model

Economic Lot Sizing and Scheduling in Distributed Permutation Flow Shops

This paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs and finished products inventory costs per unit of time. Since the proposed model is NP-hard, an efficient Water Cycle Algorithm is proposed to solve the model. To justify proposed WCA, Monarch Butterfly Optimization (MBO), Genetic Algorithm (GA) and combination of GA and simplex are utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed

An adaptive genetic algorithm with dominated genes for distributed scheduling problems

This paper proposes an adaptive genetic algorithm for distributed scheduling problems in multi-factory and multi-product environment. Distributed production strategy enables factories to be more focused on their core product types, to achieve better quality, to reduce production cost, and to reduce management risk. However, when comparing with single-factory production, scheduling problems involved in multi-factory one are more complicated, since different jobs distributed to different factories will have different production scheduling, consequently affect the performance of the supply chain. Distributed scheduling problems deal with the assignment of jobs to suitable factories and determine their production scheduling accordingly. In this paper, a new crossover mechanism named dominated gene crossover will be introduced to enhance the performance of genetic search, and eliminate the problem of determining optimal crossover rate. A number of experiments have been carried out. For the comparison purpose, five multi-factory models have been solved by different well known optimization approaches. The results indicate that significant improvement could be obtained by the proposed algorithm. © 2005 Elsevier Ltd. All rights reserved.link_to_subscribed_fulltex

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

SOLVING PROCESS PLANNING AND SCHEDULING PROBLEMS USING THE CONCEPT OF MAXIMUM WEIGHTED INDEPENDENT SET

Process planning and scheduling (PPS) is an essential and practical topic but a very intractable problem in manufacturing systems. Many research studies use iterative methods to solve such problems; however, they cannot achieve satisfactory results in both quality and computational speed. Other studies formulate scheduling problems as a graph coloring problem (GCP) or its extensions, but these formulations are limited to certain types of scheduling problems. In this dissertation, we propose a novel approach to formulate a general type of the PPS problem with resource allocation and process planning integrated towards a typical objective, minimizing the makespan. The PPS problem is formulated into an undirected weighted conflicting graph, where nodes represent operations and their resources; edges represent constraints, and weight factors are guidelines for the node selection at each time slot. Then, the Maximum Weighted Independent Set (MWIS) problem, which considers a graph with weights assigned to nodes and seeks to discover the “heaviest” independent set, that is, a set of nodes with maximum total weight so that no two nodes in the set are connected by an edge, can be solved to find the best set of operations with their desired resources for each discrete time slot. This proposed approach solves the PPS problem directly (a direct method in computational mathematics context). We establish that the proposed approach always returns a feasible optimum or near-optimum solution to the PPS problem. The performance of the proposed approach for the PPS problem depends on the accuracy and computational speed of solving the MWIS problem. We propose a divide-and-conquer algorithm structure with relatively low complexity for solving the MWIS problem. An exact MWIS algorithm and an All Maximal Independent Set Listing (AMISL) algorithm are developed based on this algorithm structure. The proposed algorithm structure can also be used to compose the exact MWIS algorithm with existing approximation MWIS algorithms. This is an effective way to improve the accuracy of existing approximation MWIS algorithms or improve the computational speed of the exact MWIS algorithm. All eight algorithms for the MWIS problem, the exact MWIS algorithm, the AMISL algorithm, two approximation algorithms from the literature, and four composed algorithms, are tested on the test instances based on the PPS application environment. The different configurations of the proposed approach for solving the PPS problem are tested on a real-world PPS example and further designated test instances to evaluate the scalability, accuracy, and robustness

Algoritmos evolutivos adaptativos para problemas de programação de pessoal

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de ProduçãoA crescente concorrência mundial tem estimulado empresas a tornar seus produtos mais competitivos e serviços mais eficazes, observando a redução de custos. Atualmente, percebe-se um rápido crescimento no setor de serviços, o que mostra a importância da utilização eficaz dos recursos materiais e humanos disponíveis. Com o foco neste crescimento, em special no setor de Call Centers, este trabalho aborda uma metodologia para a resolução de problema de Programação de Pessoal com aplicação em uma empresa neste setor. O problema foi dividido em duas etapas, sendo resolvidas na seguinte ordem: problema de Turnos de Trabalho e problema de Designação dos Turnos aos Atendentes. O primeiro, consiste em eterminar os turnos de trabalho e a quantidade de atendentes em cada turno, de modo a satisfazer à demanda. O segundo, busca a configuração de jornadas de trabalho e a designação destas aos atendentes. Os objetivos são o de minimizar a quantidade de atendentes e de encontrar jornadas que iniciem o turno o mais próximo possível de um horário determinado. Para resolver o problema, foi desenvolvido um Algoritmo Evolutivo (AE) que integra outros AEs, denominado Algoritmo Evolutivo Adaptativo (AEA). A ideia que motivou o desenvolvimento do AEA foi a introdução de um processo que leva em consideração o desempenho prévio de cada AE. Para a resolução do primeiro problema foram utilizados Algoritmos Genéticos, Evolução Diferencial Discreta e o AEA integrando os dois algoritmos anteriores. Também, um modelo de PLI foi desenvolvido e resolvido com os aplicativos XPRESS, Cbc, Gurobi e MOSEK, disponibilizados em um site na internet. Os resultados encontrados pelos AEs se mostraram próximos aos encontrados a partir da resolução do modelo em PLI. Os resultados do AEA e do modelo em PLI foram utilizados como dados de entrada para o segundo problema. Nesta segunda fase foi desenvolvida uma EDD com variáveis mistas (inteiras e binárias). Os resultados encontrados mostraram que para se encontrar resultados adequados para o problema de Programação de Pessoal, não é necessário usar os melhores resultados encontrados na primeira etapa, mas apenas resultados adequados. O AEA desenvolvido pode integrar, além de AEs, outras ferramentas e ser utilizado em outras aplicações. A metodologia adotada pode ser considerada adequada para aplicação em empresas de Call Center, podendo ser expandida para outras com características similares.Increasing global competition has encouraged companies to make their products more competitive and more efficient services, noting the cost savings. Currently, we see a rapid growth in the services sector, what shows the importance of efficient use of available human and material resources. With the focus on this growth, particularly in the Call Center industry, this paper presents a methodology for solving Human Resource problem with an application for a company in this sector. The problem was divided into two phases, resolved in the following order: Working Shift problem and Assignment of the Shifts to the Telephone Operators problem. The first one is to determine the shifts and the number of telephone operators on each shift to meet demand. The second one seeks the setting working hours and the assignment of the telephone operators. The objectives are to minimize the number of telephone operators and find working hours that begin the shift as close as possible to a certain time. To solve the problem has been developed an Evolutionary Algorithm (EA) that integrates other EAs, called Adaptive Evolutionary Algorithm (AEA). The idea that led to the development of the AEA was the introduction of a process that takes into account the previous performance of each EA. To solve the first problem was used Genetic Algorithms, Discrete Differential Evolution and AEA integrating the two previous algorithms. Also, an ILP model was developed and solved with XPRESS, Cbc, Gurobi and MOSEK applications, available on a website. The results find to AEs showed similar to those found from solving the ILP model. The results of AEA and PLI model were used as input data for the second problem. The second phase was developed with an EDD mixed variables (integer and binary). The results showed that in order to find appropriate results for the Human Resource problem, there is no need to use the best results in the first step, but only use the adequate results. The AEA developed may include, beyond the AE, others tools to be used in other applications. The methodology can be considered suitable for application in Call Center companies and can be expanded to others with similar characteristics

Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. Aplicación a la industria cerámica

En un marco de competencia global en el cual los tiempos de respuesta son cada vez más relevantes como elemento competitivo y donde, en no pocas ocasiones, las empresas tiende a ofrecer un catálogo de productos amplio y diferenciado de la competencia, existen múltiples retos que las Organizaciones deben afrontar. Dentro de éstas la Dirección de Operaciones tiene el reto de adaptar los procesos de Gestión de los Sistemas Productivos y Logísticos a las actuales necesidades. En este proceso de cambio es habitual partir de Sistemas Productivos poco flexibles y orientados a la producción en masa en los que es fundamental emplear el mejor "saber-hacer" para procurar obtener el rendimiento más adecuado de los recursos disponibles. El despliegue de unas buenas prácticas en el ámbito de la Programación de la Producción puede ayudar en buena medida a mejorar la eficiencia de los recursos. Tradicionalmente se ha venido considerando a la Programación de la Producción con una visión bastante cuantitativa en la que su misión consistía en asignar, secuenciar y temporizar los diferentes trabajos del periodo en base a los recursos disponibles. No obstante, sin dejar de ser válido este planteamiento, en esta tesis se desea enfatizar como en realidad el fin último de las técnicas y métodos desarrollados durante años en el ámbito de la Programación de la Producción no es otro que el de ser empleados dentro de un Sistemas de Ayuda a la Toma de Decisiones. Y en este sentido, las decisiones operativas que se toman en el área del Programador de la Producción deben estar conectadas en todos los casos, al menos, con su entorno decisional más directo como es el de la Planificación de la Producción. Una revisión literaria en profundidad al extenso trabajo realizado en más de 50 años de existencia de lo que se ha denominado, empleando la terminología en lengua inglesa, como "Scheduling" pone de manifiesto la existencia una necesidad que debe ser cubierta.Gómez Gasquet, P. (2010). Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. Aplicación a la industria cerámica [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7728Palanci