Minimizing Total Weighted Tardiness in Identical Parallel Machine with Sequence Dependent Setup Time Using Genetic Algorithm

This paper considers a scheduling problem in an identical parallel machine environment to minimize total weighted tardiness with the consideration of sequence dependent setup times. As the scheduling problem is proven to be NP-hard, a genetic algorithm is developed with the aim of providing good solution in a reasonable time to the scheduling problem. Computational experiments were performed to study the effectiveness of the genetic algorithm solution quality and the CPU time. Various dispatch heuristics were developed to provide initial solutions to the genetic algorithm besides comparing their solution quality with the genetic algorithm’s solution. The developed genetic algorithm has the capability to provide good results and good improvement compared to all the developed dispatching heuristics

A New Approach in Job Shop Scheduling: Overlapping Operation

In this paper, a new approach to overlapping operations in job shop scheduling is presented. In many job shops, a customer demand can be met in more than one way for each job, where demand determines the quantity of each finished job ordered by a customer. In each job, embedded operations can be performed due to overlapping considerations in which each operation may be overlapped with the others because of its nature. The effects of the new approach on job shop scheduling problems are evaluated. Since the problem is well known as NP-Hard class, a simulated annealing algorithm is developed to solve large scale problems. Moreover, a mixed integer linear programming (MILP) method is applied to validate the proposed algorithm. The approach is tested on a set of random data to evaluate and study the behavior of the proposed algorithm. Computational experiments confirmed superiority of the proposed approach. To evaluate the effect of overlapping considerations on the job shop scheduling problem, the results of classical job shop scheduling with the new approach (job shop scheduling problem with overlapping operations) are compared. It is concluded that the proposed approach can improve the criteria and machines utilization measures in job shop scheduling. The proposed approach can be applied easily in real factory conditions and for large size problems. It should thus be useful to both practitioners and researchers

Aplicação de Uma Nova Heurística em Problemas de Job Shop com Tempo Variável Dependente da Sequência

O Planejamento e Controle da Produção é uma área de estudo que pode, juntamente à Pesquisa Operacional e a modelagem matemática, otimizar o planejamento da produção. A programação da produção é ponto crucial na determinação de como sequenciar as tarefas pelas máquinas que as mesmas devem passar para então chegar ao produto final. No ambiente das oficinas de máquina pode-se considerar um possível tempo de preparação da máquina antes de receber a próxima tarefa, e este tempo depende da última tarefa sequenciada nesta máquina, o problema torna-se ainda maior e mais complexo. Para resolver este problema, este trabalho tem como objetivo aplicar uma heurística que busca minimizar o tempo total de trabalho na situação descrita. Esta heurística é implementada e testada para problemas de diversos tamanhos e os resultados obtidos são satisfatórios

Artificial intelligence effectiveness in job shop environments

The aim of this paper is to define a new methodology that allows the comparison of the effectiveness among some of the major artificial intelligence techniques (random technique, taboo search, data mining, evolutionary algorithms). This methodology is applied in the sequencing production process in job shop environments, in a problem with N orders, and M machines, where each of the orders must pass through every machine regardless of its turn. These techniques are measured by the variables of total makespan time, total idle time, and machine utilization percentage. Initially, a theoretical review was conducted and showed the usefulness and effectiveness of artificial intelligence in the sequencing production processes. Subsequently and based on the experiments presented, the obtained results showed that these techniques have an effectiveness higher than 95%, with a confidence interval of 99.5% measured by the variables under study

Metodología integral soportada en simulación para el mejoramiento de sistemas de producción job shop. aplicaciones en pymes metalmecánicas

En el municipio de Caldas (Colombia), uno de los sectores estratégicos para la economía regional es el de las pymes metalmecánicas; de hecho, este sector, según estadísticas del DANE a 2005, participa aproximadamente con el 31% de los establecimientos industriales del departamento y con un 29% del empleo industrial, además tiene vocación exportadora hacia países andinos. Sin embargo, estudios preliminares realizados al 57% del universo empresarial de este sector (no incluye microempresas ni famiempresas), demuestran serias falencias de tipo estructural (tecnologías, procesos, instalaciones) e infraestructural (programación de la producción, sistemas de calidad, etc.) en los sistemas de producción de estas organizaciones. Mediante este artículo se espera divulgar entre la comunidad académica los resultados obtenidos al aplicar una metodología integral de mejoramiento del sistema de producción en una empresa piloto del sector. A partir de la definición y ponderación de las prioridades competitivas que la empresa debe alcanzar, y siguiendo la metodología universalmente aceptada en estudios de simulación discreta, se propone un marco de experimentación para mejorar los niveles alcanzados por el sistema en dichas prioridades empleando técnicas de bifurcación secuencial, diseño factorial en experimentación y superficies de respuesta. Al final se presentan las mejoras alcanzadas en las prioridades competitivas en términos de un índice de efectividad (IE) del sistema de producción de una empresa piloto estudiada al pasar éste de 1,84 a 2,46.Metalworking companies represent one of the strategic sectors in the regional economy of the Caldas department in Colombia; in fact, this sector is involved in 31% of the department’s industrial establishments and 29% of industrial employment according to DANE (Colombian State Statistical Department) statistical data from 2005. The sector also exports to Andean countries. However, preliminary studies conducted with 57% of the entrepreneurs from this sector (excluding micro companies and family businesses) have revealed serious structural (technology, processing, installations) and infrastructure weaknesses (production planning, quality systems) in these organisations’ production systems. It is hoped that this paper will lead to disseminating the results amongst the academic community of implementing a comprehensive methodology for improving the production system of a pilot company from this particular sector. An experimental framework for improving the levels reached by the system regarding such priorities is proposed following universally accepted methodology in discrete simulation studies; it proposes using sequential bifurcation, factorial design and response surface experimentation based on defining and weighting the competing priorities which the company should achieve. The improvements in the pilot company’s production system priorities are presented in terms of an effectiveness index (EI) which rose from 1.84 to 2.46 by the end of the study

A hyper-heuristic ensemble method for static job-shop scheduling.

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyperheuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances

Scheduling of Physicians to Minimize Patients’ Waiting Time

RÉSUMÉ : Chaque phase du processus de soins en radiothérapie se compose de plusieurs étapes. Le patient est d’abord référé au centre de radiothérapie. Après une consultation avec le médecin, un scan permettra de délimiter les contours de la tumeur à soigner afin d’établir le plan de traitement. Les doses sont calculées par des dosimétristes et ensuite validées par le médecin. La phase de prétraitement commence donc par la consultation avec le médecin et se termine lorsque le traitement en tant que tel peut commencer. Dans cette étude, notre objectif est de minimiser la durée de la phase de prétraitement. Bien que plusieurs ressources (humaines et matérielles) soient impliquées dans la phase de prétraitement, nous nous concentrons dans ce projet sur les médecins. En effet, à chacune des étapes du prétraitement le médecin est impliqué et doit donner son aval avant de passer à l’étape suivante. Notre objectif est de déterminer un horaire cyclique et hebdomadaire des tâches à affecter aux médecins, dans le but d’améliorer le flux des patients et de réduire la durée de la phase de prétraitement des patients. Bien que cet objectif soit primordial, nous incluons la satisfaction des médecins quant au choix des tâches affectées chaque jour lors de l’élaboration de l’horaire. Le défi de ce problème réside dans l’incorporation d’éléments incertains (tels que l’arrivée des patients au centre de radiothérapie et leur profil). L’horaire des médecins est identique semaine après semaine tandis que la distribution de l’arrivée des patients varie au courant de l’année. Deux types de patients sont traités par le centre : les patients curatifs et palliatifs. Ces patients n’ont pas le même objectif de traitement, et surtout n’ont pas les mêmes délais d’attente. Afin de résoudre ce problème nous avons développé une méthode de recherche Tabou basée sur trois types de mouvements. Dans un premier temps nous validons la performance de notre algorithme en nous basant sur des instances déterministes. Nous montrons qu’en moyenne, notre méthode est à 0.67% de la solution obtenue par CPLEX dans un temps de calcul raisonnable. Dans un deuxième temps nous incluons les paramètres stochastiques du problème. La fonction d’évaluation du coût des mouvements dans l’algorithme tient désormais compte du fait que l’arrivée et le profil des patients ne sont pas connus d’avance. Nous montrons que l’horaire obtenu par notre algorithme est de meilleure qualité que celui utilisé en pratique sur une cinquantaine de scénarios générés.----------ABSTRACT : Patients are interacting with many different types of healthcare resources. At the same time, new technologies in laboratories, radiology departments and surgeries have increased the number of procedures in diagnosing and curing diseases. Due to financial issues, healthcare organizations are trying to provide the best quality services with reasonable cost by improving the utilization of existing resources. The variability in demand and uncertainty in treatment as well as test duration can cause situations that some resources may not be available at the time they are required which create bottlenecks. Various factors, such as the lack of physical capacity, staff, proper scheduling method, equipment, supplies and sometimes even information, can cause bottlenecks which result in a delay for patients who are receiving the treatment. According to the Canadian Cancer Society reports, every three minutes one person is diagnosed and every seven minutes one person dies from cancer, Canadian Cancer Society (2013). Besides, long waiting times for radiotherapy treatments can cause serious effects on the treatment process. In Quebec, the waiting time for radiation oncology (the time between the patient becomes ready for the treatment and the starting day of treatment) is 4 weeks, Ministère de la santé et des services sociaux (2010). However, time has a major impact on the treatment process and delay in starting radiotherapy has negative effects on treatment progress. The optimal use of existing resources along with keeping the quality of treatment can be the best possible option. In cancer facilities and radiotherapy centers, the sooner the disease is recognized and the treatment is started, strengthen the chance of success in treatment. Since a patient is referred to a radiotherapy center till the start of the treatment, the patient should go through a sequence of tasks. Therefore, reducing the time for the pre-treatment phase becomes crucial, which again explains the importance of this study in making the patient ready for the treatment, thus shortening the pre-treatment phase to less than a week. The objective of this study is Determining a task schedule for physicians in a radiotherapy center. Attempts were made to find a scheme for physicians in order to minimize the pretreatment phase for patients, which would help them to start their treatment earlier by preventing physicians from being bottleneck. Satisfaction of physicians was also considered. To reach this objective, some uncertainty items such as arrival rate of patients and their profiles were considered. A meta-heuristic approach, Tabu Search algorithm, was developed and then compared with two mathematical models, one based on patterns and the other based on tasks of physicians. Due to the size of the problem and different conditions, either task-based model or patternbased one could be used. It is shown that the method developed in this project is compatible with different situations. In addition, two heuristic approaches were developed based on physicians’ tasks

Otimização do tempo total de trabalho na programação da produção em oficina de máquinas com tempo de preparação dependente da sequência

Orientador : Prof. Dr. Arinei Carlos Lindbeck SilvaTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 10/12/2014Inclui referênciasÁrea de concentração: Programação matemáticaResumo: O planejamento da produção de uma indústria _e ponto fundamental na busca de bons resultados para metas estabelecidas e maximizar lucros. Para tal, o Planejamento e Controle da Produção e uma área de estudo que pode, com auxílio da Pesquisa Operacional e seus modelos matemáticos, otimizar diversos objetivos. Dentre as diversas decisões a serem tomadas dentro da indústria, uma e crucial para o sucesso ou não: a programação da produção, através da determinação de como sequenciar as tarefas pelas máquinas que as mesmas devem passar para então chegar ao produto final. São diversos ambientes descritos na literatura, sendo o mais abrangente a usina de máquinas. E quando neste ambiente, considera-se um possível tempo de preparação da maquina antes de receber a próxima tarefa, e este tempo depende da ultima tarefa sequenciada nesta máquina, o problema torna-se ainda maior e mais complexo. Para resolver este problema, este trabalho tem como objetivo apresentar uma nova heurística que busca minimizar o tempo total de trabalho na situação descrita. Esta heurística e implementada e testada para problemas de diversos tamanhos e os resultados obtidos são satisfatórios. Palavras-chave: Programação da Produção, Usina de maquinas, Tempo de preparação, Dependência da sequência.Abstract: An industry's planning of production is a fundamental area when seeking the best results for reaching established goals and maximizing profits. For that, with the help of Operational Research and its mathematical models, the planning and controlling of production is an area of study that can optimize many deferent areas. Amongst all these decisions made in the industry, one of them is quintessential in determining whether one succeeds or fails inside the industrial market - the scheduling of the production, through deciding how to sequence the tasks of the machines through which the product undergoes until it reaches its final state, completed and assembled. For this, there are many deferent environments in which machine sequencing is needed, but the most encompassing of them is the job shop. In this type of environment, each machine needs some sort of preparation prior to performing a certain task, and this preparation is dependant on the task that was previously performed by that machine - a matter which further complicates deciding the right sequencing. To solve this problem, this study presents a new heuristic that seeks to minimize the total time required for a complete a task similar to the one described earlier - one that considers a set of tasks with preparation dependant on the previous task performed. This heuristic is implemented and tested on several deferent problems of varying size, all of which show very successful results. Key-words: Scheduling, Job shop, Setup time, Sequence dependence

Metaheuristic approach for solving scheduling and financial derivative problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe objective of this thesis is to implement metaheuristic approaches to solve di erent types of combinatorial problems. The thesis is focused on neighborhood heuristic optimisation techniques such as Variable Neighborhood Search (VNS) and Ant Colony Optimisation (ACO) algorithms. The thesis will focus on two diverse combinatorial problems. A job shop scheduling problem, and a nancial derivative matching problem. The rst is a NP-hard 2-stage assembly problem, where we will be focussing on the rst stage. It consists of sequencing a set of jobs having multiple components to be processed. Each job has to be worked on independently on a speci c machine. We consider these jobs to form a vector of tasks. Our objective is to schedule jobs on the particular machines in order to minimise the completion time before the second stage starts. In this thesis, we have constructed a new hybrid metaheuristic approach to solve this unique job shop scheduling problem. The second problem has arisen in the nancial sector, where the nancial regulators collects transaction data across regulated assets classes. Our focus is to identify any unhedged derivative, Contract for Di erence (CFD), with its corresponding underlying asset that has been reported to the corresponding component authorities. The underlying asset and CFD transaction contain di erent variables, like volume and price. Therefore, we are looking for a combination of underlying asset variables that may hedge the equivalent CFD variables. Our aim is to identify unhedged or unmatched CFD's with their corresponding underlying asset. This problem closely relates to the goal programming problem with variable parameters. We have developed two new local search methods and embedded the newly constructed local search methods with basic VNS, to attain a new modi ed variant of the VNS algorithm. We then used these newly constructed VNS variants to solve this nancial matching problem. In tackling the Vector Job Scheduling problem, we developed a new hybrid optimisation heuristic algorithm by combining VNS and ACO. We then compared the results of this hybrid algorithm with VNS and ACO on their own. We found that the hybrid algorithm performance is better than the other two independent heuristic algorithms. In tackling the nancial derivative problem, our objective is to match the CFD trades with their corresponding underlying equity trades. Our goal is to identify the mismatched CFD trades while optimising the search process. We have developed two new local search techniques and we have implemented a VNS algorithm with the newly developed local search techniques to attain better solutions