O problema de programação de operações em ambiente no-wait flow shop tem sido abordado desde a década de 60. Por se tratar de um ambiente em que as tarefas devem ser processadas continuamente e sem interrupções entre uma máquina e outra, um tempo de espera entre o início da tarefa anterior e o início da tarefa atual deve ser determinado na primeira máquina. Neste sentido, uma vez que a tarefa inicia seu processamento, as máquinas devem estar disponíveis para que atendam a restrição de no-wait. Portanto, operações de manutenção preventiva são necessárias para que a programação seja atendida sem maiores problemas. Este trabalho aborda dois problemas: no-wait flow shop e no-wait flow shop com operações de manutenção preventiva. O critério de desempenho adotado foi a duração total da programação (makespan). Por meio de uma revisão de literatura, mecanismos de construção de soluções foram identificadas e classificadas e, baseando-se em tais, novos métodos heurísticos construtivos simples e compostos foram propostos para o problema no-wait flow shop e uma heurística composta foi desenvolvida considerando as operações de manutenção preventiva. Experimentações computacionais para os dois problemas foram realizadas para fins de comparação e avaliação dos métodos propostos com os métodos heurísticos construtivos da literatura. Para o problema Fm|no - wait|Cmax resultados evidenciaram que as heurísticas propostas H4GPSLLS e MH4GPSLLS superaram as heurísticas da literatura em qualidade de solução, com diferença estatisticamente significativa no nível de 5% de significância. Para o problema Fm|no - wait, m(k)|Cmax, pode-se constatar que a heurística BIHLS e as heurísticas H4GPSLLS e MH4GPSLLS apresentaram desempenho superior com diferença estatística significativa no nível de 5% de significância em comparação as heurísticas da literatura. / The no-wait flow shop scheduling problem has been studied since 60\'s. In this environment, jobs must be processed continuously without interruption between one machine and another, and because of this, a delay between the start time of the previous job and the start time of the current job must be determined in the first machine. In this sense, since a job starts its processing, the machines must be available to respect the no-wait constraint. Therefore, preventive maintenance operations are needed. This work adresses two problems: the m machine no-wait flow shop and the m machine no-wait flow shop with preventive maintenance operations. The performance measure adopted was the makespan. By means of a literature review, mechanisms of solution construction were identified and classified. New simple and composite constructive heuristics were proposed to the no-wait flow shop problem and a new composite constructive heuristic was developed considering the preventive maintenance operations. Computational experiments and their respective analyses for both problems were carried out to compare and evaluate the performance between the proposed methods and the constructive heuristics of the literature. Regarding Fm|no - wait|Cmax problem, results show that the proposed heuristics H4GPSLLS and MH4GPSLLS outperformed the heuristics of the literature in quality of the solution and is statistically significative to 5% of significance level. To the Fm|no - wait, m(k)|Cmax problem it can be seen that the proposed heuristic BIHLS and H4GPSLLS and MH4GPSLLS outperformed the heuristics of the literature and is statistically better to 5% of significance level.

Este trabalho aborda o problema de no-wait flowshop em um ambiente com custos de setup apartados dos tempos de processamento, são investigados os casos de setups dependentes e independentes da seqüência para makespan e total flowtime. Diversas aplicações práticas podem ser modeladas sob estas suposições, dentre elas destacamos a indústria química e alimentícia. É proposta uma metaheurística evolutiva baseada em algoritmo genético e clustering search e seus resultados são comparados com os métodos de Brown et al (2004), França et al (2006) e Ruiz e Allahverdi (2007) através dos bancos de dados de Ruiz e Stützle (2008) e Ruiz e Allahverdi (2007). Os métodos são avaliados segundo o percentual de sucesso e desvio relativo médio. Os resultados obtidos demonstram a superioridade do método proposto para problemas de grande porte. / This work intends to research the no-wait flowshop scheduling problem with setup times separated from the processing costs; the both cases where the sequence is dependent and independent are targeted for makespan and total flowtime. There are numerous practical situations that can be modeled under these assumptions, such as, chemical industry, food processing, etc. A hybrid metaheuristic method based on a genetic algorithm and clustering search is proposed and its results are compared to the methods of Brown et al (2004), França et al (2006) e Ruiz e Allahverdi (2007) using the data base from Ruiz e Stützle (2008) and Ruiz e Allahverdi (2007). The methods are evaluated as regarding the success rate and average relative deviation. The results show that the proposed method delivers better solutions for problems with higher complexity.

O problema de programação de operações em ambiente no-wait flow shop tem sido abordado desde a década de 60. Por se tratar de um ambiente em que as tarefas devem ser processadas continuamente e sem interrupções entre uma máquina e outra, um tempo de espera entre o início da tarefa anterior e o início da tarefa atual deve ser determinado na primeira máquina. Neste sentido, uma vez que a tarefa inicia seu processamento, as máquinas devem estar disponíveis para que atendam a restrição de no-wait. Portanto, operações de manutenção preventiva são necessárias para que a programação seja atendida sem maiores problemas. Este trabalho aborda dois problemas: no-wait flow shop e no-wait flow shop com operações de manutenção preventiva. O critério de desempenho adotado foi a duração total da programação (makespan). Por meio de uma revisão de literatura, mecanismos de construção de soluções foram identificadas e classificadas e, baseando-se em tais, novos métodos heurísticos construtivos simples e compostos foram propostos para o problema no-wait flow shop e uma heurística composta foi desenvolvida considerando as operações de manutenção preventiva. Experimentações computacionais para os dois problemas foram realizadas para fins de comparação e avaliação dos métodos propostos com os métodos heurísticos construtivos da literatura. Para o problema Fm|no - wait|Cmax resultados evidenciaram que as heurísticas propostas H4GPSLLS e MH4GPSLLS superaram as heurísticas da literatura em qualidade de solução, com diferença estatisticamente significativa no nível de 5% de significância. Para o problema Fm|no - wait, m(k)|Cmax, pode-se constatar que a heurística BIHLS e as heurísticas H4GPSLLS e MH4GPSLLS apresentaram desempenho superior com diferença estatística significativa no nível de 5% de significância em comparação as heurísticas da literatura. / The no-wait flow shop scheduling problem has been studied since 60\'s. In this environment, jobs must be processed continuously without interruption between one machine and another, and because of this, a delay between the start time of the previous job and the start time of the current job must be determined in the first machine. In this sense, since a job starts its processing, the machines must be available to respect the no-wait constraint. Therefore, preventive maintenance operations are needed. This work adresses two problems: the m machine no-wait flow shop and the m machine no-wait flow shop with preventive maintenance operations. The performance measure adopted was the makespan. By means of a literature review, mechanisms of solution construction were identified and classified. New simple and composite constructive heuristics were proposed to the no-wait flow shop problem and a new composite constructive heuristic was developed considering the preventive maintenance operations. Computational experiments and their respective analyses for both problems were carried out to compare and evaluate the performance between the proposed methods and the constructive heuristics of the literature. Regarding Fm|no - wait|Cmax problem, results show that the proposed heuristics H4GPSLLS and MH4GPSLLS outperformed the heuristics of the literature in quality of the solution and is statistically significative to 5% of significance level. To the Fm|no - wait, m(k)|Cmax problem it can be seen that the proposed heuristic BIHLS and H4GPSLLS and MH4GPSLLS outperformed the heuristics of the literature and is statistically better to 5% of significance level.

Este trabalho aborda o problema de no-wait flowshop em um ambiente com custos de setup apartados dos tempos de processamento, são investigados os casos de setups dependentes e independentes da seqüência para makespan e total flowtime. Diversas aplicações práticas podem ser modeladas sob estas suposições, dentre elas destacamos a indústria química e alimentícia. É proposta uma metaheurística evolutiva baseada em algoritmo genético e clustering search e seus resultados são comparados com os métodos de Brown et al (2004), França et al (2006) e Ruiz e Allahverdi (2007) através dos bancos de dados de Ruiz e Stützle (2008) e Ruiz e Allahverdi (2007). Os métodos são avaliados segundo o percentual de sucesso e desvio relativo médio. Os resultados obtidos demonstram a superioridade do método proposto para problemas de grande porte. / This work intends to research the no-wait flowshop scheduling problem with setup times separated from the processing costs; the both cases where the sequence is dependent and independent are targeted for makespan and total flowtime. There are numerous practical situations that can be modeled under these assumptions, such as, chemical industry, food processing, etc. A hybrid metaheuristic method based on a genetic algorithm and clustering search is proposed and its results are compared to the methods of Brown et al (2004), França et al (2006) e Ruiz e Allahverdi (2007) using the data base from Ruiz e Stützle (2008) and Ruiz e Allahverdi (2007). The methods are evaluated as regarding the success rate and average relative deviation. The results show that the proposed method delivers better solutions for problems with higher complexity.

Nous abordons dans cette thèse des problèmes de flowshop de permutation soumis des contraintes de délais minimaux et maximaux avec deux types de travaux principaux : 1. Nous avons modélisé, en utilisant l'algèbre MaxPlus, des problèmes de flowshop de permutation m-machines soumis une famille de contraintes : de délais minimaux, de délais maximaux, de sans attente, de délais fixes, de temps de montage indé- pendant de la séquence, de temps de démontage indépendant de la séquence, de blocage, de dates de début au plus tæt ainsi que de durées de latence. Des matrices caractérisant complètement leurs travaux associés ont été élaborées. Nous avons fait apparaître un problème central soumis des contraintes de délais minimaux et maximaux. 2. Nous avons élaboré des bornes inférieures pour le makespan et pour la somme (pondérée ou non) des dates de fin. Ces bornes inférieures ont été incorporées dans des procédures par séparation et évaluation. Nous avons généralisé les bornes inférieures de Lageweg et al. pour des contraintes quelconques et amélioré une borne inférieure de la littérature. L'utilisation de chacune de ces bornes inférieures ainsi que de leurs combinaisons ont été testées. Une famille de bornes inférieures pour la somme (pondérée ou non) des dates de fin a été élaborée basée sur la résolution d'un problème une machine et sur la résolution d'un problème de voyageur de commerce. Une politique de sélection de bornes inférieures a été proposée pour combiner les bornes inférieures. Bien qu'il s'agisse d'un problème de NP-difficile, l'efficacité de ces bornes inférieures a été vérifiée l'aide de tests. / In this thesis, permutation flowshop problems with minimal and maximal delay constraints were considered through two following principal tasks were particularly tackled. 1. In the first task, m-machine permutation flowshop problems with a family of constraints (minimal delays, maximal delays, no-wait, fixed delays, sequence-independent setup times, sequence-independent removal times, blocking, ready dates, duration of latency) were modeled using MaxPlus algebra. Job associated matrices which totally characterize these jobs were elaborated. The modeling led to reveal a central problem with constraints of minimal and maximal delays. 2. In the second task, lower bounds for makespan and for total (weighted or unweighted) completion times were elaborated. These lower bounds were incorporated in branchand-bound procedures. The lower bounds of Lageweg et al. were generalized for any constraint and a existed lower bound was improved. The usage of each of these lower bounds as well as that of their combinations was tested. A family of lower bounds for total (weighted or non-weighted) completion times was elaborated thanks to the solution of a one-machine problem and the solution of a traveling salesman problem. A lower bound selection strategy was proposed in order to combine these lower bounds. Despite necessity to solve a NP-hard problem, the effectiveness of these lower bounds was verified by numerical tests.

Background: Optimal scheduling is a common practice in various industries, facili-tating efficient workflow management. Accelerating the generation of schedules while maintaining their optimality could encourage broader adoption of this approach inindustry settings. Previous work has aimed to estimate the makespan for the JobShop Scheduling Problem, showing promising results. Objectives: Given the increasing demand for AI and Machine Learning (ML) solutions across industries, this research aims to explore the integration of ML techniquesinto optimal scheduling processes. Specifically, the goal is to develop a faster scheduling solution without compromising the optimality of the generated schedules. The proposed approach combines the effectiveness and speed of ML with the optimal results obtained from mathematical scheduling models. Methods: This thesis focuses on the Job Shop Scheduling (JSS) Problem, where a mathematical scheduler is tasked with minimizing the makespan of a set of jobs while following a predefined set of rules. An initial investigation is performed to establish if there is potential in providing the scheduler with its optimal makespan to decrease the scheduling time. To generalize the application of the concept, the study investigates the potential efficiency acceleration achieved by providing the scheduler with a Machine Learning estimated makespan. This involves training a Neural Network(NN) to estimate the optimal makespan of job sets, which is then utilized to speedup the scheduling process. Results: The preliminary investigation demonstrates that providing the scheduler with the optimal makespan results in an average speed-up of schedule generationby 24%. The results of the scheduling time with the NN estimated makespan is on the other hand not as well performing. Despite achieving a level of accuracy in estimating the makespan, the resulting speed-up in the scheduler’s performance falls short. For the scheduler to benefit from being provided an estimated makespan it is therefore theorized to require a close-to-perfect estimation of the makespan, which was not achieved with the trained NN model. The trained NN reached an average accuracy of 95.75%. Conclusions: The study concludes that while ML models can accurately estimate makespan, the observed speed-up in scheduling performance is not as significant as anticipated. The correlation between well-estimated makespan and speed-up appearsto be inconsistent, indicating potential limitations in the current approach. Further investigation into the search algorithm employed by the scheduling tool Gurobi mayprovide insights into optimizing the scheduling process more effectively. In summary, while the integration of ML techniques shows promise in accelerating scheduling processes, a higher accuracy of the ML model would be required. Additional researchis needed to refine the approach and potentially bring a faster optimal scheduling solution into the future. / Bakgrund: Optimal schemaläggning är en vanlig implemetation inom flera olika branscher och underlättar hantering och effektiviserar arbetsflöden. Att påskynda genereringen av scheman samtidigt som den optimala aspekten av schemaläggning inte går till spillo, skulle kunna främja en bredare användning av optimal schemaläggning för fler brancher. Tidigare undersökningar har gjorts för att estimera "makespan" för Job Shop problemet inom schemaläggning och har visat lovande resultat. Syfte: Med den ökande efterfrågan på AI- och maskininlärnings lösningar inom olika branscher syftar denna forskning till att utforska integrationen av ML-tekniker i den optimala schemaläggningsprocessen. Målet är att utveckla en snabbare schemaläggningslösning utan att kompromissa med det genererade schemats optimalitet. Det föreslagna tillvägagångssättet kombinerar ML’s effektivitet och hastighet med de optimala resultaten som den matematiska schemaläggningsmodellen erbjuder. Metod: Forskningen fokuserar på problemet med schemaläggning för jobbshoppen(JSSP), där en matematisk schemaläggare har i uppgift att minimera makespan fören uppsättning jobb med hänsyn till ett par fördefinierade regler. En initial under-sökning görs, vilket visar att det finns potential i att tillhandahålla schemaläggarendess optimala makespan för att minska schemaläggningstiden. För att generalisera tillämpningen undersöker studien den potentiella accelerationen som uppnås genomatt tillhandahålla schemaläggaren ett maskininlärt uppskattat makespan. Detta medför att träna ett neuralt nätverk för att uppskatta det optimala makespanet för en mängd jobbuppsättningar, som sedan används för att påskynda schemaläggningsprocessen. Resultat: Den preliminära undersökningen visar att schemaläggaren resulterar i igenomsnittlig hastighetsökning av schemagenereringen med cirka 24% när den får tillgång till det optimala makespanet för de givna jobben. Resultaten av schemaläggningstiden med det neurala nätverkets uppskattade makespan är dock lägre än förväntat. Trots att en viss noggrannhetsnivå uppnås vid estimeringen av makespanet, når den resulterande hastighetsökningen i schemaläggarens prestanda inte upp tillförväntningarna. För att schemaläggaren ska dra nytta av att tillhandahålla ett uppskattad makespan krävs en nära perfekt uppskattning av makespan, vilket inte uppnåddes med det tränade neurala nätverket. Slutsatser: Studien drar slutsatsen att även om ML-modeller kan uppskatta makespan någorlunda noggrant, är den observerade hastighetsökningen i schemaläggningen inte lika betydande som förväntat. Korrelationen mellan väl uppskattad makespan och hastighetsökning verkar vara inkonsekvent, vilket indikerar potentiella begränsningar i det nuvarande tillvägagångssättet. Vidare undersökning av sökalgoritmen som används av schemaläggningsverktyget Gurobi kan ge insikter för att optimera schemaläggningsprocessen mer effektivt. Sammanfattningsvis visar integrationen av ML-tekniker lovande resultat för att accelerera schemaläggningsprocesser, men en bättre estimering av makespan skulle krävas. Ytterligare forskning behövs för att förbättra tillvägagångssättet och potentiellt introducera en snabbare optimal schemaläggningslösning för framtiden.

Automated Guided Vehicle (AGV) Container-Job deployment is essentially a vehicle-dispatching problem. In this problem, the impact of vehicle dispatching polices on the ship makespan for discharging and/or loading operations is analyzed. In particular, given a storage location for each container to be discharged from the ship and given the current location of each container to be loaded onto the ship, the problem is to propose an efficient deployment scheme to dispatch vehicles to containers so as to minimize the makespan of the ship so as to increase the throughput. The makespan of the ship refers to the time a ship spends at the port for loading and unloading operations. In this paper, we will compare the performance of current deployment scheme used with the new proposed deployment scheme, both with deadlock prediction & avoidance algorithm done in previous study [1]. The prediction & avoidance algorithm predicts and avoids cyclic deadlock. The current deployment scheme, namely pmds makes use of a greedy heuristics which dispatches the available vehicle that will reach the quay with the minimum amount of time the vehicle has to spend waiting for the crane to discharge/load the container from/onto the ship. The new deployment scheme, namely mcf aims to formulate the problem as a minimum cost flow problem, which will then be solved by network simplex code. The two simulation models are implemented using discrete-event simulation software, AutoMod, and the performances of both deployment schemes are analyzed. The simulation results show that the new deployment scheme will result in a higher throughput and lower ship makespan than the current deployment scheme. / Singapore-MIT Alliance (SMA)

The problem of scheduling a parallel program presented by a weighted directed acyclic graph (DAG) to the set of homogeneous processors for minimizing the completion time of the program has been extensively studied as academic optimization problem which occurs in optimizing the execution time of parallel algorithm with parallel computer.In this paper, we propose an application of the Ant Colony Optimization (ACO) to a multiprocessor scheduling problem (MPSP). In the MPSP, no preemption is allowed and each operation demands a setup time on the machines. The problem seeks to compose a schedule that minimizes the total completion time.We therefore rely on heuristics to find solutions since solution methods are not feasible for most problems as such. This novel heuristic searching approach to the multiprocessor based on the ACO algorithm a collection of agents cooperate to effectively explore the search space.A computational experiment is conducted on a suit of benchmark application. By comparing our algorithm result obtained to that of previous heuristic algorithm, it is evince that the ACO algorithm exhibits competitive performance with small error ratio.

The problem of finding a schedule with the lowest makespan in the class of all flowtime-optimal schedules for parallel identical machines is an NP-hard problem. Several approximation algorithms have been suggested for this problem. We focus on algorithms that are fast and easy to implement, rather than on more involved algorithms that might provide tighter approximation bounds. A set of approaches for proving conjectured bounds on performance ratios for such algorithms is outlined. These approaches are used to examine Coffman and Sethi's conjecture for a worst-case bound on the ratio of the makespan of the schedule generated by the LD algorithm to the makespan of the optimal schedule. A significant reduction is achieved in the size of a hypothesised minimal counterexample to this conjecture.

We consider the problem of scheduling a given set of tasks on multiple pro- cessors with predefined periods of unavailability, with the aim of minimizing the maximum completion time. Since this problem is strongly NP-hard, polynomial ap- proximation algorithms are being studied for its solution. Among these, the best known are LPT (largest processing time first) and Multifit with their variants. We give a Multifit-based algorithm, FFDL Multifit, which has an optimal worst- case performance in the class of polynomial algorithms for same-speed processors with at most two downtimes on each machine, and for uniform processors with at most one downtime on each machine, assuming that P 6= NP. Our algorithm finishes within 3/2 the maximum between the end of the last downtime and the end of the optimal schedule. This bound is asymptotically tight in the class of polynomial algorithms assuming that P 6= NP. For same-speed processors with at most k downtimes on each machine our algorithm finishes within ( 3 2 + 1 2k ) the end of the last downtime or the end of the optimal schedule. For problems where the optimal schedule ends after the last downtime, and when the downtimes represent fixed jobs, the maximum completion time of FFDL Multifit is within 3 2 or ( 3 2+ 1 2k ) of the optimal maximum completion time. We also give an LPT-based algorithm, LPTX, which matches the performance of FFDL Multifit for same-speed processors with at most one downtime on each machine, and is thus optimal in the class of polynomial algorithms for this case. LPTX differs from LPT in that it uses a specific order of processors to assign tasks if two processors become available at the same time. For a similar problem, when there is at most one downtime on each machine and no more than half of the machines are shut down at the same time, we show that a bound of 2 obtained in a previous work for LPT is asymptotically tight in the class of polynomial algorithms assuming that P 6= NP.