Dynamic resource constrained multi-project scheduling problem with weighted earliness/tardiness costs

In this study, a conceptual framework is given for the dynamic multi-project scheduling problem with weighted earliness/tardiness costs (DRCMPSPWET) and a mathematical programming formulation of the problem is provided. In DRCMPSPWET, a project arrives on top of an existing project portfolio and a due date has to be quoted for the new project while minimizing the costs of schedule changes. The objective function consists of the weighted earliness tardiness costs of the activities of the existing projects in the current baseline schedule plus a term that increases linearly with the anticipated completion time of the new project. An iterated local search based approach is developed for large instances of this problem. In order to analyze the performance and behavior of the proposed method, a new multi-project data set is created by controlling the total number of activities, the due date tightness, the due date range, the number of resource types, and the completion time factor in an instance. A series of computational experiments are carried out to test the performance of the local search approach. Exact solutions are provided for the small instances. The results indicate that the local search heuristic performs well in terms of both solution quality and solution time

Train scheduling with application to the UK rail network

Nowadays, transforming the railway industry for better performance and making the best usage of the current capacity are the key issues in many countries. Operational research methods and in particular scheduling techniques have a substantial potential to offer algorithmic solutions to improve railway operation and control. This thesis looks at train scheduling and rescheduling problems in a microscopic level with regard to the track topology. All of the timetable components are fixed and we aim to minimize delay by considering a tardiness objective function and only allowing changes to the order and to the starting times of trains on blocks. Various operational and safety constraints should be considered. We have achieved further developments in the field including generalizations to the existing models in order to obtain a generic model that includes important additional constraints. We make use of the analogy between the train scheduling problem and job shop scheduling problem. The model is customized to the UK railway network and signaling system. Introduced solution methods are inspired by the successful results of the shifting bottleneck to solve the job shop scheduling problems. Several solution methods such as mathematical programming and different variants of the shifting bottleneck are investigated. The proposed methods are implemented on a real-world case study based on London Bridge area in the South East of the UK. It is a dense network of interconnected lines and complicated with regard to stations and junctions structure. Computational experiments show the efficiency and limitations of the mathematical programming model and one variant of the proposed shifting bottleneck algorithms. This study also addresses train routing and rerouting problems in a mesoscopic level regarding relaxing some of the detailed constraints. The aim is to make the best usage of routing options in the network to minimize delay propagation. In addition to train routes, train entry times and orders on track segment are defined. Hence, the routing and scheduling decisions are combined in the solutions arising from this problem. Train routing and rerouting problems are formulated as modified job shop problems to include the main safety and operational constraints. Novel shifting bottleneck algorithms are provided to solve the problem. Computational results are reported on the same case study based on London Bridge area and the results show the efficiency of one variant of the developed shifting bottleneck algorithms in terms of solution quality and runtime

Decentralized Scheduling of Discrete Production Systems with Limited Buffers

Die Steuerung der Produktion ist eine der Kernaufgaben eines jeden produzierenden Unternehmens. Sie ist insbesondere wichtig, um auf die Anforderungen des Marktes und damit auf die Wünsche der Kunden reagieren zu können. Aktuelle Trends im Markt führen dabei zu einer hochindividualisierten Produktion bei gleichzeitiger Erhöhung der produzierten Stückzahlen. Eine Konsequenz daraus ist, dass Unternehmen über flexiblere und agilere Produktionssysteme verfügen müssen, um auf die sich ständig ändernden Kundenwünsche reagieren zu können. Da starre Fertigungslinien nicht mehr geeignet sind, werden zunehmend komplexere Strukturen wie die der Werkstattfertigung oder Matrixproduktion eingesetzt. Hierfür werden geeignete Steuerungsmethoden für die Produktion benötigt. Diese Arbeit beschäftigt sich mit eben jenen Steuerungsmethoden, genauer gesagt Methoden zur Planung von Produktionsaufträgen in diesen neuen Produktionssystemen. Zur Steuerung eignen sich echtzeitfähige und autonome Entscheidungssysteme, mit denen die Steuerung der neuen Organisationsstruktur der Produktion angepasst ist. Agentenbasierte Systeme bieten genau diese Eigenschaften und erlauben es, komplexe Planungsaufgaben in kleinere Teilprobleme zu zerlegen, die schneller und genauer gelöst werden können. Sie erfordern die Verfügbarkeit von Daten in Echtzeit und eine schnelle Kommunikation zwischen den Agenten, was heute dank der vierten industriellen Revolution zur Verfügung steht. Demgegenüber steht der erhöhte Koordinierungsbedarf, der in diesen Systemen beherrscht werden muss. Das Ziel dieser Arbeit ist es, einen dezentralen Produktionsplanungs-Algorithmus zu entwickeln, der in einem Multi-Agenten-System implementiert ist. Er berücksichtigt begrenzte Verfügbarkeit von Pufferplätzen an jedem Arbeitsplatz, ein Thema, das in der Literatur wenig erforscht ist. Der Algorithmus ist in einer flexiblen Werkstattfertigung anwendbar und zeigt eine große Zeiteffizienz bei der Einplanung größerer Mengen von Aufträgen. Um dieses Ziel zu erreichen, wird zunächst der Produktionsplanungs-Algorithmus ohne das Agentensystem entworfen. Er basiert auf der von \textcite{adams1988} veröffentlichten Shifting Bottleneck Heuristik. Da viele Änderungen notwendig sind, um die geforderten Eigenschaften berücksichtigen zu können, bleibt nur die grundlegende Vorgehensweise gleich, während alle Schritte der Heuristik von Grund auf neu modelliert werden. Anschließend wird ein Multi-Agenten-System entworfen, das die genannten Anforderungen abbildet und den Algorithmus zur Planung verwendet. In diesem System hat jeder Arbeitsplatz einen Arbeitsplatzagenten, der für die Planung und Steuerung seines zugeordneten Arbeitsplatzes zuständig ist, sowie einige zusätzliche Agenten für die Kommunikation, die Datenspeicherung und allgemeine Aufgaben. Der entworfene Algorithmus wird angepasst und in das Multi-Agenten-System implementiert. Da das System im praktischen Einsatz immer eine Lösung finden muss, stellen wir mögliche Fehlerfälle vor und wie mit ihnen umgegangen wird. Abschließend findet eine numerische Evaluierung mit zwei realen Produktionssystemen statt. Da sich diese Systeme in einem wichtigen Merkmal ähneln, werden weitere zufällig erzeugte Beispiele getestet und ausgewertet

Assembly job shop scheduling problems with component availability constraints.

Job shop scheduling has been widely studied for several decades. In generalized of the job shop scheduling problem, n jobs are to be processed on m machines under specific routings and due dates. The majority of job shop scheduling research concentrates on manufacturing environments processing string-type jobs with a linear routing where no assembly operations are involved. However, many manufacturing environments produce complex products with multi-level assembly job structures and cannot be scheduled efficiently with existing job shop scheduling techniques. Little research has been done in the area of assembly job shop scheduling, and we are not aware any of those studies consider on the availability of purchased components and the impact of component availability on the performance of assembly job shops. This research focuses on scheduling job shops that process jobs requiring multiple-levels of assembly and it also considers the availability of components that are procured from outside suppliers. By considering material constraints during production scheduling, manufacturers can increase resource utilization and improve due date performance.To represent assembly job shop scheduling problems with component availability constraints, a modified disjunctive graph formulation is developed in this research. A mixed-integer programming model with the objective of minimizing the total weighted-tardiness is also developed in this research. Several heuristic methods, described as modified shifting bottleneck procedure (MSBP), efficient shifting bottleneck procedure (ESBP) and rolling horizon procedure (RHP), are proposed to reduce the computational time required for assembly job shop scheduling problems. These methods are extended from the shifting bottleneck procedure. The performance of various flavors of the MSBP and ESBP is demonstrated on a set of test instances and compared with different dispatching rules that are widely used in practice. Results show that MSBP and ESBP outperform the dispatching rules by 18% to 16% on average.This dissertation not only studies the assembly job shop scheduling problem with component availability constraints, but also demonstrates how the decomposition methodology can reduce the complexity of NP-hard problems. Based on the relative preference of solution quality and computational time, recommendations for appropriate methods to solve assembly job shop scheduling problems with different problem sizes are given in the conclusions of this dissertation

The dynamic, resource-constrained shortest path problem on an acyclic graph with application in column generation and literature review on sequence-dependent scheduling

This dissertation discusses two independent topics: a resource-constrained shortest-path problem (RCSP) and a literature review on scheduling problems involving sequence-dependent setup (SDS) times (costs). RCSP is often used as a subproblem in column generation because it can be used to solve many practical problems. This dissertation studies RCSP with multiple resource constraints on an acyclic graph, because many applications involve this configuration, especially in column genetation formulations. In particular, this research focuses on a dynamic RCSP since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-time âÂÂpreliminaryâ phase to transform RCSP into an unconstrained shortest path problem (SPP) and then solves the resulting SPP after new values of dual variables are used to update objective function coefficients (i.e., reduced costs) at each iteration. Network reduction techniques are considered to remove some nodes and/or arcs permanently in the preliminary phase. Specified techniques are explored to reoptimize when only several coefficients change and for dealing with forbidden and prescribed arcs in the context of a column generation/branch-and-bound approach. As a benchmark method, a label-setting algorithm is also proposed. Computational tests are designed to show the effectiveness of the proposed algorithms and procedures. This dissertation also gives a literature review related to the class of scheduling problems that involve SDS times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations - single machine, parallel machine, flow shop, and job shop - reviewing both optimizing and heuristic solution methods in each category. Since lot-sizing is so intimately related to scheduling, this dissertation reviews work that integrates these issues in relationship to each configuration. This dissertation provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems. since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-tim

Makespan Minimization in Re-entrant Permutation Flow Shops

Re-entrant permutation flow shop problems occur in practical applications such as wafer manufacturing, paint shops, mold and die processes and textile industry. A re-entrant material flow means that the production jobs need to visit at least one working station multiple times. A comprehensive review gives an overview of the literature on re-entrant scheduling. The influence of missing operations received just little attention so far and splitting the jobs into sublots was not examined in re-entrant permutation flow shops before. The computational complexity of makespan minimization in re-entrant permutation flow shop problems requires heuristic solution approaches for large problem sizes. The problem provides promising structural properties for the application of a variable neighborhood search because of the repeated processing of jobs on several machines. Furthermore the different characteristics of lot streaming and their impact on the makespan of a schedule are examined in this thesis and the heuristic solution methods are adjusted to manage the problem’s extension

Scheduling in assembly type job-shops

Assembly type job-shop scheduling is a generalization of the job-shop scheduling problem to include assembly operations. In the assembly type job-shops scheduling problem, there are n jobs which are to be processed on in workstations and each job has a due date. Each job visits one or more workstations in a predetermined route. The primary difference between this new problem and the classical job-shop problem is that two or more jobs can merge to foul\u27 a new job at a specified workstation, that is job convergence is permitted. This feature cannot be modeled by existing job-shop techniques. In this dissertation, we develop scheduling procedures for the assembly type job-shop with the objective of minimizing total weighted tardiness. Three types of workstations are modeled: single machine, parallel machine, and batch machine. We label this new scheduling procedure as SB. The SB procedure is heuristic in nature and is derived from the shifting bottleneck concept. SB decomposes the assembly type job-shop scheduling problem into several workstation scheduling sub-problems. Various types of techniques are used in developing the scheduling heuristics for these sub-problems including the greedy method, beam search, critical path analysis, local search, and dynamic programming. The performance of SB is validated on a set of test problems and compared with priority rules that are normally used in practice. The results show that SB outperforms the priority rules by an average of 19% - 36% for the test problems. SB is extended to solve scheduling problems with other objectives including minimizing the maximum completion time, minimizing weighted flow time and minimizing maximum weighted lateness. Comparisons with the test problems, indicate that SB outperforms the priority rules for these objectives as well. The SB procedure and its accompanying logic is programmed into an object oriented scheduling system labeled as LEKIN. The LEKIN program includes a standard library of scheduling rules and hence can be used as a platform for the development of new scheduling heuristics. In industrial applications LEKIN allows schedulers to obtain effective machine schedules rapidly. The results from this research allow us to increase shop utilization, improve customer satisfaction, and lower work-in-process inventory without a major capital investment

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