Analysis of critical machine reliability in manufacturing cells

Purpose: In an increasingly competitive business environment, machine reliability problem merits special attention in operations of manufacturing cells. This is mainly due to flow line nature of the cellular layout, interdependency of downstream and upstream of machines related to each other. This study investigates the effect of critical machine reliability improvement on production capacity and throughput time in manufacturing cells. Design/methodology/approach: A discrete-event simulation model was developed to investigate the effectiveness of a reliability plan focusing on the most critical production machines in improving the performance level as an alternative to increasing the reliability of all machines. Four machine criticality policies are examined in the simulation experiments. Findings: The results of this experimental study indicated that an improvement of reliability of a limited number of machines leads to an increase in overall production capacity and speed in cellular manufacturing operations. A reliability plan, that focuses on a set of critical machines, potentially offers a more economical alternative to increasing the reliability of all machines in such facility. Research limitations/implications: The results demonstrate that to achieve higher production capacity and shorter throughput times, managers should consider directing more resources to increase the reliability of critical machines, particularly, those with shorter mean time to failure and higher utilization. Originality/value: The designed simulation model is unique in representing the dynamics of a real world manufacturing cell environment by encoding operational functions such as machine failure, maintenance resource allocation, material flow, job sequencing and scheduling. A new machine availability metric is defined as well.Peer Reviewe

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

Analysis of critical machine reliability in manufacturing cells

Purpose: In an increasingly competitive business environment, machine reliability problem merits special attention in operations of  manufacturing cells. This is mainly due to flow line nature of the cellular layout, interdependency of downstream and upstream of machines related to each other. This study investigates the effect of critical machine reliability improvement  on production capacity and throughput time in manufacturing cells.   Design/methodology/approach: A discrete-event simulation model was developed to investigate the effectiveness of a reliability plan focusing on the most critical production machines in improving the performance level as an alternative to increasing the reliability of all machines. Four machine criticality policies are examined in the simulation experiments. Findings: The results of this experimental study indicated that an improvement of reliability of a limited number of machines leads to an increase in overall production capacity and speed in cellular manufacturing operations. A reliability plan, that focuses on a set of critical machines, potentially offers a more economical alternative to increasing the reliability of all machines in such facility. Research limitations/implications: The results demonstrate that to achieve higher production capacity and shorter throughput times, managers should consider directing more resources to increase the reliability of critical machines, particularly, those with shorter mean time to failure and higher utilization. Originality/value: The designed simulation model is unique in representing the dynamics of a real world manufacturing cell environment by encoding operational functions such as machine failure, maintenance resource allocation, material flow, job sequencing and scheduling. A new machine availability metric is defined as well

Scheduling research in multiple resource constrained job shops: a review and critique

Over the past several years, a number of survey, classification, and review articles have focused on scheduling research in machine [only] constrained job shops. Barring the work of Treleven (1989), there is no reported research that presents a detailed review of the issues related to scheduling and sequencing in job shops with multiple resource constraints. In his article, Treleven reviewed the research in job shops constrained by machines and labour. Job shops are not only constrained by machines and labour, but by auxiliary resources (in the form of tooling. etc.) as well. This paper extends the work of Treleven by reviewing the literature on scheduling in job shops constrained by more than one resource and comparing the scheduling research in auxiliary resource-constrained job shops with that of labour-constrained job shops. In addition, this article raises some issues for future scheduling research in multiple resource-constrained job shops

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

Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Planning and control of AGVs in AMRF decision hierarchy

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Sciences of Bilkent Univ., 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references leaves 90-94.Scheduling efforts made without considering the special limitations of the material handling system might lead to infeasible results. This problem especially becomes important when the Automated Guided Vehicles (AGV) are the main material handling media due to their inherent flexibility and adaptability that increase the scheduling complexity. In this thesis, an analytical model is proposed, first, to incorporate the AGV module into the overall decision making hierarchy. A mathematical formulation is developed to include interaction between the AGV module and other modules in the system by considering the restrictions of the material handling system. A micro-opportunistic approach is proposed to solve the AGV scheduling problem. Finally, the proposed method is compared with a number of dispatching rules.Yılmaz, HalukM.S

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