Artificial immune system for static and dynamic production scheduling problems

Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival of jobs or machine breakdown requiring solutions. To solve these problems for static and dynamic environments, the single and hybrid methods were introduced. Single method utilizes Artificial Immune System (AIS), whereas AIS and Variable Neighbourhood Descent (VND) are used in the hybrid method. Clonal Selection Principle (CSP) algorithm in the AIS was used in the proposed single and hybrid methods. In addition, to evaluate the significance of the proposed methods, experiments and One-Way ANOVA tests were conducted. The findings showed that the hybrid method was proven to give better performance compared to single method in producing optimized solution and reduced solution generating time. The main contribution of this thesis is the development of an algorithm used in the single and hybrid methods to solve JSSP and FJSSP in static and dynamic environment

Praktische Job-Shop Scheduling-Probleme

Die vorliegende Arbeit beschäftigt sich mit Verallgemeinerungen des klassischen Job-Shop Scheduling-Problems J||Cmax. Im ersten Kapitel werden Probleme mit verallgemeinerten Reihenfolgerestriktionen, Maschinen mit deterministischen Nichtverfügbarkeitsintervallen und erneuerbaren diskreten Ressourcen eingeführt und modelliert. Als zu minimierende Zielfunktionen werden der Makespan, die Lateness, die Tardiness und die Summe der Fertigstellungszeiten verwendet. Im zweiten Kapitel wird zur Minimierung einer beliebigen der vier Zielfunktionen eine genetische lokale Suche entwickelt. Für das Verständnis der Heuristik wird zusätzlich die Struktur des Lösungsraumes untersucht. Im dritten Kapitel wird die genetische lokale Suche für die multikriterielle Optimierung und zur Approximation der Menge der Pareto-optimalen Lösungen erweitert. Schließlich wird zur Entscheidungsunterstützung ein Short-Listing-Verfahren, das die Menge der Pareto-optimalen Lösungen reduziert, entwickelt und untersucht. Die ausgewählten Lösungen sollten dabei eine möglichst gute Diversität aufweisen

Permanente Optimierung dynamischer Probleme der Fertigungssteuerung unter Einbeziehung von Benutzerinteraktionen

Trotz enormen Forschungsaufwands erhalten die Entscheider in der Fertigungssteuerung nur rudimentäre Rechnerunterstützung. Diese Arbeit schlägt ein umfassendes Konzept für eine permanent laufende algorithmische Feinplanung vor, die basierend auf einer Analyse des Optimierungspotentials und -bedarfs intelligent mit den Entscheidern kollaboriert und zeitnah auf Fertigungsereignisse reagiert. Dynamische Simulationen mit Unternehmensdaten bestätigen die Praxistauglichkeit des Konzepts

Heuristic approaches to scheduling problems in a flexible job shop environment

Thesis (Master)--Izmir Institute of Technology, Energy Engineering, Izmir, 2004Includes bibliographical references (leaves: 81-85)Text in English, Abstract: Turkish and Englishx, 85 pages, [245] leavesModern production factories, to obtain high profits, usually maximize their profits through streamlining their productivity. This goal can be achieved, among others, by optimal or almost optimal scheduling of jobs in production process. Scheduling is a key factor for manufacturing productivity and energy save. Effective scheduling can improve on-time delivery of products, reduce inventory, reduce processing times, and utilize bottleneck resources, therefore energy is saved as a result.Process plants typically produce a family of related products that require similar processing techniques. The most important problem encountered in such manufacturing systems is scheduling of operations so that demand is fulfilled within a pre-described time horizon imposed by production planning. The typical scheduling operation that process plants involve can be formulated as a general job shop scheduling problem. Due to production flexibility, it is possible to generate many feasible process plans for each job. The two functions, process planning and scheduling are tightly interwoven with each other. The optimality of scheduling depends on the result of process planning. The integration of process planning and scheduling is therefore important for an efficient utilization of manufacturing resources.In this study, we present real cases taken from manufacturing industry, which were modeled and solved using theoretical tools of scheduling theory. According to this idea, this study was motivated by the design and implementation of a flexible job shop scheduling system for the manufacturing of Teba Oven.s Press Workshop.The manufacturing is characterized by significant machine setup times, strict local capacities, the option of choosing a few alternative processing routes, and long horizon as compared to the time resolution required by the scheduling models. Our goal is thus to obtain near-optimal schedules with quantifiable quality in computationally efficient manner. For achieving this goal, dispatching rules and shifting bottleneck heuristics are used, and solution methodology based on a combined dynamic programming. The methods have been implemented by using the object-oriented generic programming, LEKIN [43], and the outputs show that the methods generate high-quality schedules in a timely fashion to achieve on-time delivery of products and low in work-in-process inventory. Finally, the integrated treatment of machines and buffers facilitates the smooth flow of parts through the system

An Adaptive Simulation-based Decision-Making Framework for Small and Medium sized Enterprises

Abstract The rapid development of key mobile technology supporting the ‘Internet of Things’, such as 3G, Radio Frequency Identification (RFID), and Zigbee etc. and the advanced decision making methods have improved the Decision-Making System (DMS) significantly in the last decade. Advanced wireless technology can provide a real-time data collection to support DMS and the effective decision making techniques based on the real-time data can improve Supply Chain (SC) efficiency. However, it is difficult for Small and Medium sized Enterprises (SMEs) to effectively adopt this technology because of the complexity of technology and methods, and the limited resources of SMEs. Consequently, a suitable DMS which can support effective decision making is required in the operation of SMEs in SCs. This thesis conducts research on developing an adaptive simulation-based DMS for SMEs in the manufacturing sector. This research is to help and support SMEs to improve their competitiveness by reducing costs, and reacting responsively, rapidly and effectively to the demands of customers. An adaptive developed framework is able to answer flexible ‘what-if’ questions by finding, optimising and comparing solutions under the different scenarios for supporting SME-managers to make efficient and effective decisions and more customer-driven enterprises. The proposed framework consists of simulation blocks separated by data filter and convert layers. A simulation block may include cell simulators, optimisation blocks, and databases. A cell simulator is able to provide an initial solution under a special scenario. An optimisation block is able to output a group of optimum solutions based on the initial solution for decision makers. A two-phase optimisation algorithm integrated Conflicted Key Points Optimisation (CKPO) and Dispatching Optimisation Algorithm (DOA) is proposed for the condition of Jm|STsi,b with Lot-Streaming (LS). The feature of the integrated optimisation algorithm is demonstrated using a UK-based manufacture case study. Each simulation block is a relatively independent unit separated by the relevant data layers. Thus SMEs are able to design their simulation blocks according to their requirements and constraints, such as small budgets, limited professional staff, etc. A simulation block can communicate to the relative simulation block by the relevant data filter and convert layers and this constructs a communication and information network to support DMSs of Supply Chains (SCs). Two case studies have been conducted to validate the proposed simulation framework. An SME which produces gifts in a SC is adopted to validate the Make To Stock (MTS) production strategy by a developed stock-driven simulation-based DMS. A schedule-driven simulation-based DMS is implemented for a UK-based manufacturing case study using the Make To Order (MTO) production strategy. The two simulation-based DMSs are able to provide various data to support management decision making depending on different scenarios