The vast majority of researches in the scheduling context focused on finding optimal or near-optimal predictive schedules under different scheduling problem characteristics. In the construction industry, predictive schedules are often produced in advance in order to direct construction operations and to support other planning activities. However, construction projects operate in dynamic environments subject to various real-time events, which usually disrupt the predictive optimal schedules, leading to schedules neither feasible nor optimal. Accordingly, the development of a dynamic scheduling model which can accommodate these real-time events would be of great importance for the successful implementation of construction scheduling systems. This research sought to develop a dynamic scheduling based solution which can be practically used for real time analysis and scheduling of construction projects, in addition to resources optimization for construction enterprises. The literature reviews for scheduling, dynamic scheduling, and optimization showed that despite the numerous researches presented and application performed in the dynamic scheduling field within manufacturing and other industries, there was dearth in dynamic scheduling literature in relation to the construction industry. The research followed two main interacting research paths, a path related to the development of the practical solution, and another path related to the core model development. The aim of the first path (or the proposed practical solution path) was to develop a computer-based dynamic scheduling framework which can be used in practical applications within the construction industry. Following the scheduling literature review, the construction project management community s opinions about the problem under study and the user requirements for the proposed solution were collected from 364 construction project management practitioners from 52 countries via a questionnaire survey and were used to form the basis for the functional specifications of a dynamic scheduling framework. The framework was in the form of a software tool fully integrated with current planning/scheduling practices with all core modelling which can support the integration of the dynamic scheduling processes to the current planning/scheduling process with minimal experience requirement from users about optimization. The second research path, or the dynamic scheduling core model development path, started with the development of a mathematical model based on the scheduling models in literature, with several extensions according to the practical considerations related to the construction industry, as investigated in the questionnaire survey. Scheduling problems are complex from operational research perspective; so, for the proposed solution to be functional in optimizing construction schedules, an optimization algorithm was developed to suit the problem's characteristics and to be used as part of the dynamic scheduling model's core. The developed algorithm contained few contributions to the scheduling context (such as schedule justification heuristics, and rectification to schedule generation schemes), as well as suggested modifications to the formulation and process of the adopted optimization technique (particle swarm optimization) leading to considerable improvement to this techniques outputs with respect to schedules quality. After the completion of the model development path, the first research path was concluded by combining the gathered solution's functional specifications and the developed dynamic scheduling model into a software tool, which was developed to verify & validate the proposed model s functionalities and the overall solution s practicality and scalability. The verification process started with an extensive testing of the model s static functionality using several well recognized scheduling problem sets available in literature, and the results showed that the developed algorithm can be ranked as one of the best state-of-the-art algorithms for solving resource-constrained project scheduling problems. To verify the software tool and the dynamic features of the developed model (or the formulation of data transfers from one optimization stage to the next), a case study was implemented on a construction entity in the Arabian Gulf area, having a mega project under construction, with all aspects to resemble an enterprise structure. The case study results showed that the proposed solution reasonably performed under large scale practical application (where all optimization targets were met in reasonable time) for all designed schedule preparation processes (baseline, progress updates, look-ahead schedules, and what-if schedules). Finally, to confirm and validate the effectiveness and practicality of the proposed solution, the solution's framework and the verification results were presented to field experts, and their opinions were collected through validation forms. The feedbacks received were very positive, where field experts/practitioners confirmed that the proposed solution achieved the main functionalities as designed in the solution s framework, and performed efficiently under the complexity of the applied case study.

Thesis (Ph. D.)--Universität, Eichstätt-Ingolstadt, 2004. / Includes bibliographical references (p. [217]-227).

Flexibility describes the system ability to cope with events that may cause imbalance between electricity supply and demand while maintaining the system reliability in a cost-effective manner. Flexibility has always been present in the power system to cater for unplanned generator outages and demand uncertainty and variability. The arrival of wind generation with its variable and hard to predict nature increases the overall needs for system flexibility. This thesis provides a systematic approach for investigating the role of flexibility in different power system activities including generation scheduling, generation planning and market operation, and furthermore proposes two 'offline' indices for flexibility evaluation. Using the tools and metrics presented in this thesis, it is possible to perform the following tasks: • Conduct generation scheduling simulation to evaluate the impacts of wind on the flexibility requirement of power systems; • Use the unit construction and commitment algorithm to 1) estimate the maximum allowable wind capacity for an existing system; 2) find the optimal investment of new flexible units for accommodating more wind generation; and 3) decide an optimal generation mix for integrating a given wind penetration; • Use the market model to reveal the value and profitability of flexibility and evaluate the corresponding effects of alternative market design; • Use the two proposed flexibility indices to quantitatively assess the flexibility of individual generators and power systems without undertaking complex and time consuming simulations.

This thesis concentrates on implementing an efficient scheduling and planning system for a Third-Party Logistics Company as a 3PL has more restrictions and parameters on how their work gets carried out on daily basis. The problems associated with the case company is that the company has poor scheduling and planning system which is leading to decrease in productivity and increase in backlog of work. Due to lag in information flow between the stakeholders, planner is unable to predict the different situation. In this technological era, it is important to enhance planning and scheduling which directly reflects on productivity. On understanding the present problem based on interview with the traffic department, questionnaires to the shopfloor employees and observations made, authors selected particular framework by referring different journal articles which led to find the solution for the problem. This report gives the solution for enhanced scheduling system by integration of digitalization. Digitalization helps in reaching the updated information faster which is required for planning and scheduling process. An efficient scheduling and Planning system always help in a smooth functioning of any production facility. It keeps the work to be done on track and helps the employee in finishing the task for the day in the best possible way. This thesis is carried out and conclusion is achieved by solving the existing problem for the case company. This system may further enhance by incorporating RFID system which updates the information faster with more data required for planning and scheduling system.

Die Bachelorarbeit behandelt die Themen der Selbstorganisation in Produktionssystemen im Kontext von Industrie 4.0. Dabei wird gezeigt, wie man mithilfe von einer Ameisenkolonieoptimierung die Reihenfolgeplanung organisieren kann.:Abbildungsverzeichnis Tabellenverzeichnis Formelverzeichnis 1. Einleitung 1.1. Motivation 1.2. Ziele 1.3. Vorgehensweise 2. Sich selbst organisierende Produktionen 2.1. Begriffserklärung 2.2. Stand der Technik 2.3. Reihenfolgeplanung als ein Problem der Selbstorganisation 2.3.1. Begriffserklärung 2.3.2. Stand der Technik 2.3.3. Umsetzung in einer Selbstorganisation 3. Ameisenkolonieoptimierung 3.1. Begriffserklärung 3.2. Allgemeine Umsetzung 3.3. Konkrete Umsetzungen 3.4. Vor- und Nachteile 3.5. Anwendungsbeispiele 4. Entwicklung einer Ameisenkolonieoptimierung für ein sich selbst organisierendes Produktionssystem 4.1. Analyse des gegebenen sich selbst organisierenden Produktionssystems 4.1.1. Grobanalyse des Systems 4.1.2. Feinanalyse der bisherigen Reihenfolgeplanung 4.2. Entwurf der Reihenfolgeplanung durch Prinzipien der Ameisenkolonieoptimierung 4.3. Implementierung der Prinzipien der Ameisenkolonieoptimierung 5. Empirische Untersuchung der implementierten Ameisenkolonieoptimierung 5.1. Beschreibung der gegebenen Produktionsdaten 5.2. Szenarienuntersuchung zur Funktionsfähigkeit 5.2.1. Schichtwechselszenario 5.2.2. Abnutzungs- und Wartungsszenario 5.2.3. Vergleichsszenario 5.3. Untersuchung hinsichtlich der Laufzeit und des Speicherbedarfs 5.3.1. Laufzeit 5.3.2. Speicherbedarf 6. Zusammenfassung und Ausblick 6.1. Zusammenfassung 6.2. Ausblick Quellenverzeichnis / The bachelor thesis is about self organization in production systems in the context of Industry 4.0. Its about ant colony optimization for scheduling in the production planning.:Abbildungsverzeichnis Tabellenverzeichnis Formelverzeichnis 1. Einleitung 1.1. Motivation 1.2. Ziele 1.3. Vorgehensweise 2. Sich selbst organisierende Produktionen 2.1. Begriffserklärung 2.2. Stand der Technik 2.3. Reihenfolgeplanung als ein Problem der Selbstorganisation 2.3.1. Begriffserklärung 2.3.2. Stand der Technik 2.3.3. Umsetzung in einer Selbstorganisation 3. Ameisenkolonieoptimierung 3.1. Begriffserklärung 3.2. Allgemeine Umsetzung 3.3. Konkrete Umsetzungen 3.4. Vor- und Nachteile 3.5. Anwendungsbeispiele 4. Entwicklung einer Ameisenkolonieoptimierung für ein sich selbst organisierendes Produktionssystem 4.1. Analyse des gegebenen sich selbst organisierenden Produktionssystems 4.1.1. Grobanalyse des Systems 4.1.2. Feinanalyse der bisherigen Reihenfolgeplanung 4.2. Entwurf der Reihenfolgeplanung durch Prinzipien der Ameisenkolonieoptimierung 4.3. Implementierung der Prinzipien der Ameisenkolonieoptimierung 5. Empirische Untersuchung der implementierten Ameisenkolonieoptimierung 5.1. Beschreibung der gegebenen Produktionsdaten 5.2. Szenarienuntersuchung zur Funktionsfähigkeit 5.2.1. Schichtwechselszenario 5.2.2. Abnutzungs- und Wartungsszenario 5.2.3. Vergleichsszenario 5.3. Untersuchung hinsichtlich der Laufzeit und des Speicherbedarfs 5.3.1. Laufzeit 5.3.2. Speicherbedarf 6. Zusammenfassung und Ausblick 6.1. Zusammenfassung 6.2. Ausblick Quellenverzeichnis