The objective of this thesis is to introduce MES systems with their properties and relations to other automation systems. Furthermore production scheduling theory is introduced with applicable mathematical methods. For given scheduling problem is created optimization model and basic serie of simulations is accomplished. The core of an existing MES system is transformed to web non-comercial platform. All necessary changes are listed in order to integrate production scheduling subsystem to the existing MES system.

The Covid-19 crisis has highlighted the vulnerability of access to food and the need for local and circular food supply chains in urban environments. Nowadays, Indoor Vertical Farming has been increased in large cities and started deploying Artificial Intelligence to control vegetations remotely. This thesis aims to monitor and control the vertical farm by scheduling the farming activities by solving a newly proposed Job-shop scheduling problem to enhance food productivity. The Job-shop scheduling problem is one of the best-known optimization problems as the execution of an operation may depend on the completion of another operation running at the same time. This paper presents an efficient method based on genetic algorithms developed to solve the proposed scheduling problem. To efficiently solve the problem, a determination of the assignment of operations to the processors and the order of each operation so that the execution time is minimized. An adaptive penalty function is designed so that the algorithm can search in both feasible and infeasible regions of the solution space. The results show the effectiveness of the proposed algorithm and how it can be applied for monitoring the farm remotely. / <p>The presentation was held in zoom</p>

Railway scheduling problems are quite popular scheduling and optimization problems which are treated in a large variety of papers and projects. Many special and even quite general situations have been investigated theoretically and also a variety of applied approaches tested on real-world instances has been developed.This thesis mainly deals with the problem of scheduling trains in railway networks with respect to given routings, fixed minimal travelling times, and other constraints like time-windows. It combines the theory of some well-known scheduling models with its applications in railway scheduling. The railway scheduling problems considered in this work are closely related to job-shop scheduling problems with blocking and some additional constraints. Therefore part of this research is related to these shop scheduling problems. Theoretical scheduling models are extended, complexity results are derived and solution methods are proposed. Most results are applied to the considered railway scheduling problems. In addition to approaches which treat railway problems as a whole also decomposition methods for these problems and corresponding solution methods are presented. These solution methods are tested and compared with simple greedy procedures.

With the increase in fuel prices, the efficient movement of aircraft around an airport can impact the profitability of a flight and an airline. The assignment of a flight to a specific gate not only impacts passenger satisfaction, but also impacts the efficient movement of aircraft from the departure gate to the runway. There have been bodies of research investigating aircraft taxi problems and gate assignment problems. However, each of these research bodies has not included the effects of the other research areas into their respective areas. This research presents a proposed framework that integrates the passenger or freight movement within a terminal with the taxiing of the aircraft to support an integrated approach to solving the gate assignment problem. A solution technique that incorporates a job shop scheduling solution method is presented and demonstrates that a large problem can be solved efficiently and in a short time using both deterministic and stochastic data.

No / The Job Shop Scheduling Problem (JSSP) is a hard combinatorial optimisation problem. This paper presents a heuristic-based Genetic Algorithm (GA) or Hybrid Genetic Algorithm (HGA) with the aim of overcoming the GA deficiency of fine tuning of solution around the optimum, and to achieve optimal or near optimal solutions for benchmark JSSP. The paper also presents a detail GA parameter analysis (also called sensitivity analysis) for a wide range of benchmark problems from JSSP. The findings from the sensitivity analysis or best possible parameter combination are then used in the proposed HGA for optimal or near optimal solutions. The experimental results of the HGA for several benchmark problems are encouraging and show that HGA has achieved optimal solutions for more than 90% of the benchmark problems considered in this paper. The presented results will provide a reference for selection of GA parameters for heuristic-based GAs for JSSP.

This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques. The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems. In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results. / Board of Trustees, Endowment Fund Project, KPK University of Engineering and Technology (UET), Peshawar and Higher Education Commission (HEC), Pakistan

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Over the last decade, the semiconductor industry has witnessed a steady increase in its complexity based on improvements in manufacturing processes and equipment. Progress in the technology used is no longer the key to success, however. In fact, the semiconductor technology has reached such a high level of complexity that improvements appear at a slow pace. Moreover, the diffusion of technology among competitors shows that traditional approaches based on technological advances and innovations are not sufficient to remain competitive. A recent crisis in the semiconductor field in the summer 2001 made it even clearer that optimizing the operational control of semiconductor wafer fabrication facilities is a vital key to success. Operating research-oriented studies have been carried out to this end for the last 5 years. None of them, however, suggest a comprehensive model and solution to the operational control problem of a semiconductor manufacturing facility. Two main approaches, namely mathematical programming and dispatching rules, have been explored in the literature so far, either partially or entirely dealing with this problem. Adapting the Shifting Bottleneck (SB) procedure is a third approach that has motivated many studies. Most research focuses on optimizing a certain objective function under idealized conditions and thus does not take into consideration system disruptions such as machine breakdown. While many papers address the adaptations of the SB procedure, the problem of re-scheduling jobs dynamically to take disruptions and local disturbances (machines breakdown, maintenance...) into consideration shows interesting perspectives for research. Dealing with local disturbances in a production environment and analyzing their impact on scheduling policies is a complex issue. It becomes even more complex in the semiconductor industry because of the numerous inherent constraints to take into account. The problem that is addressed in this thesis consists of studying dynamic scheduling in a job-shop environment where local disturbances occur. This research focuses on scheduling a large job shop and developing re-scheduling policies when local disturbances occur. The re-scheduling can be applied to the whole production horizon considered in the instance, or applied to a restricted period T that becomes a decision variable of the problem. The length of the restricted horizon T of re-scheduling can influence significantly the overall results. Its impact on the general performance is studied. Future extensions can be made to include constraints that arise in the semiconductors industry, such as the presence of parallel and batching machines, reentrant flows and the lot dedication problem. The theoretical results developed through this research will be applied to data sets to study their efficiency. We hope this methodology will bring useful insights to dealing effectively with local disturbances in production environments. / Master of Science