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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The purpose of this study is to investigate and gain a better understanding of the consumer's point of view of visual sustainability communication with a primary focus on eco-labels on food products. The study will further look into if an environmentally conscious consumer has the ability to make sustainable choices with the guidance of food packaging information. The aim of this study was to answer the question through a questionnaire survey and in further in-depth interviews with comparative visual analysis material of food packaging: Does an environmentally conscious consumer have the ability to make sustainable choices with the support of food packaging information? Based on data collected from the survey and the interviews there are many pitfalls for the consumer when choosing sustainable products because of a number of heuristics that are taken into account. Price and brand still play a big role in purchasing decisions. However, this study showed that there is a desire to act more sustainably. It also showed that there is a need for a new perspective of sustainability and how it is presented on food products. Eco-labels are of great importance, but as for the conclusion: the consumer needs more information to know whether sustainability claims are true or not.

Workforce scheduling is widely used within most industries. A well-outlined and efficient schedule gives cost savings, such as reduced number of overtime hours, increases overall utilization, and facilitates meeting demands. A large and complex schedule, for example, scheduling of a health care workforce, needs to consider many parameters when constructed; it is essential to account for all critical constraints regarding who can dispense a particular medicine, laws restricting the health care system, etcetera. This thesis evaluates two different methods for implementing a workforce scheduling system for one of Linköping’s most well-known restaurants and bars for students, using mixed integer programming and heuristics. Flamman Pub & Disco recruits new employees prior to every semester. Usually, the workforce consists of around 100 employees, and the vast majority of them work either in the bar or in the kitchen. Historically, the scheduling process has been handled manually using Excel. This does, however, take up much time for the operations manager, something considered frowned upon. Therefore, this thesis suggests an automated scheme for future scheduling processes. Because Flamman is a student organization, they do not hold the capital to invest in expensive licensed optimization software. However, literature studies have shown that heuristics such as large neighborhood search can generate sufficient performance, and therefore the investigation of free-of-charge software using a heuristic approach is conducted. The constructed framework uses a mixed integer programming model, which also lays the cornerstone for the two heuristics: a reverse constructive heuristic and a large neighborhood search. The results retrieved from the analysis prove that a heuristic can be a helpful tool for upcoming recruitment periods. There are, however, recommended areas for improvement regarding the current state of the heuristic.

For larger instances of multi-objective optimisation problems, the exact Pareto frontier can be both difficult and time-consuming to calculate. There is a wide range of methods to find feasible solutions to such problems, but techniques for finding good optimistic bounds to compare the feasible solutions with are missing. In this study, we investigate the use of Lagrangian relaxation to create optimistic bounds to bi-objective optimisation problems with complicating side constraints. The aim is to develop an effective method to produce optimistic bounds that are closer to the Pareto frontier than the commonly used linear programming bounds.  In order to use Lagrangian relaxation on the bi-objective problem, the objectives are combined using the weighted sum method. A Lagrangian dual function is then constructed by relaxing the complicating constraints and the subgradient method is used to optimise the dual problem in order to find an optimistic solution. By solving the single-objective problem for multiple weights, an optimistic bound to the Pareto frontier can be constructed. The subgradient method also includes a heuristic to find feasible solutions. The feasible solutions found by the heuristic form a pessimistic bound to the frontier. The method has been implemented and tested on several instances of a capacitated facility location problem with cost and CO2 emission as objectives. The results indicate that, by using Lagrangian relaxation, an optimistic bound close to the Pareto frontier can be found in a relatively short time. The heuristic used also manages to produce good pessimistic bounds, and hence the Pareto frontier can be tightly enclosed. The optimistic bounds found by Lagrangian relaxation are better and more consistent along the Pareto frontier than the bounds found by linear programming.