Modeling of assembly line balancing problems with uncertain parameters by the aid of grey linear programming

Events and situations occurring in the world may not always be certain. Companies that want to gain an edge over their competitors in the field in which they operate must also shape their work by taking into account the uncertainty in their processes. With the emergence of fields where uncertainty should be digitized, different theories have been put forward. One of these theories is Grey System Theory (GST), which emerged in order to model and analyze complex systems with incomplete information. GST aims to quantify uncertainty situations by means of grey numbers. Grey numbers are numbers that have definite upper and lower limits, but whose exact values are not known. If at least one parameter in the linear programming model has uncertainty expressed with a grey number, the model is called Grey Linear Programming (GLP). GLP is capable of dealing with systems-related problems with poor or incomplete information and is a powerful tool for decision-making problems under uncertainty. Uncertainty situations may be encountered in the parameters of assembly line balancing problems and the related uncertainty may need to be included in the planning. In this thesis, it is aimed to include the uncertainties in the parameters of assembly line balancing problems into the mathematical model and solve them. In this context, uncertain parameters are expressed with grey numbers and the problems are solved with GLP. GLP models have been created and solved with different solution approaches to solve Type-1 and Type-2 problems for Straight and U-shaped assembly lines with grey operation times and/or grey cycle times. The solutions obtained were compared with the solutions in the reference studies, and the superiorities of the solutions found according to the obtained line efficiency values and the satisfaction degree values were stated.