Akış tipi ve paralel makineli robotik hücrelerde enerji tasarruflu çizelgeleme

Robotic cell scheduling studies mostly focus on increasing the throughput of the cell. Therefore, those studies consider cycle time or makespan minimization objectives. However, energy-efficient and environmentally sensitive manufacturing operations become more important and receive attention in recent studies in the literature. In this thesis, we study two robotic cell scheduling problems with machines and a material handling robot. We consider robot energy consumption as an objective to minimize while maximizing the throughput. The problem is to find efficient solutions for these objectives. Two scheduling environments are studied: a two machine flow-shop robotic cell and a two parallel machine robotic cell. In a flow-shop scheduling environment, parts are processed on both machines, but in a parallel machine scheduling environment, parts are processed on one of two machines. In both robotic cells, a robot performs all handling and loading-unloading operations. The robot consumes energy during its moves. We assume that the energy consumption can be formulated as a convex nonlinear function of robot's speed. We try to find the optimal robot move sequence and optimal speed of the robot to minimize energy consumption and makespan (or cycle time). We also make other scheduling decisions such as part sequencing and machine-part assignment. For the flow-shop robotic cell scheduling problem we propose a mathematical model that finds efficient solutions for energy consumption and cycle time objectives. We solve the model using mixed-integer second-order conic programming (MISOCP) reformulation. For the parallel machine robotic cell, similarly, we propose a mathematical model and its MISOCP reformulation to find efficient solutions. We also propose alternative neighborhood search algorithms based on Simulated Annealing. For both problems, we test the computational performance of proposed solution approaches and present energy saving achieved by robot speed control strategy.