Balancing and Sequencing of Mixed Model Assembly Lines

Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this dissertation, a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers is proposed. Upon observing the limitation of the IP, a Constraint Programming (CP) model that is based on CPLEX CP Optimizer is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems. A sensitivity analysis is done to assess the cost of enforcing some of the constraint on the computation complexity and the amount of violations to these constraints once they are disabled. Results show that some of the constraints are helpful in reducing the computation time. Specifically, the assignment constraints in which decision variables are fixed or bounded result in a smaller search space. Finally, since the line balance for mixed model is based on task duration averages, we propose a mixed model sequencing model that minimize the number of overload situation that might occur due to variability in tasks times by providing an optimal production sequence. We consider the skip-policy to manage overload situations and allow interactions between stations via workers swimming. An IP model formulation is proposed and a GRASP solution heuristic is developed to solve the problem. Data from the literature are used to assess the performance of the developed heuristic and to show the benefit of swimming in reducing work overload situations

Design and construction of a small gas turbine to drive a permanent magnet high speed generator

Radial gas turbines engines have established prominence in the field of small turbomachinery because of their simplicity, relatively high performance and installation features. Thus they have been used in a variety of applications such as generator sets, small auxiliary power units (APu), air conditioning of aircraft cabins and hybrid electric vehicles turbines. The current research describes the design, manufacturing, construction and testing a radial type small gas turbine. The aim was to design and build the engine to drive directly a high-speed permanent magnet alternator running at 60000 rpmand developing a maximum of 60 W. This direct coupling arrangement produces a portable, light, compact, reliable and environment friendly power generator. These features make the generator set very attractive to use in many applications including emergency power generation for hospitals, in areas of natural disasters such as floods and earthquakes, in remote areas that cannot be served from the national grid, oil rigs, and in confined places of limited spaces. It is important to recognize that the design of the main components, that is, the inward flow radial UFR turbines, the centrifugal compressor and the combustion chamber involve consideration of aero-dynamics, thermodynamics, fluid mechanics, stress analysis, vibration analysis, selection of bearings, selection of suitable materials and the requirements for manufacturing. These considerations are all inter-linked and a procedure has been followed to reach an optimum design. This research was divided into three phases: phase I dealt with the complete design of the inward radial turbine, the centrifugal compressor, the power transmission shaft, the selection of combustion chamber and the bearing housing including the selection of bearings. Phase 2 dealt with mechanical consideration of the rotating components that is stress, thermal and vibration analyses of the turbine rotor, the impeller and the rotating shaft, respectively. Also it dealt with the selection of a suitable fuel and oil lubrication systems and a suitable starting system. Phase 3 dealt with the manufacturing of the gas turbine components, balancing the rotating components, assembling the engine and finally commissioning and then testing the engine. The current work in this thesis has put the light on a new design methodology on determining the optimum principal dimensions of the rotor and the impeller. This method, also, has defined the optimum number of blades and the axial length of the rotor and the impeller. Mathematical models linking the performance parameters and the design variables for the turbine and the compressor have been developed to assist in carrying out parametric studies to study the influence of the design parameters on the performance and on each other. Also, a new graphical matching procedure has been developed for the gas turbine components. This technique can serve as a valuable tool to determine the operating range and the engine running line. Furthermore, it would decide whether the gas turbine engine operates in a region of satisfactory compressor and turbine efficiencie

Meta-Heuristics for Dynamic Lot Sizing: a review and comparison of solution approaches

Proofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search, genetic algorithms and simulated annealing, have become popular and efficient tools for solving hard combinational optimization problems. We review the various meta-heuristics that have been specifically developed to solve lot sizing problems, discussing their main components such as representation, evaluation neighborhood definition and genetic operators. Further, we briefly review other solution approaches, such as dynamic programming, cutting planes, Dantzig-Wolfe decomposition, Lagrange relaxation and dedicated heuristics. This allows us to compare these techniques. Understanding their respective advantages and disadvantages gives insight into how we can integrate elements from several solution approaches into more powerful hybrid algorithms. Finally, we discuss general guidelines for computational experiments and illustrate these with several examples