Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.

12 V / kilo-Farad Range Lead-Carbon Hybrid Ultracapacitors and Their Envisaged Applications

12 V / kilo-Farad (kF) range substrate-integrated lead-carbon hybrid ultracapacitors (HUCs) wherein the conventional positive plates of lead-acid batteries are replaced with substrate-integrated PbO2 positive plates and the negative plates are replaced with carbon-coated graphitic electrodes, providing totally non-faradaic and corrosion-free electrodes, are developed and performance tested. Constant-current discharge data at varying load-currents, constant-power discharge data at varying power values, and the capacitance data at different temperature for a 12 V / kF range substrate-integrated lead-carbon HUC are described along with its resistance, leakage current, self-discharge and cycle-life characteristics.