Design of Experiment Sample Clauses

Design of Experiment. Executing the Experiment 3. Analysis & feedback

Design of Experiment. This TQAPP will verify the performance of the Formula 5 coating submitted by Evermore Paints and Coatings A mean value and variance (or standard deviation) will be reported for each critical response factor, and a default 95% confidence limit will be applied. This verification test will consist of five runs with two racks of eight panels in a single row per run; therefore, a total of eighty panels will be coated and used for analysis of the coatings performance characteristics. The statistical analyses for all response factors will be carried out using Minitab statistical software.

Design of Experiment. To study this problem, we use the design of experiment methodology [19] to extract the key factors in the process. The design of experiment can also draw out the interactions of two or more factors. To plan a two-level full factorial design of experiment, all the factor have to be determined. All the costs have to be considered: storage cost, and for each supplier purchasing cost and setup cost. The contract parameters are Li, Ui, Qi and Ri for each supplier i. Assuming that the demands are generated by a normal law, the mean µ and the variance σ are also factors of the model. ∞ In this experiment two suppliers are considered, namely A and B. The contract defined by L, U, Q and R is between the supplier A and the retailer. The contract with the supplier B does not constrain the order quantities nor the order frequency, i.e. Q = 0, R = T , L = 0 and U = , thus B’s purchasing cost is greater. Since the demands must be satisfied, and the total costs are minimized, supplier A’s purchasing costs can be discarded. Both suppliers share the same setup costs. The two-level full factorial design of experiment parameter values are summarized in Table 1. parameter level 1 level 2 R maximum number of periods between two orders from supplier A 3 T Q minimum number of periods between two orders from supplier A 0 3 U maximum amount in one order from supplier A L minimum order quantity for the supplier A 0 30 σ demand variance 2 10 µ demand mean 5 20 f setup production cost for both suppliers 2 10 c supplier B unit production cost 0.1 4 h storage cost 0.1 1 Since 9 parameters are studied, 29 parameter combinations have to be performed. For each parameter combination (referred to as sets herein), 25 instances are generated and solved. Solving a single instance takes less than 2 seconds, but considering the number of instances is 12800, the whole experiment takes about 5 hours to compute.