Test Instances Clause Samples
Test Instances. Test instances used in this paper consists of two sets of single item instances generated by ▇▇▇▇▇▇▇▇ et al. (2005). Both sets contains 96 instances divided into four classes. The first set has T = 6 time periods, and N = 50,75,100,125,150 customers. The second set have T = 30 time periods and N = 50 customers. The different classes have different costs for the given parameters to emulate different situations in real world examples: unitary production cost cp, fixed production cost per period fp, inventory holding cost at customer k hk and fixed transportation cost per period fv. In class 1 these parameters are set to h0 = 3, cp = 10h0, fp = 100cp and fv = 32181. For evaluation of situations where there are no fixed transportation costs specified, class 2 has fixed transportation cost fv = 0. To see what impact on the solution there will be if the production cost is reduced compared to the inventory holding cost at the plant, the instances in class 3 have a unit production cost cp = h0. In the last class the customer inventory holding cost hk, k = 1, ..., N are set to zero. This will emulate situations where the plant does not have to pay for customer inventory holding costs. These instances have been applied by both ▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇▇▇▇ in their papers about the IPDP. ▇▇▇▇▇▇▇▇ used the more constrained VMI-OU and VMI-FFD strategies in his paper, while ▇▇▇▇▇▇▇▇▇▇ used the less constrained VMI-ML strategy to measure the cost reduction this strategy gives over the results reported by ▇▇▇▇▇▇▇▇. As mentioned earlier the tests performed in this thesis uses the VMI-ML strategy which give a god basis for result comparizon against results reported by ▇▇▇▇▇▇▇▇▇▇. Intel Core 2 Duo , 3 Ghz Intel Quad i7 920, 3.66 Ghz MacBook Pro 2.2 Ghz Core 2 Duo
Test Instances. There are 9 test instances that were created to test GT-EDD algorithm. Parameters for the test instances are shown in Table 2. Test instance, # Number of products Probability of a defective operation Demand, range, units Setup time, range, min Processing time, range, min Due date, range, days Buffer capacity, units Buffer time, min 1 30 30% [10, 20] [10, 20] [2, 10] [1, 3] 2 20 2 30 30% [10, 20] [10, 20] [2, 10] [1, 3] 3 20 3 30 30% [10, 20] [10, 20] [2, 10] [1, 3] 5 20 4 40 30% [10, 20] [10, 20] [2, 10] [1, 3] 2 20 5 40 30% [10, 20] [10, 20] [2, 10] [1, 3] 3 20 6 40 30% [10, 20] [10, 20] [2, 10] [1, 3] 5 20 7 50 30% [10, 20] [10, 20] [2, 10] [1, 3] 2 20 8 50 30% [10, 20] [10, 20] [2, 10] [1, 3] 3 20 9 50 30% [10, 20] [10, 20] [2, 10] [1, 2] 5 20 Test instances are divided into 3 groups of 30, 40 and 50 products. Each group consists of 3 instances. These instances differ from each other only by the buffer capacity which takes the values of 2, 3, or 5 units. Probability of a defective operation and a fixed time that any item spends in the buffer denoted in Table 2 as buffer time are held constant for all instances. Demand, setup times, processing times and due dates are randomly generated within the ranges specified in Table 2. Due dates vary between 1 and 3 days in minutes. Notice that buffer time is twice larger than the largest processing time. Moreover, the generated G(g,f,r) function has not a concave staircase structure in
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