For the sake of com
testing. Each problem includes 25
e 100 jobs would be processed on the 25 parallel identical machines and each machine capacity is set to be three days, 4320 minutes. Here “minute” is used as the time unit for job processing time, job due dates, setup time, and machine capacity.
In this paper, we highlight the impact of setup time of consecutive jobs from different product families or different operation temperatures. So the time cost by changing
probe card before the machine is ready to process the coming job with different product family is set to 80 or 120 minutes (80 or 120 minutes is according to different product family). The required times of adjusting temperature from room to high is set to be 60 minutes, from high to room is set to be 80 minutes, and from high to high is set to be 140 minutes. Because the time of adjusting temperature from room to room does not need to warm up or cool down the machine, it is set to be 0 minutes.
The time of loading code before the machine is ready to process the coming job with different product type is set to be 5 minutes. And the initial setup time of machine from idle to processing state is set to be 100 minutes. The setup time of consecutive jobs from same product type is set to be 0 minutes under all operation temperatures.
The problem design is based on the wafer probing shop floor in an IC manufacturing factory of the Science-based Industrial Park, Taiwan. The problem test is divided into four factors, which contains (1) the product family ratio, including tw
milies is related to the setup time of ed to evaluate the influence of product families on the performance of scheduling solutions via the factor, product family ratio. If a product fa
o grouping levels R2 and R6, (2) the tightness of due dates, including stable and increasing states, (3) the consideration of adjusting temperature, including setup time with temperature consideration or not, (4) the total processing time, including low and high levels.
Product Family Ratio (R)
The distribution of jobs to the product fa consecutive jobs. We ne
mily has large number of jobs, it may lead to a smaller value of total setup time of scheduling solutions. Oppositely, if a product family has small number of jobs, it may result in a larger value of total setup time of machine schedules. Here we define an index, product family ratio, which is the division of the number of job product types by the number of job product families. There are 100 jobs divided into 30 product types in our test problem. For example, if the value of product family ratio is 2, it means that 30 product types of 100 jobs are distributed into 15 product families randomly. In our design, there are two levels for testing, R2 and R6, which means
30 product types of jobs are divided into 15 and 5 product families individually. The evaluation of product family ratio is expressed in equation (41).
F
Tightness of Due Dates (T_Due)
Here we use tightness of due dates for evaluating the density of job due dates. It me, the expected setup time, the machine capacity before due dates, and the number of jobs with given due dates. The tightness index is
is including the job processing ti
defined as below:
where the number of available machines
K
and the expected setup time are expressed in Section 2 and 3. Due dates of Jobs in the test problem arethree time points, which are 1, 2, and 3 days. is denoted as the total t.
If the tightness of due dates is stable, that means there are 30 jobs assigned for 1440 minutes of due dates, 35 jobs assigned for 2880 minutes of du
ES
divided into )P
(Yprocessing time of jobs of which due dates are given before Yth due day poin We define the notation
Cap
(Y) as the available capacity of machine before Yth due day point. AndNum
(Y) is to express the number of jobs of which due dates are given before Yth due day point.According to equation (42), we can evaluate three tightness indexes under three time points of due dates.
e dates, and 35 jobs assigned for 4320 minutes of due dates randomly. And the tightness values of due dates would be nearly equal. If the tightness of due dates is increasing, that means there are 5 jobs assigned for 1440 minutes of due dates, 15 jobs assigned for 2880 minutes of due dates, and 80 jobs assigned for 4320 minutes of due dates randomly. Besides, the tightness of due date 1440 minutes would be smaller than the tightness of due date 2880 minutes, and the tightness of due date 2880 minutes would be smaller than the tightness of due date 4320 minutes
Temperature Consideration (Te)
Because the setup time of loading temperature is longer than the setup time of peripheral hardware, we take the factor, temperature changing, into consideration in of setup time is related to the product types, product families of two consecutive jobs generally. If temperature change of machine is considered in tes
e of scheduling difficulties, so it is an index for evaluating the performance of scheduling heuristics.
cessing time, high and low, to represent the size of total machine workload. High and low levels of total processing time are set to be 54126 m
our problem design. The value
ting situation, it should be added in setup time. Our problem is designed to consider setup time with temperature changing or not.
Total Processing Time (Total_PT)
The value of total processing time would influence the degre
We generate two levels of total pro
inutes and 66379 minutes, which have 1.5 and 1.85 days of machine utilization individually. Table 2 below shows the summary of 16 testing problems, and other related information suchlike product types, product families, tightness of due dates, and setup time of two consecutive jobs, is shown in the appendix.
Table 2. Summary of 16 problem design