Question 1, Scheduling (points) The "m" parallel machines single stage configuration is shown in figure below. The configuration is made up of "m" identical machines; each one can process any job. Machine 1 M Machine m M.. m 72 Figure 1. Single stage "m" parallel machines Notation: MIP models for scheduling single stage "m" parallel machines Indices Input parameters Data K machine no. k (1, ...m} total number of machines 2 i = job, ie (1, ... n) number of jobs 3 1 = job,j E (1, ...) P processing time of job "" [7.10.12) di due date of job "" [10,15,20] M-large number Decision variables Xi startup time of job "" Yuj binary variable, 1:if job."" precedes job"}" in the processing sequence, 0: otherwise binary variable, 1:if job "" is processed on machine "k",0; otherwise WIK Required Formulate linear programming model for the scheduling problem that minimize total tardiness, and solve it using python Question 1, Scheduling (points) The "m" parallel machines single stage configuration is shown in figure below. The configuration is made up of "m" identical machines; each one can process any job. Machine 1 M Machine m M.. m 72 Figure 1. Single stage "m" parallel machines Notation: MIP models for scheduling single stage "m" parallel machines Indices Input parameters Data K machine no. k (1, ...m} total number of machines 2 i = job, ie (1, ... n) number of jobs 3 1 = job,j E (1, ...) P processing time of job "" [7.10.12) di due date of job "" [10,15,20] M-large number Decision variables Xi startup time of job "" Yuj binary variable, 1:if job."" precedes job"}" in the processing sequence, 0: otherwise binary variable, 1:if job "" is processed on machine "k",0; otherwise WIK Required Formulate linear programming model for the scheduling problem that minimize total tardiness, and solve it using python