Optimal job-shop scheduling with random operations and cost objectives

We consider a job-shop manufacturing cell of n jobs (orders), J(i), 1 less than or equal to i less than or equal to n, and m machines M-k, 1 less than or equal to k less than or equal to m. Each job-operation O-il (the lth operation of job i) has a random time duration t(il) with the average value (t) over bar (il) and the variance V-il. Each job J(i) has its due date D-i and the penalty cost C-i* for not delivering the job on time (to be paid once to the customer). An additional penalty C-i** has to be paid for each time unit of delay, i.e., when waiting for the job's delivery after the due date. If job Ji is accomplished before Di it has to be stored until the due date with the expenses C-i*** per time unit. The problem is to determine optimal earliest start times S-i of jobs J(i), 1 less than or equal to i less than or equal to n, in order to minimize the average value of total penalty and storage expenses. Three basic principles are incorporated in the model: 1. At each time moment when several jobs are ready to be served on one and the same machine, a competition among them is introduced. It is based on the newly developed heuristic decision-making rule with cost objectives. 2. A simulation model of manufacturing the job-shop and comprising decision-making for each competitive situation, is developed. 3. Optimization is carried out by applying to the simulation model the coordinate descent search method. The variables to be optimized are the earliest start times Si. A numerical example of a simulation run is presented to clarify the decision-making rule, The optimization model is verified via extensive simulation. (C) 2002 Elsevier Science B.V. All rights reserved.