makespan

The time it takes to complete all of the jobs is called Makespan.

This would mean simply an action profile whose makespan is minimum.

But at a critically short makespan, the number of solutions starts to fall abruptly.

This assumes no resource conflicts and gives a makespan of 22.

Now examine all "nearby" schedules and test if any is of an equal or better makespan.

We consider the egalitarian cost function , here called the makespan.

The objective is to determine the schedule that will produce the shortest makespan.

Hence, the shorter the makespan, the higher the profitability of the solution path.

The number of batches to produce is set, and we try to minimize the makespan (processing time).

Does the phase transition occur in typical job shop problems as makespan is tuned from long to short?

The total length of time it takes to construct the set of objects is called the "makespan."

We guess the optimum value of makespan and write the following LP.

Note that this is exactly the same makespan if MN gloves were used.

The makespan is the total length of the schedule (that is, when all the jobs have finished processing).

We want to minimize makespan.

The chromatic number of the graph is exactly the minimum makespan, the optimal time to finish all jobs without conflicts.

The makespan is then given by:

Clearly, the minimum G(M, N) increases the makespan significantly, sometimes by a factor of 3.

Objective function can be to minimize the makespan, the L norm, tardiness, maximum lateness etc.

So schedules with low makespan correspond to points of high eciency on the job-shop fitness landscape.

A second test looks at the "Hausdorf dimensionality" of the acceptable solutions at a given makespan or better.

These new paths can be considered the disjunctive constraints and they need to be taken into consideration when determining the new makespan.

The due date, denoted d, is determined by subtracting the processing times of the jobs preceding the job on the respective machine from the makespan.

Its primary objective is to find an optimal sequence of jobs to reduce makespan (the total amount of time it takes to complete all jobs).

But to keep our mountainous landscape metaphor where high peaks are good, let us consider optimizing eciency by minimizing makespan.