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Technological and industrial advances have resulted in the growth of large enterprises.Optimization models have been developed to increase the efficiency of partsof these systems, but models that optimize entire enterprises are frequently immenseand very complex to solve. Sequential solution techniques have resulted, which leadto useful, but not globally optimal, solutions. For example, airlines develop flightschedules based on strategic business objectives, and sequentially plan operationalprocesses to execute the schedule. Proven models that exist for the operationalsubproblems are solved sequentially, begin with a flight schedule, and allow limitedfeedback in the planning process. Since small changes to the individual parts haveproduced millions of dollars in improvement, an overall optimal solution could yielda significant increase in the airline's profit.We consider a modelling paradigm that moves toward integrated methods for theairline schedule planning phase using surrogate representations of the operationalproblems. In this context, surrogate models are relatively easy to solve, yet suffi-ciently representative of the operational problem to reflect its impact on schedulechoices. To illustrate, we develop surrogate models of maintenance scheduling, crewscheduling, and revenue generation. We solve the master schedule problem with eachsurrogate model using well-known decomposition techniques, and then combine thesurrogates into a single model that is readily decomposed into multiple subproblemsand solved.The model developments include additional considerations in constructing surrogatemodels. For example, to demonstrate validation of a surrogate's utility, wecompared the feasibility indications from the maintenance subproblem surrogate tothose from a larger, exact model of maintenance feasibility. The crew scheduling surrogatemodel development incorporates disruptions in the master schedule, drivingthe schedule to account for both crew costs and the impact of random disruptions.Finally, in the revenue management subproblem, we consider random demand thatimpacts a schedule's profitability.While surrogate solutions are inherently of little utility operationally, the resultsare useful for shaping the master schedule towards a global optimum. The paradigmallows for consideration of the subproblems in initial planning, so that solutionsobtained from the full models are based on a schedule that may lead to a betteroverall bottom line.

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A new method is developed to schedule jobs on parallel machines with availability constraints. The objective of the problem is to minimize the makespan of the total production schedule. Without the availability constraints the scheduling of machines is a Pm || Cmax problem. The scheduling of this problem was the topic of many earlier papers. The main contribution of this research is that the schedule of the jobs on parallel machines with availability constraints is determined within a single implicit enumer- ation algorithm. Within the general enumeration scheme, the loads of each machine are enumerated in a lexicographic order. An exact integer linear programming model is provided, too. The difficulty of the problem depends on the properties of the pro- cessing times, the number of machines, and the number of availability constraints on the machines. In some subclasses, problems with very large number of jobs are solved. The largest problems solved within one hour limit have 1, 000, 000 jobs.

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