Examples of Incremental Optimization Applications:

Scheduling and Planning Models

Business operations plan the allocation of scarce resources so as to maximize capability or minimize cost. Airlines schedule aircraft and personnel to minimize costs; airports allocate gates, runways and taxi ways to maximize on-time flights; delivery companies create shortest routes for their vehicles; call centers schedule their staff to provide adequate coverage at minimal cost. In all of these examples a resource allocation schedule is created in advance for a fixed time period, ranging from a month to a few days. Typically the plan is regenerated some portion of the way through the planning period. This "rolling horizon" planning process has been a main focus of analytic computing for decades. Now, as additional computing power, real-time data, and the ability to instantly communicate changes are becoming widely available, we are seeing increasing interest in the real-time component of scheduling and planning. Real-time planning presents new challenges, both in creating the underlying models and in providing implementations that can be executed in the allowed time. Incremental methods, which update plans rather than re-compute from scratch, are likely to be used for most of the "plan revisions" with full updates done when the solution, or the input data, has significantly deviated from the base case. A near-term application of real-time planning involves recovering, during operation, from disruptions to the plan. Uncertainties in demand prediction as well as unexpected events necessitate schedule recovery actions. Weather disruptions or mechanical problems require airlines to reschedule personnel and aircraft; emergencies may require reallocation of service personnel. Recovery plans must be generated and deployed very quickly. Users cannot wait for several days of processing as is common with planning models. Traditional planning models and algorithms must be dramatically modified for use in operational recovery systems.

Service Operations
Since more than 60% of the US economy is in the service industry, applying optimization techniques to this industry has the potential for great benefits. One obvious application involves tools for service personnel optimization. In addition to cost reduction, the use of optimization techniques can result in a competitive edge since it would allow the corporation to be more aggressive in setting service level agreements, pricing service contracts, planning and acquiring resources, and in increasing customer loyalty. As an example, consider the hardware maintenance services business. A variety of customer contracts are sold, with price determined by the type of hardware being serviced and the contracted response time. A variety of alternate pricing structures have been proposed, but currently minimal analytic support for pricing is available. Much of the data to support contract pricing models (failure rates, technician cost, inventory holding cost) is available, but analytic models that estimate the marginal cost of new contracts, or of new terms in existing contracts, are missing. However, at least part of the dynamic resource allocation problem has been addressed by analytic models that support the scheduling of service technicians. These models include several cost criteria, including customer service levels and cost. Work is under way to provide robust capacity planning models that determine the location of parts facilities and the inventory levels for parts within these facilities. If the run time of these planning models can be reduced from days to hours or minutes, then the increased inventory costs of a proposed contract can be evaluated as part of the pricing process. Similarly, coupled with a failure simulation model, the service technician scheduling tool can be used to estimate increases in service technician costs, or the requirements for additional skilled technicians, associated with a new contract.