Under changing market conditions for the hospitality industry, the Carlson Rezidor Hotel Group (CRHG) collaborated with JDA Software Group to use operations research to drive higher revenue for its hoteliers and to stay ahead of the competition. This highly innovative revenue optimization project, Stay Night Automated Pricing (SNAP), started with enterprise demand forecasting across 600 US hotels in 2007. It was followed by a large-scale network optimization solution to dynamically optimize hotel room rates based on price elasticity of demand, competitor rates, availability of remaining inventory, demand forecasts, and business rules. All North American hotels were operational in SNAP by March 2011. Starting from the optimization prototyping results in 2008, CRHG consistently measured a 2–4 percent revenue improvement in compliant hotels over noncompliant ones. To date, compliant hotels have increased revenue by more than $16 million annually. After a successful deployment in the Americas, CRHG extended the partnership with JDA to globally roll out SNAP, with an initial focus on Europe, the Middle East, Africa, and the Asia Pacific region. CRHG anticipates that the worldwide incremental revenue from this solution will exceed $30 million annually.
This article studies a markdown optimization problem commonly faced by many large retailers that involves joint decisions on inventory allocation and markdown pricing at multiple stores subject to various business rules. At the beginning of the markdown planning horizon, there is a certain amount of inventory of a product at a warehouse that needs to be allocated to many retail stores served by the warehouse over the planning horizon. In the same time, a markdown pricing scheme needs to be determined for each store over the planning horizon. A number of business rules for inventory allocation and markdown prices at the stores must be satisfied. The retailer does not have a complete knowledge about the probability distribution of the demand at a given store in a given time period. The retailer's knowledge about the demand distributions improves over time as new information becomes available. Hence, the retailer employs a rolling horizon approach where the problem is re-solved at the beginning of each period by incorporating the latest demand information. It is shown that the problem involved at the beginning of each period is NP-hard even if the demand functions are deterministic and there is only a single store or a single time period. Thus, attention is focused on heuristic solution approaches. The stochastic demand is modeled using discrete demand scenarios based on the retailer's latest knowledge about the demand distributions. This enables possible demand correlations to be modeled across different time periods. The problem involved at the beginning of each period is formulated as a mixed-integer program with demand scenarios and it is solved using a Lagrangian relaxation -based decomposition approach. The approach is implimented on a rolling horizon basis and it is compared with several commonly used benchmark approaches in practice. An extensive set of computational experiments is perfomed under various practical situations, and it is demonstrated that the proposed approach significantly outperforms the benchmark approaches. A number of managerial insights are derived about the impact of business rules and price sensitivity of individual stores on the total expected revenue and on the optimal inventory allocation and pricing decisions.
Assigning fleets of aircraft to a weekend schedule is more difficult than assigning them to a weekday schedule. The reason is that we must balance two conflicting objectives. We must meet passenger demand that is different from weekday demand. We must also minimize the costs of realigning airport facilities and personnel that we would incur by changing flight patterns too much. To support US Airways' schedule planners in this balancing act, we built a specialized fleet-assignment model and integrated it into a graphical environment for schedule development. The planners use the system to create safe, profitable, and robust flight plans.
The authors describe the implementation of a swapper optimization suite (SOS) for All Nippon Airways. SOS uses optimization models to generate optimal swaps of flight legs among different equipment types for the allotted fleet assignments. All Nippon Airways has integrated SOS into its information systems to incorporate optimization into its decision-making process.
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