YIELD MANAGEMENT CHAPTER 1

Transcription

1 CHAPTER 1 YIELD MANAGEMENT Yield Management (YM) is a term used in replication with revenue management, the purpose of which is to maximize revenue. The revenue can be maximized simply by increasing the price, but if the prices are more, it will be very hard to sell anything in this competitive world. Therefore the concept of yield management has come into force. This chapter takes an insight into this concept. This chapter is broadly divided into three parts which are yield management, optimization and simulation. Yield management came into inception in 1978 via American Airlines. Since its inception it has grown its stature by leaps and bounds. YM is to sell inventory to the right person at right time for the right price. In the present chapter, various aspects of YM such as its process, system, applications, levels, advantages and risks are discussed. YM is based on the conditions fixed capacity, perishable inventory and price discrimination. Due to these conditions YM becomes a perfect candidate for optimization. For solving optimization problems a number of techniques are available such as linear programming, non-linear programming, integer programming which comes under the banner of mathematical optimization. Another category for solving optimization problems is heuristic which comprises of methods such as hill climbing, memetic algorithms, genetic algorithms, tabu search, simulated annealing etc. The second category which is also known as evolutionary algorithms is more useful when there is no specific model available for solution, which is the case with yield management. For implementing these algorithms simulation is required. Simulation can act as a very good way to implement these algorithms for solving the problem of optimization, specifically yield management in present case. Yield Management Page 1

2 1.1 Introduction Yield management is the concept of identifying various strategies for optimizing yield in various capacity-constrained services. The basic objective of yield management is to provide the right service to the right customer at the right time for the right price. The concept of yield management basically involves four things (i) service (ii) customer (iii) time and (iv) price. The term service may be defined as according to a particular service, when and to whom it is to be delivered, and how it is to be delivered and whether the service should be reserved or not. Time can be defined in terms of both service delivery, and customer desire for the service i.e. at what time customer wants a service and at what time the service is provided to the customer. Price is defined in terms of timing of service, service type, reservation timing and some other rules followed by a particular service. The customer can be defined in terms of demand characteristics related to price, time and service. The term "Yield Management" is somewhat misleading since revenue rather than yield should be maximized. Therefore yield management is generally referred to as revenue management. 1.2 Meaning of Yield Management A number of definitions about yield (revenue) management are available in the literature. Some of them are as under: Jauncey et al. (1995) have come up with a definition through an analysis of literature as: An integrated, continuous and systematic approach to maximizing room revenue through the manipulation of room rates in response to forecasted patterns of demand. Anderson A. (1997) defines yield management in terms of inventory, pricing and sales. The trade-off between pricing and capacity management is also taken into account in this definition. The definition is: Yield Management is an approach to maximizing profit by carefully monitoring and managing pricing, inventory availability and sales. It means managing the trade-off between filling all available capacity and charging the highest unit price, and ensuring that those customers most willing to pay for a product or service can do so. Cross (1997) defines revenue management as: Revenue Management is the art and science of predicting real-time customer demand at the micro market and optimizing the price and availability of products. One of the most popular definitions of yield management is given by Ingold A. et.al. (2000) as: Yield Management Page 2

3 Yield Management is a method which can help a firm to sell the right inventory unit to the right type customer, at the right time and for the right price. Further, YM is often associated with the definition from Kimes (2000): The application of information systems and pricing strategies to allocate the right capacity to the right customer at the right place at the right time. YM is therefore related to how an organisation applies different tools, e.g. computer systems, in order to control the price so that it will be correct according to each customer and that the room sale takes place at the right time, in the correct way, and at the right place. Netessine and Shumsky in 2002 defined yield management in terms of consumer behaviour as: Yield management is the process of understanding, anticipating and reacting to consumer behaviour in order to maximize revenue or profits. Talluri (2004) defined revenue management as: Revenue Management is concerned with such demand-management decision and the Revenue Management methodology and systems required to make them. It involves managing the firm s interface with the market as it were with the objectives of increasing revenues. Yield Management is defined in terms of optimality by Lai and Ng (2005) as: Revenue management is used to find optimal inventory allocation and scheduling strategies as well as price setting for perishable assets so as to maximize revenue within the planning. According to Mauri, A.G. (2007) yield management may be defined as: Yield management is the process of understanding, anticipating and influencing consumer behaviour to maximize yield or profits from a fixed, perishable resource. 1.3 Background of Yield Management The airlines are credited for developing the foundational science behind revenue management. Almost since the beginning of commercial flight, airlines had attempted to maximize their revenues by focusing on filling as many seats as possible on every flight. According to Trevor Stuart Hill, the idea of yield management originated from airline companies in the United States. These were the first ones to apply this strategy. Before 1978 the airline industry in the United States did not have much freedom, in fact the fares and the schedules were controlled by the Civil Aeronautics Board (CAB). During this period flying was a luxury and fares were very high. In 1978 the whole airline industry was shocked and everything changed, when the American Congress passed the Airline Deregulation Act. This meant company were free to decide their fares, and their domestic routes. This was a huge change from a totally restricted industry to complete freedom. The main reason this Act was Yield Management Page 3

4 passed, was to encourage new entrants into the business. One of these newcomers was PeopleExpress. It was a small company with extremely low prices, 70% below the bigger airlines. It came as a shock for major airlines, like American Airlines, because they just simply could not compete with such low prices. They had to cover their costs, and this meant low fares were not an option. This was a very hard time, and something needed to be done. If they were to lower their fares they were going to go bankrupt, but if they kept the higher fares they would lose their passengers. It seemed like there was no solution to this problem, and American Airlines will fail to compete in this new deregulated environment. But Robert Crandall, former CEO of American Airlines, took it as a challenge and thought it otherwise. In 1985 American Airlines announced its Ultimate Super Saver Fares. American Airlines introduced low fares, just like PeoleExpress, or in some cases even lower. There were only two differences: If a passenger wanted to purchase an Ultimate Super Saver fare he needed to book at least two weeks prior to departure, and stay at his destination over a Saturday night. The number of seats that could be sold for discount price was restricted. In this way American Airlines could save seats for full fare customers who book just days before departure. As per Marta Treszl, with these two changes American Airlines segmented the market between leisure and business travelers. Both segments preferred the major airline s better service, so eventually PeoleExpress was on the edge of bankruptcy. This is considered to be the birth of yield management. In recent time, more and more airline companies started applying it and later even other industries too, like hotels and car rental companies. That s why today the name revenue management is preferred; it is a more general naming. Yield management is generally used by airline companies. 1.4 Pre-Requisite for Yield Management: The big question is when yield management should be applied. Are there any conditions under which it can be applied? The answer lies in the following three basic conditions: There is a fixed amount of resources available for sale. The resources sold are perishable. Different customers are willing to pay a different price for using the same amount of resources. If the resources available are not fixed or not perishable, the problem becomes a simple inventory or production management. If all customers would pay the same price for using the Yield Management Page 4

5 same amount of resources, the challenge would only be limited to selling as quickly as possible as there might be some cost for holding inventory. That s why the above three conditions are necessary for applying yield management to any business/industry. 1.5 Reasons to use yield management systems Revenue Management Systems Inc. defines the following reasons to use a Yield Management System: "Accurately assess future consumer behaviour under dynamically changing market conditions." "Determine the most effective way to price and allocate inventory to reach every future consumer, each and every day, making real-time adjustments as market conditions change, with the consumer in real-time." "Serve as a decision-support resource for marketing and operational functions, including but not limited to: pricing, scheduling, product development, advertising, sales, distribution, human resource utilization and capacity planning." 1.6 Applications of Yield Management: Yield management can be applied in a number of industries, although it is more popular in airlines industry and hotel industry. Let us now consider some of the major applications of yield management as provided by Wikipedia: Airlines: In an airline flight capacity is obviously fixed. Also when the aircraft departs, the unsold seats cannot generate any revenue and thus can be termed as perished. Airlines now-a-days use specialized software to monitor how seats are reserved and react accordingly. For example, airlines can offer discounts on lowdemand flights and selling more-expensive seats when there is excess demand. Actually, it the airlines industry, which is being most benefitted with the yield management. Hotels: Hotels also use this system in more or less the same way i.e. to calculate the rates, rooms and restrictions on sales in order to best optimize their return. These systems measure constrained and unconstrained demand e.g. length of stay, nonrefundable rate, or close to arrival, seasonal changes, group booking etc. Rental: In the rental car industry, yield management deals with the sale of optional insurance, damage waivers and vehicle upgrades. It accounts for a major portion of the rental company's profitability, and is monitored on a daily basis. According to Yield Management Page 5

6 Peter Lucy (2005), in the equipment rental industry, yield management is a method to manage rental rates against capacity and demand. Sea-Cargo: In the sea-cargo the basic concept remain the same as in airlines i.e. what should be the prices for the seats so that no seats remain unsold and what pricing is to be done at what time. Therefore some strategy is to be found to optimize the revenue. Telecommunications: According to Kaul (2009) and Smyck (2011), generally communication service providers utilize an average of just 35 to 40% of available network capacity. Now-a-days a number of telecommunications software vendors have promoted yield management as a strategy for communications service providers to generate additional revenue and reduce capital expenditures by maximizing subscriber use of available network bandwidth. Stadiums: Various stadiums can also follow yield management as when a match is going on only then tickets can be sold. When the match is over no more tickets can be sold and hence the tickets are perishable in nature. Therefore some strategy is to be followed so that more and more tickets can be sold so as to maximize revenue. Grid Computing: Grid computing and other various types of computing also involves a number of application of yield management. Yield management can be used to determine the pricing of reservations of grids in order to increase profit [Sulistio, A. et.al. (2007)]. Using YM it can also be ensured that resources are allocated to the applications that are highly valued by the customers. Cloud Computing: Cloud Computing is a promising approach with a high impact on business models. One aspect of business models is clearly the revenue model, which defines how prices should be set to achieve predefined revenue level. The decision about accepting or denying requests has a high impact on the revenue of the provider. Therefore YM can play a big role in pricing within cloud computing.[puschel, Tim et.al.] 1.7 Yield Management System The industries that are engaged in yield management generally use computerized yield management systems to do so. The Internet is the major source for this process. Firms that use yield management review transactions time-to-time for goods or services already supplied and for goods or services to be supplied in the future. Sometimes firms also review information about events such as holidays, competitive information (including prices), seasonal patterns, and other major factors that affect sales. The yield management attempts to Yield Management Page 6

7 forecast total demand for all products/services they provide, and attempts to optimize the firm's outputs to maximize revenue. The optimization attempts to answer the question: "Given our operating constraints, what is the best mix of products and/or services to produce and sell in the period, and at what prices, to generate the highest expected revenue?" Optimization can help to adjust prices and to allocate capacity among market segments to maximize expected revenues. This can be done at different levels of detail: (i) by goods (such as a seat on a flight or a seat in a stadium) (ii) by group of goods (such as the entire hotel or all the seats on a flight) (iii) by market (such as sales for a flight or a hotel) (iv) overall (on all the routes an airline flies, or all the seats in a stadium) Yield management is particularly suitable when selling perishable products, i.e. goods that become unsellable at a point in time (for example air tickets just after a flight takes off). With an advance forecast of demand and pricing flexibility, buyers will self-sort based on their price sensitivity, demand sensitivity, and time of purchase. In this way, yield management's overall aim is to provide an optimal mix of goods at a variety of price points at different points in time or for different baskets of features. Good yield management maximizes revenue production for the same number of units, by taking advantage of the forecast of high demand/low demand periods, effectively shifting demand from high demand periods to low demand periods and by charging a premium for late bookings. While yield management systems tend to generate higher revenues, the revenue streams tends to arrive later in the booking horizon as more capacity is held for late sale at premium prices. Firms faced with lack of profit sometimes turn to yield management as a last resort. After a year or two using yield management, many of them are surprised to discover they have actually lowered prices for the majority of their products. That is, they offer far higher discounts more frequently for off-peak times, while raising prices only marginally for peak times, resulting in higher revenue overall. By doing this, they have actually increased quantity demanded by selectively introducing many more price points, as they learn about and react to the diversity of interests and purchase drivers of their customers. Yield Management Page 7

8 1.8 Yield Management Process Although there are no specific guidelines for a yield management process, following steps can be considered: Data Collection: The Yield Management process begins with data collection. A system must collect and store historical data for inventory, prices, demand, and other causal factors. Any data that reflects the details of products offered, their prices, competition, and customer behaviour must be collected, stored, and analysed. For example, in the case with the hotel sector where key operating indicators are monitored, such as Occupancy Rate, Average Daily Rate and Revenue per Available Room. Information about customer behaviour is a valuable asset that can reveal consumer behavioural patterns, the impact of competitors actions, and other important market information. This information is crucial to start the Yield Management process according to Cross (1997). Segmentation: After collecting the relevant data, market segmentation is the key to market-based pricing and revenue maximization. Success depends on the ability to segment customers into similar groups based on a calculation of price responsiveness of customers to certain products based upon the circumstances of time and place. Forecasting: Yield Management requires forecasting various elements such as demand, inventory availability, market share, and total market. Its performance depends critically on the quality of these forecasts. Forecasting is a critical task of Revenue Management and takes much time to develop, maintain, and implement. Quantity-based forecasts, which may use time-series models, booking curves, cancellation curves, etc., project future quantities of demand, such as reservations or products bought. Price-based forecasts seek to forecast demand as a function of marketing variables, such as price or promotion. According to Mcgill and Ryzin (1999), by combining these forecasts with calculated price sensitivities and price ratios, a Revenue Management System can then quantify these benefits and develop price optimization strategies to maximize revenue. Optimization: While forecasting suggests what customers are likely to do, optimization suggests how a firm should respond. Often considered the pinnacle of the Revenue Management process, optimization is about evaluating multiple options on how to sell your product and to whom to sell your product. According to Cross (1997), optimization involves solving two important problems in order to achieve the Yield Management Page 8

9 highest possible revenue. The first is determining which objective function to optimize. Secondly, the business must decide which optimization technique to utilize. Dynamic Re-evaluation: Revenue Management requires that a firm must continually re-evaluate their prices, products, and processes in order to maximize revenue. In a dynamic market, an effective Revenue Management System constantly re-evaluates the variables involved in order to move dynamically with the market. The process is explain through the fig.1.1 given by Talluri(2000) Fig.1.1 Revenue Management Process Flow [Talluri, K.T. (2000)] Yield Management Page 9

10 1.9 Myths about Yield Management What yield management offers is a systematic approach to reach the objective. Through that systemization, industry discovers that they can deliver their product differently, and more profitably. The Yield management generally include: Setting the most effective pricing structure; Limiting the number of reservations accepted for any given night or room type, based on the expected incremental profitability of a reservation; Reviewing reservation activity to determine whether any inventory-control actions should be taken (e.g., controlling the availability of discounted rates); Negotiating volume discounts with wholesalers and groups; Providing customers with the "right" product (e.g., room type, rate); Obtaining more revenues from current and potential business; and Enabling reservations agents to be effective sales agents rather than merely order takers. Yield management uses information about customer purchasing behaviour and product sales to develop pricing and inventory controls that produce greater revenues and deliver products that are better matched to the customers' needs. It is a blending of information-systems technology, probability, statistics, organizational theory, and business experience and knowledge. But still there are number of myths given by Warren H. Lieberman regarding Yield Management. Myth 1: YM Is a Computer System Yield management is neither a computer system nor a set of mathematical techniques. It is an approach to increasing revenues and improving service by responding to current demand. It is a process, a way of conducting business. Certainly, computer-based tools can be a key component of a yield-management program. The full range of the benefits of yield management will not be achieved without computer-based tools that do the following: forecast demand, cancellation, and no-show activity; determine when to restrict the sale of discounts; estimate the revenue displacement of transient demand caused by a group; recommend and control reservation availability on the basis of length of stay and daily rate; and perform many other actions that could not otherwise be effectively carried out. But other yield-management practices can be implemented with little or no investment in computer resources. Upselling programs, enhanced scripting for reservations agents that Yield Management Page 10

11 enables them to be more-effective sales agents, revised performance measures, and marketing programs and packages that produce incremental revenue gains are a few of those actions. Myth 2: YM Takes Control Away from Employees Yield management tools do not replace employee decision making and control. Yieldmanagement programs offer information to a firm's staff members so that they can make better decisions. Yield management tools may accomplish some routine actions previously performed by employees, but yield management tools do not take over a staff member's decision-making functions or responsibilities. A more sophisticated yield-management program will contain tools that recommend specific courses of action. For example, yield management tools might suggest that no more reservations be accepted for a specific discount rate for a particular night, or that a particular group rate is too low for a certain set of nights. But the ultimate decision and responsibility for accepting those recommendations, or overriding them and choosing a different course of action, rest with staff members. Myth 3: YM Works Only When Demand Exceeds Supply The best-known applications of yield management overbooking and discount control are most useful when excess demand exists and the industry is in the luxurious position of having to choose the type and amount of demand it will satisfy. But for most industries such conditions are not typical, and there is a widespread perception that yield management provides little help when supply exceeds demand. The myth is probably less true for hotels than for airlines. Hoteliers can manage their demand to a finer level than airline executives. If demand for a particular flight on a specific day is low, it is extremely difficult, or at least expensive, for an airline to stimulate demand for that flight. But, given sufficient notice, a hotel can stimulate demand for specific dates in costeffective ways. Myth 4: YM Is Price Discounting Yield management focuses on how much of a product to sell at established prices. It does not tell a firm what prices to charge or whether to change prices. But it does indicate when to open and close rate classes. Although yield management does not tell a hotel the right prices to charge, the mechanisms used to monitor demand can identify opportunities to improve the pricing structure. It can easily be seen that yield management is not price discounting, but a way to improve the pricing structure. Yield Management Page 11

12 Myth 5: Yield Management Is Incompatible with Good Customer Service Actively using pricing and inventory controls to increase profitability increases the risk of providing reduced customer service unless appropriate actions are taken. For example, by implementing an overly simplistic yield-management program that does not recognize the long-term value of regular customers, firm is likely to anger or disappoint those valuable customers and thereby lose potential repeat business. Consistent pricing is often more important for repeat customers than for occasional customers. Therefore, even when demand is unusually heavy for an upcoming date, it is unwise to refuse a loyal customer's request for a normally available discount rate as that guest represents future sales. A firm s yieldmanagement system should be able to evaluate both the short-term and the long-term revenue implications of inventory-control decisions. Yield management should focus on increasing long-term and not just short-term profits. Another area that is particularly susceptible to customer-service problems involves overbooking. When a firm takes greater risks to obtain additional sales and profits, it may not be able to honour all reservations. Customer-service programs should identify and resolve potential conflicts so that all customers are satisfied. As part of an overall yield-management effort, well-designed customer-service programs often replace ad hoc operations, thereby benefiting customers and employees and improving customer-employee relations. Unfortunately, many of the early implementations of yield management concepts focused on short-term revenue gains. Customer service, which is important from a long-term perspective, was not a major focus. The lack of a long term revenue perspective was a flaw in the design and implementation of those early programs many of which continue to be used not a flaw in the concept of yield management. When a yield-management program focuses on both short-term and long-term revenue gains, improved customer service is a byproduct Myth 6: YM Is Too Complex. If a yield-management program is allowed to evolve slowly, it will not be too complex. Some airlines have trained their staffs and developed corporate procedures and policies that support sophisticated implementations. But they developed those programs over a long period. Had the current programs been implemented all at once, ten years ago, they might have had disastrous consequences for the companies. Airlines that have recently begun benefiting from yield management are implementing much simpler programs than their competitors with established programs. Their programs will increase in complexity over the years. Yield Management Page 12

13 Myth 7: YM Doesn't Address "My" Problems When firms claim that yield management concepts are not relevant to their operations, they are usually thinking of how yield management is implemented at another firm. And when they explain why the yield management techniques they have seen don't apply to their operations, they are usually correct. What they don't realize is that the yield management techniques appropriate for their firm could differ significantly from those they have seen. A firm can implement yield management in many ways. The best way depends on the firm's particular characteristics. Myth 8: YM Programs Automatically Increase Revenues Yield management supports an active approach to revenue enhancement. But if it is not carried out carefully, it can result in reduced, rather than increased, revenues. One of the reasons it has been successful is that it stresses the need to quantify the impacts of pricing and inventory-control decisions. Implementing a yield-management program without including that focus is risky. The techniques can also be used to identify strategies that, although they look good on paper, would result in lost revenue if they were implemented. The techniques have convinced senior hotel executives of the value of inventory-control strategies Levels of Yield Management There can be various levels of potential to apply yield management in various levels. The various levels are described in the following table 1.1: Table 1.1 Levels of Yield Management in Various Applications Application Perishable Fixed Advance Potential to Actual use of Inventory Capacity Purchase apply Yield Yield Management Management Airlines Yes Yes Yes High High-Very High Hotels Yes Yes Yes High Low-Very High Sea-Cargo Yes Yes Yes High High-Very High Rental Cars Yes Yes Sometimes High Medium-High Tour Yes Sometimes Yes Medium-High Medium-High Operator Railways Yes Yes Sometimes Medium-High Medium-Very High Stadiums Yes Yes Sometimes Medium Medium Theatres Yes Yes Sometimes Medium Low-Medium 1.11 Effectiveness and advantages This section describes the real-life examples from literature on how the companies realized financial benefits at different levels of company functioning through effective Revenue (Yield) Management or advice. These examples are taken from big-scale (hotel, airlines) and Yield Management Page 13

14 small-scale (Small and medium (SME)) enterprises. The following examples shows how the implementation of the Revenue Management system through low-cost and high revenue policies, modernization of the company and investment into the Revenue Management decision support system or changing the way the business were done previously has helped the companies to increase their revenue. Several authors have reported success stories on how effective Revenue Management has been done in practice. For example, in 1989 The Carlton Beach Hotel in The Hague has successfully introduced and implemented its own Revenue Management system, designed to serve specific needs of the Hotel. As reported by Anderson (1997), since the implementation, the hotel reported an increase in revenues by 20% and the profits by 17%. Another example of successful Revenue Management system can be found in British Airways provided by Unternehmen and Märkte (1992). Among the reported in 1992 reasons of company success were cost cutting and sophisticated Yield Management. The company attributed its success to being a low-cost and high-revenue carrier. The next successful Revenue Management implementation example is given by Cross (1997) and can be found within the Austrian Airlines company that has been one of the most consistently profitable airlines in Europe in the past decades. During the Gulf War that has caused profits of the most airlines to decline, the Austrian Airlines has experienced its twenty-first consecutive year of profits. This has proven the company s capability to perform effectively during extremely cyclical periods. The company owes its success to smart investment choice into a Revenue Management and decision-support computer system. This system has helped to monitor all historical data on the company flights as well as has made it possible to perform flights forecasts up to a one year period with high precision. Moreover, the new Revenue Management system has allowed the company management to look for future business opportunities by supplying it with decision-making tools to forecast future flights demand. In addition, the implementation of this system has enabled the company to keep her prices at 16 stable levels while other companies were trying to survive by lowering their flight prices. Finally, the Revenue Management success stories can be also found within Small and Medium Enterprises (SME). For example, Cross (1997) gives an example of the barbershop whose owner was not initially able to run her business optimally. On some week days, her barber salon had few clients while in the weekends it was packed, resulting in unhappy and un-served customers. By functioning in such a way, the owner had constantly incurred high losses. By following the Revenue Management advice which was to offer the customers Yield Management Page 14

15 discount (20%) if they visit the salon on the week-days and raise the price slightly (20%) if they visit it in the weekends, the owner was able to change the way her business were functioning. The implementation of the advice has created the smooth customer flow, made her clientele happy due to better customer services and, most importantly for the owner itself, has increased business revenue up to 30%. The previous examples demonstrate that the effectiveness of Revenue Management should be company or industry specific. While companies have a lot of common features that are important for the Revenue Management (for example, demand for customers, operating costs), they differ in offering different products and services. Those product and services, depending on the industry, require specific attention while implementing the Revenue Management system. Most companies start thinking of Revenue Management after a few years when everything is running as desired. They think there is some strategy that tells them what to base their prices on to help them earn more. However, there is no one strategy that tells you at which price you could sell a ticket. Nor is there a strategy which tells you at which price you could offer your hotel room. Nevertheless, as per Phillips, R. (2005), Revenue Management is not based on setting and updating prices, but on setting and updating availability of price classes, where each class has an associated price that remains constant through the booking period. Finally, Revenue Management is not only based on maximising revenues either. Besides the fact that airlines benefit from implementing Revenue Management, it also has advantages for the customers. Indeed, they make use of the various offers. Customers for whom a seat or a service is very important are happy to get this seat or service, in spite of the price The Risks In the previous section the aspects and the importance of Revenue Management for a company were discussed. Nevertheless, there are risks that are extremely important for the implementation of Revenue Management. In this section these risks will be taken care of. In the historical background it has been discussed how People Express s eventually declared bankrupt. Former People Express chairman Donald Burr claims many of People Express s problems were due to the Revenue Management system. Because Donald Burr did not know about Revenue Management and did not realize its power, the People Express was killed. In summary, what Burr did was ignoring Revenue Management, and this has cost him his business. Another risk that is also very important for a company who plans to start Revenue Management is making good and confirmable analyses. Nowadays, more companies know of Yield Management Page 15

16 the existence of Revenue Management. On the one side, that is fine, as more companies implement Revenue Management, the more applicable it will be in different industries, but on the other side, more people will proclaim themselves as experts. Hence, it is difficult for companies who start implementing Revenue Management to divide the sheep from the goats. The disadvantage of Revenue Management for the customer is that guests can regard it as unjust if they have paid more than others. Imagine for example that you are sitting on a plane from Amsterdam to New York and you have paid in total six hundred Euros. On board you are sitting next to a man who paid three hundred euro s for his flight. How do you feel? The point is, Revenue Management gives consumers a wide range of options. It isn t unethical for businesses to charge different rates for the same product because, in fact, the product is not the same. For instance, a seat available six months in advance when the plane is half booked, is not the same product as a seat saved for the last few days when there is limited inventory and peak demand. Similarly, a Saturday haircut with a two-hour wait is not the same as one with a brief wait. Therefore is it important for companies to know how to handle with customer that contact the company, because of feeling mislead Optimization In mathematics, computer science, or management science, optimization is the selection of a best element (with regard to some criteria) from some set of available alternatives. In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. Generally, optimization includes finding "best available" values of some objective function given a defined domain, including a variety of different types of objective functions and different types of domains. The optimization problems can be classified according to objective function in the following ways: Linear Programming (LP), studies the case in which the objective function f is linear and the set of constraints is specified using only linear equalities and inequalities. Integer Programming studies linear programs in which some or all variables are constrained to take on integer values. This is in general much more difficult than regular linear programming. Quadratic Programming allows the objective function to have quadratic terms, while the feasible set must be specified with linear equalities and inequalities. Yield Management Page 16

17 Nonlinear Programming studies the general case in which the objective function or the constraints or both contain nonlinear parts. Stochastic Programming studies the case in which some of the constraints or parameters depend on random variables. Combinatorial Optimization is concerned with problems where the set of feasible solutions is discrete or can be reduced to a discrete one. Heuristics and Metaheuristic make few or no assumptions about the problem being optimized. Usually, heuristics do not guarantee that any optimal solution need be found. On the other hand, heuristics are used to find approximate solutions for many complicated optimization problems. Dynamic Programming studies the case in which the optimization strategy is based on splitting the problem into smaller subproblems. Another classification of optimization can be done according to variables and number of solutions as under: Single Objective Optimization: In this type of optimization only one variable act as an objective and on the basis of various constraints the problem of maximization or minimization is solved using any above-specified methods. These types of problems are the basic form of optimization problems. Multi-objective optimization: Adding more than one objective to an optimization problem adds complexity. For example, to optimize a structural design, one would want a design that is both light and rigid. Because these two objectives conflict, a trade-off may exists. There will be one lightest design, one stiffest design, and an infinite number of designs that are some compromise of weight and stiffness. The set of trade-off designs that cannot be improved upon according to one criterion without hurting another criterion is known as the Pareto set. The curve created plotting weight against stiffness of the best designs is known as the Pareto frontier. A design is judged to be "Pareto optimal" or "Pareto efficient" or in the Pareto set, if it is not dominated by any other design. If it is worse than another design in some respects and no better in any respect, then it is dominated and is not Pareto optimal. The choice among "Pareto optimal" solutions to determine the "favourite solution" is delegated to the decision maker. In other words, defining the problem as multiobjective optimization signals that some information is missing: desirable objectives are given but not their detailed combination. Yield Management Page 17

18 Multi-Modal optimization: Optimization problems are often multi-modal; that is, they possess multiple good solutions. They could all be globally good (same cost function value) or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multi-modal optimizer. Classical optimization techniques due to their iterative approach do not perform satisfactorily when they are used to obtain multiple solutions, since it is not guaranteed that different solutions will be obtained even with different starting points in multiple runs of the algorithm. Evolutionary Algorithms are however a very popular approaches to obtain multiple solutions in a multi-modal optimization task Computational Optimization Techniques To solve various optimization problems, one may use algorithms that terminate in a finite number of steps, or iterative methods that converge to a solution (on some specified class of problems), or heuristics that may provide approximate solutions to some problems. I Optimization algorithms II Simplex algorithm designed for linear programming. Extensions of the simplex algorithm, designed for quadratic programming. Combinatorial algorithms, etc. Iterative Methods The iterative methods used to solve problems of nonlinear programming differ according to whether they evaluate Hessians, gradients, or only functional values. While evaluating Hessians (H) and gradients (G) improves the rate of convergence, for functions for which these quantities exist and vary sufficiently smoothly, such evaluations increase the computational complexity (or computational cost) of each iteration. In some cases, the computational complexity may be excessively high. The method that evaluate Hessian is Newton s method and the methods that evaluate gradients are Quasi-Newton, Interior point, Ellipsoid, etc. III Heuristics Besides (finitely terminating) algorithms and (convergent) iterative methods, there are heuristics that can provide approximate solutions to some optimization problems: Memetic algorithms (MA) represent one of the recent growing areas of research in evolutionary computation. The term MA is now widely used as a synergy of Yield Management Page 18

19 evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Genetic algorithms (GA) are a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as encoding, mutation, selection, and crossover. Hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm that starts with an arbitrary solution to a problem, then attempts to find a better solution by incrementally changing a single element of the solution. If the change produces a better solution, an incremental change is made to the new solution, repeating until no further improvements can be found. Hill climbing is good for finding a local optimum but it is not guaranteed to find the best possible solution (the global optimum) out of all possible solutions (the search space). Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, called dubbed particles, and moving these particles around in the searchspace according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position and is also guided toward the best known positions in the searchspace, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions. However, metaheuristics such as PSO do not guarantee an optimal solution is ever found. Ant colony optimization (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. The first algorithm was aiming to search for an optimal path in a graph, based on the behavior Yield Management Page 19

20 of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more efficient than exhaustive enumeration, provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution. Tabu search is a local search method used for mathematical optimization. Local searches take a potential solution to a problem and check its immediate neighbours (that is, solutions that are similar except for one or two minor details) in the hope of finding an improved solution. Local search methods have a tendency to become stuck in suboptimal regions or on plateaus where many solutions are equally fit. Tabu search enhances the performance of these techniques by using memory structures that describe the visited solutions or user-provided sets of rules. If a potential solution has been previously visited within a certain short-term period or if it has violated a rule, it is marked as "tabu" (forbidden) so that the algorithm does not consider that possibility repeatedly. Now it can be seen that a number of optimization techniques are existing. The main thing while selecting an optimization method is to consider the parameters of the problem in question. In our case the problem is to optimize (maximize) the yield and the parameters that are to be considered are perishable inventory, fixed items availability, advance booking, different segments, etc. While looking at these parameters it can be easily seen that these parameters are most suitable to Genetic Algorithms as the solution space in stochastic and deterministic in nature. Also the concept of adaptability is to be applied as shifting from one segment to another may be required at random. Although some other heuristic algorithms such as simulated annealing, PSO may also be useful in some cases. But overall GA has proven to be the best technique in solving Yield management problems. For implementing GA one has to use simulation techniques. Now a glimpse of simulation will be taken. Yield Management Page 20

21 1.15 Simulation Simulation is the imitation of the operation of a real-world process or system over time. [Banks J. et.al. (2001)] The act of simulating something first requires that a model be developed; this model represents the key characteristics or behaviours/functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time. Simulation is a technique that refers to some representation or model of a system that can be studied in order to better understand the behaviour of the actual system itself and to make predictions about the future. Simulation is used in a number of applications, such as simulation of technology for performance optimization, testing, training, education, and video games. Generally, computer experiments are used to study simulation models. Simulation can also be used with scientific modelling of natural systems or human systems to gain insight into their functioning. Simulation can also be used when the real system cannot be designed, because it may not be accessible, or it may be dangerous or unacceptable to designed, or it is being designed but not yet built, or it may simply not exist. [Sokolowski and Banks (2009)] Simulation basically involves the following step for processing: Construction of a symbolic model which describes system operations. Dividing the system into smaller components and combining them in their natural and logical order. Analysing the effect of their (component) interactions on one another. Studying various specific alternatives with reference to performance of the model and choosing the best one Computer Simulation A computer simulation is a simulation, run on a single computer, or a network of computers, to reproduce behaviour of a system. The simulation uses an abstract model (a computer model, or a computational model) to simulate the system. Computer simulations have become a useful part of mathematical modelling of many natural systems in physics, astrophysics, chemistry and biology, human systems in economics, psychology, social science, science, and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions. Computer simulations vary Yield Management Page 21

22 from computer programs that run a few minutes to network-based groups of computers running for hours to ongoing simulations that run for days Simulation and Modelling A computer model refers to the algorithms and equations used to capture the behaviour of the system being modelled. By contrast, a computer simulation refers to the actual running of the program that contains these equations or algorithms. Simulation, therefore, refers to the result of running a model. In other words, one cannot build a simulation; rather one would build a model, and then either run a model or run a simulation. The relationship between model and simulation and theory can be understood by the following fig. 1.2 Fig. 1.2: Process of building a computer model, and the interplay between experiment, simulation, and theory Classification of Simulation Computer Simulation can be classified according to several independent pairs of attributes, including: Stochastic or deterministic: Stochastic simulation use random number generators to model chance or random events; while deterministic simulation, given a particular input, will always produce the same output, with the underlying machine always passing through the same sequence of states. Steady-state or dynamic: Steady-state simulation is somewhat similar to deterministic simulation i.e. it does not change its state in response to the changing Yield Management Page 22