Keywords: resort development, simulation, hybrid system, genetic algorithm.

Resorts have become one of the preferred options for investment in the leisure industry over the past few decades. However, inadequate development planning may lead to the failure of the resort project due to the factors such as overestimation of the operation revenue and negligence of potential risks. Thoughtful planning balances the project development expenditure and the expectation of future revenue, and avoids excessive development and budget deficits.

A resort consists of amenities and supporting resources (e.g., infrastructure). Some popular amenities alone may attract tourists while others may only attract tourists when they are simultaneously accessible. The inter-dependences among the amenities and supporting resources make the development problem too difficult for experts to optimize. Several decision models or problem-solving techniques for the project portfolio selection and plan optimization are available. Examples are applied linear and integer programming [1], CAPM [2], and, more recently, real options analysis [3]. Most of these techniques, however, still rely on a series of assumptions that limit the complexity of the model [4]. Besides, these models are unsuitable for resort development projects because they cannot simultaneously deal with selection, ordering, and planning the level and schedule of feasible investment items.

This paper presents a new decision-support model by integrating Monte Carlo simulation and polyploidy genetic algorithms (GAs) to optimize the development levels of resort amenities in each project phase. Given a set of constraints (e.g. size of available land), there may be more than one feasible combination of development level for the amenities. Each combination results in a different profit in terms of net present value. The simulation model allows the planner to define amenities, supporting resources, and their probability distributions (e.g., probabilities of attracting various levels of number of tourists once the amenity is available). Traditional GAs usually apply the genetic structure of a haploid, in which a single-dimensional genetic encoding is severely limited to express potential solutions. A polyploidy genetic structure may express and reveal more practical solutions, thus is suitable to deal with the problems featuring multi-periods, multi-steps, or multi-situations as in this research. The flowchart of the proposed system, called RIP (Resort Investment Planner) includes four major functions: (1) model setting (2) amenity and activity (3) resource edit, and (4) resource flow. We also evaluated the RIP performance using a realistic project and compared it with the planning carried out by five experts. The results showed that the RIP can propose a better plan (in terms of net present value) faster than the human experts. With regards to the dynamics of resources and activities, the human experts couldn't provide a thorough consideration in their planning, while the RIP provided a fully integrated planning.

1

E. Gori, "Portfolio selection of capital investment projects in the Durban Metropolitan region", Construction Management and Economics, 14(5), 451-456, 1996. doi:10.1080/014461996373313

2

M. Sandsmark, H. Vennemo, "A portfolio approach to climate investments: CAPM and endogenous risk", Environment Resource Economics, 37(4), 681-695, 2007. doi:10.1007/s10640-006-9049-4

3

C. Carlsson, R. Fuller, M. Heikkila, P. Majlender, "A fuzzy approach to R&D project portfolio selection", International Journal of Approximate Reasoning, 44(2), 93-105, 2007. doi:10.1016/j.ijar.2006.07.003

4

M. Better, F. Glover, "Selecting project portfolios by optimizing simulations", The Engineering Economist, 51(1), 81-97, 2006. doi:10.1080/00137910600695593

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description
purchase this book (price £95 +P&P)