Обзорный реферат по переведенной литературе

Tourists are interested in visiting tourist attractions that have a unique tourist attraction. This case study is limited to the Bandung area. The European Tourism Commission (ETC) states that the Internet is the main source of information for tourists looking for tourist attractions that they want to visit 1. However, information received from the Internet is not complete and detailed.

It is usually difficult for tourists to find tourist attractions that they have never visited. In addition, it is difficult for tourists to estimate the distance and travel time when visiting several tourist attractions at the same time. Tourists will begin a tour of the hotel where they will stop. Some of the problems tourists face are part of the Traveling Salesman Problem (TSP). The Traveling Salesman Problem (TSP) is a problem when a person travels between cities, but must start from the city of origin and return to the city of origin with a minimum total distance and minimum cost. To date, many studies have been conducted in which optimization methods have been developed to determine the optimal routes for visiting tourists.

As a critical place on the tourist route, the city plays an important role in terms of the tourist route. The city is a tourist attraction, as well as an important tourist destination for attracting tourists. The versatility of the city determines that urban tourism will become multifunctional, intelligent and integrated. Urban tourism is associated with services such as food and accommodation, transportation, shopping and entertainment, medical treatment, finance and postal services, etc.

In 1986, Fred Glover discovered a method for solving problems of local search optimization. This optimization method is usually called tabu search 10. The basic concept of the taboo search method is to guide each process to get the optimal solution without being trapped in the original solution found during the continuous process. The goal of the tabu search method is to find ways to prevent repetition and to find the same solution in an iteration. Some parameters contained in the tabu search method:

˗ Local Search Procedure: The local search procedure consists of insertion and sharing. Insertion is the process of randomly selecting one part of a structure to move to another part. Meanwhile, the exchange is a process of random exchange of positions between two parts of the structure.

˗ Neighborhood structure. Neighborhood structures are functions that are used to identify any other solutions obtained by exchanging two nodes in a solution. Hotels and tourist attractions are considered anodes.

˗ Tabu conditions. Tabu conditions are conditions that prohibit the use of solutions that were found earlier.

˗ The condition of aspiration: The condition of aspiration is a condition for ignoring the status of tabu. Aspiration conditions arise when there is a process of exchanging notes at iteration.

˗ Finishing Criteria: The tabu search algorithm stops based on the number of user iterations and the number of consecutive iterations without increasing the value of the best objective function.

When assessing the tourism opportunities of the region, one should take into account: - the target audience (CA) of the tourism product formed in the region, i.e. consumers who will be interested in this tourist route. The target audience of the route is children, adults, senior citizens, families with children, people with disabilities, etc. When determining CA, it is important to consider the income level of a potential traveler. because each social group has different material capabilities.

Modern software development is usually conducted in a dynamic environment with limited resources, which is subject to the accumulation of technical debt. Although this common phenomenon is recognized, it remains unknown how technical debt specifically manifests itself in software processes and affects it, and how the software development methods used take into account or mitigate the presence of this debt.

The term «tech debt» was coined by Ward Cunningham [3] when he described the phenomenon of meeting the deadline for production through adaptation and concessions in the product. He also outlined how the subsequently felt consequences were similar to those associated with financial debt. Cunningham [3] acknowledged that more often than not, technical debt requires payback, while failure to manage assets can lead to a complete halt, as the interest and results of adaptations (or lack thereof) become unbearable.

The definition of technical debt was later revised in a number of cases, usually to generalize what Cunningham had previously described for all applicable situations, while classifying its characteristics. The definition of Steve McConnell, which shares the intentional and unintentional accumulation of technical debt [7], has been widely accepted by academia (for example, in [8,9] and recognized in [5]): the first type of technical debt is this kind of occurring unintentionally.

For example, a design approach is error prone, or a junior programmer simply wrote some bad code. This technical debt is a strategic result of poor performance. In some cases, these kinds of debts can arise out of ignorance.

The second type of technical debt is the one that arose intentionally. This usually happens when an organization makes a conscious decision to optimize for the present and not for the future. «If we do not complete this release on time, there will be no next release».

3.1. Selection of tourist places and hotels.

At the first stage, tourists choose the hotels and tourist attractions that they want to visit. The hotel is used as the starting point of departure and the end point when searching for routes in the tabu search method. However, in this article we will try to include all the tourist attractions contained in the data set.

3.2. Determination of optimal tours (cost, popularity and number of tours).

At the second stage, tourists choose a priority scale of tourist visits based on criteria of cost, popularity and the number of tours. The values 0 - 1 are used as weight values for each attribute. The calculations at this stage use the concept of multiple attribute theory (MAUT). The MAUT calculation is aimed at obtaining a suitable value that will be used in the process of creating a tabu list.

3.3. Normalization

The results of the calculation of the suitability values are obtained from the normalization results. Normalization data is a form of data transformation for grouping different ranges of values into the same scale of values. There are several normalization methods that you can use, but in this example, the min-max normalization method was used.

In this study, we use a combination of the taboo search method and the MAUT calculation concept to solve the traveling salesman problem. The taboo search method acts as a determinant to find the best route based on the MAUT calculation results that were made so that we can get the optimal values. We recommend staying in one hotel and making a schedule of tourist routes for maximum tourist trips for 3 days.

As for the situation when a tourist chooses a city attraction according to subjective impressions, this article discusses decision support for tourist routes based on a neural network buffer analysis model. Based on the classification of the viewing spot, he quantifies the factors of the service object in the radius of the buffer neighborhood of the viewing spot and confirms the tourist motivational index and the fluctuating value. Use the neural network model to derive the value of the iteration of the motive to ensure the optimal route. As an example of an algorithm, take three Zhengzhou city attractions to do research and provide tourist support. The algorithm proves that the model in the work is feasible and valuable, which can provide an effective support solution.

 

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