The Problem of Constructing Specialized Routes: a Case Study of the Vienna – Venice "Wine Route"

This chapter reviews how wine tourism has evolved across different French regions. It discusses the range and types of wine attractions which have been developed in France; the importance of wine routes; the establishment of wine events; and the structure of the market in terms of individual and packaged products. The development of tourist routes has become increasingly popular as a tourism promotional tool in France since the mid-1980s, with 270 being recorded across the country in 1997. A tourist route targeted at a specific and restricted topic, such as wine, and therefore some wine routes are combined with other attractions and products to form diversified tourist routes. Consumer interest in wine, particularly quality wine, has also been aroused by the so-called 'French paradox', whereby a variety of health benefits have been attributed to moderate wine consumption. Despite the significance of wine tourism little research has been conducted on wine tourist motivations and behaviour.

Logistics means management of goods transportation from one place into another in an effective way. Most of the logistic companies is very effective in transportation of goods through proper design of logistics network. When the logistics network design is effective, then the delivery of goods to the right place at right time is effective. In general travelling salesman problem (TSP) supports to design logistics network and plays a vital role in effective logistics management. Hence, solving TSP in polynomial time will greatly influences the proper design logistics network. As a result the delivering of goods will be very effective. Thus the objective of this project is to solve TSP using optimization algorithm in order to design an effective logistics network. Many researchers have proposed different kinds of optimization algorithms to solve NP hard problem TSP. But still there is a need to solve TSP in polynomial time and also to handle uncertainty situation. Thus the aim of this project is to use adaptive based Donkey-Smugglers optimization (DSO) algorithm to solve TSP problem in polynomial time and handle uncertainty events in more effective. The salient feature of this proposed system is able to handle uncertainty situation with the help of adding adaptive part in DSO algorithm to reduce time delay in delivery of goods. Since logistics network is heavily depends upon time, traffic, load weight, competency of driver etc. Hence there is a possible for uncertainty happens during delivering of goods. In order to handle this uncertainty situation, the project aims to solve TSP problem using adaptive based DSO algorithm. Meanwhile the proposed system also compares the results with other adaptive optimization algorithms namely Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The results have been proved that the Adaptive based DSO algorithm works well even in uncertainty situation and able to solve TSP in polynomial time. As well as the proposed system achieves minimum average time taken even when there is large number of instances. Hence the proposed system reduces time delay effectively and helps to deliver goods at right place at right minimum time.

In this paper, a minimum time path planning strategy is proposed for multi points manufacturing problems in drilling/spot welding tasks. By optimizing the travelling schedule of the set points and the detailed transfer path between points, the minimum time manufacturing task is realized under fully utilizing the dynamic performance of robotic manipulator. According to the start-stop movement in drilling/spot welding task, the path planning problem can be converted into a traveling salesman problem (TSP) and a series of point to point minimum time transfer path planning problems. Cubic Hermite interpolation polynomial is used to parameterize the transfer path and then the path parameters are optimized to obtain minimum point to point transfer time. A new TSP with minimum time index is constructed by using point-point transfer time as the TSP parameter. The classical genetic algorithm (GA) is applied to obtain the optimal travelling schedule. Several minimum time drilling tasks of a 3-DOF robotic manipulator are used as examples to demonstrate the effectiveness of the proposed approach. Highlights In this paper, an optimization strategy is proposed for solving minimum time manufacturing path planning in multi points manufacturing tasks. According to the start-stop movement in drilling/spot welding task, the path planning problem is converted into a traveling salesman problem (TSP) and a series of point to point minimum time transfer path planning problems. Cubic Hermite interpolation polynomial is used to parameterize the transfer path and then the path parameters are optimized to obtain minimum point to point transfer time. A new TSP with minimum time index is constructed and then solved by using a classical genetic algorithm (GA). Numerical test is executed to demonstrate the effectiveness of the proposed approach.

Wine and tourism represent a perfect symbiosis, as they offer a different experience to tourists and also promote the economic, social, and environmental development of wine regions. In Spain, wine tourism is an increasingly important research field and several studies have been conducted on the role of wine routes in boosting the competitiveness of a territory, increasing wine production, improving the quality of life of citizens, and respecting the environment. However, to the best of our knowledge, the economic impact generated by these routes in Spain has not been addressed in the academic literature. To overcome this research gap, this paper aims to analyze the supply and demand of tourism activities by examining the evolution of the institutions involved in the Spanish wine routes, on the one hand, and the economic impact of these routes, on the other. The results show a sharp drop in the supply of institutions and the demand for tourist routes in 2020 as a result of COVID-19, with a decrease of 2.58% in the total number of institutions adhered to the wine routes, a decrease of 73.53% in the total number of visitors to the different Wine Routes of Spain and a decrease of 74.7% in the economic impact compared to the previous year. Likewise, the research shows the existence of differences between the Wine Routes of Spain in terms of the number of visitors and their economic impact, which could serve as a guide for managers to make investments through acquisitions and/or their own investments in wineries located on the routes with the greatest economic impact..

This chapter sets forth results from an empirical study that aimed to explore the purchasing and consumption behaviour of the wine tourist on the Setubal Peninsula Wine Route, one of the first studies of this subject in Portugal. Surveys of 819 visitors on this routes were analysed to reveal holiday and leisure habits and behaviours, the process of deciding on a wine route, the degree of satisfaction with the Setubal route and visitor profiles. It was found that the majority of visitors were Portuguese couples, from the districts of Setubal and Lisbon, aged over 35 and with university-level education. Visitors over 55 years old, retired, from households of more than four people or from the USA and Brazil were also significant segments. The price of visits and tastings and the prestige of the wineries and wines are the most important criteria in the choice of route. Specialist internet portals and recommendations by friends are the preferred sources of information when planning a visit. The research also included interviews with 17 different travel agencies to understand what they offer in terms of wine tourism. It was found that wine tourism does not generate significant business for travel agencies, but is a growing market. Recommendations are made for improving the marketing mix of the wine tourism product, namely for greater flexibility and customisation of what is on offer and greater promotion to agencies, tour operators and the final customer by the wine route organisation itself.

Summary The Niagara Region is not only home to Niagara Falls but also home to an emerging wine route with more than 50 wineries. The Niagara Wine Route has two distinct clusters. The wineries in the east are in the tourist town of Niagara-on-the-Lake, which receives many international visitors while the wineries in the west are not typically on a tourist route and receive more domestic tourists. Through the results of a visitor survey at eight wineries, the paper will illustrate the different markets visiting the Niagara Wine Route and suggests possible implications for marketing strategies for this emerging wine route.

The Mendoza Wine Route can be conceived as a Latin American benchmark for the articulation of history, oenology, and culture, which embodies SDG 11 (Sustainable Cities and Communities) in several of its goals. From a historical and heritage perspective, the network of irrigation ditches and public trees protects and manages an urban-productive oasis landscape that makes the Andean desert habitable and constitutes a natural and cultural asset in line with target 11.4 to protect cultural and natural heritage. This green and water-based fabric supports both the city (tree-lined streets, plazas, waterways) and the winemaking foundation that gave rise to the Wine Route. Mendoza (Argentina), is the heart of the South American wine industry because of its natural elements, production tradition, and the strength derived from distinct stages of the sector’s growth. The Wine Route, running through the province’s grape-growing districts, benefits the region’s economy, society, and environment. Popular festivals and traditional uses of wine and grapes in gastronomy are expressions of this integration. Yet, economic and social development cannot be detached from environmental preservation. Hence, sustainable wine tourism routes organize wine areas to protect the biodiversity of productive systems and satisfy growing visitor interest.

This paper addresses a dynamic vehicle routing problem related to courier mail services with explicit consideration of multiple objectives. This problem is modeled as a dynamic traveling repairman problem with time windows, and with three objectives considered: maximization of the number of serviced customers, minimization of customer waiting, and minimization of total travel time. A solution (simulation) framework, extending techniques developed for single-objective dynamic routing problems in the literature, is proposed to tackle this multiobjective problem. An efficient chained local search heuristic is embedded in this solution framework to solve the underlying static problem, the traveling salesman problem with time windows. Comprehensive simulation experiments conducted on data sets derived from benchmark problems show that the proposed algorithmic approach is able to provide high-quality solutions with limited computing effort. These experiments also indicate that the multiobjective model proposed in this study can offer much more attractive solutions to decision makers than single-objective models.

This article discusses the annealing simulation method for solving the fuzzy traveling salesman problem, which is formulated as the problem of finding a route to visit a given number of cities without repetitions with a minimum travel time. The content of the annealing simulation method is presented, the method formalization algorithm is described. The axiomatics of fuzzy triangular numbers are given. A fuzzy traveling salesman problem is formulated, in which the time parameters of movement between cities are given in the form of right fuzzy numbers, the carrier value in which depends on various external conditions and factors. The results of calculations for solving the traveling salesman problem in clear and fuzzy forms with different parameters of slices of fuzzy numbers are presented. Key words: traveling salesman problem, simulated annealing method, algorithm, fuzzy numbers, level set, formalization of time intervals.

Limited battery capacity is one of the major hurdles towards perpetual operation of wireless sensor networks. In this paper, a novel framework for charging the sensor nodes using unmanned aerial vehicle (UAV)-assisted radio frequency energy transfer (RFET) is presented. First, the notion of RFET zone is conceptualized and a closed-form expression for RFET zone radius is obtained. The sensor nodes located inside this zone can harvest energy from the transmitter mounted on UAV. The effective power harvested at the sensor node situated at different spatial locations is evaluated by considering the impact of shadowing statistics of path loss and non-linear RF-to-direct current conversion efficiency. With these findings on sensor nodes deployed in a given area, an optimization problem is formulated with the objective of minimizing the total time in a charging cycle, which is comprised of travel time and charging time. This problem is decomposed into two sub-problems and they are solved individually in sequential steps. The optimal solution of the first sub-problem, which provides the sequence of charging having minimum travel time, is a Traveling Salesman Problem (TSP). In the second sub-problem, the presence of Lambert function makes it analytically intractable, and hence, approximations are presented to solve this. Subsequently, to account for the health parameters of the sensor nodes in estimating the charging cycle, three variants of order of charging, namely, Voltage-aware Charging Sequence , Operational Time-aware Charging Sequence , and Iterative Charging Sequence , are proposed. Through system simulations it is demonstrated that, in a generalized setting, the charging sequence offered by the proposed variants perform increasingly better in comparison to the state-of-the-art TSP approach.

Applying the automation in covering the areas entirely eases manual jobs in various domestic fields such as site investigation, search, rescue, security, cleaning, and maintenance. A self-reconfigurable robot with adjustable dimensions is a viable answer to improve the coverage percentage for predefined map areas. However, the shape-shifting of this robot class also adds to the complexity of locomotion components and the need for an optimal complete coverage strategy for this new type of robot. The typical complete coverage route, including the least times of shape-shifting, the shortest navigation route, and the minimum travel time, is presented in the article. By splitting the map into the sub-areas similar to the self-reconfigurable robot's available shapes, the robot can design the ideal tileset and optimal navigation strategies to cover the workspace. To this end, we propose a Complete Tileset Energy-Aware Coverage Path Planning (CTPP) framework for a tiling self-reconfigurable robot named hRombo with four rhombus-shaped modules. The robot can reconfigure its base structure into seven distinct forms by activating the servo motors to drive the three robot hinges connecting robot modules. The problem of optimal path planning assisting the proposed hRombo robot to clear optimally all predefined tiles within the arbitrary workspace is considered a classic Travel Salesman Problem (TSP), and this TSP is solved by the reinforcement learning (RL) approach. The RL's reward function and action space are based on robot kinematic and the required energies, including transformation, translation, and orientation actions, to move the robot inside the workspace. The CTPP for the hRombo robot is validated with conventional complete coverage methods in simulation and real workspace conditions. The results showed that the CTPP is suitable for producing Pareto plans that enable robots to navigate from source to target in different workspaces with the least consumed energy and time among considered methods.

We study the approximation of minimum travel time paths in time dependent networks. The travel time on each link of the network is a piecewise linear function of the departure time from the start node of the link. The objective is to find the minimum travel time to a destination node d, for all possible departure times at source node s. Dehne et al. proposed an exact output-sensitive algorithm for this problem [6, 7] that improves, in most cases, upon the existing algorithms. They also provide an approximation algorithm. In [10, 11], Foschini et al. show that this problem has super-polynomial complexity and present an e–approximation algorithm that runs \(O( {\lambda \over \epsilon} \log ({T_{max} \over T_{min}}) \log({L \over \lambda \epsilon T_{min}}))\) shortest path computations, where λ is the total number of linear pieces in travel time functions on links, L is the horizontal span of the travel time function and T min and T max are the minimum and maximum travel time values, respectively.

The traditional underway replenishment path planning model takes the minimum sailing time as the optimization objective, does not consider the time window factor, and therefore does not consider the impossible waiting replenishment time, which is not applicable to the situation where replenishment is prohibited for multiple time periods. In this paper, the problem of underway replenishment path planning considering time window is analogized as Traveling Salesman Problem with Time Windows. Considering that the time of wartime underway replenishment is tight, and the time window for replenishment by supply ships is mostly the time window for prohibiting replenishment, an underway replenishment path planning model with minimum replenishment time as the goal was designed. Finally, an improved genetic algorithm is designed to solve the problem, and the feasibility of the method is verified by an example.

Optimization of flexible lift processes on high-rise building construction sites

Considering passengers' travel psychoanalysis, the public traffic model of optimum route is proposed, which primary goal is minimal transfer times and the second goal is shortest travel time. On the integrated analysis of the space relationship between stations, the public traffic network's searching model is proposed. Basing on the searching model, theminimal transfer times problem is solved by translating to the problem of the shortest route between two stations. The whole shortest route algorithm of the minimal transfer times is issued, which can efficiently solve the travel project of all the least minimal times between two stations. Finally, basing on the analysis of public traffic travel time chain, the time evaluating function is provided to calculate the public traffic travel time.