Tour Duration Research Articles

Tackling the crowdsourced shared-trip delivery problem at scale with a novel decomposition heuristic

This paper presents a set-partitioning formulation and a novel decomposition heuristic (D-H) solution algorithm to solve large-scale instances of the urban crowdsourced shared-trip delivery (CSD) problem. The CSD problem involves dedicated vehicles (DVs) and shared personal vehicles (SPVs) fulfilling delivery orders, wherein the SPVs have their own trip origins and destinations. The D-H begins by assigning as many package delivery orders (PDOs) to SPVs as possible, where the D-H enumerates the set of routes each SPV can feasibly traverse and then solves a PDO-SPV-route assignment problem. For PDO-DV assignment and DV routing, the D-H solves a multi-vehicle routing problem with time-window, tour duration, and capacity constraints using an insertion heuristic. Finally, the D-H seeks potential solution improvements by switching PDOs between SPV and DV routes through a simulated annealing (SA)-inspired procedure. The D-H outperforms a commercial solver in terms of computational efficiency while obtaining near-optimal solutions for small problem instances. The SA-inspired switching procedure outperforms a large neighborhood search algorithm regarding run time, and the two are comparable regarding solution quality. Finally, the paper uses the D-H to analyze the impact of several relevant factors on city-scale CSD system performance, namely the number of participating SPVs and the maximum willingness to detour of SPVs. Consistent with the existing literature, we find that CSD can substantially reduce delivery costs. However, we find that CSD can increase vehicle miles traveled. Our findings provide meaningful insights for logistics practitioners, while the algorithms illustrate promise for large real-world systems.

Impact of water exhaled out by visitors in show caves: a case study from the Moravian Karst (Czech Republic)

The anthropogenic impact of the water and CO2 exhaled by visitors was studied in the show caves of the Moravian Karst (Czech Republic), especially in the Balcarka and Výpustek Caves. Two alternative models based on (1) the known/presumed composition of the breathed air and physical activity of visitors and (2) the detailed monitoring microclimatic data were proposed. The CO2 fluxes of 2.4 × 10−4 and (2.0–3.9) × 10−4 mol person−1 s−1 and the water vapor fluxes of (3.2–8.9) × 10−3 and (0.6–1.2) × 10−2 g person−1 s−1 were found for a slightly increased physical load. The total attendance and cave tour duration were the main driving factors. For the available data on attendance and accessibility periods, the total mass of water vapor exhaled by visitors in all show caves in the Moravian Karst was estimated between 9.6 × 106 and 4.3 × 108 g with significant seasonality. According to the geochemical model, this mass of water is capable of dissolving 1280 to 59,038 g of calcite, assuming a mean winter and summer CO2 concentration in the cave air of 1000 and 3000 ppmv. The larger extent of water condensation can lead to the so-called condensation corrosion, whereas the lower extent of condensation probably causes a recrystallization of calcite on the surface of speleothems and rocks.

System for Selecting Tourist Routes Based on Genetic Programming

One of the most important tasks in tourism is helping tourists plan a trip to a given place. Since it is impossible to visit all places, tourists try to be rational and choose what they find most acceptable and attractive. Each plan has limitations (e. g., length of tour, limited budget) and preferences (e. g., art, culture, historical sites, architecture, modernism) that should be considered when planning your travel trip. This is a special case of the Orientation problem — Tourist trip design problem. The goal is to maximize the total score achieved within a given tour duration limit. This paper focuses on developing a recommendation system that considers users limitations during tour planning and their preferences. Since this problem is an NP-hard problem, heuristic algorithms such as the genetic algorithm (GA) are well suited for solving it. However, this algorithm can take a very long time to find the optimal solution, so this article focuses on developing a greedy strategy of GA to find optimal or near-optimal solutions. Instead of random genetic transformations, the algorithm consciously modifies optimal routes in order to find the desired tour for the users in a shorter time. After this, the developed algorithm was compared with a GA using various generated user profiles. Over 700 locations in Novosibirsk were used as a dataset for making recommendations. These modifications made it possible to obtain optimal routes faster than the standard implementation of the genetic algorithm.

Optimization of transport constraints and quality of service for joint resolution of uncertain scheduling and the job-shop problem with routing (JSSPR) as opposed to the job-shop problem with transport (JSSPT)

To better meet the qualitative and quantitative requirements of customers or relevant sector managers, workshop environments are implementing increasingly complex task management systems. The job shop scheduling problem (JSSP) involves assigning each task to a single machine while scheduling many tasks on different machines. Finding the best scheduling for machines is one of the challenging optimizations of difficult non-deterministic polynomial (NP) time problems. The fundamental goal of optimization is to shorten the makespan (total execution time of all tasks). This paper is interested in the joint resolution of scheduling and transport problems and more particularly the Job-shop problem with Routing (JSSPR) as opposed to the Job-shop problem with Transport (JSSPT). These two problems are modeled in the form of a disjunctive graph. For the JSSPT, the solution to the transport problem is not linked to any quality of service (QoS) criterion and the solution is therefore often semi-active. The Job-shop with Routing explicitly considers transport operations and uses algorithms from the transport community to solve the transport problem. It is shown that the routing part of the JSSPR is a problem of the vehicle routing family and of the Pickup and Delivery Problem family. QoS in the JSSPR is defined by the duration of tours, the duration of transport of parts and the waiting time for them. A new evaluation function – named Time-Lag Insertion Heuristic (TLH) – is proposed to evaluate a disjunctive graph by simultaneously minimizing the makespan and maximizing the quality of service. Thus, the solution obtained is not semi-active, but a compromise between the different criteria. This evaluation function is included in a metaheuristic. Our numerical evaluations demonstrate that, on the one hand, the TLH evaluation can find almost optimal solutions regarding the QoS criterion; and on the other hand, the TLH evaluation is not very sensitive to the order of insertion of the maximum time-lags during the different minimization steps.

Analyzing the Influence of Demographics and Logistical Provisions on Knowledge Acquisition in Computer Engineering Educational Tours

Educational tours, acknowledged as essential supplements to traditional classroom learning, provided students with direct, invaluable experiences relevant to their academic pursuits. The primary focus of the study was to assess the impact of industry educational tours on the demographic characteristics, particularly age and gender, of computer engineering students who had participated in the educational tour, as well as to examine the level of satisfaction among computer engineering students with the provisions of food, transportation, hotel accommodation, and knowledge acquisition during the educational tour, and the significant relationship between demographic characteristics of computer engineering students and their knowledge acquisition. Descriptive statistics were applied to summarize the demographic information and satisfaction levels, providing a clear overview of the participants’ backgrounds and their experiences. To delve deeper, inferential statistical methods, including correlation and regression analysis, were used to explore and determine the relationships between demographic characteristics, logistical factors, and knowledge acquisition. The majority of participants were young, predominantly around 22 years old, with a higher representation of males. Notably, satisfaction with food and hotel accommodations emerged as pivotal factors influencing knowledge acquisition, while transportation satisfaction showed a lesser impact. These findings underscored the critical importance of ensuring top-notch logistical arrangements, including food and accommodations, to optimize the educational outcomes of such tours. Future investigations could incorporate an evaluation of the factors driving students’ inclination towards engaging in educational tours, aiming to discern the underlying motivations that influenced their decision to participate. Additionally, it would be valuable to identify and analyze the challenges and obstacles confronted by students both prior to and during their involvement in such tours, thereby offering a comprehensive examination of the various aspects impacting students’ experiences throughout the tour duration.

A general variable neighborhood search for the traveling salesman problem with time windows under various objectives

The traveling salesman problem with time windows (TSPTW) has wide practical applications in transportation and scheduling operations. We study the TSPTW in the deterministic case as well as under travel time uncertainty. We consider the three classical TSPTW variants with the objectives of minimizing cost, completion time, and tour duration. In addition, a new TSPTW variant that maximizes the minimum slack of a tour, i.e., the smallest time buffer between the arrival time and the end of the time window over all nodes visited on the tour, is introduced. We further address a variant of the TSPTW in which the arc travel times are uncertain. This problem is modeled by means of an uncertainty set, and the goal is to determine a tour that remains feasible in the worst case within a budget stipulating the sum of all travel time deviations from their nominal values. To solve all targeted problem variants, we develop a two-phase general variable neighborhood search (GVNS) that applies an efficient move evaluation approach within the local search. Extensive numerical experiments on benchmark instances from the literature show that our GVNS finds high-quality solutions that are competitive with those obtained by the state-of-the-art heuristics for all problem variants.

The supply of convenience stores: Challenges of short-distance routing within the constraints of working time regulations

Convenience stores are small shops typically located in dense urban areas. They are an important element in modern retailing considering growing urbanization. The layout of convenience stores usually differs significantly for individual stores depending on the local conditions and the types of store (e.g., grocery stores, kiosks, gas stations). Convenience stores have specific delivery requirements that differ significantly due to their variety. In this context, retailers have to cope with small order sizes and frequent deliveries that need to occur within opening hours or predetermined time windows. In addition to these requirements, working hour regulations for the drivers, i.e., the consideration of break times and tour duration restrictions, have to be respected. These regulations have so far been neglected within short-distance routing but significantly impact the planning in practice. The supply of convenience stores is thus a major challenge for retailers.This article addresses the supply of convenience stores as it takes place at a major European retailer. We develop a mathematical model that integrates existing working time regulations and actual requirements in retail practice. The resulting routing problem is solved using an adaptive large neighborhood search. We demonstrate the impact of the requirements imposed on the routing problem and corresponding delivery solutions in numerical studies. Our experiments show that ignoring break times within route planning when strict time windows are given leads to non-feasible solutions and a potential delay in deliveries of more than 18 h for a single planning week.

The features of organizing tourist routes as one of the methods to increase the efficiency of rolling stock usage

The article deals with the main advantages of railway transport in the field of tourist transportation and the capabilities of effective rolling stock use for organizing high-quality transport service and attracting new customers. Currently, the attention of a great number of transport industry scientists is focused on passenger transportation. The foundations of interaction between railway transport and the tourism industry are being formed. The importance of the railway tourism sector cannot be underestimated in terms of economic, social and environmental aspects of the society. There are advantages of railway transport compared with other modes of transport: the longest railway network in Europe, ability to transport a large number of passengers, use of railway cars as a hotel on wheels, high degree of reliability and safety, lack of dependence on weather conditions, high level of comfort and amenities on board, providing a choice of travel conditions depending on the carriage types and train categories, regularity. The development of the route network contributes to the increase in the competitive abilities of regional tourist destinations. At the same time, the key factors in choosing the method of organizing a tourist route are as follows: the volume of passenger demand for an assigned route, size of expenses, tour duration, etc. The field of tourist transportation is not limited to the use of rail transport only. Its collaboration with other types of transportation allows for maximum effect from tourist service while maintaining a high level of service. This interaction is one of the components of the tourism product aimed at addressing all the key aspects and stages of tour organization.

A novel greedy genetic algorithm-based personalized travel recommendation system

In recent years, there has been a significant increase in the utilization of Tourism Recommendation Systems (TRS) to enhance tourist satisfaction. However, planning a trip can be a daunting and time-consuming process, leading to concerns for travelers. This paper focuses on developing a highly personalized TRS that considers the complexities and limitations of tour itinerary planning. To achieve this, we propose an extension of the constrained orienteering problem that selects the most suitable attractions based on various constraints, such as maximum tour duration, mandatory visits, and start and end locations. In addition, we introduce the use of tier constraints to limit the time spent on similar attractions. In this study, we also propose a novel personalization approach that considers the individual preferences of tourists and generates personalized ratings for points of interest. Next, we focus on developing a new greedy genetic algorithm to address the NP-hard problem of finding optimal or near-optimal solutions. To assess the performance of the developed algorithm, we conducted a sensitivity analysis of the input parameters. Using different user profiles, we demonstrated its effectiveness on a real-world London city dataset. Moreover, we conducted a comparative experimental analysis of the algorithm with four baseline algorithms, and the results were statistically analyzed using non-parametric tests such as Wilcoxon and Friedman. Our algorithm achieved the best-known solutions for 100% of the instances tested, demonstrating its efficiency for large-scale problems with 200-300 points of interest. Furthermore, our TRS provides highly personalized tourist trips, making it a valuable tool for tourists. The new greedy genetic algorithm and unique personalization approach are significant features of the new GlobeTrott tourist recommendation system, which is accessible on popular mobile operating systems and through the website at https://www.globetrott.com/.

Expert assessment and consumer preferences for the implementation of educational tourism in the learning system of universities students: the experience of Kazakhstan

Our research is devoted to the implementation of educational tourism in the education system of higher education students, identifying the prospects for its development based on expert and consumer surveys. The article proposes the author’s definition of educational tourism, reveals its essence and component composition, as well as possible positive effects from its implementation; the connection between the educational and tourist components is emphasized, the primary and secondary nature of which is determined by motivational factors. The key directions for the development of educational tourism are identified through the study of current works presented in the scientometric databases WoS and Scopus. We conducted a study of the main participants in the educational tourism market in Kazakhstan, in particular, we analyzed the indicators of academic mobility of students and employees of scientific and educational institutions; international activities of universities in countries; as well as the number of students, teaching staff in organizations of technical and professional, higher and postgraduate education. The examples of educational tours provided by travel agencies in Kazakhstan are shown. Based on expert surveys and polls, the features of consumer demand and supply in the field of educational tourism were identified, as well as the main communication channels for promoting this product on the market; main promising directions for the development of educational tourism; the degree of awareness of students of higher educational institutions about the essence and specifics of educational tourism; the need to include educational tourism in the educational trajectory of university students. In terms of the characteristics of consumption and provision of services in the field of educational tourism, the authors identified: seasonality and frequency of consumers’ requests to obtain information, form and arrange an educational tour; key motivational factors that guide consumers when choosing an educational tour; average duration and geographic focus of organized educational tours; problems that travel companies face when designing and organizing such tours. In terms of the selection of information and communication tools used to promote educational tours, the main and additional communication channels for promoting educational tours have been identified. In terms of identifying the main promising directions for the development of educational tourism, the average expert assessment of the level of development of outbound and inbound educational tourism was determined. Experts proposed the main promising directions for the development of educational tourism in Kazakhstan, including studying abroad, participation in educational events, learning foreign languages and ecological tours with a scientific focus; the need to include educational tourism in the educational process of university students is emphasized.

Dynamic Programming for the Time-Dependent Traveling Salesman Problem with Time Windows

The time-dependent traveling salesman problem with time windows (TDTSPTW) is a variant of the well-known traveling salesman problem with time windows, in which travel times are not assumed to be constant. The TDTSPTW accounts for the effects of congestion at the planning level, being particularly suited for distribution problems in large cities. In this paper we develop a labeling-based algorithm for the TDTSPTW that incorporates partial dominance and generalizes several state-of-the-art components from the time-independent related literature. We propose a framework general enough to be applied to the TDTSPTW and its variant without time windows, with the objective of minimizing the duration or the makespan. As part of the framework, we introduce a new state-space relaxation specifically designed for the time-dependent context. Extensive computational experiments show the effectiveness of the overall approach and the impact of the new relaxation, outperforming several recent algorithms proposed for these variants on more than 9,000 benchmark instances. In addition, we frame the minimum tour duration problem within the time-dependent literature and include it as a benchmark for our algorithm, obtaining improved computation times and 31 new optimal solutions. Summary of Contribution: In this paper, we study the time-dependent traveling salesman problem with time windows (TDTSPTW), a difficult single-vehicle routing problem that incorporates more realistic travel time functions than its classic time-independent counterpart. As a result, the TDTSPTW is harder to solve, as it requires more complex models and algorithms. Using state-of-the-art optimization techniques, we propose an efficient solution approach for the TDTSPTW and some related variants that outperforms the previous approaches in the literature. Our paper emphasizes the importance of algorithmic design and efficient implementations to tackle relevant practical combinatorial optimization problems—in particular, for time-dependent problems. Moreover, the resulting algorithm fosters a new research direction regarding exact algorithms for time-dependent problems using dynamic programming and relaxation techniques. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Funding: This research has been funded by Fondo para la Investigación Científica y Tecnológica (FONCyT) [Grants PICT-2016-2677 and PICT-2018-2961] from the Ministry of Science, Argentina, and by the Google Latin America Research Award (LARA) 2019. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1236 .

Solving the time dependent minimum tour duration and delivery man problems with dynamic discretization discovery

In this paper, we present exact methods for solving the Time Dependent Minimum Tour Duration Problem (TD-MTDP) and the Time Dependent Delivery Man Problem (TD-DMP). Both methods are based on a Dynamic Discretization Discovery (DDD) approach for solving the Time Dependent Traveling Salesman Problem with Time Windows (TD-TSPTW). Unlike the TD-TSPTW, these problems involve objective functions that depend in part on the time at which the vehicle departs the depot. As such, optimizing these problems adds a scheduling dimension to the problem. We present multiple enhancements to the DDD method, including enabling it to dynamically determine which waiting opportunities at the depot to model. With an extensive computational study we demonstrate that the resulting methods outperform all known methods for both the TD-MTDP and TD-DMP on instances taken from the literature.

Activity-based travel behavior modeling of dormitory students

To date, extensive studies have been conducted on the urban travel behavior of individuals, while the travel behavior of special trip generators, such as dormitory students, has rarely been addressed despite their significant role in urban travel demand. The purpose of this study is to provide an activity-based model for dormitory students to understand the factors that influence students' travel demand and behavior. To this end, questionnaires were distributed among students living in the dormitories of Imam Khomeini International University on May 28, 2018. Finally, an activity-based model at twelve levels was proposed and built using activity-travel information of 336 students. Each level was constructed through standard and mixed multinomial logit models. The study results show that the daily activity pattern, in addition to individual characteristics, is a function of class schedule, friends and roommates’ activities, and the individual’s activities when present at the dormitory. Furthermore, the levels of tour duration choice are mainly dependent on the duration of each person’s daily classes that have been held. The levels of travel mode choice also depend on the time and cost of travel and their mode choice in previous activities. Additionally, it is indicated that destination levels mostly depend on the location of the university, dormitory, and central business district at a specific zone. This study concludes that students’ activity-based model, in addition to socioeconomic factors, is a function of some policy-making factors that can be used for improving students’ commutation in the city.

Optimization of Truck-Drone Parcel Delivery Using Metaheuristics

This research addresses a variant of the traveling salesman problem in drone-based delivery systems known as the TSP-D. The TSP-D is a combinatorial optimization problem in which a truck and a drone collaborate to deliver parcels to customers, with the objective of minimizing the total delivery time. Determining the optimal solution is NP-hard; thus, the size of the problems that can be solved optimally is limited. Therefore, metaheuristics are used to solve the problem. Metaheuristics are adaptive and intelligent algorithms that have proved their success in many similar problems. In this study, a solution to the TSP-D problem using the greedy, randomized adaptive search procedure with two local search alternatives and a self-adaptive neighborhood selection scheme is presented. The proposed approach was tested on 200 instances with different properties from the publicly available “Instances of TSP with Drone” benchmark. Results were evaluated against state-of-the-art algorithms. Non-parametric statistical tests concluded that the proposed approach has comparable performance to the rival algorithms (p=0.074) in terms of tour duration. The proposed approach has better or similar performance in instances where the drone and truck have the same speed (α=1).

A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem

The Multi-Depot Green Vehicle Routing Problem (MDGVRP) is an extension of the well-known Green Vehicle Routing Problem (GVRP) where a fleet of alternative fuel-powered vehicles (AFVs) are used to serve the customers. GVRP consists of determining AFV tours such that the total distance travelled is minimum. The AFVs depart from the depot, serve a set of customers, and complete their tours at the depot without exceeding their driving range and the maximum tour duration. AFVs may refuel en-route at public refueling stations. In MDGVRP, the AFVs are dispatched from different depot locations and may refuel during the day at any depot or refueling station. We formulate MDGVRP as a mixed integer linear programming model and develop a hybrid General Variable Neighborhood Search and Tabu Search approach by proposing new problem-specific neighborhood structures to solve the problem effectively. We assess the performance of our method using the GVRP dataset from the literature. Our results show that the proposed method can provide high quality solutions in short computation times. Then, we extend these instances to the multi-depot case and compare our solutions for small-size instances with the optimal solutions. We also report our results for large-size problems and investigate the trade-offs associated with operating multiple depots and adopting different refueling policies to provide further insights for both academicians and practitioners.

A route planning framework for smart wearable assistive navigation systems

Route planning (RP) enables individuals to navigate in unfamiliar environments. Current RP methodologies generate routes that optimize criteria relevant to the traveling distance or time, whereas most of them do not consider personal preferences or needs. Also, most of the current smart wearable assistive navigation systems offer limited support to individuals with disabilities by providing obstacle avoidance instructions, but often neglecting their special requirements with respect to the route quality. Motivated by the mobility needs of such individuals, this study proposes a novel RP framework for assistive navigation that copes these open issues. The framework is based on a novel mixed 0–1 integer nonlinear programming model for solving the RP problem with constraints originating from the needs of individuals with disabilities; unlike previous models, it minimizes: (1) the collision risk with obstacles within a path by prioritizing the safer paths; (2) the walking time; (3) the number of turns by constructing smooth paths, and (4) the loss of cultural interest by penalizing multiple crossovers of the same paths, while satisfying user preferences, such as points of interest to visit and a desired tour duration. The proposed framework is applied for the development of a system module for safe navigation of visually impaired individuals (VIIs) in outdoor cultural spaces. The module is evaluated in a variety of navigation scenarios with different parameters. The results demonstrate the comparative advantage of our RP model over relevant state-of-the-art models, by generating safer and more convenient routes for the VIIs.

On the impact of spatio-temporal granularity of traffic conditions on the quality of pickup and delivery optimal tours

Optimizing the duration of delivery tours is a crucial issue in urban logistics. In most cases, travel times between locations are considered as constant for the whole optimization horizon. Making these travel times time-dependent is particularly relevant in real urban traffic environments as traffic conditions and thus travel speeds vary according to the time of the day. In this paper, we review the literature on time-dependent routing problems, with a specific focus on benchmarks and performance criteria used to experimentally evaluate the interest of exploiting time-dependent data, showing the lack of studies on the impact of spatio-temporal features of the benchmark on solutions. Hence, we introduce a new benchmark produced from a realistic traffic flow micro-simulation of Lyon city, allowing us to consider different levels of spatial granularity (i.e., number of sensors used to measure traffic conditions) and temporal granularity (i.e., frequency of measures). Finally, we experimentally evaluate the impact of the spatio-temporal granularity on the quality of solutions for different classical problems, including the traveling salesman problem, the pickup and delivery problem, and the dial-a-ride problem.

КОНКУРЕНТНИЙ АНАЛІЗ ГАСТРОНОМІЧНИХ ТУРІВ ДО ЛАТИНСЬКОІ АМЕРИКИ ТУРИСТИЧНИХ АГЕНЦІЙ УКРАЇНИ

The article analyzes the proposals of gastronomic tours to Latin America, which are provided by travel agencies of Ukraine. Features of gastronomic tourism are highlighted. The most popular Latin American gastronomic tours are presented as such countries: Brazil, Argentina, Mexico, Peru and Chile. Fourteen travel agencies, which provide gastronomic and beverage tours to Latin America, were selected for the analysis. The geographic and species structure of gastronomic tours is given. The cost and duration of Latin American gastronomic tours, which are offered by Ukrainian travel agencies, have been investigated. The expenses, that are included or not included in the cost of the gastronomic tour, are determined. It is concluded that Latin America has many resources for organizing a gastronomic tour, but travel agencies in Ukraine offer few tours, which indicates the need to develop this direction.

Evaluation of the size of time windows for the travelling salesman problem in delivery operations

A great challenge in operational research is to apply time-efficient algorithms to find the optimal solutions to the travelling salesman problem (TSP) and its many variations. The TSP with time windows (TSPTW) arises due to intense pressure for business to improve customer service. As online shopping becomes more popular, customer satisfaction increases if customers can decide when their orders are delivered to them. Customers may choose a time window, which is defined by an earliest delivery time and a latest delivery time, during which the package is delivered. Delivering packages to multiple customers is a typical TSPTW. One main challenge for a delivery business is to determine the size of the time window (i.e., the difference between the latest and earliest delivery times), which affects delivery cost and customer satisfaction. Although many previous studies investigated the TSPTW, none of those focused on the size of time windows. This study is the first that experiments with different time window sizes and determines their impact on tour duration, customer satisfaction, and solution time of the optimal delivery routes. The experiment results show that increasing the size of the time window decreases tour duration and customer satisfaction and increases solution time. Decreasing the size of the time window increases tour duration and customer satisfaction and decreases solution time. A small solution time is necessary for the scheduling of deliveries to many customers. A large solution time prevents a delivery business from delivering packages using optimal routes, which increases delivery cost and decreases customer satisfaction. The results of this study indicate that a general guideline for business is to allow customers to choose a time window size that is within the cost limit but is sufficiently small to maximize customer satisfaction and optimize delivery routes.

The Effect of COVID-19 Pandemic: How are the Future Tourist Behavior?

Aims: This research examines tourist travel intentions after the end of the pandemic.
 Study Design: Descriptive quantitative study.
 Place and Duration of Study: Research from February - April 2020 in Jakarta, Indonesia.
 Methodology: This research is a descriptive study describing the current situation using a quantitative approach. Probability sampling with simple random sampling. Data collection using survey methods by distributing online questionnaires (Google Form) through WhatsApp broadcast messages and reach 128 respondents. Simple quantitative data analysis.
 Results: The travel preferences are (78%), or the majority of respondents said they would go back on tour. About (65%) will return to travel in the near term, which is 0-6 months after the pandemic is declared over, of the type of tourism desired by respondents is nature tourism by (66%), The majority of the desired tour duration is short-period, which is 1-4 days. The survey results show that travel intention mean value is higher than travel anxiety.
 Conclusion: Empirical predictions of tourist behavior after this pandemic ends. There are passion and optimism that tourism will recover faster because the majority of respondents in this study have planned when and where they will immediately after COVID-19 pandemic end with new travel preferences.