The regular language-constrained orienteering problem with time windows

Indonesia has a large number of favorable tourism destinations, for example like Besuki in East Java that has some potential tourist attractions. The complexity to plan a tourism travel routes for particular tourist category become a problem. Hence, travel recommendations for tourists are required. To solve this problem, the first step is accumulating data from questionnaires, Google Map, TripAdvisor, and website of the Tourism Office in Besuki. The questionnaires were distributed to respondents with dominant age between 15-24 years (60%) and most of them were students (44%). Most respondents were single (74%) and traveling with their family (52%). Using LINGO, model Orienteering Problem with Time Windows (OPTW) with objective function is maximizing scores and having restrictions in opening and closing hours of each tourist attractions. The database that required are opening and closing hours, the distance between tourist attractions, and maximum tourist’s hours per day. Score for each tourist attraction and service time will be taken from each tourist category. There are 3 tourists’ categories such as Simplicity Searchers (42%), Cultural Purists (34%), and Reward Hunters (24%). Eventhough there is no significant difference among service time for all categories, the median service time will give different results in total score and routes.

In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD) - Orienteering Problem (OP) - Time Windows (TW), which analyzes in real time the user's constraints and the points of interest's constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users' interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users' perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries.

We introduce a new variation of the Orienteering Problem (OP), the Minimum-Maximum Category Constraints Orienteering Problem with Time Windows. In the Orienteering Problem we seek to determine a path from node \( S \) to node \( T \) in a weighted graph where each node has a score. The total weight of the path must not exceed a predetermined budget and the goal is to maximize the total score. In this variation, each Activity is associated with a category and the final solution is required to contain at least a minimum and at most a maximum of specific categories. This variation better captures the problem of tourists visiting cities. For example, the tourists can decide to visit exactly one restaurant at a specific time window and at least one park. We present a Replace Local Search and an Iterated Local Search which utilizes Stochastic Gradient Descent to identify the tightness of the constraints. We perform exhaustive experimental evaluation of our results against state of the art implementations for the unconstrained problem and examine how it performs against increasingly more restricting settings.

ABSTRACTThe orienteering problem (OP) is a routing problem that has numerous applications in various domains such as logistics and tourism. The objective is to determine a subset of vertices to visit for a vehicle so that the total collected score is maximized and a given time budget is not exceeded. The extensive application of the OP has led to many different variants, including the team orienteering problem (TOP) and the team orienteering problem with time windows. The TOP extends the OP by considering multiple vehicles. In this article, the team orienteering problem with variable profits (TOPVP) is studied. The main characteristic of the TOPVP is that the amount of score collected from a visited vertex depends on the duration of stay on that vertex. A mathematical programming model for the TOPVP is first presented and an algorithm based on iterated local search (ILS) that is able to solve modified benchmark instances is then proposed. It is concluded that ILS produces solutions which are comparable to those obtained by the commercial solver CPLEX for smaller instances. For the larger instances, ILS obtains good-quality solutions that have significantly better objective value than those found by CPLEX under reasonable computational times.

Technological developments in recent years led to the emergence of increasingly sophisticated recommender systems to support multi-day travel itineraries that fall under the Tourist Trip Design Problem (TTDP). Various problem analogies are widely used to solve TTDP, such as Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), Orienteering Problem (OP), and Team Orienteering Problem with Time Windows (TOPTW). For multi-day route recommendation, TOPTW is suitable as a problem analogy since there is a per-day travel duration constraint. So far, TTDP with TOPTW does not consider the weighting (priority level of users) for each requirement attribute in a multi-attribute-based TOPTW to ensure personalized recommendations. In addition, running time remains a challenge in many studies in the TOPTW area. Many metaheuristic algorithms have been adopted to TOPTW for generating a time-efficient approach. Komodo Mlipir Algorithm (KMA) emerges as a new algorithm that promises good scalability. Therefore, we propose KomoTrip, a method that adopts the discrete version of KMA and Multi-Attribute Utility Theory (MAUT) to recommend optimal travel routes per day by accommodating the multi-attribute preferences of users. We perform three evaluation scenarios, i.e., general performance, Degree of Interest (DOI) combinations, and varying numbers of Points of Interest (POI), consistently demonstrating that KomoTrip outperforms several benchmark algorithms in terms of computational time efficiency and also exhibits robust fitness values across different problem dimension scales. Thus, KomoTrip can be regarded as an efficient algorithm to recommend optimal multi-day tour routes, effectively incorporating weighted multi-attribute preferences into its optimization process. We further benchmarked KomoTrip against state-of-the-art TOPTW heuristics on the public Solomon dataset, where it demonstrated competitive profit values, particularly for a larger number of days (tours), and consistently achieved superior runtime performance.

A GRASP × ILS for the vehicle routing problem with time windows, synchronization and precedence constraints

The problem of finding matches of a regular expression (RE) on a string exists in many applications, such as text editing, biosequence search, and shell commands. Existing techniques first identify candidates using substrings in the RE, then verify each of them using an automaton. These techniques become inefficient when there are many candidate occurrences that need to be verified. In this article, we propose a novel technique that prunes false negatives by utilizing negative factors , which are substrings that cannot appear in an answer. A main advantage of the technique is that it can be integrated with many existing algorithms to improve their efficiency significantly. We present a detailed description of this technique. We develop an efficient algorithm that utilizes negative factors to prune candidates, then improve it by using bit operations to process negative factors in parallel. We show that negative factors, when used with necessary factors (substrings that must appear in each answer), can achieve much better pruning power. We analyze the large number of negative factors, and develop an algorithm for finding a small number of high-quality negative factors. We conducted a thorough experimental study of this technique on real datasets, including DNA sequences, proteins, and text documents, and show significant performance improvement of the state-of-the-art tools by an order of magnitude.

We present a solution to the winner determination problem which takes into account not only costs but also risk aversion of the agent that accepts the bids, and which works for auctioning tasks that have time and precedence constraints. We use Expected Utility Theory as the basic mechanism for decision-making. Our theoretical and experimental analysis shows that Expected Utility is useful for choosing between cheap-but-risky and costly-but-safe bids. Moreover, we show how bids with similar costs and similar probabilities of being successfully completed but different time windows can be efficiently selected or rejected.

Learning or writing regular expressions to identify instances of a specific concept within text documents with a high precision and recall is challenging. It is relatively easy to improve the precision of an initial regular expression by identifying false positives covered and tweaking the expression to avoid the false positives. However, modifying the expression to improve recall is difficult since false negatives can only be identified by manually analyzing all documents, in the absence of any tools to identify the missing instances. We focus on partially automating the discovery of missing instances by soliciting minimal user feedback. We present a technique to identify good generalizations of a regular expression that have improved recall while retaining high precision. We empirically demonstrate the effectiveness of the proposed technique as compared to existing methods and show results for a variety of tasks such as identification of dates, phone numbers, product names, and course numbers on real world datasets.

Heritage tourism is a tourist trip by visiting places that have historical and heritage value as a tourist attraction. In this study selected three locations in Indonesia are Yogyakarta, Bandung, and Bali. The tourism phone application is an application service tour guide encourages tourists to get information on the journey of heritage tourism. With this application, the user chooses the tourism package according to the area that the user wants to visit. Heritage tour guide application uses the user-centred design (UCD) method. UCD is an iterative design process in which designers focus on the users and their needs in each phase of the design process. This application makes it easier for tourists because it is a tool for carrying out heritage tourism trips. Tourist can access routes, and tourist destinations will be addressed in the Heritage journey. The results of the design of tour guide services with a confidence scale of 95% intervals, which showed this study was successful in designing tour guide services that were in line with user expectations.

The state-of-the-art approaches for regular expression matching on LZ78 compressed strings do not perform efficiently. Moreover, LZ78 compression has some shortcomings, such as higher compression ratio and slower decompression speed than LZ77 (a variant of LZ78). In this paper, we study regular expression matching on LZ77 compressed strings. To address this problem, we propose an efficient algorithm, namely, RELZ, utilizing the positive factors, i.e., the prefix and the suffix, and negative factors (Negative factors are substrings that cannot appear in an answer.) of the regular expression to prune the candidates. For the sake of quickly locating these two kinds of factors on the compressed string without decompression, we design a variant of suffix trie index, called SSLZ. We construct bitmaps for factors of regular expression to detect candidates. Moreover, due to the high space cost of SSLZ, we propose a variant index that partially maintain suffixes of the phrases with high frequency and develop an efficient regular expression algorithm based on the novel index, namely, RELZ+. In addition, two optimization strategies employing block filtering and LZ filtering are proposed to prune false negative candidates. At last, we conduct a comprehensive performance evaluation depending on four real data sets to validate our ideas and the proposed algorithms. The experimental results show that our RELZ and RELZ+ algorithms significantly outperform the existing algorithms.

In response to the rapid increase in e-commerce, distribution services, and smart technology, businesses need to solve the vehicle routing problem (VRP) and its variants to improve service quality and cut operating costs. In this paper, we introduce the VRP with time windows and time costs (VRPTWTC), which is a new but practical VRP variant that adds time windows of customers and time costs of service constraints into the optimization of the traditional VRP problem. Furthermore, we propose a masking algorithm and combine it into our proposed end-to-end and data-driven deep reinforcement learning framework to solve the VRPTWTC. Experimental results show a shorter tour length and reasonable processing time than benchmarks both on a simulated dataset and a real enterprise-level dataset. Besides, violation of time window and total time cost, which is one of the most important criterions in our problem formulation, never occurred in our experiment under the real road connection conditions when using a real map API. To the best of our knowledge, our framework is the first to effectively and efficiently solve a VRP variant problem with practical time windows and time costs constraints with a real map API to calculate routes of VRP considering real road connection conditions on an enterprise-level dataset.

TWINS: Efficient time-windowed in-network joins for sensor networks

A target-time-windows technique for project scheduling under uncertainty

The authors focus on the integration of symbolic and sub-symbolic knowledge for the execution of path-planning tasks in autonomous agents. Environmental knowledge is represented through a multilayered architecture whose different abstraction levels are identified by means of meta-knowledge for classification and clustering of distinctive places. The path-planning problem considered consists of determining the cheapest path for visiting a set of resources in the environment, each resource being expressed as either a cluster or a category of clusters at any abstraction level. Time windows and precedence constraints between resources are taken into account. The algorithm proposed finds a sub-optimal solution to this problem by decomposing it at the different abstraction levels through a divide-et-impera technique. >