Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of shape need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of shape need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb real-world itineraries using three operations: REPLACE, ADD, and DELETE. Each perturbation is grounded in three intents: disruptions of popularity, spatial distance, and category diversity. Furthermore, hybrid metrics are proposed to ensure perturbation effectiveness. We conduct comprehensive benchmarking on iTIMO to analyze the capabilities and limitations of state-of-the-art LLMs. Overall, iTIMO provides a comprehensive testbed for the modification task, and empowers the evolution of traditional travel recommender systems into adaptive frameworks capable of handling dynamic travel needs. Dataset, code and supplementary materials are available at https://github.com/zelo2/iTIMO.

It is challenging to achieve an optimal worker allocation for production lines of large-scale industrial enterprises due to the complex requirements for workers’ capabilities. Some optimization approaches have been proposed to solve this problem from different perspectives. However, they have not fully explored the multidimensional features of both workers and production lines, making it hard to obtain an optimal allocation. This article proposes a novel approach to collaborative allocation optimization of production line workers by incorporating multidimensional feature measurement and the environment-classes, agents, roles, and objects (E-CARGO) model. First, we develop a comprehensive evaluation system to quantify the diverse features of workers and production lines. Based on it, an adaptability assessment mechanism is designed to measure the matching degree of workers for different production lines. Afterward, the role-based collaboration theory and the E-CARGO model are innovatively utilized to formalize the worker allocation problem. Meanwhile, the key constraints are identified to guarantee the reasonability of allocation, and an efficient solution via CPLEX package is proposed. Finally, the case analysis and simulation experiments verify the effectiveness of the proposed approach.

Recently, the city road trips have become one of the mainstream travel styles for Chinese tourists. Existing research does not comprehensively consider tourists' subjective preferences for sightseeing, dining, and accommodation, and analyze the objective constraints related to city road trips. It is difficult for tourists to obtain route planning solutions with high satisfaction of city road trips. A route planning approach for city road trips is proposed to meet these preferences and constraints. This approach defines two types of POIs (points of interest), i.e., attractions and rest spots, and identifies key constraints possibly affecting POI selections. Based on them, the tourist’s route for one day is divided into three sub-routes. Each sub-route consists of two different POIs including an attraction and a rest spot. First, an appropriate attraction is selected as the center of a sub-route. Second, a suitable rest spot is assigned to each sub-route according to its center. The E-CARGO model is utilized to formalize the route planning problem of city road trips and an effective solution is provided. Case study and simulation experiments show that the approach is efficient and feasible to achieve the maximum tourist satisfaction in city road trips.

With the massive popularization of e-learning, collaborative learning via learning teams has become indispensable to enhancing the learning efficiency and learning quality of overall learners. The team leader usually plays a key role in collaborative learning. However, the existing research ignores the key characteristics of learners and constraints relevant to e-learners when identifying appropriate team leaders and compatible members. A novel collaborative optimization approach to learning team formation is proposed based on a refined learner model and the environments—classes, agents, roles, groups, and objects (E-CARGO) model. With the proposed approach, a learner is modeled by combining 5-D characteristics (i.e., cognitive ability, leadership, sociability, learning style, and personality) and three types of constraints (e.g., conflicts, genders, and the number of members), and an assessment mechanism is designed to measure the comprehensive abilities of learners for identifying an ideal team leader and selecting the team members for a team. By innovatively introducing the role-based collaboration theory and E-CARGO model, the leader-centered learning team formation problem is formalized as a collaborative optimization problem. The mathematical model and the constraint relations are established for this problem, which is solved based on the IBM CPLEX package. Finally, a case study and experiments demonstrate that the proposed approach is efficient and feasible, in favor of improving the satisfaction degree of learners.

Recently, the road trip in regional central cities has become one of the mainstream travel styles for Chinese tourists. However, existing research fails to plan the road trip route in Chinese regional central cities due to its flexibility, complexity, and long-term characteristic. A collaborative route planning approach for road trips, namely CoRPoRT, is proposed. This approach decomposes the route planning of a road trip into two role-based collaboration subproblems to alleviate the complexity issue. Then, the environments–classes, agents, roles, groups, and objects (E-CARGO) model is innovatively employed to formalize the problems and guide the optimization process for dealing with the long-term characteristic of road trips. Moreover, we identify the complex constraints affecting point of interest selections and propose an efficient solution via the IBM CPLEX solver to handle the flexibility of road trips. Finally, a case study and simulation experiments verify the effectiveness of CoRPoRT. CoRPoRT presents a general problem modeling method and a novel research paradigm for the route planning of road trips.

The prediction of examinee performance via cognitive diagnosis models might provide an important decision-making support for personalized learning instruction in an e-learning system. Aiming at the uncertainty of learners' skill proficiency caused by the complexity of skills, and the large-scale volume of score profiles, a collaborative prediction approach of examinee performance is proposed based on a new fuzzy cloud cognitive diagnosis model. In this approach, the normal cloud models are used to measure the uncertainty of the skill proficiency from three aspects (i.e., expectation, variation degree, and variation frequency), and an e-learner’s skill proficiency is characterized with a fuzzy interval number. Based on a collaborative parameter estimation method, the predicted scores on every test item could be obtained for learners. Finally, the experiments demonstrate that this approach provides good accuracy and less execution time for predicting examinee performance than other approaches.

It is a fundamental function of a personalized elearning system to recommend suitable exercises to learners for improving their learning efficiencies and qualities. These exercises relevant to the current learning progress and the skill proficiency of learners could be selected by analyzing their score profiles in the past exams. Aiming at the limitations of existing research, a new exercise recommendation approach is proposed based on cognitive diagnosis and Neutrosophic set. In it, the learners' cognitive status is measured from multiple perspectives comprehensively by introducing the Neutrosophic set theory. The similarity between the learners is calculated with a Neutrosophic set method. The learner's performance on the new exercises could be predicted by collaborative filtering algorithm, and the exercises suitable to learners are recommended to them according to their preferences. The experiments show that the accuracy of proposed approach is higher than the existing approaches.