The tourism industry is a significant contributor to the global economy, responsible for generating nearly 10% of the world’s GDP and employing around 9% of the global workforce. A crucial aspect of this industry is personalised itinerary recommendation, where visitors’ preferences and constraints are taken into account to create customised travel plans. This task involves selecting the best points of interests (POIs) for visitors in various cities and then schedule these POIs as an itinerary considering numerous constraints. However, due to the varied ways in which researchers have defined the itinerary recommendations, it can be challenging for new researchers to locate up-to-date literature on the topic. As a result, this paper aims to review existing research in this area and provide a taxonomy of the works based on problem formulations, proposed techniques, constraints, and features used. We divide the study into two directions: user satisfaction and provider satisfaction, where user satisfaction is derived non–personalised and personalised POI/ Itinerary recommendations. We also discuss the data sources, techniques ranging from optimization approaches to deep learning and evaluation methodologies commonly used in this field. Finally, we highlight the importance of personalised itinerary recommendation and identify areas for future research to address the current challenges. 

A survey on personalized itinerary recommendation: From optimisation to deep learning

Multi-objective home healthcare routing and scheduling problem based on sustainability and “physician–patient” satisfaction

The green vehicle routing problem (GVRP) has been a prominent topic in the literature on logistics and transportation, leading to extensive research and previous review studies covering various aspects. Operations research has seen the development of various exact and approximation approaches for different extensions of the GVRP. This paper presents an up-to-date and thorough review of GVRP literature spanning from 2016 to 2023, encompassing 458 papers. significant contribution lies in the updated solution approaches and algorithms applied to both single-objective and multi-objective GVRP. Notably, 92.58% of the papers introduced a mathematical model for GVRP, with many researchers adopting mixed integer linear programming as the preferred modeling approach. The findings indicate that both metaheuristics and hybrid are the most employed solution approaches for addressing single-objective GVRP. Among hybrid approaches, the combination of metaheuristics-metaheuristics is particularly favored by GVRP researchers. Furthermore, large neighborhood search (LNS) and its variants (especially adaptive large neighborhood search) emerges as the most widely adopted algorithm in single-objective GVRP. These algorithms are proposed within both metaheuristic and hybrid approaches, where A-/LNS is often combined with other algorithms. Conversely, metaheuristics are predominant in addressing multi-objective GVRP, with NSGA-II being the most frequently proposed algorithm. Researchers frequently utilize GAMS and CPLEX as optimization modeling software and solvers. Furthermore, MATLAB is a commonly employed programming language for implementing proposed algorithms. 

A Recent Review of Solution Approaches for Green Vehicle Routing Problem and its variants

Home Health Care (HHC) services have emerged as a result of the need to provide care to patients/people who cannot be treated in hospital due to their special conditions and/or need home care. The care and treatment processes provided by this service have been appreciated by researchers as they can be less expensive and just as effective than care provided by a hospital or a skilled care facility. HHC gains importance due to the increasing epidemic disease cases, the increase in the elderly population, etc. In this study, it is aimed to provide high quality service as well as minimizing travel costs by considering the constraints related to travel balancing and type of transportation vehicle. So, environmental and economic factors could be included in the model. The proposed Home Health Care Routing Problem with Health Team Skill Level (HTSL-HHCRP) model was coded in GAMS 24.2.1 software. A new hybrid method named as Genetic Algorithm Simulated Annealing-Marine Predators Algorithm (GASA-MPA) has been developed to solve the large dimensions of the problem. In hybrid approach, SA has been used with an initial point obtained by GA. On the other hand, feasibility of the neighborhood solutions is guaranteed by solving a constraint satisfaction problem via MPA. Test problems’ performance was analyzed using the Relative Deviation Index (RDI) evaluation criteria. In addition, the sensitivity of the results according to some parameters selected by scenario analysis was examined. Results confirm the efficiency of the GASA-MPA algorithm in terms of time and objective function. Furthermore, by addressing skill levels of the healthcare team, it is shown that the proposed algorithm performs better than more traditional evolutionary algorithms in HHCRP. The comparisons show that the proposed method outperformed the classical methods not only in terms of best solution, but also the Gap (%) and RDI values. 

A Modified Metaheuristic Algorithm for a Home Health Care Routing Problem with Health Team Skill Levels

While the provision of Online Consultation Services (OCS) has brought convenience to patients, the original offline outpatient workflow may struggle to maintain efficiency. Hospital managers' concerns lie on how to reconcile the operations of offline service with the provision of OCS and what are the latent impacts of OCS on the operational performances of healthcare system. The outpatient appointment scheduling is a crucial part of the workflow decision that affects both quality of offline services and the fragmented idle time that doctors can reply to their Follow-up patients (FUP) during working hours. To smooth the fluctuations in workload of OCS, we introduce a novel decision variable—the recommended maximum number of FUP that a doctor should serve during overtime hours (FUP-MN)—and propose an inter-day service shifting strategy that any requests for OCS exceeding the FUP-MN will be deferred to be served in the fragmented idle time of the next session. We formulate a stochastic programming model to search for the optimal combination of the appointment schedule and FUP-MN. Propositions are conducted to demonstrate the model is valid not only in coordinating the dual demand for online and offline services, but also in restricting the potential overflow of OCS requests. We provide a general solution approach and also develop a heuristic algorithm to quickly obtain optimal workflows at different OCS demand levels. The numerical experimental results indicate that an appropriate introduction of OCS can give the healthcare system a strong ability to resist the adverse effects of uncertainty. 

When outpatient appointment meets online consultation: a joint scheduling optimization framework

An exact algorithm for the home health care routing and scheduling with electric vehicles and synergistic-transport mode

Time-of-Use (ToU) electricity pricing scheme has been widely implemented to alleviate the grid's peak load, under which manufacturing companies obtain a good opportunity to save energy cost through more reasonable production scheduling. As a typical production system, batch processing machine manufacturing system has been widely used in modern manufacturing industry because of its advantages in improving production efficiency and reducing production costs. In this work, a new bi-objective uniform parallel batch machine scheduling problem with different job sizes under ToU tariffs is explored, with the goal of minimizing the total electricity cost and the number of enabled machines. We first establish a mixed integer linear programming model, and then propose an improved model. Both models are solved by CPLEX using the $\varepsilon$-constraint method. The calculation results of randomly generated instances prove the effectiveness of the proposed model. At the same time, the calculation results show that the improved model is more effective than the original one.

Bi-Objective Optimization for Uniform Parallel Batch Machine Scheduling under Time-of-Use Tariffs

We study the multiple Hamiltonian path problem (MHPP) defined on a complete undirected graph G with n vertices. The edge weights of G are non-negative and satisfy the triangle inequality. The MHPP seeks to find a collection of k paths with exactly one visit to each vertex of G with the minimum total edge weight, where endpoints of the paths are not prefixed. We present a 3/2-approximation algorithm for MHPP with time complexity O(n3) for arbitrary k≥1. 

A 3/2-approximation algorithm for the multiple Hamiltonian path problem with no prefixed endpoints

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. An efficient bi-objective personalized route planning over two-fold time-dependent road networks Liping Gao, Feng Chu

An eﬀicient bi-objective personalized route planning over two-fold time-dependent road networks

Utility-rich (e.g., more attractive or safer) route planning on city-scale road networks is a common yet time-consuming task. Although both travel time and utility on edges are time-dependent concurrently in real cases, they are overlooked in most prior work. In this paper, we focus on the route planning over two-fold time-dependent road networks, i.e., both travel time and utility on edges are varying over time. We aim to find a route from an origin to a destination by maximizing the accumulated utility score within a time budget. Moreover, to satisfy users' real-time requests, the fast response is usually mandatory. Here, we propose a novel two-phase framework called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MOOP , i.e., <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M anaging <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O ffline data for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O nline route <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P lanning, to discover the near-optimal driving routes efficiently. Specifically, in the offline phase, we construct the auxiliary data structure, i.e., the edge table, to manage the time-dependent information about edges. In the following online phase, the route is generated sequentially by an iterative edge table visiting process. We evaluate the proposed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MOOP thoroughly based on synthetic road networks and two real-world road networks in the city of Chengdu (4,819 nodes and 6,385 edges) and the city of Chongqing (5,056 nodes and 7,355 edges) in China. Results show that: (i) our framework can work adaptively for different time-varying utility patterns; (ii) the edge table is economic yet effective; (iii) our route planning algorithm outperforms other baselines in obtaining the highest utility value while costing the least running time.

MOOP: An Efficient Utility-Rich Route Planning Framework Over Two-Fold Time-Dependent Road Networks

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