Clustered coverage orienteering problem of unmanned surface vehicles for water sampling

202105 bchyNot applicableOthersNSFC projectsPublished12 month

Multi-objective Analysis of Approaches to Dynamic Routing of a Vehicle

We consider a routing problem for a single vehicle serving customer Locations in the course of time. A subset of these customers must necessarily be served, while the complement of this subset contains dynamic customers which request for service over time, and which do not necessarily need to be served. The decision maker’s conflicting goals are serving as many customers as possible as well as minimizing total travel distance. We solve this bi-objective Problem with an evolutionary multi-objective algorithm in order to provide an a-posteriori evaluation tool for enabling decision makers to assess the single objective solution strategies that they actually use in real-time. We present the modifications to be applied to the evolutionary multi-objective algorithm NSGA2 in order to solve the routing problem, we describe a number of real-time single-objective solution strategies, and we finally use the gained efficient trade-off solutions of NSGA2 to exemplarily evaluate the real-time strategies. Our results show that the evolutionary multi-objective approach is well-suited to generate benchmarks for assessing dynamic heuristic strategies. Our findings point into future directions for designing dynamic multi-objective approaches for the vehicle routing problem with time windows

MODEL OPTIMISASI UNTUK MASALAH MINIMISASI WAKTU PERJALANAN WISATA TUR-TUNGGAL DI DAERAH KEPULAUAN

Penelitian ini bertujuan untuk mengembangkan model optimasi penyelesaian masalah minimisasi waktu perjalanan wisata tur-tunggal di daerah kepulauan. Model dikembangkan dengan menggunakan pendekatan integer programming dan diformulasikan dalam bentuk non-linear. Faktor-faktor yang dipertimbangkan meliputi klaster pulau, rute dan jadwal keberangkatan di setiap titik koneksi, dan seleksi titik akomodasi. Validasi model dilakukan melalui percobaan numerikal untuk menguji konsistensi dan adaptabilitas nilai keluaran model terhadap perubahan parameter yang diberikan. Skenario percobaan direncanakan berdasarkan variasi hari keberangkatan dan maksimum waktu kover titik akomodasi. Hasil menunjukkan bahwa model memiliki adaptabilitas dan konsisten terhadap perubahan parameter berdasarkan empat belas skenario yang diberikan

The Vehicle Routing Problem with Service Level Constraints

We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs. Our computational experiments show that the proposed heuristic returns very high-quality solutions for this difficult problem, matches all optimal solutions found for small and medium-scale benchmark instances, and improves upon existing algorithms for two important special cases: the vehicle routing problem with private fleet and common carrier, and the capacitated profitable tour problem. The branch-and-price algorithm also produces new optimal solutions for all three problems

MODEL OPTIMISASI UNTUK MASALAH MINIMISASI BIAYA PERJALANAN WISATA TUR-TUNGGAL DI ZONA KEPULAUAN

This study aims to develop an optimization model for solving the insular cost minimization single-tour travel problems. The model was developed using an integer non-linear programming approach by considering the selection of departure and arrival points of an island, selecting accommodation points, selecting transportation mode departure schedules, and selecting travel routes both within islands and between islands. The cost components considered include inter-island travel costs, land travel costs, costs at selective points, and costs waiting for departure. A numerical experiment was conducted on the case of planning a tourist route in Maluku, Indonesia. The departure day scenario is built to find out the exact route and schedule on each day of departure with a minimum total cost. In addition, comparisons were also made to the results obtained in the time minimization model that was introduced earlier. The results showed that the model can adapt through variations of solutions to changes in the given parameters and objectives

Profitable mixed capacitated arc routing and related problems

Mixed Capacitated Arc Routing Problems (MCARP) aim to identify a set of vehicle trips that, starting and ending at a depot node, serve a given number of links, regarding the vehicles capacity, and minimizing a cost function. If both profits and costs on arcs are considered, the Profitable Mixed Capacitated Arc Routing Problem (PMCARP) may be defined. We present compact flow based models for the PMCARP, where two types of services are tackled, mandatory and optional. Adaptations of the models to fit into some other related problems are also proposed. The models are evaluated, according to their bounds quality as well as the CPU times, over large sets of test instances. New instances have been created from benchmark ones in order to solve variants that have been introduced here for the first time. Results show the new models performance within CPLEX and compare, whenever available, the proposed models against other resolution methods.info:eu-repo/semantics/publishedVersio

Incorporating A New Class of Uncertainty in Disaster Relief Logistics Planning

In recent years, there has been a growing interest among emergency managers in using Social data in disaster response planning. However, the trustworthiness and reliability of posted information are two of the most significant concerns, because much of the user-generated data is initially not verified. Therefore, a key tradeoff exists for emergency managers when considering whether to incorporate Social data in disaster planning efforts. By considering Social data, a larger number of needs can be identified in a shorter amount of time, potentially enabling a faster response and satisfying a class of demand that might not otherwise be discovered. However, some critical resources can be allocated to inaccurate demands in this manner. This dissertation research is dedicated to evaluating this tradeoff by creating routing plans while considering two separate streams of information: (i) unverified data describing demand that is not known with certainty, obtained from Social media platforms and (ii) verified data describing demand known with certainty, obtained from trusted traditional sources (i.e. on the ground assessment teams). These projects extend previous models in the disaster relief routing literature that address uncertainty in demand. More broadly, this research contributes to the body of literature that addresses questions surrounding the usefulness of Social data for response planning

Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information Discovery

Uncertain optimization problems with decision dependent information discovery allow the decision maker to control the timing of information discovery, in contrast to the classic multistage setting where uncertain parameters are revealed sequentially based on a prescribed filtration. This problem class is useful in a wide range of applications, however, its assimilation is partly limited by the lack of efficient solution schemes. In this paper we study two-stage robust optimization problems with decision dependent information discovery where uncertainty appears in the objective function. The contributions of the paper are twofold: (i) we develop an exact solution scheme based on a nested decomposition algorithm, and (ii) we improve upon the existing K-adaptability approximate by strengthening its formulation using techniques from the integer programming literature. Throughout the paper we use the orienteering problem as our working example, a challenging problem from the logistics literature which naturally fits within this framework. The complex structure of the routing recourse problem forms a challenging test bed for the proposed solution schemes, in which we show that exact solution method outperforms at times the K-adaptability approximation, however, the strengthened K-adaptability formulation can provide good quality solutions in larger instances while significantly outperforming existing approximation schemes even in the decision independent information discovery setting. We leverage the effectiveness of the proposed solution schemes and the orienteering problem in a case study from Alrijne hospital in the Netherlands, where we try to improve the collection process of empty medicine delivery crates by co-optimizing sensor placement and routing decisions

IoT analytics and agile optimization for solving dynamic team orienteering problems with mandatory visits

Transport activities and citizen mobility have a deep impact on enlarged smart cities. By analyzing Big Data streams generated through Internet of Things (IoT) devices, this paper aims to show the efficiency of using IoT analytics, as an agile optimization input for solving real-time problems in smart cities. IoT analytics has become the main core of large-scale Internet applications, however, its utilization in optimization approaches for real-time configuration and dynamic conditions of a smart city has been less discussed. The challenging research topic is how to reach real-time IoT analytics for use in optimization approaches. In this paper, we consider integrating IoT analytics into agile optimization problems. A realistic waste collection problem is modeled as a dynamic team orienteering problem with mandatory visits. Open data repositories from smart cities are used for extracting the IoT analytics to achieve maximum advantage under the city environment condition. Our developed methodology allows us to process real-time information gathered from IoT systems in order to optimize the vehicle routing decision under dynamic changes of the traffic environments. A series of computational experiments is provided in order to illustrate our approach and discuss its effectiveness. In these experiments, a traditional static approach is compared against a dynamic one. In the former, the solution is calculated only once at the beginning, while in the latter, the solution is re-calculated periodically as new data are obtained. The results of the experiments clearly show that our proposed dynamic approach outperforms the static one in terms of rewardsThis project has received the support of the Ajuntament of Barcelona and the Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001)Peer ReviewedPostprint (published version