Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page

Model and algorithm of two-stage distribution location routing with hard time window for city cold-chain logistics

Taking cold-chain logistics as the research background and combining with the overall optimisation of logistics distribution networks, we develop two-stage distribution location-routing model with the minimum total cost as the objective function and varying vehicle capacity in different delivery stages. A hybrid genetic algorithm is designed based on coupling and collaboration of the two-stage routing and transfer stations. The validity and feasibility of the model and algorithm are verified by conducting a randomly generated test. The optimal solutions for different objective functions of two-stage distribution location-routing are compared and analysed. Results turn out that for different distribution objectives, different distribution schemes should be employed. Finally, we compare the two-stage distribution location-routing to single-stage vehicle routing problems. It is found that a two-stage distribution location-routing system is feasible and effective for the cold-chain logistics network, and can decrease distribution costs for cold-chain logistics enterprises.Peer ReviewedPostprint (published version

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Shared Mobility Optimization in Large Scale Transportation Networks: Methodology and Applications

abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Application of selection hyper-heuristics to the simultaneous optimisation of turbines and cabling within an offshore windfarm

Global warming has focused attention on how the world produces the energy required to power the planet. It has driven a major need to move away from using fossil fuels for energy production toward cleaner and more sustainable methods of producing renewable energy. The development of offshore windfarms, which harness the power of the wind, is seen as a viable approach to creating renewable energy but they can be difficult to design efficiently. The complexity of their design can benefit significantly from the use of computational optimisation. The windfarm optimisation problem typically consists of two smaller optimisation problems: turbine placement and cable routing, which are generally solved separately. This paper aims to utilise selection hyper-heuristics to optimise both turbine placement and cable routing simultaneously within one optimisation problem. This paper identifies and confirms the feasibility of using selection hyper-heuristics within windfarm optimisation to consider both cabling and turbine positioning within the same single optimisation problem. Key results could not identify a conclusive advantage to combining this into one optimisation problem as opposed to considering both as two sequential optimisation problems.</p

An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

This article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints

Deep Learning and Deep Reinforcement Learning for Graph Based Applications

Dyp læring har gitt state-of-the-art ytelse i mange applikasjoner som datasyn, tekstanalyse, biologi, osv. Suksessen med dyp læring har også hjulpet fremveksten av dyp forsterkende læring for optimal beslutningstaking og har vist stort potensiale, spesielt i optimaliseringsproblemer. I tillegg har grafer som matematisk representasjon for strukturerte komplekse systemer vist seg å være et kraftig verktøy for analyse og problemløsning, og gitt et nytt perspektiv på formuleringen av problemet. Ved å introdusere grafer som en inputmodalitet for maskinlæringsproblemer kan dyplæringsmodeller enten bruke strukturen til grafen i sine representasjonslæringsskjema, eller optimalisere grafstrukturen i en nedstrøms evalueringsoppgave. Dette vil også føre til modellmetoder og pipelines som utnytter den strukturelle informasjonen gitt av grafer til forbedret ytelse, sammenlignet med tradisjonelle maskinlæringsmodellers kapasitet. I denne oppgaven introduserer vi fem forskjellige use-case-applikasjoner, gjennom fem forskningsartikler, som kan modelleres som grafer og tar sikte på å skape nye modeller som adresserer problemer ved bruk av dyp grafrepresentasjonslæring og dype forsterkningslæringsmodeller. Våre tre viktigste applikasjonsdomener er bioinformatikk, datasyn og logistikk. Først tar vi sikte på å adressere to problemer innen bioinformatikk. I Paper I tar vi opp spørsmålet om integrering av kontinuerlige omics-datasett med biologiske nettverk. Vi introduserer et auto-koderskjema fokusert på representasjonslæring av nodefunksjoner i biologiske nettverk, og viser anvendelsen av det utformede rammeverket i et virkelighetseksempel gjennom imputering av manglende verdier i et eksempeldatasett for omics. Paper II ser på bruk av grafrepresentasjonslæring for å behandle metabolske nettverk. I den foreslåtte tilnærmingen introduserer vi en maskinlæringspipeline (fra funksjonsekstraksjon til modellarkitektur) basert på grafiske nevrale nettverk og evaluerer pipelinen basert på prediksjon av genessensalitet, som er en velkjent bruk av metabolske banenettverk. Det andre domenet av applikasjoner er datasynsdomenet, spesifikt problemet med gjenkjennelse av menneskelige gester. I Paper III, og oppfølgingen Paper IV, introduserer vi et gestgjenkjenningssystem som er både raskere og mer nøyaktig enn den avanserte prediksjonen av menneskelige motivbevegelser fra mmWave Radar genererte punktskyer. Vi oppnår dette ved å modellere inngangspunktskyen som en spatio-temporal graf og å bearbeide den opprettede grafen ved bruk av den foreslåtte læringsteknikken for grafrepresentasjon. Videre evaluerer vi systemet under forskjellige eksperimentelle forhold ut ifra vinkelen til emnet med hensyn til sansing, og foreslår en ensembletilnærming for å dempe effekten av å endre sansevinkelen på ytelsen til modellen. Den siste applikasjonen vi tar for oss er bruken av dyp forsterkningslæring for å optimalisere strukturen til grafene i kombinatoriske optimaliseringsproblemer i logistikk. Paper V introduserer en generell problemuavhengig hyperheuristikk som utnytter beslutningsevnen til dyp forsterkende læring, ved å bruke en problemuavhengig tilstandsfunksjonsinformasjon. Det foreslåtte rammeverket er trent på en generell belønningsfunksjon for å oppnå høykvalitets ytelse blant populære løsere innen kombinatorisk optimalisering. Vi evaluerer ytelsen til den foreslåtte tilnærmingen med tre eksempler på ruting problemer samt et planleggingsproblem, for å vise effektiviteten til metoden vår i forskjellige typer problemstillinger.Deep learning has provided state-of-the-art performance in many applications such as computer vision, text analysis, biology, etc. The success of deep learning has also helped with the emergence of deep reinforcement learning for optimal decision-making and has shown great promise, especially in optimization problems. Additionally, graphs as a mathematical representation for structured complex systems have proven to be a powerful tool for analysis and problem-solving that offer a fresh perspective on the formulation of the problem. Introducing graphs as an input modality for machine learning problems enables deep learning models to either utilize the structure of the graph in their representation learning scheme or optimize the graph structure for a downstream evaluation task. Doing so will also lead to model methods and pipelines that leverage the structural information provided by graphs to improve performance compared to traditional machine learning models. In this thesis, we introduce five different use-case applications, in the format of five research papers, that can be modeled as graphs and aim to provide novel models that address problems using deep graph representation learning and deep reinforcement learning models. Our main three application domains are bioinformatics, computer vision, and logistics. First, we aim to address two problems in the domain of bioinformatics. In Paper I, we address the issue of integration of continuous omics datasets with biological networks. We introduce an auto-encoder scheme focused on representation learning of node features in biological networks and showcase the application of the designed framework in a real-world example through the imputation of missing values in an example omics dataset. Paper II looks at utilizing graph representation learning for processing metabolic networks. In the proposed approach, we introduce a machine learning pipeline (from feature extraction to model architecture) based on graph neural networks and evaluate the pipeline on the task of gene essentiality prediction which is a well-known application of metabolic pathway networks. The second domain of applications is the computer vision domain specifically the problem of human gesture recognition. In Paper III and the follow-up Paper IV, we introduce a gesture recognition system that is both faster and more accurate compared to the state-of-the-art prediction of human subject gestures from mmWave Radar generated point clouds. We achieve this by modeling the input point cloud as a spatio-temporal graph and processing the created graph using the proposed graph representation learning technique. We further evaluate the system in different experimental conditions in terms of the angle of the subject with respect to sensing and propose an ensemble approach for mitigating the effect of changing the sensing angle on the performance of the model. The last application that we address is the use of deep reinforcement learning to optimize the structure of the graphs in combinatorial optimization problems in logistics. Paper V introduces a general problem-independent hyperheuristic that utilizes the decision-making capability of deep reinforcement learning using a problem-independent state feature information. The proposed framework is trained on a general reward function to achieve state-of-the-art performance among popular solvers in the field of combinatorial optimization. We evaluate the performance of the proposed approach on three example routing problems as well as a scheduling problem to showcase the effectiveness of our method in different problems.Doktorgradsavhandlin

A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container transportation problem. In a long planning horizon of multiple shifts, transport tasks are completed satisfying the time constraints. Due to the different travel distances and time of tasks, there are two types of shifts (long shift and short shift) in this problem. The unit driver cost for long shifts is higher than that of short shifts. A mathematical model of this Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW) is established in this paper, with two objectives of minimizing the total driver payment and the total travel distance. Due to the large scale and nonlinear constraints, the exact search showed is not suitable to MS-VRPTW. An initial solution construction heuristic (EBIH) and a selective perturbation Hyper-Heuristic (GIHH) are thus developed. In GIHH, five heuristics with different extents of perturbation at the low level are adaptively selected by a high level selection scheme with the Hill Climbing acceptance criterion. Two guidance indicators are devised at the high level to adaptively adjust the selection of the low level heuristics for this bi-objective problem. The two indicators estimate the objective value improvement and the improvement direction over the Pareto Front, respectively. To evaluate the generality of the proposed algorithms, a set of benchmark instances with various features is extracted from real-life historical datasets. The experiment results show that GIHH significantly improves the quality of the final Pareto Solution Set, outperforming the state-of-the-art algorithms for similar problems. Its application on VRPTW also obtains promising results

REVISIÓN DE LA LITERATURA DEL PROBLEMA DE RUTEO DE VEHÍCULOS EN UN CONTEXTO DE TRANSPORTE VERDE

In the efficient management of the supply chain the optimal management of transport of consumables and finished products appears. The costs associated with transport have direct impact on the final value consumers must pay, which in addition to requiring competitive products also demand that they are generated in environmentally friendly organizations. Aware of this reality, this document is intended to be a starting point for Master's and Doctoral degree students who want to work in a line of research recently proposed: green routing. The state of the art of the vehicle routing problem is presented in this paper, listing its variants, models and methodologies for solution. Furthermore, the proposed interaction between variants of classical routing problems and environmental effects of its operations, known in the literature as Green-VRP is presented. The goal is to generate a discussion in which mathematical models and solution strategies that can be applied within organizations that consider within their objectives an efficient and sustainable operation are posed. En el gerenciamiento eficiente de la cadena de suministro aparece la gestión óptima del transporte de insumos y productos terminados. Los costos asociados al transporte tienen impacto directo sobre el valor final que deben pagar los consumidores, que además de requerir productos competitivos también exigen que los mismos sean generados en organizaciones amigables con el medioambiente. Consientes de esa realidad este documento pretende ser un punto de partida para estudiantes de maestría y doctorado que quieran trabajar en una línea de investigación propuesta recientemente: el ruteo verde. En este trabajo se muestra un estado del arte del problema de ruteo de vehículos, enumerando sus variantes, modelos y metodologías de solución. Además, se presenta la interacción que se ha propuesto entre variantes clásicas de los problemas de ruteo y los efectos ambientales de su operación, denominados en la literatura como Green-VRP. El objetivo es generar una discusión donde se planteen modelos matemáticos y estrategias de solución que puedan ser aplicadas en organizaciones que consideren dentro de sus objetivos una operación eficiente y sustentable. Document type: Articl

Revisión de la literatura del problema de ruteo de vehículos en un contexto de transporte verde

In the efficient management of the supply chain the optimal management of transport of consumables and finished products appears. The costs associated with transport have direct impact on the final value consumers must pay, which in addition to requiring competitive products also demand that they are generated in environmentally friendly organizations. Aware of this reality, this document is intended to be a starting point for Master’s and Doctoral degree students who want to work in a line of research recently proposed: green routing. The state of the art of the vehicle routing problem is presented in this paper, listing its variants, models and methodologies for solution. Furthermore, the proposed interaction between variants of classical routing problems and environmental effects of its operations, known in the literature as Green- VRP is presented. The goal is to generate a discussion in which mathematical models and solution strategies that can be applied within organizations that consider within their objectives an efficient and sustainable operation are posed.En el gerenciamiento eficiente de la cadena de suministro aparece la gestión óptima del transporte de insumos y productos terminados. Los costos asociados al transporte tienen impacto directo sobre el valor final que deben pagar los consumidores, que además de requerir productos competitivos también exigen que los mismos sean generados en organizaciones amigables con el medioambiente. Consientes de esa realidad este documento pretende ser un punto de partida para estudiantes de maestría y doctorado que quieran trabajar en una línea de investigación propuesta recientemente: el ruteo verde. En este trabajo se muestra un estado del arte del problema de ruteo de vehículos, enumerando sus variantes, modelos y metodologías de solución. Además, se presenta la interacción que se ha propuesto entre variantes clásicas de los problemas de ruteo y los efectos ambientales de su operación, denominados en la literatura como Green-VRP. El objetivo es generar una discusión donde se planteen modelos matemáticos y estrategias de solución que puedan ser aplicadas en organizaciones que consideren dentro de sus objetivos una operación eficiente y sustentable