A Big-Data-Analytics Framework for Supporting Logistics Problems in Smart-City Environments

Abstract Containers delivery management is a problem widely studied. Typically, it concerns the container movement on a truck from ships to factories or wholesalers and vice-versa. As there is an increasing interest in shipping goods by container, and that delivery points can be far from railways in various areas of interest, it is important to evaluate techniques for managing container transport that involves several days. The time horizon considered is a whole working week, rather than a single day as in classical drayage problems. Truck fleet management companies are typically interested in such optimization, as they plan how to match their truck to the incoming transportation order. This planning is a relevant both for strategical consideration and operational ones, as prices of transportation orders strictly depends on how they are fulfilled. It is worth noting that, from a mathematical point of view, this is an NP-Hard problem. In this paper, a Decision Support System for managing the tasks to be assigned to each truck of a fleet is presented, in order to optimize the number of transportation order fulfilled in a week. The proposed system implements a hybrid optimization algorithm capable of improving the performances typically presented in literature. The proposed heuristic implements an hybrid genetic algorithm that generate chains of consecutive orders that can be executed by a truck. Moreover, it uses an assignment algorithm based to evaluate the optimal solution on the selected order chains

Transportation-mission-based Optimization of Heterogeneous Heavy-vehicle Fleet Including Electrified Propulsion

Commercial-vehicle manufacturers design vehicles to operate over a wide range of transportation tasks and driving cycles. However, certain possibilities of reducing emissions, manufacturing and operational costs from end vehicles are neglected if the target range of transportation tasks is narrow and known in advance, especially in case of electrified propulsion. Apart from real-time energy optimization, vehicle hardware can be meticulously tailored to best fit a known transportation task. As proposed in this study, a heterogeneous fleet of heavy-vehicles can be designed in a more cost- and energy-efficient manner, if the coupling between vehicle hardware, transportation mission, and infrastructure is considered during initial conceptual-design stages. To this end, a rather large optimization problem was defined and solved to minimize the total cost of fleet ownership in an integrated manner for a real-world case study. In the said case-study, design variables of optimization problem included mission, recharging infrastructure, loading--unloading scheme, number of vehicles of each type, number of trips, vehicle-loading capacity, selection between conventional, fully electric, and hybrid powertrains, size of internal-combustion engines and electric motors, number of axles being powered, and type and size of battery packs. This study demonstrated that by means of integrated fleet customization, battery-electric heavy-vehicles could strongly compete against their conventional combustion-powered counterparts. Primary focus has been put on optimizing vehicle propulsion, transport mission, infrastructure and fleet size rather than routing

Development of a procedure for the statewide distribution and assignment of truck commodity flows: a case study of Iowa

The purpose of this research is to develop a procedure for statewide planning of truck commodity flows and apply it to the State of Iowa. The methodology utilizes available relevant data sources at the state level and state-of-the-art freight transportation planning tools. A case study consisting of two manufacturing sectors and a simplistic transportation network is developed for Iowa to demonstrate the procedure. Difficulties encountered in the modeling process are identified and categorized by cause into modeling capability or data related. Data deficiencies having the greatest impact on the modeling process and the accuracy of the results are identified. Some methods to improve freight data are offered as are estimates of the effort entailed in improving and developing new data;The procedure for the statewide distribution and assignment of truck commodity flows provides a practical tool for state level freight transportation planning. The procedure examines major commodity movements on dense corridors. The general scheme of the methodology is to: (1) identify major commodities shipped in the state; (2) identify producing and attracting zones of the major commodities in the state; (3) estimate truck shipments between origin-destination pairs; and (4) assign estimated truck trips onto the primary highways within the state;Analysis zones within Iowa represent counties, while external analysis zones represent states. The total zonal freight tonnage generated is estimated using socioeconomic indicators, employment and population. Produced manufacturing freight is correlated with employment rates. Attracted freight is allocated to industrial inputs by employment and to consumption using population size. The truck freight share is estimated as the total freight generated less the freight tonnage shipped by rail. A gravity model is used to distribute the estimated truck tonnage among major origin-destination pairs. The impedance factor in the gravity model is equal to the inverse of travel time on links. Estimated truck tonnage is converted to vehicle trips using typical vehicle equivalent weights. Truck trips are assigned to shortest paths calculated using a tree building algorithm. Estimated truck trips are validated against truck counts on selected highway links

Optimization of customer orders routing in a collaborative distribution network

This paper presents a sequential approach for the assessment of a multi-layered distribution network from a cluster of collaborating suppliers to a large set of customers. The transportation network includes three segments: suppliers routes from suppliers to a consolidation and distribution center, full truckload routes toward regional distribution centers, and less-than-truckload distribution toward final customers. In every shipping date, the optimization problem consists of assigning customers to regional distribution centers and determining the routes of vehicles through the whole distribution network. This problem is first modeled as a Mixed Integer Linear Problem (MILP). Then, we propose to decompose it into three smaller MILPs that are solved sequentially in order to quickly provide a good approximate solution. The experiments on real data show that the decomposition method provides near optimal solutions within a few minutes while the original model would require hours of calculation

Models for Reducing Deadheading through Carrier and Shipper Collaboration

The competitive nature in the trucking industry has forced trucking firms to develop innovative solutions to improve their operational efficiency and decrease marginal costs. There is also a great need to reduce deadheading miles of heavy trucks to help reduce the amount of air pollutants they emit. One way carriers and shippers are attempting to accomplish these goals is through various collaborative operational strategies. This work focuses on developing multiple collaboration frameworks and formulating optimization models for each framework that demonstrates the operations and reveals the potential cost savings of each framework.;The first collaboration framework focuses on how a medium level shipper or carrier can introduce collaboration in their operations by fulfilling a collaborative carrier\u27s or shipper\u27s delivery requests on its backhaul route. Two optimization models are developed to route the carrier of interest\u27s backhaul routes and select collaborative shipments to fulfill; one is formulated as an integer program and the other is formulated as a mixed integer program. Two solution methodologies, a greedy heuristic and tabu search, are used to solve the two problems, and numerical analysis is performed with a real world freight network. Numerical analysis on a real world freight network reveals that the percentage of cost savings for backhaul routes can be as high as 27%.;The second collaboration framework focuses on a group of shippers that collaborate their operations and form cycles between their long-haul shipping lanes. If the shippers provide the bundled lanes, as loops, to a common carrier they can realize cost savings from the carrier. The problem is formulated as a mixed integer program and forms least cost loops between the shipping lanes. A tabu search heuristic is used to solve the second collaboration framework and results using a real freight network reveal collaborative network costs savings between 7% to 12%. Three cost allocation mechanisms are proposed for the problem to distribute the costs to the shippers involved in the collaboration and computational results are provided for each of the allocation mechanisms

Design and Analysis of Efficient Freight Transportation Networks in a Collaborative Logistics Environment

The increase in total freight volumes, reducing volume per freight unit, and delivery deadlines have increased the burden on freight transportation systems of today. With the evolution of freight demand trends, there also needs to be an evolution in the freight distribution processes. Today\u27s freight transportation processes have a lot of inefficiencies that could be streamlined, thus preventing concerns like increased operational costs, road congestion, and environmental degradation. Collaborative logistics is one of the approaches where supply chain partners collaborate horizontally or/and vertically to create a centralized network that is more efficient and serves towards a common goal or objective. In this dissertation, we study intermodal transportation, and cross-docking, two major pillars of efficient, cheap, and faster freight transportation in a collaborative environment. We design an intermodal network from a centralized network perspective where all the participants intermodal operators, shippers, carriers, and customers strive towards a synchronized and cost-efficient freight network. Also, a cross-dock scheduling problem is presented for competitive shippers using a centralized cross-dock facility. The problem develops a fast heuristic and meta-heuristic approach to solve large-scale real-world problems and draws key insights from a cross-dock operator and inbound carrier\u27s perspectives

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed