Green Maritime Logistics:The Quest for Win-win Solutions

By green maritime logistics we mean achieving an acceptable environmental performance of the maritime transport logistical supply chain while at the same time respecting traditional economic criteria. In this paper the environmental focus is on maritime emissions. Achieving such goal may involve several trade-offs, and win-win solutions are typically sought. However, finding these solutions may be more difficult than may appear at first glance. The purpose of this paper is to provide a concise overview of the challenges of green maritime logistics and present some examples, both for greenhouse gas (GHG) and non-GHG emissions

The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services

The operation of a demand responsive transport service usually involves the management of dynamic requests. The underlying algorithms are mainly adaptations of procedures carefully designed to solve static versions of the problem, in which all the requests are known in advance. However there is no guarantee that the effectiveness of an algorithm stays unchanged when it is manipulated to work in a dynamic environment. On the other hand, the way the input is revealed to the algorithm has a decisive role on the schedule quality. We analyze three characteristics of the information flow (percentage of real-time requests, interval between call-in and requested pickup time and length of the computational cycle time), assessing their influence on the effectiveness of the scheduling proces

Model-based corridor performance analysis – An application to a European case

The paper proposes a methodology for freight corridor performance monitoring that is suitable for sustainability assessments. The methodology, initiated by the EU-funded project SuperGreen, involves the periodic monitoring of a standard set of transport chains along the corridor in relation to a number of Key Performance Indicators (KPIs). It consists of decomposing the corridor into transport chains, selecting a sample of typical chains, assessing these chains through a set of KPIs, and then aggregating the chain-level KPIs to corridor-level ones using proper weights. A critical feature of this methodology concerns the selection of the sample chains and the calculation of the corresponding weights. After several rounds of development, the proposed methodology suggests a combined approach involving the use of a transport model for sample construction and weight calculation followed by stakeholder refinement and verification. The sample construction part of the methodology was tested on GreCOR, a green corridor project in the North Sea Region, using the Danish National Traffic Model as the principal source of information for both sample construction and KPI estimation. The results show that, to the extent covered by the GreCOR application, the proposed methodology can effectively assess the performance of a freight transport corridor. Combining the model-based approach for the sample construction with the study-based approach for the estimation of chain-level indicators exploits the strengths of each method and avoids their weaknesses. Possible improvements are also suggested by the paper

A Multistage Stochastic Programming Approach to the Dynamic and Stochastic VRPTW - Extended version

We consider a dynamic vehicle routing problem with time windows and stochastic customers (DS-VRPTW), such that customers may request for services as vehicles have already started their tours. To solve this problem, the goal is to provide a decision rule for choosing, at each time step, the next action to perform in light of known requests and probabilistic knowledge on requests likelihood. We introduce a new decision rule, called Global Stochastic Assessment (GSA) rule for the DS-VRPTW, and we compare it with existing decision rules, such as MSA. In particular, we show that GSA fully integrates nonanticipativity constraints so that it leads to better decisions in our stochastic context. We describe a new heuristic approach for efficiently approximating our GSA rule. We introduce a new waiting strategy. Experiments on dynamic and stochastic benchmarks, which include instances of different degrees of dynamism, show that not only our approach is competitive with state-of-the-art methods, but also enables to compute meaningful offline solutions to fully dynamic problems where absolutely no a priori customer request is provided.Comment: Extended version of the same-name study submitted for publication in conference CPAIOR201

A Basic Problem of Resource Allocation in Target Tracking

Stochastic dynamic programming techniques are used to formulate and solve the problem of tracking two independent and stationary targets with one sensor in order to maximize a certain measure of performance. At any point in time, the sensor, usually a passive sonar array, can be allocated to only one of the two targets. Assuming the fluctuation process in the ocean to be governed by a phase‐random multipath law, the sensor ’’holds’’ the target when ρ, the root‐mean‐square pressure at the receiver, is above a user‐specified threshold. Using discrete time models for the ocean acoustic detection process formulated in earlier papers, we solve the problem for a finite horizon of observations using several alternative objective and reward/penalty functions. Delays of user‐specified magnitude in ’’switching’’ from one target to the other are also incorporated in our algorithms. Examples using both real and simulated data are presented and discussed. Finally, future research directions are suggested.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86218/1/Perakis18.pd