Harilaos N. Psaraftis

DYNAMIC VEHICLE ROUTING : STATUS AND PROSPECTS

Although most real-world vehicle routing problems are dynamic, the traditional methodological arsenal for this class of problems has been based on adaptations of static algorithms. Still, some important new methodological approaches have recently emerged. In addition, computer-based technologies such as electronic data interchange (EDI), geographic information systems (GIS), global positioning systems (GPS), and intelligent vehicle-highway systems (IVHS) have significantly enhanced the possibilities for efficient dynamic routing and have opened interesting directions for new research. This paper examines the main issues in this rapidly growing area, and surveys recent results and other advances. The assessment of possible impact of new technologies and the distinction of dynamic problems vis-à-vis their static counterparts are given emphasis.

Cyclic transfer algorithms for multivehicle routing and scheduling problems

This paper investigates the application of a new class of neighborhood search algorithms—cyclic transfers—to multivehicle routing and scheduling problems. These algorithms exploit the two-faceted decision structure inherent to this problem class: First, assigning demands to vehicles and, second, routing each vehicle through its assigned demand stops. We describe the application of cyclic transfers to vehicle routing and scheduling problems. Then we determine the worst-case performance of these algorithms for several classes of vehicle routing and scheduling problems. Next, we develop computationally efficient methods for finding negative cost cyclic transfers. Finally, we present computational results for three diverse vehicle routing and scheduling problems, which collectively incorporate a variety of constraint and objective function structures. Our results show that cyclic transfer methods are either comparable to or better than the best published heuristic algorithms for several complex and important vehicle routing and scheduling problems. Most importantly, they represent a novel approach to solution improvement which holds promise in many vehicle routing and scheduling problem domains.

A Dynamic Programming Approach for Sequencing Groups of Identical Jobs

A Dynamic Programming approach for sequencing a given set of jobs in a single machine is developed, so that the total processing cost is minimized. Assume that there are N distinct groups of jobs, where the jobs within each group are identical. A very general, yet additive cost function is assumed. This function includes the overall completion time minimization problem as well as the total weighted completion time minimization problem as special cases. Priority considerations are included; no job may be shifted by more than a prespecified number of positions from its initial, First Come-First Served position in a prescribed sequence. The running time and the storage requirement of the Dynamic Programming algorithm are both polynomial functions of the maximum number of jobs per group, and exponential functions of the number of groups N. This makes our approach practical for real-world problems in which this latter number is small. More importantly, the algorithm offers savings in computational effort as compared to the classical Dynamic Programming approach to sequencing problems, savings which are solely due to taking advantage of group classifications. Specific cost functions, as well as a real-world problem for which the algorithm is particularly well-suited, are examined. The problem application is the optimal sequencing of aircraft landings at an airport. A numerical example as well as suggestions on possible extensions to the model are also presented.

Dynamic shortest paths in acyclic networks with Markovian arc costs

We examine shortest path problems in acyclic networks in which arc costs are known functions of certain environment variables at network nodes. Each of these variables evolves according to an independent Markov process. The vehicle can wait at a node (at a cost) in anticipation of more favorable arc costs. We first develop two recursive procedures for the individual arc case, one based on successive approximations, and the other on policy iteration. We also solve the same problem via parametric linear programming. We show that the optimal policy essentially classifies the state of the environment variable at a node into two categories: green states for which the optimal action is to immediately traverse the arc, and red states for which the optimal action is to wait. We then extend these concepts for the entire network by developing a dynamic programming procedure that solves the corresponding problem. The complexity of this method is shown to be O(n2K + nK3), where n is the number of network nodes and K ...

CO2 emission statistics for the world commercial fleet

The purpose of this paper is to present an analysis of carbon dioxide (CO2) emissions of the world commercial fleet. The analysis is based on the Lloyds-Fairplay world ship database for 2007 and produces various emissions statistics of the following major ship types: bulk carriers, crude oil tankers, container vessels, product/chemical carriers, LNG carriers, LPG carriers, reefer vessels, Ro-Ro vessels and general cargo ships. A separate analysis is carried out for small vessels under 400 GRT and for passenger vessels. The main outputs from this analysis for each ship type-size bracket are the emitted grams of CO2 per tonne-km and an estimate of the total CO2 produced in a year. The methodology for estimating these statistics is described, and a comparison with other studies is made.

k-Interchange procedures for local search in a precedence-constrained routing problem

Abstract We develop k-interchange procedures to perform local search in a precedence-constrained routing problem. The problem in question is known in the Transportation literature as the single vehicle many-to-many Dial-A-Ride Problem, or DARP. The DARP is the problem of minimizing the length of the tour traveled by a vehicle to service N customers, each of whom wishes to go from a distinct origin to a distinct destination. The vehicle departs from a specified point and returns to that point upon service of all customers. Precedence constraints in the DARP exist because the origin of each customer must precede his/her destination on the route. As in the interchange procedure of Lin for the Traveling Salesman Problem (TSP), a k-interchange is a substitution of k of the links of an initial feasible DARP tour with k other links, and a DARP tour is k-optimal if it is impossible to obtain a shorter tour by replacing any k of its links by k other links. However, in contrast to the TSP where each individual interchange takes O(1) time, checking whether each individual DARP interchange satisfies the origin-destination precedence constraints normally requires O(N2) time. In this paper we develop a method which still finds the best k-interchange that can be produced from an initial feasible DARP tour in O(Nk) time, the same order of magnitude as in the Lin heuristic for the TSP. This method is then embedded in a breadth-first or a depth-first search procedure to produce a k-optimal DARP tour. The paper focuses on the k = 2 and k = 3 cases. Experience with the procedures is presented. in which k-optimal tours are produced by applying a 2-opt or 3-opt search to initial DARP tours produced either randomly or by a fast O(N2) heuristic. The breadth-first and depth-first search modes are compared. The heuristics are seen to produce very good or near-optimal DARP tours.

Analysis of an O(N2) heuristic for the single vehicle many-to-many Euclidean dial-a-ride problem

We develop an O(N2) heuristic to solve the single vehicle many-to-many Euclidean Dial-A-Ride problem. The heuristic is based on the Minimum Spanning Tree of the modes of the problem. The algorithms worst case performance is four times the length of the optimal Dial-A-Ride tour. An analysis of the algorithms average performance reveals that in terms of sizes of single-vehicle problems that are likely to be encountered in the real world (up to 100 nodes) and in terms of computational complexity, the O(N2) heuristic performs equally well, or, in many cases, better than heuristics described earlier by Stein for the same problem. The performance of the heuristic exhibits statistical stability over a broad range of problem sizes.

Reduction of emissions along the maritime intermodal container chain: operational models and policies

Emissions from commercial shipping are currently the subject of intense scrutiny. Among the top fuel-consuming categories of ships and hence air polluters are container vessels. The main reason is their high service speed. Lately, speed reduction has become a very popular operational measure to reduce fuel consumption and can obviously be used to curb emissions. This paper examines such an operational scenario. Since time at sea increases with slow steaming, there is a parallel and strong interest to investigate possible ways to decrease time in port. One way to do so is to reduce port service time. Another possible way to minimize disruption and maximize efficiency is the prompt berthing of vessels upon arrival. To that effect, a related berthing policy is investigated as a measure to reduce waiting time. The objective of reducing emissions along the maritime intermodal container chain is investigated vis-à-vis reduction in operational costs and other service attributes. Some illustrative examples are presented.

An empirical analysis of IOPCF oil spill cost data

This paper reports on recent analysis of oil spill cost data assembled by the International Oil Pollution Compensation Fund (IOPCF). Regression analyses of clean-up costs and total costs have been carried out, after taking care to convert to current prices and remove outliers. In the first place, the results of this analysis have been useful in the context of the ongoing discussion within the International Maritime Organization (IMO) on environmental risk evaluation criteria. Furthermore, these results can be useful in estimating the benefit of regulations that deal with the protection of marine environment and oil pollution prevention.

Optimal response to oil spill: The strategic decision case

In this paper we develop a model for the problem of a locating appropriate levels and types of cleanup capability to respond to oil spills and b allocating such capability among points of high oil spill potential. The model takes into account frequency of spill occurrence, variability of spill volumes, different cleanup technologies, equipment efficiency and operability, fixed costs to open a facility, equipment acquisition, transportation and operating costs, and costs of damage as functions of spill volume and level of response. The model can also accept policy stipulations on response times. We present an illustrative application of the model in the New England region and discuss its possible uses within existing and alternative policy environments.