Emrah Demir

A review of recent research on green road freight transportation

Road freight transportation is a major contributor to carbon dioxide equivalent emissions. Reducing these emissions in transportation route planning requires an understanding of vehicle emission models and their inclusion into the existing optimization methods. This paper provides a review of recent research on green road freight transportation.

An adaptive large neighborhood search heuristic for the Pollution-Routing Problem

The Pollution-Routing Problem (PRP) is a recently introduced extension of the classical Vehicle Routing Problem with Time Windows which consists of routing a number of vehicles to serve a set of customers, and determining their speed on each route segment so as to minimize a function comprising fuel, emission and driver costs. This paper presents an adaptive large neighborhood search for the PRP. Results of extensive computational experimentation confirm the efficiency of the algorithm.

The bi-objective pollution-routing problem

The bi-objective Pollution-Routing Problem is an extension of the Pollution-Routing Problem (PRP) which consists of routing a number of vehicles to serve a set of customers, and determining their speed on each route segment. The two objective functions pertaining to minimization of fuel consumption and driving time are conflicting and are thus considered separately. This paper presents an adaptive large neighborhood search algorithm (ALNS), combined with a speed optimization procedure, to solve the bi-objective PRP. Using the ALNS as the search engine, four a posteriori methods, namely the weighting method, the weighting method with normalization, the epsilon-constraint method and a new hybrid method (HM), are tested using a scalarization of the two objective functions. The HM combines adaptive weighting with the epsilon-constraint method. To evaluate the effectiveness of the algorithm, new sets of instances based on real geographic data are generated, and a library of bi-criteria PRP instances is compiled. Results of extensive computational experiments with the four methods are presented and compared with one another by means of the hypervolume and epsilon indicators. The results show that HM is highly effective in finding good-quality non-dominated solutions on PRP instances with 100 nodes.

Green vehicle routing

Green vehicle routing is a branch of green logistics which refers to vehicle routing problems where externalities of using vehicles, such as carbon dioxide-equivalents emissions, are explicitly taken into account so that they are reduced through better planning. This chapter presents an overview of some of the recent developments in the area, including the description of some vehicle emission models and their applications in road freight transportation.

An Exact Approach for a Variant of the Pollution-Routing Problem

The pollution-routing problem (PRP) is a recently introduced green vehicle routing problem in the field of green logistics. It concerns routing a number of vehicles to serve a set of geographically dispersed customers within their time windows, jointly with determining their speed on each arc so as to minimize fuel and driving costs. Because of its complexity, all known solution methods are based on (meta-)heuristics. This paper presents an exact solution based on a branch-and-price algorithm for a variant of the PRP. The master problem is a set-partitioning problem, and the pricing problem is a speed- and start-time elementary shortest path problem with resource constraints, in which the speed and start time at the depot needs to be decided on for each individual route. The master problem is solved by means of column generation, and a tailored labeling algorithm is used to solve the pricing problem. New dominance criteria are developed to discard unpromising labels by exploiting the structure of the read...

A metaheuristic for the time-dependent pollution-routing problem

We propose a metaheuristic for the Time-Dependent Pollution-Routing Problem, which consists of routing a number of vehicles to serve a set of customers and determining their speed on each route segment with the objective of minimizing the cost of driver’s wage and greenhouse gases emissions. The vehicles face traffic congestion which, at peak periods, significantly restricts vehicle speeds and leads to increased emissions. Our algorithm is based on an adaptive large neighborhood search heuristic and uses new removal and insertion operators which significantly improve the quality of the solution. A previously developed departure time and speed optimization procedure is used as a subroutine to optimize departure times and vehicle speeds. Results from extensive computational experiments demonstrate the effectiveness of our algorithm.

Multidepot Distribution Planning at Logistics Service Provider Nabuurs B.V.

Distribution networks of many logistics service providers have evolved from single-depot to complex, dynamic multidepot networks. In a single-depot network, the deliveries from each depot are planned for that depot only, and drivers return to the starting depot to pick up each new order. In a multidepot network, the deliveries from multiple depots can be planned simultaneously; therefore, a logistics service provider can efficiently combine its resources, thus reducing its labor and transport costs. However, an increasing emphasis on reliability, customization, and flexibility is affecting the logistics structures. This paper describes the shift from single-depot planning to multidepot planning for Nabuurs B.V., a large Benelux logistics service provider that implemented a centralized, automated multidepot planning process throughout its organization. We developed a simulation model to evaluate system performance and to address performance challenges. In this paper, we discuss the results of extensive simulation tests and the specific recommendations that Nabuurs B.V. management implemented.

The pickup and delivery problem with time windows and scheduled lines

ABSTRACT Integrating freight flows with scheduled public transportation services creates attractive business opportunities as the same transportation needs can be met with fewer operating costs. The pickup and delivery problem with time windows and scheduled lines (PDPTW-SL) aims at routing a given set of vehicles to transport freight requests from their origins to their corresponding destinations, where the requests can use scheduled passenger transportation services as a part of their journeys. We describe the PDPTW-SL as an arc-based mixed-integer program. Computational results on a set of small-size instances provide a clear understanding of the benefits of using scheduled line services as a part of freights journey.

Branch-and-Price for the Pickup and Delivery Problem with Time Windows and Scheduled Lines

The Pickup and Delivery Problem with Time Windows and Scheduled Lines (PDPTW-SL) consists of routing and scheduling a set of vehicles, by integrating them with scheduled public transportation lines, to serve a set of freight requests within their time windows. This paper presents an exact solution approach based on a branch-and-price algorithm. A path-based set partitioning formulation is used as the master problem, and a variant of the elementary shortest path problem with resource constraints is solved as the pricing problem. In addition, the proposed algorithm can also be used to solve the PDPTW with transfers (PDPTW-T) as a special case. Results of extensive computational experiments confirm the efficiency of the algorithm: it is able to solve small- and medium-size instances to optimality within reasonable execution time. More specifically, our algorithm solves the PDPTW-SL with up to 50 requests and the PDPTW-T with up to 40 requests on the considered instances.

Mathematical Modeling of CO2e emissions in one-to-one pickup and delivery problems

The road freight transportation sector is a significant emitter of carbon dioxide equivalents (CO2e), which are directly proportional to fuel consumption. Multi-vehicle, multi-depot one-to-one pickup and delivery pollution-routing problem (PDPRP) is an extension of the classical vehicle routing problem with time windows (PDVRPTW) which consists of routing a number of vehicles to serve a set of customers and deciding on their speed on each route so as to minimize a total function comprising fuel and driver costs. A mathematical model of the one-to-one PDPRP is non-linear in nature, although it can be linearized and represented as a mixed integer linear programming formulation. Computational results show the importance of using fuel consumption objective instead of distance- or time-based cost functions.