Jan Fabian Ehmke

Advanced routing for city logistics service providers based on time-dependent travel times

Abstract Increasing traffic demand, recurring congestion and sophisticated e-commerce business models lead to enormous challenges for routing in city logistics. We introduce a planning system for city logistics service providers, which faces those challenges by more realistic vehicle routing considering time-dependent travel times. Time-dependent travel times arise from telematics-based traffic data collection city-wide. Well-known heuristics for the Traveling Salesman Problem and for the Vehicle Routing Problem are adapted to time-dependent planning data. Computational experiments allow for insights into the efficiency of individual heuristics, their adaptability to time-dependent planning data sets, and the quality and structure of resulting delivery tours.

Ensuring service levels in routing problems with time windows and stochastic travel times

In the stochastic variant of the vehicle routing problem with time windows, known as the SVRPTW, travel times are assumed to be stochastic. In our chance-constrained approach to the problem, restrictions are placed on the probability that individual time window constraints are violated, while the objective remains based on traditional routing costs. In this paper, we propose a way to offer this probability, or service level, for all customers. Our approach carefully considers how to compute the start-service time and arrival time distributions for each customer. These distributions are used to create a feasibility check that can be “plugged” into any algorithm for the VRPTW and thus be used to solve large problems fairly quickly. Our computational experiments show how the solutions change for some well-known data sets across different levels of customer service, two travel time distributions, and several parameter settings.

Integration of Information and Optimization Models for Routing in City Logistics

As urban congestion continues to be an ever increasing problem, routing in these settings has become an important area of operations research. This monograph provides cutting-edge research, utilizing the recent advances in technology, to quantify the value of dynamic, time-dependent information for advanced vehicle routing in city logistics. The methodology of traffic data collection is enhanced by GPS based data collection, resulting in a comprehensive number of travel time records. Data Mining is also applied to derive dynamic information models as required by time-dependent optimization. Finally, well-known approaches of vehicle routing are adapted in order to handle dynamic information models. This book interweaves the usually distinct areas of traffic data collection, information retrieval and time-dependent optimization by an integrated methodological approach, which refers to synergies of Data Mining and Operations Research techniques by example of city logistics applications. These procedures will help improve the reliability of logistics services in congested urban areas.

Customer acceptance mechanisms for home deliveries in metropolitan areas

Efficient and reliable home delivery is crucial for the economic success of online retailers. This is especially challenging for attended home deliveries in metropolitan areas where logistics service providers face congested traffic networks and customers expect deliveries in tight delivery time windows. Our goal is to develop and compare strategies that maximize the profits of a logistics service provider by accepting as many delivery requests as possible, while assessing the potential impact of a request on the service quality of a delivery tour. Several acceptance mechanisms are introduced, differing in the amount of travel time information that is considered in the decision of whether a delivery request can be accommodated or not. A real-world inspired simulation framework is used for comparison of acceptance mechanisms with regard to profits and service quality. Computational experiments utilizing this simulation framework investigate the effectiveness of acceptance mechanisms and help identify when more advanced travel time information may be worth the additional data collection and computational efforts.

Vehicle routing to minimize time-dependent emissions in urban areas

This paper focuses on the problem of minimizing CO2 emissions in the routing of vehicles in urban areas. While many authors have realized the importance of speed in minimizing emissions, most of the existing literature assumes that vehicles can travel at the emissions-minimizing speed on each arc in the road network. In urban areas, vehicles must travel at the speed of traffic, which is variable and time-dependent. The best routes also depend on the vehicle load. To solve the problem, we take advantage of previous work that transforms the stochastic shortest path subproblems into deterministic problems. While in general, these paths must be computed for each combination of start time and load, we introduce a result that identifies when the emissions-minimizing path between customers is the same for all loads. When this occurs, we can precompute the paths and store them in a lookup table which saves on runtime. To solve the routing problem, we adapt an existing tabu search algorithm. We test our approach on instances from a real road network dataset and 230 million speed observations. Experiments with different numbers of vehicles, vehicle weights, and pickup quantities demonstrate the value of our approach. We show that large savings in emissions can occur particularly in the suburbs, with heavier vehicles, and with heterogeneous pickup quantities as compared with routes created with more traditional objectives. We show that the savings in emissions are proportionally larger than the associated increases in duration, indicating improved emissions are achievable at a fairly low cost.

Floating Car Data Based Analysis of Urban Travel Times for the Provision of Traffic Quality

The management of urban traffic systems demands information for the real-time control of traffic flows as well as for strategic traffic management. In this context, state-of-the-art traffic information systems are mainly used to control varying traffic flows and to provide collective and individual information about the current traffic situation. However, the provision of information for strategic traffic management as well as for traffic demand dependent planning activities (e.g., in city logistics) is still a potential field of research due to the former lack of reliable city-wide traffic information. Recently, historical traffic data arising from telematics-based data sources provided information for time-dependent route planning, for the improvement of traffic flow models as well as for spatial and time-dependent forecasts. In this chapter, we focus on the analysis of historical traffic data, which serves as a background for sophisticated real-time applications.

Data chain management for planning in city logistics

This contribution is about data chain management enabling route planning in city logistics. The transformation of raw data into reliable decisions requires effective data chain management. The data chain closes the gap between empirical collection of raw traffic data and decision-making in terms of route planning. We define the data chain for the support of route planning in city logistics. The data chain transforms raw empirical traffic data into planning data by first and second level aggregation. The single elements of the data chain are investigated in detail. We discuss basic issues of telematics-based data collection, data cleaning and data integration. The key element of the data chain is the aggregation by cluster analysis. Aggregated data is evaluated by explorative data analysis. Finally, the efficient application of aggregated data for route planning is illustrated.

Data-driven approaches for emissions-minimized paths in urban areas

Concerns about air quality and global warming have led to numerous initiatives to reduce emissions. In general, emissions are proportional to the amount of fuel consumed, and the amount of fuel consumed is a function of speed, distance, acceleration, and weight of the vehicle. In urban areas, vehicles must often travel at the speed of traffic, and congestion can impact this speed particularly at certain times of day. Further, for any given time of day, the observations of speeds on an arc can exhibit significant variability. Because of the nonlinearity of emissions curves, optimizing emissions in an urban area requires explicit consideration of the variability in the speed of traffic on arcs in the network. We introduce a shortest path algorithm that incorporates sampling to both account for variability in travel speeds and to estimate arrival time distributions at nodes on a path. We also suggest a method for transforming speed data into time-dependent emissions values thus converting the problem into a time-dependent, but deterministic shortest path problem. Our results demonstrate the effectiveness of the proposed approaches in reducing emissions relative to the use of minimum distance and time-dependent paths. In this paper, we also identify some of the challenges associated with using large data sets. HighlightsWe present a new way of constructing expected emissions-minimized paths in urban areas.We discuss the usage of sampling to determine expected emissions-minimized paths.We compare the sampling-based method to a deterministic path generation method.We discuss how to overcome technical challenges of using large data sets in shortest path construction.We analyze the performance and the quality of the proposed methods based on 230 million real speed observations.

When Are Deliveries Profitable

The paper aims to optimize the final part of a firm’s value chain with regard to attended last-mile deliveries. It is assumed that to be profitable, e-commerce businesses need to maximize the overall value of fulfilled orders (rather than their number), while also limiting costs of delivery. To do so, it is essential to decide which delivery requests to accept and which time windows to offer to which consumers. This is especially relevant for attended deliveries, as delivery fees usually cannot fully compensate costs of delivery given tight delivery time windows. The literature review shows that existing order acceptance techniques often ignore either the order value or the expected costs of delivery. The paper presents an iterative solution approach: after calculating an approximate transport capacity based on forecasted expected delivery requests and a cost-minimizing routing, actual delivery requests are accepted or rejected aiming to maximize the overall value of orders given the computed transport capacity. With the final set of accepted requests, the routing solution is updated to minimize costs of delivery. The presented solution approach combines well-known methods from revenue management and time-dependent vehicle routing. In a computational study for a German metropolitan area, the potential and the limits of value-based demand fulfillment as well as its sensitivity regarding forecast accuracy and demand composition are investigated.

Interactive analysis of discrete-event logistics systems with support of a data warehouse

We propose an interactive approach for the flexible analysis of detailed state-transition data collected from discrete-event simulation models of logistics systems. To this end, we focus on multidimensional modeling in order to reveal the drivers of time-variant system performance. Online Analytical Processing functionality provides fast and flexible organization, aggregation and visualization of information. We illustrate the advantages of such an approach with data from simulation studies of the Upper Mississippi River waterway system.