A Flexible Transport Service for Passengers

Demand-responsive transport (DRT) systems, where users generate requests for transportation from a pickup point to a delivery point, are expected to increase in usage dramatically as the inconvenience of privately-owned cars in metropolitan areas becomes excessive. However, despite the increasing role of DRT systems, there are very few rigorous results characterizing achievable performance (in terms, e.g., of stability conditions). In this paper, our aim is to bridge this gap for a rather general model of DRT systems, which takes the form of a generalized Dynamic Pickup and Delivery Problem. The key strategy is to develop analytical bounds for the optimal cost of the Euclidean Stacker Crane Problem (ESCP), which represents a general static model for DRT systems. By leveraging such bounds, we characterize a necessary and sufficient condition for the stability of DRT systems; the condition depends only on the workspace geometry, the stochastic distributions of pickup and delivery points, customers' arrival rate, and the number of vehicles. Our results exhibit some surprising features that are absent in traditional spatially-distributed queueing systems.

The pickup and delivery problem with time windows (PDPTW) is an NP-hard discrete optimization problem of serving transportation requests using a fleet of homogeneous trucks. Its main objective is to minimize the number of vehicles, and the secondary objective is to minimize the distance traveled during the service. In this paper, we propose the longest common subsequence based selective route exchange crossover (LCS-SREX), and apply this operator in the memetic algorithm (MA) for the PDPTW. Also, we suggest the new solution representation which helps handle the crossover efficiently. Extensive experimental study performed on the benchmark set showed that using LCS-SREX leads to very high-quality feasible solutions. The analysis is backed with the statistical tests to verify the importance of the elaborated results. Finally, we report one new world’s best routing schedule found using a parallel version of the MA exploiting LCS-SREX.

This paper presents a generic two-stage optimization model and a simulation framework for on-demand grocery delivery. The proposed simulation framework can be used to reproduce any historical day's behaviour or evaluating various what-if scenarios. The two-stage optimization model's objective is to minimize the Cost Per Delivery (CPD) and maximize Customer Experience (CX). In the first stage, Last-Mile (LM) delivery optimization is modelled as a Pickup and Delivery Problem with Time Windows (PDPTW). Both static and dynamic variants of the PDPTW model are tried out and compared to their performances. A Just-In-Time (JIT) heuristic is integrated with PDPTW to minimize waiting time. The second stage solves the First-Mile (FM) delivery optimization using a multi-objective assignment model for trading off between the CPD and CX. The proposed framework is simulated with actual order data and it was found that the the dynamic PDPTW model results in significant savings in CPD while maintaining a good CX.

Optimised flexible transport mode without advanced reservations by a DES approach

This study investigates a new variant of the pickup and delivery problem with time windows (PDPTW) applied in cold chain transportation, which quantifies the effect of time on the quality of perishable products. Multiple commodities with incompatibility constraints are considered, where some types of products cannot be transported in a vehicle simultaneously because of their different properties and requirements for storage temperatures. The aim is to determine vehicles’ pickup and delivery routes as well as their departure times from the depot such that the travel cost and refrigeration cost of vehicles and the quality decay cost of products are minimized. We formulate this problem as a set partitioning model, which is solved exactly by a tailored branch-and-price (B&P) algorithm. To tackle the asymmetry issue arising from the pricing problem of the B&P framework, we develop a novel asymmetric bidirectional labeling algorithm. Benchmark instance sets based on real-world statistical data and classic PDPTW instance sets are first generated for this problem. Numerical results show that our B&P algorithm can solve most instances to optimality in an acceptable time frame. Moreover, our results demonstrate that integrating the refrigeration and quality decay costs into the objective function can significantly lower the total cost of cold chain transportation activities, compared with the widely adopted objective function minimizing only the travel cost. Funding: This work was supported by the National Key R&D Program of China [Grant 2018YFB1700600] and the National Natural Science Foundation of China [Grants 72101049, 71971090, 71821001]. Supplemental Material: The online supplement is available at https://doi.org/10.1287/trsc.2022.1167 .

In this paper problems of planning in transportation systems based on Pickup and Delivery Problem with Time Windows (PDPTW) are discussed. The results of two variants of evolutionary algorithms illustrate the pros and cons of using different approaches, and their cooperation in hybrid island model indicates how they can help each other in achieving better solutions. This leads to the general idea of an agent-based cooperative system, in which many different techniques may be used simultaneously, exchanging the obtained solutions. Experimental study of such a system that uses evolutionary algorithms and tabu search concludes the work.Keywordstransport planning and schedulingPickup and Delivery Problem with Time Windowsevolutionary algorithmsmulti-agent systems

In this paper we present a parallel solver for the vehicle routing problem with time windows (VRPTW) and the pickup and delivery problem with time windows (PDPTW). The solver is based on parallel competition of particular solvers solving the given problem instance. The particular solvers are based on negotiation between a fleet of agents representing individual vehicles using a cost structure corresponding to the wellknown travel time savings insertion heuristic. The performance of the solver is assessed on the Homberger-Gehring and Li-Lim benchmark sets for the VRPTW and PDPTW case respectively. While both sets are widely used across the routing community, they were not addressed by previous agent-based studies. Thus the achieved average solution quality of 95% and 87% for the VRPTW and PDPTW cases represents a new best known result for these benchmark sets for agent-based approaches. An analysis of the solver's convergence, runtime and parameter sensitivity is provided within the experimental evaluation section, that has also not been provided by previous agent-based studies.

In recent decades, the rapid development of international economy has led to significant inter-regional trade imbalances. In marine transportation, the voluminous empty containers accumulated at ports in import dominated countries have to be re-circulated back to ports in export dominated countries for re-deployment. This repositioning of empty containers is crucial for shipping companies to provide effective transportation services. At present, empty container repositioning has proved to be highly expensive, contributing to 27% of the overall operation cost of shipping companies. Consequently, major stakeholders including the shipping companies have strived to reduce the repositioning cost. Unfortunately, few major improvements have been made over the years and there are still lingering research issues. The objective of this research, therefore, is to develop efficient and effective methodologies to obtain empty container repositioning plans that can significantly reduce transportation cost while fulfilling customer demands of empty and laden containers.
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\nIn this research, the empty container repositioning problem under two related real marine transportation scenarios has been studied. The first scenario considers multi-vessel transportation for both laden and empty containers with split vessel routing (denoted as ECRSVR). A mathematical model of ECRSVR is developed based on the formulation of the Pickup and Delivery Problem with Time Windows (PDPTW). However, a major research issue arises due to such integrated consideration of split vessel routing and transportation of empty and laden containers. The resulting model will encompass many more new decision variables and constraints. Thus, it cannot be readily solved by existing algorithms. To address this issue, a new Branch-and-Price (B&P) algorithm incorporating B&B and column generation is developed. Specifically, a new dynamic programming algorithm with new label structure and dominance rules is established to solve the pricing problem in B&P, and a new multi-layer branching scheme is developed for implementing the algorithm. The effectiveness and robustness of the proposed methodology are then tested on a set of randomly generated problems.
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\nThe second scenario extends the first by including transshipment activities in the ECRSVR (denoted as ECRSVRTS). Apparently, the inclusion of transshipment activities will significantly increase the size of the solution space of feasible routing and distribution schemes. Also, more constraints have to be included in the model to account for the interdependence between the distribution schemes of separate vessels. To tackle this problem, two mathematical models are developed. The first is a port-based model that explicitly characterizes the routing plans and distribution volumes for each vessel at each time interval as decision variables. The resulting integer program is non-linear and difficult to solve. Therefore, an equivalent event-driven node-based model inspired by the formulation of PDPTW is constructed. In this model, the pickup/delivery tasks and transshipment events are represented as nodes in a generated topology. The B&P scheme is then modified to solve the node-based model and a Hybrid Scatter Tabu Search (HSTS) is developed to solve the pricing problems. Results obtained from the numerical experiments using randomly generated test problems show that the proposed HSTS algorithm outperforms conventional scatter search and tabu search algorithms.

In recent years, a change in demand trends has influenced the development of public passenger transport, causing a number of critical issues and consequences for the environment, the management policies of these services and, especially, the users. Various passenger transport services have evolved to reduce the use of private vehicles, including DRT systems that consider technological developments like Mobility ad a Service (MaaS). This article focuses on a European literature review on user-oriented research development. The overall objective is to: i) highlight, among all the critical aspects of service development, the factors influencing the economic and ethical aspects; ii) finally identify a discrepancy between the perception of DRT and the empirical research design. The study concludes by providing potential suggestions for improving DRT service deployment and reducing some of the factors influencing users' modal choices. Finally, the paper proposes implications for policy makers and operators.

Over the last decade, platforms have emerged in numerous industries and often transformed them, posing new challenges for transportation research. Platform providers such as Uber, Uber Freight, Blackbuck, or Lyft mostly do not have immediate control over the physical resources needed to move people or goods. They often operate in a multi-sided market setting, where it is crucial to design clear incentives to motivate a third party to engage in collaboration. As a consequence, collaboration incentives become an integral part of decision support models for platform providers and they need to be developed at the operational level and applied dynamically. Naturally, this involves a trade-off between the interests of platform providers, shippers, and carriers. In this work, we investigate the real-world case of a platform provider operating as an intermediary between shippers and carriers in a less-than-truckload (LTL) business. We propose a new mixed-integer programming (MIP) formulation for the underlying collaborative pickup and delivery problem with time windows (PDPTW) that minimizes the price the platform pays to the carriers and enforces collaboration incentives for carriers through individual rationality constraints. This is facilitated by a dynamic pricing approach which ensures that carriers are better off collaborating than working on their own. The pricing is bounded by the costs and market conditions to keep the price range reasonable. We explore possible policies to be implemented by the platform and find that their business remains profitable when individual rationality is enforced and the platform could even guarantee increased profit margins to the carriers as incentives.

This paper addresses a new variant of Pickup and Delivery Problem with Time Windows (PDPTW) for enhancing customer satisfaction. In particular, a huge number of requests is served in the system, where each request includes a pickup node and several delivery nodes instead of a pair of pickup and delivery nodes. It is named Pickup and Multi-Delivery Problem with Time Windows (PMDPTW). A mixed-integer programming model is formulated with the objective of minimizing total travel costs. Computational experiments are conducted to test the correctness of the model with a newly generated benchmark based on the PDPTW benchmark instances. Results show that our proposed model is able to solve small-size instances. Alternative approaches for solving larger problems are proposed for future research.

This paper compares the performance of the classic genetic algorithm with the more recently proposed ant colony optimisation for solving real-world pickup and delivery problems with hard time windows. A real-world problem that is present worldwide - waste collection - is used to evaluate the algorithms. As in most real-world waste collection problems, many of the waste bins have time windows. The time windows stem from such things as safety regulations and customer agreements, and must be strictly adhered to. The optimisation showed that the genetic algorithm is better than the ant colony optimisation when utilising standard implementations of both algorithms. However, when the algorithms are enhanced with a local search procedure, the ant colony optimisation immediately becomes superior and achieves improved results. Local search seems to be a drawback for the genetic algorithm when hard time windows are involved. Various implementations of the local search procedure are evaluated in this paper using five different test sets. Recommendations for future implementations are given as well as additional enhancements which could improve the performance of the ant colony optimisation.

This research presents a multi-agent based metropolitan demand responsive transport (DRT) system intelligent control model, which adopts a practical multi-agents planning approach for urban DRT services control that satisfies the main constraints: minimize total slack time, travel time, waiting time, client's special requests, and using minimum number of vehicle. In this paper, we propose an agent based multi-layer distributed hybrid planning model for the real-time problem. By computational experiments, we examine an effectiveness of the proposed method for the large real-time metropolitan transport system.

In the dial-a-ride services, customers specify pickup and drop-off locations and their times for transport. The operator aims at constructing cheap itineraries while minimizing customer inconvenience. This problem has been studied as the Dial-a-Ride Problem (DARP), which consists of both spatial and temporal factors. This article studies the impact of spatial and temporal variation on multivehicle DARP using route construction heuristics. Our work highlights the impact of these variations on the customer disutility and transportation cost. The study will help the service operators in selecting an appropriate construction heuristic while expanding the DARP service in terms of service area, service duration, or planning for a sudden growth in the number of customers. The results show that if the operator is planning to expand the area of service, the Saving Heuristic is more appropriate for larger operators while Insertion Heuristic with spatial sorting scheme is better for smaller operators with limited number of vehicles. If the operator is expanding the service in terms of service duration, from 8 to 24 hours daily, for instance, or preparing for a growth in the number of customers, the Saving Heuristic is better for both smaller and larger service operators.

Demand responsive transport (DRT) may be a feasible alternative when conventional transportation services provided by private companies are not available at a satisfactory level in terms of both price and quality. Nevertheless, these unconventional transportation systems are still not consolidated due to technical and economic issues. Beyond technical feasibility, DRT needs to be based on prices and pricing policies that ensure the attractiveness of the service without compromising its economic viability. Nevertheless, there is little evidence on the conditions of economic viability of DRT services and on the cost structure of such systems. In this article, we use a strategic activity based costing model to analyze the conditions of economic viability of a DRT system in a Portuguese rural municipality. The case presented was analyzed in terms of costs and profitability. They were defined a set of important variables which should be taken into account in the design and economic evaluation of DRT projects namely, ticket price, trip distance and passengers. Thus, this costing model can be used to assist decision making and support strategic and operational decisions. Furthermore, it can be embedded in a decision support system designed to support the real time operation of intelligent flexible transportation systems.