Set Of Transportation Requests Research Articles

The real-time on-demand bus routing problem: The cost of dynamic requests

The real-time on-demand bus routing problem (ODBRP) supports the online routing of buses in a large-scale ride-sharing system. Given are a set of buses with fixed capacity, a set of bus stations and a set of transportation requests, only part of which are known before the planning horizon. A request consists of a set of possible departure and arrival stations, as well as an earliest departure and latest arrival time. The aim is to (1) assign each passenger to a departure and arrival bus station and (2) develop a set of bus routes to fulfill each request within its time window while minimizing the sum of the total waiting time and the total user ride time.Including the possibility for requests to be issued after the start of the planning horizon, i.e., when buses have already started servicing other requests, requires a dynamic re-optimization of a partially executed solution. Compared to the case in which all requests are known beforehand, the solution quality, expressed as the total waiting and user ride time, is expected to decline. This decline in objective function value can be seen as the “cost” of the dynamic requests. In this paper, we introduce the real-time ODBRP as an extension of its static problem variant and present a heuristic to deal with dynamic requests. In addition, an extensive set of experiments allows us to conclude that dynamic requests indeed lead to higher waiting and user ride times, especially for passengers who submit their request at the last minute. Passengers are therefore encouraged, if possible, to send their request well in advance, as this results in lower and more stable service promises, higher customer satisfaction, and higher revenues for the operating on-demand bus company.

The static on‐demand bus routing problem: large neighborhood search for a dial‐a‐ride problem with bus station assignment

AbstractWe introduce a novel optimization problem to support the planning and routing of on‐demand buses in an urban context. We call this problem the on‐demand bus routing problem (ODBRP). Given are a fleet of buses with fixed capacity, a set of bus stations and travel times between them, and a set of transportation requests. Each transportation request consists of a set of potential departure and a set of potential arrival bus stations, as well as a time window, that is, an earliest departure time and a latest arrival time. The aim of the ODBRP is to (1) assign each passenger to a departure and arrival bus station and (2) develop a set of bus routes to fulfill each request in time while minimizing the total travel time of all users. We present the static version of the ODBRP, as well as a straightforward large neighborhood search heuristic to solve it. The performance of the heuristic is established by comparing it to an off‐the‐shelve heuristic solver (LocalSolver). We also use our heuristic to solve (slightly modified instances of) the well‐known dial‐a‐ride problem. The results found by the heuristic for the on‐demand bus system are compared to those of a simulated traditional public bus system with fixed lines and timetables. A thorough analysis of the comparison demonstrates that total user ride times can be significantly lower in an on‐demand public bus system and shows that an on‐demand bus system works best with a large number of small buses.

Pickup and delivery problems with autonomous vehicles on rings

In this paper we introduce a new class of Pickup and Delivery problems on circles (or rings). These problems arise in the field of public transportation systems where autonomous (i.e. driverless) vehicles travel on circular networks. We consider a set of stations arranged in a circle and a set of transportation requests. Each request asks for the transportation of a certain quantity from a pickup station to a delivery station. A fleet of capacitated vehicles is available at the depot. In the first part of the paper we propose a classification scheme for these problems. In the second part, we address the variants in which the vehicles are allowed to move in a single direction of the circle (either clockwise or counterclockwise) and the objective is to minimize the number of tours on the ring while serving all the requests. We provide a complexity analysis for this class of problems. We develop polynomial time algorithms for the variants that are polynomially solvable and proofs of NP-hardness for the variants that are NP-hard. In addition, for the latter, we provide mathematical formulations and perform computational tests that show the effectiveness of these formulations. Finally, we compare optimal solutions with those obtained using a straightforward greedy algorithm.

Logic-based benders decomposition for scheduling a batching machine

This paper investigates the problem of scheduling a set of jobs on a single batching machine to minimize the maximum lateness, where jobs may be subject to precedence constraints and incompatibilities. Single batching machine scheduling has many applications, but this study is particularly motivated by single crane scheduling in an automated storage and retrieval system (AS/RS): given a set of transport requests, which requests should be processed together in the same dual command cycle, and in what order should the cycles be processed? Since storage and retrieval requests may refer to the same physical item, precedence constraints must be observed. Moreover, the crane may not be capable of handling multiple storage or retrieval requests in the same cycle, hence the need to account for incompatibilities. We present a novel exact algorithm based on branch & Benders cut, which is shown to solve even large instances with more than 100 jobs to optimality in many cases. For the special case without precedence constraints and incompatibilities, it improves on several best-known upper bounds from the literature.

A matheuristic for a multimodal long haul routing problem

We address a planning problem faced by logistics service providers who transport freight over long distances. Given a set of transportation requests, where the origin and the destination of each request are located far apart from each other, a logistics service provider must find feasible vehicle routes to fulfil those requests at minimum cost. When transporting freight over long distances, multimodal transportation provides a viable alternative to traditional unimodal road transportation. We introduce this new problem, which we call the multimodal long haul routing problem (MMLHRP), and present a mathematical formulation for it. Furthermore, we propose a matheuristic, using iterated local search within a column generation framework, for solving the MMLHRP. Results show that large cost savings can be achieved through multimodal transportation compared to unimodal road transportation.

A new mathematical formulation for the static demand responsive transport problem

Demand responsive transport is a new transportation mode aiming to make the transportation system easier and more flexible. This mode allows passengers to be carried to their destinations through a set of transportation requests established by customers. These requests are to be answered by a single depot with different vehicle configurations. The idea is to group a number of passengers in the same vehicle respecting of the capacity of each vehicle in order to reduce the operational cost while maintaining a good quality of service. In this paper, we treated the static case of demand responsive transport problem. A new mathematical model for this problem is presented aiming to optimise the cost and the quality of service. This model integrates an original concept; penalty concept to enhance the quality of service. The model is validated on small and medium size instances.

The Pickup and Delivery Problem: a Many-objective Analysis

The pickup and delivery problem (PDP) considers a set of transportation requests, which specify the quantity of product that has to be picked up from an origin and delivered to a destination. There exist a number of vehicles available to be used for completing these tasks. PDP consists of finding a collection of routes with minimum cost, such that all transportation request are serviced. Traditionally, cost has been associated with the number of routes and the total travel distance. However, in many applications, some other objectives emerge, for example, the minimization of travel time and the maximization of the collected profit. If we consider all these four objectives equally important, PDP can be tackled as a many-objective problem. In this paper we are interested in analyzing this many-objective problem in order to study some of its properties, specifically, (i) the change of difficulty when the number of objectives is increased, and (ii) the conflict degree between each pair of objectives. In order to analyze these topics, we compare the performance of a recently proposed multi-objective evolutionary algorithm against that of the well-known -MOEA, which has shown good results in many-objective problems.

An ELS-based approach with dynamic probabilities management in local search for the Dial-A-Ride Problem

This paper addresses the Dial-A-Ride Problem (DARP) which is used to model on-demand transportation services. This kind of transportation is quickly becoming an essential service for modern public mobility or logistics providers. In the DARP, a set of transportation requests has to be handled by a fleet of vehicles. Each request corresponds to a client to be transported from a pickup point to a delivery point. The routing costs have to be minimized, while respecting a set of constraints including time windows on nodes, maximum riding time per client and a maximal total duration of trips.An Evolutionary Local Search (ELS) based approach is proposed to solve this problem. A new greedy randomized heuristic to compute initial solutions is developed. A dynamic probabilities management mechanism is used in the local search to improve the convergence. The method is benchmarked on a classic set of instances from the literature and it is compared against state of the art methods. The numerical results show the effectiveness of this approach.

A Branch-and-Price Method for the Vehicle Routing Problem with Cross-Docking and Time Windows

One important factor in supply chain management is to efficiently control the supply chain flows. Due to its importance, many companies are trying to develop efficient methods to increase customer satisfaction and reduce costs. Cross-docking is considered a good method to reduce inventory and improve responsiveness. The Vehicle Routing Problem with Cross-Docking and Time Windows (VRP-CD-TW) consists on designing the minimum-cost set of routes to serve a given set of transportation requests while respecting constraints on vehicles capacity, customer time windows and using transfers on a cross-docking base. Each customer must be visited just once and mixed tours comprising pick-up and delivery stops are not allowed. For a given vehicle, the designed pick-up tour must precede its delivery tour. In this work, we model the VRP-CD-TW assuming that all feasible orders are known in advance. We present a new mixed integer program to model the VRP-CD-TW and reformulate it via Dantzig–Wolfe decomposition to later develop a column generation procedure. The proposed branch-and-price algorithm shows encouraging results on solving some Solomon-based instances.

A non-dominated sorting genetic algorithm for a bi-objective pick-up and delivery problem

Some companies must transport their personnel within facilities. This is especially the case for oil companies that use helicopters to transport engineers, technicians and assistant personnel from platform to platform. This operation has the potential to become expensive if the transportation routes are not correctly planned and provide a bad quality of service. Here this issue is modelled as a pick-up and delivery problem where a set of transportation requests should be scheduled in routes, minimizing the total transportation cost while the most urgent requests are satisfied by priority. To solve the problem, a method based on a Non-dominated Sorting Genetic Algorithm (NSGA-II) is proposed. This algorithm is tested on both randomly generated and real instances provided by a petroleum company. The results show that the proposed algorithm improves the best-known solutions.

Comparison of fuzzy C-mean clustering and 0-1 integer programming model for employee routing problem

In a real time life, vehicle routing problems (VRP) arises whenever a set of vehicles is available to serve a set of transportation requests. The work carried in this paper focuses on understanding the concept of VRP and applying the techniques to a real time problem, which relates to the picking up of employees of an organisation by a fleet of buses. The problem is modelled as a employee routing problem (ERP), i.e., each bus has a limited number of seats and the number of employees picked up by each bus should be less than or equal to the maximum capacity of the bus. The techniques used here are Fuzzy C-mean clustering and 0-1 integer programming model for obtaining the allocation of employees to each bus. The other techniques include nearest neighbourhood, which is used to obtain the initial route for the buses and 2-opt method, which is used to improve the travelling route by reducing the total cost and thus it helps in achieving the objective of ERP.

Models and branch-and-cut algorithms for pickup and delivery problems with time windows

In the pickup and delivery problem with time windows (PDPTW), capacitated vehicles must be routed to satisfy a set of transportation requests between given origins and destinations. In addition to ...