Transportation Requests Research Articles

수익을 최대로 하는 픽업 및 배송문제의 세분화 타부서치 알고리즘 기법 연구

In this paper, we propose the granular tabu search heuristic for the pickup and delivery problem to maximize the total revenue by transportation of customer demands with a limited fleet of vehicles. The problem, denoted as the pickup and delivery maximum covering problem, is designed for the efficient operation of military air transportation system, it also can be applied to the competitive door-to-door transportation systems. In the problem, there exist several transportation requests, each of which consists of demand volume, price value, and unique pickup and delivery locations. The company maximizes the sum of the price value from allocated customers under the vehicle capacity and the maximum route length constraints. Since the suggested mathematical formulation is too hard to solve within the reasonable computational time, the local search heuristic algorithm is suggested. The algorithm contains two procedures for the efficient search of neighbor solutions, and the first suggestion is the granularity condition that only considers the edges with smaller cost-per-price value than the given threshold. The procedure not only reduces the computational requirements for the local search significantly but also keeps the solution quality compared to the results without the granularity condition. Also, we use the short-term memory procedure from the tabu search heuristic to prevent the repetition in a sequence of solutions. Test results show that the mathematical formulation is difficult to solve with commercial optimization software due to the NP-hardness. Also, we analyze the algorithm’s performance with various value of granular threshold, and the results show the advantages of the granularity condition.

May the Fourth (Industrial) Revolution be with You: Value Convergence within Uber’s Sharing Economy

This research provides empirical insights to reveal how value convergence occurs within Uber’s sharing economy. Uber’s business model is built on a digital platform that links private vehicle owners and their under-utilized assets to be economic revenue generating units for on-demand transport requests from potential clients. By analyzing adoption values for drivers and users of Uber, we identify antecedent operational and behavioral values for Uber adoption. We add to extant Industry 4.0 research and show value convergence emanating from economic and convenience motives. On the basis of our findings, we develop propositions for value convergence leveraging on cyber-physical systems embedded in Industry 4.0, and its application for theory and practice within the broader domains of the sharing economy.

A Generic GPU-Accelerated Framework for the Dial-A-Ride Problem

Accelerating the performance of optimization algorithms is crucial for many day-to-day applications. Mobility-on-demand is one such application that is transforming urban mobility by offering reliable and convenient on-demand door-to-door transportation at any time. Dial-a-ride problem (DARP) is an underlying optimization problem in the operational planning of mobility-on-demand systems. The primary objective of DARP is to design routes and schedules to serve passenger transportation requests with high-level user comfort. DARP often arises in dynamic real-world scenarios, where rapid route planning is essential. The traditional CPU-based algorithms are generally too slow to be useful in practice. Since customers expect quick response for their mobility requests, there has been a growing interest in fast solution methods. Therefore, in this paper, we introduce a GPU-based solution methodology for the dial-a-ride problem to produce good solutions in a short time. Specifically, we develop a GPU framework to accelerate time-critical neighborhood exploration of local search operations under the guidance of metaheuristics such as tabu search and variable neighborhood search. Besides, we propose device-oriented optimization strategies to enhance the utilization of a current-generation GPU architecture (Tesla P100). We report speedup achieved by our GPU approach when compared to its classical CPU counterpart, and the effect of each device optimization strategy on computational speedup. Results are based on standard test instances from the literature. Ultimately, the proposed GPU methodology generates better solutions in a short time when compared to the existing sequential approaches.

A simulation-optimization model for analyzing a demand responsive transit system for last-mile transportation: A case study in São Paulo, Brazil

Ride-hailing services, in particular Demand Responsive Transit (DRT) systems, have emerged in many cities worldwide as a potential solution to bridge the public transport supply gap. In such services, passengers whose itineraries and times are coincident to a certain degree can share vehicles that follow flexible routes to accommodate distinct boarding and alighting locations with minor disturbances to their convenience. In this context, this paper aims to evaluate the potential of a DRT as an alternative for a feeder to a trunk transport system, such as BRT (Bus Rapid Transit), or a metro or rail system as well. This is accomplished by a simulation–optimization model that allows determining, in very short time, the optimized way to serve each new transport request that arises dynamically over time. The model was applied to a first/last mile transportation system in an area adjacent to a subway station in the city of São Paulo, Brazil. We analyze and measure the impacts of different operational characteristics of supply and demand, measured in terms of service level to riders, number of vehicles, average occupation and total kilometer traveled per vehicle. The results evidence the potential of these DRT services as an alternative to complement the existing bus lines, as well as to attract users of individual transport, as they can offer adequate service level at a competitive cost.

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.

A novel proof of useful work for a blockchain storing transportation transactions

Proof-of-Work (PoW) is a common mechanism used to validate peer-to-peer transactions and maintain highly secured immutability of the blockchain. However, this mechanism has been criticized due to its inefficient use of computing resources and its limited usefulness. In this paper, we propose the Proof-of-Useful-Work (PoUW) as an alternative mechanism for transaction validation that puts the squandered computing resources to beneficial use. The main premise is to replace the mathematical puzzle, which constitutes a fundamental part of the Proof-of-Work mechanism, with NP-hard optimization problems whose solutions benefit the participants of the blockchain. We demonstrate its usefulness in the context of transportation. Accordingly, PoUW-based blockchain not only tracks, manages and validates transactions, but also optimizes transportation requests profiting its ecosystem. We describe the framework of the proposed PoUW along with the associated optimization model and the miner’s reward mechanism.

Scheduling heterogeneous multi-load AGVs with battery constraints

Recent trends towards larger and more complex systems necessitate the use of heterogeneous and flexible automated guided vehicles (AGVs) to fulfill the transport demand within a factory. To operate the fleet of AGVs efficiently, it is also important to consider their limited battery capacity. In this context, we tackle the problem of scheduling transport requests on multi-load and multi-ability AGVs with battery management. Each AGV can carry more than one load at a time and have specific capabilities such as lift loads, tow loads, or handle loads with a mounted robot arm. Each request consists of a pickup and a delivery task associated with an origin, a destination, a soft time window, and a priority. Each transport request may also require different AGV capabilities, and the AGV batteries can be recharged partially under consideration of a critical battery threshold. The decisions involve assigning transport and charging requests to AGVs, sequencing these requests, and determining the arrival times and charging duration. A mixed-integer linear programming model is formulated. A hybrid adaptive large neighborhood search with an integrated local search method is proposed to find a feasible schedule with the aim to minimize the tardiness costs of requests and travel costs of AGVs. We illustrate the efficacy of the hybrid algorithm with an industry case study using real-world data. The computational results reveal a 20%–50% cost reduction in current practice by using our hybrid algorithm, and around 50% cost reduction with respect to a single-load AGV scheduling approach proposed in the literature.

A matheuristic for AGV scheduling with battery constraints

This paper considers the problem of scheduling automated guided vehicles (AGVs) with battery constraints. Each transport request involves a soft time window, and the AGV fleet used to service those requests is heterogeneous with a diverse set of capabilities and travel costs. In contrast to the existing literature, each transport request may require different AGV material handling capabilities (such as lift loads, tow loads, or handle loads with a mounted robot arm), and the AGV batteries can be recharged partially under consideration of a critical battery threshold. The problem is to assign the transport and charging requests to AGVs, sequence the requests, and determine their starting times and the recharging durations of the AGVs with the objective of minimizing a weighted sum of the tardiness costs of transport requests and travel costs of AGVs. A mixed-integer linear programming model is formulated. We also propose a new matheuristic that makes use of an adaptive large neighborhood search algorithm and a linear program to solve industry-size instances. We illustrate the efficacy of our approach with an industry case study using real-world data.

Anticipatory approach for dynamic and stochastic shipment matching in hinterland synchromodal transportation

This paper investigates a dynamic and stochastic shipment matching problem faced by network operators in hinterland synchromodal transportation. We consider a platform that receives contractual and spot shipment requests from shippers, and receives multimodal services from carriers. The platform aims to provide optimal matches between shipment requests and multimodal services within a finite horizon under spot request uncertainty. Due to the capacity limitation of multimodal services, the matching decisions made for current requests will affect the ability to make good matches for future requests. To solve the problem, this paper proposes an anticipatory approach which consists of a rolling horizon framework that handles dynamic events, a sample average approximation method that addresses uncertainties, and a progressive hedging algorithm that generates solutions at each decision epoch. Compared with the greedy approach which is commonly used in practice, the anticipatory approach has total cost savings up to 8.18% under realistic instances. The experimental results highlight the benefits of incorporating stochastic information in dynamic decision making processes of the synchromodal matching system.

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.

Global synchromodal transport with dynamic and stochastic shipment matching

This paper investigates a dynamic and stochastic shipment matching problem, in which a platform aims to provide online decisions on accepting or rejecting newly received shipment requests and decisions on shipment-to-service matches in global synchromodal transportation. The problem is considered dynamic since the platform receives requests and travel times continuously in real time. The problem is considered stochastic since the information of requests and travel times is not known with certainty. To solve the problem, we develop a rolling horizon framework to handle dynamic events, a hybrid stochastic approach to address uncertainties, and a preprocessing-based heuristic algorithm to generate timely solutions at each decision epoch. The experimental results indicate that for instances with above 50% degrees of dynamism, the hybrid stochastic approach that considers shipment request and travel time uncertainties simultaneously outperforms the approaches that do not consider any uncertainty or just consider one type of uncertainties in terms of total profits, the number of infeasible transshipments, and delay in deliveries.

A GRASP-ALNS combination for robust pickup and delivery problem

One of the well-studied routing issues is the Pickup and Delivery Problem (PDP). It aims to route a fleet of vehicles based on a depot to satisfy a set of transport requests subject to numerous constraints. In real-life cases, the satisfaction of those requests over a given time slot (1 day for example) may become impossible while respecting all problem constraints. Therefore, the studied variant is characterised by the selective aspect which consists of choosing a subset of demands to be honoured. In addition, travel times between sites may be uncertain and may vary according to several scenarios. The robustness consists then at finding a solution which remains feasible over all scenarios. Therefore, the goal of this study is to introduce lexicographic and metaheuristic approaches to solve the Robust Selective PDP (RSPDP). Numerous tests have been done on new instances for the considered variant, in order to show the efficiency of proposed approaches.

Simulation-Based Assessment of Parking Constraints for Automated Mobility on Demand: A Case Study of Zurich

In a coordinated mobility-on-demand system, a fleet of vehicles is controlled by a central unit and serves transportation requests in an on-demand fashion. An emerging field of research aims at finding the best way to operate these systems given certain targets, e.g., customer service level or the minimization of fleet distance. In this work, we introduce a new element of fleet operation: the assignment of idle vehicles to a limited set of parking spots. We present two different parking operating policies governing this process and then evaluate them individually and together on different parking space distributions. We show that even for a highly restricted number of available parking spaces, the system can perform quite well, even though the total fleet distance is increased by 20% and waiting time by 10%. With only one parking space available per vehicle, the waiting times can be reduced by 30% with 20% increase in total fleet distance. Our findings suggest that increasing the parking capacity beyond one parking space per vehicle does not bring additional benefits. Finally, we also highlight possible directions for future research such as to find the best distribution of parking spaces for a given mobility-on-demand system and city.

AUTOMATIC TRANSPORTATION OF PASSENGERS IN AN INTELLIGENT TRANSPORTATION SYSTEM BASED ON UNMANNED ELECTROCARS

The process of planning and organizing transportation in an intelligent information transport system is considered through the tasks of forecasting the receipt of requests for transportation from passengers, drawing up a transportation plan and calculating the required number of vehicles for the implementation of the transportation plan. The proposed methods are aimed at providing the most high-quality and timely passenger service of an intelligent information transport system with the most efficient use of rolling stock.     &nbsp

Autonomous wheelchair for patient\u2019s transportation on healthcare institutions

The transport of patients from the inpatient service to the operating room is a recurrent task in a hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented a system, named Connected Driverless Wheelchair, that can receive transportation requests directly from the hospital information management system, pick up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated operating room. As a result, a prototype capable of transporting patients autonomously in hospital environments was obtained. Although it was impossible to test the final developed system at the hospital as planned, due to the COVID-19 pandemic, the extensive tests conducted at the robotics laboratory facilities, and our previous experience in integrating mobile robots in hospitals, allowed to conclude that it is perfectly prepared for this integration to be carried out. The achieved results are relevant since this is a system that may be applied to support these types of tasks in the future, making the transport of patients more efficient (both from a cost and time perspective), without unpredictable delays and, in some cases, safer.

Bi-objective optimization model for the heterogeneous dynamic dial-a-ride problem with no rejects

This work proposes a bi-objective mathematical optimization model and a two-stage heuristic for a real-world application of the heterogeneous Dynamic Dial-a-Ride Problem with no rejects, i.e., a patient transportation system. The problem consists of calculating route plans to meet a set of transportation requests by using a given heterogeneous vehicle fleet. These transportation requests can be either static or dynamic, and all of them must be attended to. In the first stage of the proposed heuristic, the problem’s static part is solved by applying a General Variable neighborhood Search based algorithm. In the second stage, the dynamic requests are dealt with by implementing a simple insertion heuristic. We create different instances based on the real data provided by a Brazilian city’s public health care system and test the proposed approach on them. The analysis of the results shows that the higher the level of dynamism, i.e., the number of urgent requests on each instance, the smaller the objective function value will be in the static part. The results also demonstrate that a higher level of dynamism increases the chance of a time window violation happening. Besides, we use the weighted sum method of the two conflicting objectives to analyze the trade-off between them and create an approximation for the Pareto frontier.

Hospital transfer for patients with postpartum hemorrhage in Yokohama, Japan: a single-center descriptive study.

AimTo describe the transport time and timing of transfer of patients with postpartum hemorrhage to a tertiary care institution in an urban area.MethodsWe included patients with postpartum hemorrhage transferred to our hospital from a perinatal care facility. We defined transfer time as the time between the referring physician's request for transfer and the patient's arrival at our hospital. We examined transfer time and its breakdown, the shock index before and after transfer, and the prognosis.ResultsOverall, 79 patients with postpartum hemorrhage were transported to our hospital within a median (interquartile range) of 53 min (47–63 min). In 70 cases (89%), the request for transport was made before the shock index reached 1.5, and two cases had cardiac arrest during transport.ConclusionsMost patients who were transferred to our hospital were transferred according to the criteria recommended by the guideline. However, the occurrence of cardiac arrest during transport indicates the need to shorten the time from transfer requests to emergency calls by strengthening cooperation with regional perinatal care facilities.

Choosing the logistics chain structure for deliveries of bulk loads: case study of the Republic Kazakhstan

Purpose. The paper aims to develop the methodology for choosing the best available structure of a logistics chain for deliveries of bulk cargoes. Methodology. The systematic approach is used to formalize the problem of choosing the optimal structure of a logistics chain: the total expenses are used to define the goal function, alternative logistics chain structures and numeric parameters of the request flow (the delivery distance and the consignment weight) are defined as control variables, and the random variables representing technological parameters of servicing processes are used to consider the influence of the environment on the logistics chain. The mathematical model defines the functional dependence of the total expenses on the entities within the delivery chain on the request flow parameters for two alternative structures: the delivery with transshipment in one freight terminal and the delivery through two terminals. By using functional analysis, we define the ranges of the request flow parameters where the use of a given logistics chain structure is characterized by minimal total expenses. Findings. The experimental studies conducted considering two alternative logistics chain structures have shown that the better solution varies depending on the values of the request parameters. It allowed us to state that the ranges of the numeric parameters of the request flow may be defined to substantiate the optimal logistics chain structure for deliveries of bulk loads. Originality. The dependencies of the total logistics expenses on the delivery distance and the consignment weight as the parameters of the flow of requests for bulk cargo transportation have been defined in the paper for the first time. Practical value. The proposed methodological approach can be used by freight forwarders to substantiate the best option out of available alternative structures of a logistics chain for deliveries of loads with the given delivery distance and the consignment weight.

SOUP: Spatial-Temporal Demand Forecasting and Competitive Supply

We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis (agents) to meet incoming passenger requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) model that predicts the requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (DROP) algorithm that considers both the spatial-temporal predictions and the supply-demand state. We report on extensive experiments with real-world data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.

Интегрированное управление контейнерной транспортной системой региона

Nowadays, regional container transport systems operate in the context of incomplete information and lack of integration of economic entities. Pursuance of personal economic gains by companies often leads to an imbalance in the development of regional terminal capacities, irrational empty container flows and a decreased efficiency of the regional container system in general. The present article proposes an approach to the integrated management of the regional container transport system based on the coordinated container flow distribution between terminals. This technology aims to increase the efficiency of the whole system, including due to the flow enlargement and the accelerated container trains formation. The approach involves the data sharing between cargo owners, terminals, the railway and resource owners on a digital logistics platform that forms logistics chains taking into account the transportation geography, infrastructure capacity and the interests of container market entities. The research presents a mathematical formalisation of the optimal container delivery chains by the time criterion, as well as a model of the integrated distribution of container flows between terminals. The model distributes the total container flow generated by a region between the existing terminal facilities in a way to minimise the total time of cargo delivery (taking into account the accumulation time of the full-length container block train at the terminal). The model, tested in the conditions of the container transport system in the Ural region, showed that an integrated management approach reduces the average container delivery time by 5 days due to the flow enlargement and regular container block trains. The effect size depends on the total regional container flow, as the larger number of requests for transportation to a certain destination leads to the accelerated formation of loaded container trains. The proposed technology is of economic interest for various participants of the container transport system. For cargo owners, it reduces the delivery time and, consequently, increases the capital turnover; for container owners, it accelerates the container turnover and reduces empty running; for terminals, it increases the throughput by reducing the accumulation time. To improve the model, it is planned to develop approaches for coordinating not only outgoing, but also incoming container flows between existing terminals.