Vehicle Scheduling Problem Research Articles

Heterogeneous fleet vehicle scheduling problems for dynamic pickup and delivery problem with time windows in shared logistics platform: formulation, instances and algorithms

ABSTRACT his paper introduces a dynamic pickup and delivery problem with time windows, heterogeneous fleet vehicles, no depot, and dynamic priority (DPDP-TWHVNDDP) in the shared logistics platform. The distinguishing feature of DPDP-TWHVNDDP is that the priority and number of requests and the number and types of vehicles available are dynamic, and that all the requests in a scheduling are not necessarily served to maximize the total profit according to their priorities. A mathematical formulation of maximizing profit dynamic scheduling adapting to dynamic priority is established, and a hybrid scatter search parallel algorithm combined with the genetic algorithm, variable neighborhood search algorithm, and tabu search algorithm is proposed to solve the problem with high quality in a relatively short time. Comparison results of our algorithm against the best-known solutions on the benchmark instances available in the literatures show that our algorithm can improve over 35% (52 out of 146) of the total distance and over 30% (44 out of 146) of the vehicle used. A set of DPDP-TWHVNDDP instances based on extending Li and Lim's benchmark instances are introduced. Experiments on benchmarks and actual datasets show that the proposed hybrid heuristic is effective and efficient especially in large scale datasets.

Vehicle Routing and Scheduling of Flex-Route Transit under a Dynamic Operating Environment

To improve the reliability, responsiveness, and productivity of the flex-route transit service, this paper investigates the vehicle scheduling and routing problem under a dynamic operating environment. First, we discuss the new operating polices after the introduction of intelligent transportation systems (ITSs), including automatic vehicle location (AVL) system, mobile data terminal (MDT), and computer-aided dispatch (CAD) system. Second, a mixed integer programming (MIP) formulation is employed to solve the offline routing problem. Third, an online scheduling scheme is presented to tackle different dynamic events, such as dynamic requests, travel time fluctuations, cancellations of requests, and customer no-shows. Finally, simulation experiments based on a real-life flex-route transit service are conducted to assess the influence of different dynamic events. The results demonstrate that the proposed scheduling scheme is reliable for coping with various dynamic events, and our findings can be used to guide the policy making of flex-route transit services.

Generating multi depot vehicle scheduling problems with known optimal tours

Validation of heuristics that aim to solve efficiently vehicle scheduling problems is usually done by generating random datasets and comparing one approach versus another. How far from the optimal solution are their results is usually not known, especially for larger workloads. The Multiple-Depot Vehicle Scheduling Problem (MDVSP) is a well-known and important optimization problem that aims to assign a set of trips to a fleet of vehicles provided by several depots in order to minimize the total cost of empty travel and waiting. Addressing the need to test different heuristics on various problem types, our paper introduces several generation algorithms that create random MDVSP instances, of any size, with known optimum. In order to evaluate the results, we have used both an exact MDVSP solver and a fast heuristic, analyzing the correctness and the runtime performance. Empirical testing indicates that these instances are still difficult to solve which means that they could provide a valuable tool for measuring the scalability of various MDVSP heuristics.

Research on Vehicle Scheduling Problem of Multi Warehouse Collaborative Distribution and Its Application

In the modern society with highly developed logistics, while paying attention to structural and functional strategic layout and global scale services, it is also our ultimate goal to pursue efficient transportation / high-quality logistics services, reduce logistics transportation costs and reduce CO₂ emissions. This paper proposes a new concept of multi warehouse collaborative distribution Vehicle Dispatching Problem (VDP/MD) and its customization, and provides a solver for complex scenarios in real new transportation problems. Aiming at the VDP/MD problem, an enhanced computing model with hierarchical and multiple structure is introduced, which is calledHIMS++computing model. It consists of three layers: the atomic layer is the fluctuation area of system regulation cost, the molecular layer is the formation area of system stable state, and the individual layer is the optimization decision area of scheduling plan. TheHIMS++model is constructed by using object-oriented programming software components; The optimization algorithm is realized by heuristic probability exploration and fuzzy decision-making method. The experiment was carried out using the 3-day order data obtained from the actual dispatching center in Tokyo Metropolitan Area, which has 27 oil tank vehicles (including 2 types). It is proved that theHIMS++model is more accurate (10% improvement) and faster (12 times) than the results given by experts.HIMS++model is also a convenient tool to solve the actual transportation combination optimization problem (multi parameter, multi constraint and multi-objective optimization problem), This study introduces a novel solution for digital logistics support and accurate Vehicle distribution scheduling.

Modeling and solving the multimodal car- and ride-sharing problem

We introduce the multimodal car- and ride-sharing problem (MMCRP), in which a pool of cars is used to cover a set of ride requests while uncovered requests are assigned to other modes of transport (MOT). A car’s route consists of one or more trips. Each trip must have a specific but non-predetermined driver, start in a depot and finish in a (possibly different) depot. Ride-sharing between users is allowed, even when two rides do not have the same origin and/or destination. A user has always the option of using other modes of transport according to an individual list of preferences.The problem can be formulated as a vehicle scheduling problem. In order to solve the problem, an auxiliary graph is constructed in which each trip starting and ending in a depot, and covering possible ride-shares, is modeled as an arc in a time-space graph. We propose a two-layer decomposition algorithm based on column generation, where the master problem ensures that each request can only be covered at most once, and the pricing problem generates new promising routes by solving a kind of shortest-path problem in a time-space network. Computational experiments based on realistic instances are reported. The benchmark instances are based on demographic, spatial, and economic data of Vienna, Austria. We solve large instances with the column generation based approach to near optimality in reasonable time, and we further investigate various exact and heuristic pricing schemes.

An effective iterated greedy algorithm for solving a multi-compartment AGV scheduling problem in a matrix manufacturing workshop

In this paper, we address a multi-compartment automatic guided vehicle scheduling (MC-AGVS) problem from a matrix manufacturing workshop that has attracted more and more attention of manufacturing firms in recent years. The problem aims to determine a solution to minimize the total cost including the travel cost, the service cost, and the cost of vehicles involved. For this purpose, a mixed-integer linear programming model is first constructed. Then, a novel iterated greedy (IG) algorithm including accelerations for evaluating objective functions of neighboring solutions; an improved nearest-neighbor-based constructive heuristic; an improved sweep-based constructive heuristic; an improved destruction procedure; and a simulated annealing type of acceptance criterion is proposed. At last, a series of comparative experiments are implemented based on some real-world instances from an electronic equipment manufacturing enterprise. The computational results demonstrate that the proposed IG algorithm has generated substantially better solutions than the existing algorithms in solving the problem under consideration.

Hybrid Evolutionary Algorithm with an Optimal Sample Allocation Strategy for Multifidelity Simulation Optimization Problems

Simulation has been applied to evaluate system performance even when the target system does not exist in practice. Dealing with model fidelity is required to apply simulation to practice. A high-fidelity (HF) simulation model is generally more accurate and requires more computational resources than a low-fidelity (LF) one. A low-fidelity model may have less accuracy than a HF one, but it can rapidly evaluate a design alternative. Consequently, the performance accuracy of the constructed simulation model and its computational cost involves a tradeoff. In this research, the simulation optimization problem under a large design space, where a LF model may not be able to evaluate all design alternatives in the limited computational resource, is studied. We extended multifidelity (MF) optimization with ordinal transformation and optimal sampling (MO2TOS), which enables the use of LF models to search for a HF one efficiently, and proposed a combination of the genetic algorithm and MO2TOS. A novel optimal sample allocation strategy called MO2TOSAS was proposed to improve search efficiency. We applied the proposed methods to two experiments on MF function optimization and a simultaneous scheduling problem of machine and vehicles (SSPMV) in flexible manufacturing systems. In SSPMV, we developed three fidelity simulation models that capture important characteristics, including the preventive deadlock situation of vehicles and alternative machines. Simulation results show that the combination of more than one fidelity level of simulation models can improve search efficiency and reduce computational costs.

A Primal Adjacency-Based Algorithm for the Shortest Path Problem with Resource Constraints

The shortest path problem with resource constraints (SPPRC) is often used as a subproblem within a column-generation approach for routing and scheduling problems. It aims to find a least-cost path between the source and the destination nodes in a network while satisfying the resource consumption limitations on every node. The SPPRC is usually solved using dynamic programming. Such approaches are effective in practice, but they can be inefficient when the network is large and especially when the number of resources is high. To cope with this major drawback, we propose a new exact primal algorithm to solve the SPPRC defined on acyclic networks. The proposed algorithm explores the solution space iteratively using a path adjacency–based partition. Numerical experiments for vehicle and crew scheduling problem instances demonstrate that the new approach outperforms both the standard dynamic programming and the multidirectional dynamic programming methods.

A study on flow decomposition methods for scheduling of electric buses in public transport based on aggregated time–space network models

Over the past few years, many public transport companies have launched pilot projects testing the operation of electric buses. The basic objective of these projects is to substitute diesel buses with electric buses within the companies’ daily operations. Despite an extensive media coverage, the share of electric buses deployed still remains very small in practice. In this context, new challenges arise for a company’s planning process due to the considerably shorter ranges of electric buses compared to traditional combustion engine buses and to the necessity to recharge their batteries at charging stations. Vehicle scheduling, an essential planning task within the planning process, is especially affected by these additional challenges. In this paper, we define the mixed fleet vehicle scheduling problem with electric vehicles. We extend the traditional vehicle scheduling problem by considering a mixed fleet consisting of electric buses with limited driving ranges and rechargeable batteries as well as traditional diesel buses without such range limitations. To solve the problem, we introduce a three-phase solution approach based on an aggregated time–space network consisting of an exact solution method for the vehicle scheduling problem without range limitations, innovative flow decomposition methods, and a novel algorithm for the consideration of charging procedures. Through a computational study using real-world bus timetables, we show that our solution approach meets the requirements of a first application of electric buses in practice. Since the employment of electric buses is mainly influenced by the availability of charging infrastructure, which is determined by the distribution of charging stations within the route network, we particularly focus on the influence of the charging infrastructure.

Stochastic Scheduling of Mobile Energy Storage in Coupled Distribution and Transportation Networks for Conversion Capacity Enhancement

Mobile energy storage systems (MESSs) is a promising solution to enhancing the operational flexibility of coupled distribution and transportation networks (CDTNs), as well as the conversion capacities of hybrid AC/DC microgrids (MGs). To achieve the coordination among MESSs, hybrid AC/DC microgrids and CDTNs, while considering the system uncertainties from variable renewable energy (VRE) sources and the daily traffic demands of common vehicles in transportation networks, a two-stage stochastic management scheme is proposed to minimize the expected system operational cost. In the first-stage, the vehicle scheduling problem of MESSs is modeled and optimized based on the proposed user-equilibrium-based multi-layer multi-timescale time-space network model to fully utilize the mobility of MESSs under traffic uncertainties, i.e., MESSs are scheduled to travel within CDTNs for energy transfer or local energy scheduling. In the second-stage, the actual charging/discharging behaviors of MESSs, outputs of VRE sources and the power conversions at each MG are adjusted in accordance with uncertainty realizations. The proposed model is verified on a test system with Sioux Falls transportation network and a 33-bus distribution system. The results demonstrate the effectiveness of MESSs mobility to serve as potential power conversion enhancements for MGs with mismatched generation and conversion capabilities.

Shift Planning Under Delay Uncertainty at Air France: A Vehicle-Scheduling Problem with Outsourcing

Airlines must operate many jobs in airports, such as passenger check-in or runway tasks. In airlines’ hubs, airlines generally choose to perform these jobs with their own agents. Shift planning aims at building the sequences of jobs operated by the airline agents and has been widely studied given its impact on operating costs. The impact of delayed flights is generally not taken into account despite the propagation of flight delays along these sequences: If a flight is late, then the agents doing the corresponding jobs are delayed, and may arrive late to their next jobs and delay the corresponding flights. Since delay costs are much higher than the costs of outsourcing jobs, if the agent who is supposed to operate a job is still working elsewhere when the job begins, then airlines tend to outsource the job to their own dedicated team or to a third party. We introduce a stochastic version of the shift-planning problem that takes into account outsourcing costs due to delay. It can be seen as a natural stochastic generalization of the vehicle-scheduling problem in which delayed jobs are outsourced. We propose a column-generation approach to solve it, whose key element is the pricing subproblem algorithm, modeled as a stochastic resource-constrained shortest-path problem. Numerical results on Air France industrial instances show the benefits of using our stochastic version of the shift-planning problem and the efficiency of the solution method. Moving to the stochastic version enables Air France to reduce total operating costs by 3.5%–4.8% on instances with more than 200 jobs, and our algorithm can solve to near optimality instances with up to 400 jobs.

Reference model of milk-run traffic systems prototyping

A declarative model is developed in this study. It allows us to formulate a constraint satisfaction problem that supports making decisions in the process of seeking a periodic distribution policy with a plan of whom to serve, how much to deliver, by which fleet of logistic trains travelling regularly, and on which routes. Implemented through a constrained, programming-driven solver, it allows for the formulation of forward and reverse milk-run vehicle routing and scheduling problems, subject to the constraints imposed by an in-plant distribution network. Consequently, it can be used to determine the number of transport trips and their organisation in time and space needed for the timely delivery of material to specific loading/unloading points. In this context, the proposed model meets the needs of an interactive search for solutions by the alternate and iterative formulation of successive forward and reverse decision problems.

Integrated Outbound Vehicle Routing and Scheduling Problem at a Multi-Door Cross-Dock Terminal

This paper presents a newly formulated model for the integrated vehicle routing and scheduling problem on the outbound side of a multi-door cross-dock terminal (or system or center) operating in post-distribution mode involving multiple product types, and develops a new decomposition-based hybrid optimization method for its solution. The hybrid optimization method solves the problem iteratively. In each iteration, a solution of the outbound vehicle scheduling sub-problem is first obtained and the vehicle routing sub-problem with time windows is then subsequently solved. The hybrid optimization method makes use of an iterated local search (ILS) algorithm for solving the vehicle routing sub-problem with time windows and a greedy search for solving the sub-problem of outbound vehicle scheduling involving the assignment and sequencing of vehicles at each dock-door, and determining the vehicle departure times from the cross-dock. The proposed solution was shown capable of finding the optimal solution identified by the enumeration method for two small datasets and further tested with 20 newly generated moderate-to-large data sets. Based on the results obtained, managerial insights are highlighted.

Research on Path Planning of Robot Based on Artificial Intelligence Algorithm

the Task of Robot Path Planning Based on Artificial Intelligence Algorithm is to Determine an Optimal Strategy, with the Goal of Maximizing the Return. When the Robot is in a New State, It Needs to Select an Action from the Action Set and Execute It. in Order to Improve the Quality of the Algorithm, It is Necessary to Apply the Search Strategy to the Action Selection. in the Path Planning System of the Robot, One of the Most Critical Links is the Realization of Directional Reference Vehicle Scheduling. Generally, Artificial Intelligence Algorithm is the Most Widely Used in Robot Path Planning. in This Paper, an Artificial Intelligence Algorithm is Used to Optimize the Vehicle Scheduling Problem. the Path Variable of the Problem Can Be Defined Correspondingly, Which Originates from the Planning Node. the Sequence of Sending Points Reached by Completing Other Sending Requirements is Called the Legitimate Sub Path, While the Corresponding Traversal Path Does Not Include the Sequence of Several Legitimate Sub Paths of Repeated Planning Points. the Algorithm Can Significantly Improve the Efficiency of Multi-Robot System, Reduce the Number of Explorations and Speed Up the Process of Convergence.

Scheduling Just-in-Time Transport Vehicles to Feed Parts for Mixed Model Assembly Lines

In order to solve the problem of vehicle scheduling to feed parts at automobile assembly line, this study proposes a just-in-time delivery method combined with the mode of material supermarket. A mixed integer linear programming model with the primary objective of using the least number of tow trains is constructed by considering capacity of vehicle and inventory levels of line. On the basis of the minimum number of tow trains, the schedule of each tour is reasonably planned to minimize inventory of assembly line, which is the secondary objective of the part supply problem. Additionally, a heuristic algorithm which can obtain a satisfactory solution in a short time is designed to solve large-scale problems after considering continuity and complexity of modern automobile production. Furthermore, some cases are analyzed and compared with the widely used periodic delivery strategy, and the feasibility of just-in-time model and algorithm is verified. The results reveal that just-in-time delivery strategy has more advantages in reducing inventory level than periodic delivery strategy.

Primal column generation framework for vehicle and crew scheduling problems

AbstractThe primal adjacency‐based algorithm and the multidirectional dynamic programming algorithm are two exact methods that have recently been developed to efficiently solve the shortest path problem with resource constraints (SPPRCs). These methods are primal in the sense that they are able to produce sequences of feasible solutions using iterative exploration of the search space. Since the SPPRCs often appear as a subproblem (SP) in the solution of vehicle and crew scheduling problems (VCSP) using column generation (CG), we propose a new primal column generation framework that embeds these primal methods in a CG scheme. The primal column generation solves at each iteration a sequence of appropriate restricted SP and stops solving the SP when there is no need to continue. This approach introduces a large degree of flexibility, and allows performing good cost improvements in a very limited time. Computational experiments on VCSP instances show that the proposed approach is able to find optimal solutions while reducing the time spent solving the SP by factors of up to seven compared to the standard CG algorithm. This leads to significant improvements in the overall solution times, with an average reduction factor of 3.5.

Scheduling the vehicles of bus rapid transit systems: a case study

AbstractBus rapid transit (BRT) is a cost‐efficient, traffic‐free bus‐based transportation system competing with subways. There are 205 municipalities around the world that implemented their own BRT systems. Istanbul, having the sixth‐most congested traffic in the world, built its own BRT system (Metrobüs), which serves more than 830,000 people (6.45% of all public transportation usage) in a day with 6254 trips covered by its current fleet of 496 vehicles. In this study, we model the vehicle scheduling problem of Metrobüs as a multiple depot vehicle scheduling problem. The model aims to minimize the fleet size and total deadhead kilometers while covering all timetabled trips. We propose a new heuristic, trips merger (TM), to solve the model and show that there exists cost reduction opportunities in terms of both fleet size and deadhead kilometers. The proposed heuristic is a member of the state‐space reduction heuristics family, which first reduces the problem size, then solves the reduced problem. Computational study reveals that TM performed better than the existing state‐space reduction heuristics for the Metrobüs case.

Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses

To address sustainable development issues of urban traffic, electric buses will join traditional bus system, and the scheduling of bus fleet should be adjusted due to the distinct features of electric buses. To this end, this paper develops a Multi-objective Bi-level programming model to collaboratively optimize the vehicle scheduling and charging scheduling of the mixed bus fleet under the operating conditions of a single depot. The upper level determines the vehicle scheduling to minimize the operating cost and carbon emissions under the constraints of connecting time between trips and the limited driving range of electric buses. The lower level is a charging scheduling problem that considers the charging time and the limited driving distance constraint to minimize the charging cost. The proposed model is solved with an integrated heuristic algorithm. The vehicle scheduling problem is addressed with the iterative neighborhood search algorithm based on simulated annealing, while the charging scheduling problem is solved with a greedy dynamic selection strategy based on the approach of multi-stage decision. Finally, case study is carried out based on a mixed bus fleet in Beijing, and the results validate the availability of the proposed model and solution algorithm.

The Vehicle Scheduling Problem of Third-Party Passenger Finished Vehicle Logistics Transportation: Formulation, Algorithms, and Instances

In this article, a vehicle scheduling problem of third-party passenger finished vehicle logistics transportation networks is studied. An integer programming model of open heterogeneous fleet pickup and delivery problem with time windows and split load (OHFPDPTWSL) is established to maximize the total profit, and a hybrid parallel heuristic algorithm combining with path buffer clustering operator (PBC), multi-mark split operator (MMS) and four variable neighborhood search operators (PBCMMSVNSHPA) is proposed to solve this problem with high quality in a relatively short time. The PBC operator with three different clustering types can effectively cluster the orders and the transport vehicles before the route planning, and the MMS operator can greatly reduce the complexity and computation of the path planning at the expense of little algorithm precision. Then a set of instances which represents the realistic characters of OHFPDPTWSL modified from benchmark instances is introduced. The experimental results on these instances show that PBCMMSVNSHPA is suitable for real-time requirement or large-scale dataset, and the experimental results on the actual instance of an enterprise show that this algorithm can solve the instance with 200 orders and 500 vehicles within 3 minutes.

Collaborative Optimization of Dock Door Assignment and Vehicle Scheduling in Cross-Docking

Cross-docking is a logistic strategy that can transport goods directly from suppliers or manufacturers to retailers or customers. In daily life, the requirements for timeliness of goods distribution have been continuously improved. Cross-docking can realize the rapid transshipment of goods and improve the process efficiency of distribution greatly. Meanwhile, during the cross-docking process, goods are deposited in the temporary storage area, which reduces the storage cost. This paper focuses on the analysis of reasonable vehicle scheduling and dock door allocation problems in cross-docking. The goal is to minimize the working time of cross-docks by the research on this combinatorial optimization problem. This paper proposes the genetic algorithm (GA) and the hybrid particle swarm optimization to solve the three-scale (small, medium and large) cross-docks. Optimal completion time, average completion time and average solution time are considered as factors to evaluate the efficiency of two algorithms on three scales. And then the concept of mixed-mode dock door is introduced. GA is used to conduct numerical experiments with mixed dock doors on different scales. Finally, by comparing the utilization rate of mixed dock doors, we can analyze the influence of mixed dock door on vehicles’ waiting time.