Pickup Vehicle Research Articles

DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks

Milk-run tours with time windows are an essential strategy to collect goods to minimize production and transportation costs. Due to unexpected events at the supplier production or traffic congestion, delays can occur during the vehicle route execution, causing non-compliance between the logistics operator and the company. This paper describes the DMRVR (Dynamic Milk-Run Vehicle Routing) solution that uses a dynamic routing algorithm along with fog-based vehicular ad hoc networks for implementing the collection of goods in milk-run operations that respect the company’s time window. When a production delay occurs, the supplier sends a message through the vehicular network to alert the pickup vehicle, forcing it to make dynamic route changes to satisfy the constraints. We have implemented a queue with a timeout and retransmission features to improve the vehicular network’s message delivery. To assess the DMRVR solution, we analyzed the efficiency of the dynamic vehicle routing and the vehicular network impacts. In the experiments, we used an event-based network simulator OMNeT++ bidirectionally coupled with SUMO (Simulation of Urban Mobility), aiming to make the most realistic simulations. Simulation results show the average route time was lower than the time limit imposed by the company with the DMRVR solution. In dense vehicular network scenarios, the message delivery success rate is higher. Conversely, when the vehicular network scenario is sparse, it is necessary to balance network coverage and distribute more RSUs in specific places.

Analysis of vehicle stability when using two-post above-ground automotive lifts: Distribution of forces in arms

Vehicles falling off two-post above-ground (2PAG) lifts in garages is a fairly frequent occurrence. As knowledge about the determining factors of such falls is limited, this paper looks at the technical determinants influencing the static stability of lifting with a 2PAG lift. The forces at the support pads and the moments induced in the lift arms were measured according to different configurations. After consultation, the selected controlled factors in the experimental design were lift wear and tear, vehicle type, vehicle position, loading of vehicle and use of fall-arrest latches. Based on analysis of variance, it was concluded that the factors that have a significant influence on the distribution of forces at the support pads are (i)the “loading * vehicle” interaction, (ii)the “lift * vehicle” interaction and (iii)the use of fall-arrest “latches.” Test results show that a compact vehicle with a loaded trunk better distributes its weight over the four arms, whereas the force distribution is evenly spread out on the four arms for an empty pick-up vehicle. A worn lift will amplify the uneven force distribution in the four arms for a compact vehicle, but lift wear does not seem to have practical implications for the force distribution in the case of a pick-up. Asymmetrically positionned fall-arrest latches tended to place a greater load on the two arms next to the latches. Finally, excessive vertical and horizontal forces on an arm were found in some configurations. Preliminary recommandations concerning inspection, vehicule placement and loading were made in accordance with these results.

Гибридный алгоритм оптимизации, основанный на гравитационной аналогии и алгоритме роя частиц, для решения задачи о заборе и доставке груза одним транспортным средством

Гибридный алгоритм оптимизации, основанный на гравитационной аналогии и алгоритме роя частиц, для решения задачи о заборе и доставке груза одним транспортным средством

Free-floating carsharing users’ willingness-to-pay/accept for logistics management mechanisms

The spatio-temporal flexibility of free-floating carsharing (FFCS) fleets leads to vehicle stock imbalances across the network. One set of strategies for managing fleet distribution involves incentivising users to participate in relocating the vehicles. The objective of this study is to establish FFCS customers’ preferences for each of four incentivisation mechanisms: 1) vehicle delivery, 2) paid relocation, and 3–4) incentivisation for alternate vehicle pick-up and drop-off locations. Survey data (n = 311; collected Sept. 2017) from FFCS users in Vancouver and Washington D.C. are employed to quantify willingness-to-pay/accept (WTP/WTA) for these mechanisms. We find that a majority of respondents report positive attitudes (“definitely” or “possibly” willing to use) toward each of the four incentivisation mechanisms, with alternate drop-off the highest (57%) and paid relocation the lowest (40%). Regression analysis finds that user experiences using FFCS are generally stronger predictors of WTP/WTA than socio-demographic features, with (intuitively) the frequency of FFCS unavailability the strongest predictor. Age is the strongest socio-demographic predictor, with the WTP for vehicle delivery increasing and the size of required incentives for alternate pick-up/drop-off locations decreasing with age. Finally, we performed k-means cluster analysis of respondents based on the times-of-week that they report experiencing difficulty finding an available FFCS vehicle, and identified four distinct segments of users. However, we found generally weak relationships between WTP/WTA and the specific time-of-week periods that unavailability is experienced.

A learning-based memetic algorithm for the multiple vehicle pickup and delivery problem with LIFO loading

The multiple vehicle pickup and delivery problem is a generalization of the traveling salesman problem that has many important applications in supply chain logistics. One of the most prominent variants requires the route durations and the capacity of each vehicle to lie within given limits, while performing the loading and unloading operations by a last-in-first-out (LIFO) protocol. We propose a learning-based memetic algorithm to solve this problem that incorporates a hybrid initial solution construction method, a learning-based local search procedure, an effective component-based crossover operator utilizing the concept of structured combinations, and a longest-common-subsequence-based population updating strategy. Experimental results show that our approach is highly effective in terms of both computational efficiency and solution quality in comparison with the current state-of-the-art, improving the previous best-known results for 132 out of 158 problem instances, while matching the best-known results for all but three of the remaining instances.

Optimization model for simultaneous delivery and pickup vehicle routing problem with time windows

Optimization model for simultaneous delivery and pickup vehicle routing problem with time windows

The research on two phase pickup vehicle routing based on the K-means++ and genetic algorithms

The research on two phase pickup vehicle routing based on the K-means++ and genetic algorithms

The research on two phase pickup vehicle routing based on the K-means++ and genetic algorithms

A popular topic of interest is the development of an efficient vehicle routing plan, which needs to meet customer requirements and ensure delivery with the lowest cost. This paper established a model of the vehicle routing problem with a time window and static network considering the vehicle type, type of goods, and customer satisfaction requirements to build an optimisation model. By optimising the combination of the K-means++ and genetic algorithms, the problem is transformed into a two stage solution, supplier clustering is performed using the K-means++ algorithm, and the vehicle path is determined using the genetic algorithm in each cluster arrangement. Finally, the optimisation results are compared with the actual delivery data, which demonstrates that the optimisation results are superior to the current vehicle arrangement in terms of vehicle utilisation and cost. Finally, an example is presented to illustrate the feasibility of the proposed algorithm.

Multi‐objective modeling and analysis of an electric pickup vehicle for range, performance, drivability, handling and ride comfort attributes

AbstractIn today's electric vehicle arena, range, performance, drivability, handling safety and ride comfort are few important attributes an OEM would like to improve on a vehicle. However, they are almost mutually exclusive like greater range & extreme handling safety would need to compromise on drivability & ride comfort and vice versa respectively. Automotive engineers constantly focus on to find the right balance among these attributes by optimum design of the vehicle components. Often these components are multi‐disciplinary involving different engineering domains like mechanical, thermal, electrical, hydraulic & electronics, etc. These complexities demand for model based and systems engineering approach from the start of the development. This emphasizes the need for high fidelity, dynamic vehicle system models suitable for co‐development of different components and controls. In this study, detailed vehicle model representing an electric pickup truck including battery, traction motors, driveline, chassis (suspension, brakes, wheels & mounts) is created in Modelica platform using which multiple vehicle level attributes like range, performance, drivability, handling and ride comfort are studied. Further, it is shown that the simulation speed can be increased by varying fidelity of the subsystems based on study intended within the same model framework.

Predictive Analysis based Efficient Routing of Smart Garbage Bins for Effective Waste Management

The crux of an effective waste management system is a well-organized routing algorithm that aids to collect the garbage from the bins efficiently and thereby eliminate the overflow of the garbage bins. To automate the garbage collection, the bins use a smart integrated sensing system. Three sensors (ultrasonic, load and gas sensors) are equipped in the proposed garbage bin to sense the level, weight and Carbon-di-oxide (CO2) concentration of the garbage in the bin. A dedicated webpage is assigned to monitor the collected garbage bin sensor data continuously. If the bin reaches the maximum waste level or weight or CO2 level (threshold) it will be marked as “Ready to be picked up”. Scheduling the route(s) and allocating pickup vehicle(s) to collect the filled bins is a significant concern for the efficiency of any waste management system. This scheduling problem boils down to a capacitated arc routing problem (CARP).Considering multiple trips for the available vehicles, capacity of the vehicle, capacity of the bins to be collected, etc., as factors a heuristic algorithm for an efficient routing is proposed in this paper. The primary aimof the proposed heuristic routing algorithm is to reduce the total usage cost of vehicles by minimizing total traversed distance. Predictive analysis algorithms will aid the pickup trucks to be used to the fullest capacity even though there are only few bins to collect so that better efficiency is achieved. In this paper, simple linear regression and multiple linear regression algorithms are applied to suggest the bins (that will be filled in near future) to be added to the route that will help achieve maximum usage of truck capacity. The waste management web application allows the admin to add these bins to the route and authorize the same for the drivers.

Hybrid Adaptive Memory Programming to optimize the multi-commodity many to many vehicle routing problem

With the quick development of urban transport networks, the multi-commodity many to many variants of pickup and delivery vehicle routing problem (PDVRP) becomes more and more important. A critical issue is to solve this variant through optimisation techniques. We address a new variant of the multi-commodity many to many PDVRP (m-MMPDVRP). The m-MMPDVRP problem is when one or multi-commodities are collected from many sites to be transported to many destinations. In this problem, we assumed that all commodities share the same vehicle capacity during transportation. All vehicles are non-homogeneous and each commodity has to be stored separately during transportation. A new model is developed, based on multiple commodities. The objective is to generate an optimal path plan, ensuring that the demand for heterogeneous commodities can be satisfied by an arbitrary set of suppliers. We propose an adaptive memory-programming (AMP) technique based on the Scatter Search (SS). The solution quality of the suggested methodology is assessed and compared with the result presented in the previous works for the same instances. Numerical experimentation shows the distinction of the AMP with Scatter Search compared with other existing techniques; and establishing an efficient metaheuristic method for the m-MMPDVRP problem.

Heterogeneous vehicle pickup and delivery problems: Formulation and exact solution

The heterogeneous green pickup and delivery problem (GPDP) aims to minimize carbon emissions of pickups and deliveries by a fleet of heterogeneous vehicles. We propose the first exact algorithm for solving the heterogeneous GPDP. This exact algorithm is based on a set partitioning model and the key characteristics of its optimal solution. We prove that the optimal solution is composed of the optimal trips of heterogeneous vehicles; thus, the solution space is significantly reduced by cutting the non-optimal trips. The proposed exact algorithm can rapidly find the optimal solution of the largest-scale instance from Li & Lim benchmarks and three types of vehicles. The exact algorithm is also validated by solving a practical case of heterogeneous GPDP in China. Based on the extensive experiments, we conclude an interesting managerial insight that heterogeneous vehicles can always effectively reduce carbon emissions except for two situations.

Freight Consolidation Problem with Time Windows, Pickup and Delivery Sequence

The number of online market places for freight matching is on the rise. Online market places help small trucking companies find shipping customers. However, they do not provide cargo consolidation strategies. This lack of effective consolidation has adverse effects on the shipping industry and greenhouse gas emission. To that extent, this article addresses Multiple Vehicle Pickup and Delivery Problem with Time Windows (MVPDPTW). We propose a Mixed Integer Programming (MIP) model and a branch-and-cut algorithm geared towards identifying effective freight consolidation opportunities. For emission studies, we used a cost conversion technique from the literature to convert emission levels into monetary values. On real-world logistics company test instances, our model identified routes with lower cost and lower emission levels than the actual routes.

Regional goods delivery: How to reduce its CO2-, NOx- and PM10-emissions?

Regional goods delivery fulfils an essential socio-economic function, in particular, in dense urbanized areas in countries like The Netherlands. Shippers (producers or traders), transport service providers, businesses and private households favour road transport, because of logistic and financial reasons. Delivery and pick-up vehicles are mainly powered by internal combustion engines (ICE). ICE is a major source of ambient air pollution by NOx and PM10 and of global warming (CO2). More recent and well-maintained engines have much lower emissions of NOX and PM10 than older and less well maintained ones, but their CO2-emission has not been reduced as much. With (local) freight transport growing exponentially, these emissions are likely to rise. The aim of the paper is to estimate how a combination of logistic, technical and policy choices may reduce emissions of CO2, NOX and PM10. The authors follow an integrative, interdisciplinary approach, because the past has taught that align- ment of decisions by companies and government is inevitable to effectively deal with the root causes of these emissions. The main research question is as follows: What is needed to reduce the key emissions by regional goods delivery? This was addressed by literature analysis and micro simulation. The lat- ter was used to compare the emissions of diesel, CNG and electricity to power trucks and truck-vans combinations in a region to inner-city delivery scenario. CNG is currently the better option in terms of emissions for the whole trip, largely because of the Dutch grey electric power mix. Replacing the city leg of the trip by vans leads to more emissions, more vehicles, higher parking needs and logistic complexity. Full electric trucks and vans cut out the local air pollution, which is beneficial, but not the emissions by power plants. A green(er) electric power mix is necessary to go to zero emission regional goods delivery.

NUMERICAL AND EXPERIMENTAL ANALYSIS OF THE FLOW OVER A COMMERCIAL VEHICLE - PICKUP

This work presents an ongoing numerical and experimental study of the flow around a pickup vehicle by means of CFD simulations and wind tunnel experiments. The model was based on the light-pickup market in Brazil and it was designed with flat surfaces and sharp edges. One of the objectives of this research was the understanding about the flow pattern around the vehicle, especially in the region behind the cabin and the wake. Another goal was to obtain original data from experimental measurements which could be used on further computational investigations. The experiments were carried out in a low-speed wind tunnel at Reynolds number of 5 x 105. Hot-wire anemometry was used to obtain the velocity profiles. Wall tufts were applied to describe the flow direction and regions of attached/detached and recirculation zones. Acceleration due to the underbody and the shear layer formed on the cabin were well defined, also indicating a region of reverse flow behind the tailgate. The flow visualization allowed the identification of recirculation regions inside the trunk and regions of detached flow. These flow patterns were also reproduced in the CFD simulations resulting satisfactory information to describe the main flow pattern over the pickup vehicle.

Vehicle Routing, Scheduling and Decision Utility Environment

In this paper, we will try to summarize research on vehicle scheduling and decision utility. We assume a given framework with centralized management distribution system replenishment and given sets of distribution centers. We provide a solution to the issues of how vehicles can be scheduled or routed to achieve a company’s logistic objectives. The problem, as typically formulated, is to determine the order turn in which the customers will be visited by delivery or pick-up vehicles otherwise called the route. Lastly, we discuss the facets of priority class scheduling in decision utility management and its advantages.

공유 자전거 시스템의 수요기반 재배치

As the environmental effect is becoming increasingly important in urban life, an eco-friendly bike sharing system has been considered as a good alternative transportation system. Most of the bike-sharing systems operate in a one-way mode, in which users can borrow a bike from any station and return it wherever they want. Hence, an imbalance exists between the supply and the demand of each station, and in such a situation, the operators must relocate bicycles using an inter-station vehicle (truck). In this study, we focused on dynamic repositioning, which relocates bikes several times a day. A mathematical model for an optimal dynamic repositioning operation plan is proposed, which can be executed once a day. This model considers pickup and delivery vehicle routing under time constraints and aims to minimize the number of unbalanced bikes while ensuring target fulfillment as close as possible. The relocating vehicle may visit several stations to pick up and deliver bikes needed within a time allowed for operation. The suggested model is evaluated through the standard cases with reasonable size and the real cases of Seoul city as well. Results indicate that the bike-sharing system can be operated efficiently to meet demand with practical implementation validity

Cross Sectional Crash Severity Analysis among Various Vehicle Driver Characteristics

The current study evaluated road crashes in three categories of drivers: passenger car drivers, heavy vehicle drivers and pickup truck drivers. The crash data of road crashes that occurred from 2009 to 2012 in Iranian suburban roads were analyzed. The crashes involved 194,041 damage, 9,677 injury and 1,303 fatality crashes. Because of the ordinal nature of crash severity, ordered logit model was selected for each vehicle driver category. In the passenger car driver category, the independent variables of driver’s gender, driver’s age and driver’s educational level (uneducated, less than high school diploma, high school diploma) were obtained for modeling; in the heavy vehicle driver category, the independent variables of driver’s age and driver’s educational level (uneducated, less than high school diploma) were obtained for modeling, and in the pickup truck driver category, the independent variables of driver’s age and driver’s educational level (uneducated, high school diploma) were obtained for modeling. The variable of driver’s gender with respect to passenger cars shows that crashes by female drivers are more severe than those involving male drivers in suburban roads. Regarding the variable of driver’s age, if the driver’s age increases in each vehicle driver category, the probability of occurrence of a severe crash will decrease. The variable of driver’s educational level shows that the severity of crashes involving a driver that has a low level of education is more compared with a driver that has a high level of education.

A hybrid genetic algorithm for multi-depot vehicle routing problem with considering time window repair and pick-up

PurposeThis paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW).Design/methodology/approachThe objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms, namely, simple genetic algorithm (GA) and hybrid genetic algorithm (HGA) are used to find the best solution for this problem. A comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA.FindingsA comparison on the results of these two algorithms has been done and based on the outcome, it has been proved that HGA has better performance than GA.Originality/valueThis paper, considers the multi-depot vehicle routing problem with time window considering two repair and pickup vehicles (CMDVRPTW). The defined problem is a practical problem in the supply management and logistic. The repair vehicle services the customers who have goods, while the pickup vehicle visits the customer with nonrepaired goods. All the vehicles belong to an internal fleet of a company and have different capacities and fixed/variable cost. Moreover, vehicles have different limitations in their time of traveling. The objective of this problem is minimization of the total traveling cost and the time window violations. Two meta-heuristic algorithms (simple genetic algorithm and hybrid one) are used to find the best solution for this problem.

Algorithms for electric vehicle scheduling in large-scale mobility-on-demand schemes

We study a setting where Electric Vehicles (EVs) can be hired to drive from pick-up to drop-off points in a Mobility-on-Demand (MoD) scheme. The goal of the system is, either to maximize the number of customers that are serviced, or the total EV utilization. To do so, we characterise the optimisation problem as a max-flow problem in order to determine the set of feasible trips given the available EVs at each location. We then model and solve the EV-to-trip scheduling problem offline and optimally using Mixed Integer Programming (MIP) techniques and show that the solution scales up to medium sized problems. Given this, we develop two non-optimal algorithms, namely an incremental-MIP algorithm for medium to large problems and a greedy heuristic algorithm for very large problems. Moreover, we develop a tabu search-based local search technique to further improve upon and compare against the solution of the non-optimal algorithms. We study the performance of these algorithms in settings where either battery swap or battery charge at each station is used to cope with the EVs' limited driving range. Moreover, in settings where EVs need to be scheduled online, we propose a novel algorithm that accounts for the uncertainty in future trip requests. All algorithms are empirically evaluated using real-world data of locations of shared vehicle pick-up and drop-off stations. In our experiments, we observe that when all EVs carry the same battery which is large enough for the longest trips, the greedy algorithm with battery swap with the max-flow solution as a pre-processing step, provides the optimal solution. At the same time, the greedy algorithm with battery charge is close to the optimal (97% on average) and is further improved when local search is used. When some EVs do not have a large enough battery to execute some of the longest trips, the incremental-MIP generates solutions slightly better than the greedy, while the optimal algorithm is the best but scales up to medium sized problems only. Moreover, the online algorithm is shown to be on average at least 90% of the optimal. Finally, the greedy algorithm scales to 10-times more tasks than the incremental-MIP and 1000-times more than the static MIP in reasonable time.