Free-floating bike sharing: Solving real-life large-scale static rebalancing problems

Free-floating bike sharing (FFBS) is an innovative bike sharing model. FFBS saves on start-up cost, in comparison to station-based bike sharing (SBBS), by avoiding construction of expensive docking stations and kiosk machines. FFBS prevents bike theft and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. However, like SBBS, the success of FFBS depends on the efficiency of its rebalancing operations to serve the maximal demand as possible.Bicycle rebalancing refers to the reestablishment of the number of bikes at sites to desired quantities by using a fleet of vehicles transporting the bicycles. Static rebalancing for SBBS is a challenging combinatorial optimization problem. FFBS takes it a step further, with an increase in the scale of the problem. This article is the first effort in a series of studies of FFBS planning and management, tackling static rebalancing with single and multiple vehicles. We present a Novel Mixed Integer Linear Program for solving the Static Complete Rebalancing Problem. The proposed formulation, can not only handle single as well as multiple vehicles, but also allows for multiple visits to a node by the same vehicle. We present a hybrid nested large neighborhood search with variable neighborhood descent algorithm, which is both effective and efficient in solving static complete rebalancing problems for large-scale bike sharing programs.Computational experiments were carried out on the 1 Commodity Pickup and Delivery Traveling Salesman Problem (1-PDTSP) instances used previously in the literature and on three new sets of instances, two (one real-life and one general) based on Share-A-Bull Bikes (SABB) FFBS program recently launched at the Tampa campus of University of South Florida and the other based on Divvy SBBS in Chicago. Computational experiments on the 1-PDTSP instances demonstrate that the proposed algorithm outperforms a tabu search algorithm and is highly competitive with exact algorithms previously reported in the literature for solving static rebalancing problems in SBSS. Computational experiments on the SABB and Divvy instances, demonstrate that the proposed algorithm is able to deal with the increase in scale of the static rebalancing problem pertaining to both FFBS and SBBS, while deriving high-quality solutions in a reasonable amount of CPU time.     

皮卡:回顾2002,展望2003

2002年是我国皮卡增长的又一个高峰期,激烈的竞争促进了皮卡行业整体的发展,也促使皮卡新品层出,产品呈现多元化。2003年将是皮卡的巩固阶段,皮卡国内外市场将会稳定增长,竞争层面将会拉开,各有侧重。继2002年低价位SUV畅销后,2003年皮卡向多功能商务车升级将是一个热点,另外在竞争方面将更多地打服务牌。     

Route design for last-in,first-out deliveries with backhauling

The retail route design problem extends the capacitated vehicle routing problem with time windows by introducing several operational constraints, including order loading and delivery restrictions (last-in, first-out), order-dependent vehicle capacity, material handling limits at the warehouse, backhauling, and driving time bounds. In this paper, the problem is modeled on a directed network for an application associated with a major grocery chain. Because the corresponding mixed-integer program proved too difficult to solve with commercial software for real instances, we developed a greedy randomized adaptive search procedure (GRASP) augmented with tabu search to provide solutions. Testing was done using data sets provided Kroger, the largest grocery chain in the US, and benchmarked against a previously developed column generation algorithm. The results showed that cost reductions of $4887 per day or 5.58% per day on average, compared to Kroger’s corresponding solutions.     

On activity-based network design problems

This paper examines network design where OD demand is not known a priori, but is the subject of responses in household or user itinerary choices to infrastructure improvements. Using simple examples, we show that falsely assuming that household itineraries are not elastic can result in a lack in understanding of certain phenomena; e.g., increasing traffic even without increasing economic activity due to relaxing of space–time prism constraints, or worsening of utility despite infrastructure investments in cases where household objectives may conflict. An activity-based network design problem is proposed using the location routing problem (LRP) as inspiration. The bilevel formulation includes an upper level network design and shortest path problem while the lower level includes a set of disaggregate household itinerary optimization problems, posed as household activity pattern problem (HAPP) (or in the case with location choice, as generalized HAPP) models. As a bilevel problem with an NP-hard lower level problem, there is no algorithm for solving the model exactly. Simple numerical examples show optimality gaps of as much as 5% for a decomposition heuristic algorithm derived from the LRP. A large numerical case study based on Southern California data and setting suggest that even if infrastructure investments do not result in major changes in link investment decisions compared to a conventional model, the results provide much higher resolution temporal OD information to a decision maker. Whereas a conventional model would output the best set of links to invest given an assumed OD matrix, the proposed model can output the same best set of links, the same daily OD matrix, and a detailed temporal distribution of activity participation and travel from which changes in peak period OD patterns can be observed.     

Designing optimal autonomous vehicle sharing and reservation systems: A linear programming approach

Autonomous vehicle (AV) technology holds great promise for improving the efficiency of traditional vehicle sharing systems. In this paper, we investigate a new vehicle sharing system using AVs, referred to as autonomous vehicle sharing and reservation (AVSR). In such a system, travelers can request AV trips ahead of time and the AVSR system operator will optimally arrange AV pickup and delivery schedules and AV trip chains based on these requests. A linear programming model is proposed to efficiently solve for optimal solutions for AV trip chains and required fleet size through constructed AVSR networks. Case studies show that AVSR can significantly increase vehicle use rate (VUR) and consequentially reduce vehicle ownership significantly. In the meantime, it is found that the actual vehicle miles traveled (VMT) in AVSR systems is not significantly more than that of conventional taxis, despite inevitable empty hauls for vehicle relocation in AVSR systems. The results imply huge potential benefits from AVSR systems on improving mobility and sustainability of our current transportation systems.     

Combining pickups and deliveries in vehicle routing – An assessment of carbon emission effects

This paper studies the effect on carbon emissions of consolidation of shipments on trucks. New positioning and communication technologies, as well as decision support systems for vehicle routing, enable better utilization of vehicle capacity, reduced travel distance, and thereby carbon emission reductions. We present a novel carbon emission analysis method that determines the emission savings obtained by an individual transport provider, who receives customer orders for outbound deliveries as well as pickup orders from supply locations. The transport provider can improve vehicle utilization by performing pickups and deliveries jointly instead of using separate trucks. In our model we assume that the transport provider minimizes costs by use of a tool that calculates detailed vehicle routing plans, i.e., an assignment of each transport order to a specific vehicle in the fleet, and the sequence of customer visit for each vehicle. We compare a basic set-up, in which pickups and deliveries are segregated and performed with separate vehicles, with two consolidation set-ups where pickups and deliveries may be mixed more or less freely on a single vehicle. By allowing mixing, the average vehicle load will increase and the total driven distance will decrease. To compare carbon emissions for the three set-ups, we use a carbon assessment method that uses the distance driven and the average load factor. An increase in the load factor can reduce part of the emission savings from consolidation. We find that emission savings are relatively large in case of small vehicles and for delivery and pickup locations that are relatively far from the depot. However, if a truck visits many demand and supply locations before returning to the depot, we observe negligible carbon emission decreases or even emission increases for consolidation set-ups, meaning that in such cases investing in consolidation through joint pickups and deliveries may not be effective. The results of our study will be useful for transport users and providers, policymakers, as well as vehicle routing technology vendors.     

Reducing carbon emission of pickup and delivery using integrated scheduling

Integrated scheduling is a powerful way to reduce carbon emission of pickup and delivery because the vehicle loading efficiency can be improved. Our study investigates theoretically the potential carbon emission reduction brought by integrated scheduling, and develops the upper and lower bounds. To demonstrate the practical advantage of carbon emission reduction brought by integrated scheduling, we use the real application of pickup and delivery, i.e., “picking up and delivering customers to airport” (PDCA). Using based-on-set-partitioning formulation, we accurately obtain the minimal carbon emissions of non-integrated scheduling and integrated scheduling. We conclude that integrated scheduling reduces carbon emission by approximate 10.8%. However, the cost in integrated scheduling may be more than before, which causes a company unwilling to accept integrated scheduling. The acceptable integrated scheduling is proposed. We identify the situations under which carbon emission can be efficiently reduced in acceptable integrated scheduling based on the extensive experiments of PDCA.