Optimizing EV-based P&R subsidy policies for commuting corridor based on cross-nested logit model

The promotion of electric vehicles (EVs) is restricted by cruising range limitation and charging station deficiency. Given the mature development of Park and Ride (P&R) mode, which is used in many cities worldwide to attract more travelers to use public transit, a new travel mode, i.e., EV-based P&R is introduced as an alternative for commuters’ daily travel. This seems quite attractive to expand the use of EVs and further increase their market share. This paper aims to investigate the impact of EV-based P&R introduction on travel mode choice along commuting corridor, and further aid in the optimal subsidy policies decision for the government. A bi-level model is proposed to model the presented problem. The lower level describes commuters’ joint mode and transfer choice behavior through a cross-nested logit (CNL) model, while the upper level minimizes the system cost. A genetic algorithm is developed to solve the formulated model with a partial linearization algorithm for solving the lower level model. And a numerical example is then used to demonstrate the effectiveness of the methodology and illustrate how the network flow pattern reshapes due to the introduction of EVs into the P&R mode and the change of corresponding subsidy policies. As the results show, improving the EV hardware, applying the intelligent supporting service system, developing new technologies for EV fast charging, appropriately improving the parking space capacity, and increasing the parking fee of transfer stations near the central business district (CBD) are all helpful to save the social cost and promote the usage of EVs.     

Vehicle routing optimization with soft time windows in a fuzzy random environment

This paper is concerned with a vehicle routing problem with soft time windows (VRPSTW) in a fuzzy random environment. Two objectives are considered: (1) minimize the total travel cost and (2) maximize the average satisfaction level of all customers. After setting up the model for the VRPSTW in a fuzzy random environment, the fuzzy random expected value concept is used to deal with the constraints and its equivalent crisp model is derived. The global–local–neighbor particle swarm optimization with exchangeable particles (GLNPSO-ep) is employed to solve the equivalent crisp model. A case study is also presented to illustrate the effectiveness of the proposed approach.     

Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters

Bicyclists are among the most vulnerable road users in the urban transportation system. It is critical to investigate the contributing factors to bicycle-related crashes and to identify the hotspots for efficient allocation of treatment resources. A grid-cell-based modeling framework was used to incorporate heterogeneous data sources and to explore the overall safety patterns of bicyclists in Manhattan, New York City. A random parameters (RP) Tobit model was developed in the Bayesian framework to correlate transportation, land use, and sociodemographic data with bicycle crash costs. It is worth mentioning that a new algorithm was proposed to estimate bicyclist-involved crash exposure using large-scale bicycle ridership data from 2014 to 2016 obtained from Citi Bike, which is the largest bicycle sharing program in the United States. The proposed RP Tobit model could deal with left-censored crash cost data and was found to outperform the Tobit model by accounting for the unobserved heterogeneity across neighborhoods. Results indicated that bicycle ridership, bicycle rack density, subway ridership, taxi trips, bus stop density, population, and ratio of population under 14 were positively associated with bicycle crash cost, whereas residential ratio and median age had a negative impact on bicycle crash cost. The RP Tobit model was used to estimate the cell-specific potential for safety improvement (PSI) for hotspot ranking. The advantages of using the RP Tobit crash cost model to capture PSI are that injury severity is considered by being converted into unit costs, and varying effects of certain explanatory variables are accounted for by incorporating random parameters. The cell-based hotspot identification method can provide a complete risk map for bicyclists with high resolution. Most locations with high PSIs either had unprotected bicycle lanes or were close to the access points to protected bicycle routes.     

Charging station location problem of plug-in electric vehicles

A novel model for the charging station location problem of plug-in electric vehicles is proposed in this paper. With the objective of minimizing the total cost, the proposed model simultaneously handles the problem of where to locate the charging stations and how many chargers should be established in each charging station. Furthermore, the model is expanded to consider a more general case. A genetic algorithm-based method is proposed for solving the expanded model. The results show that a better location scheme can be obtained using the expanded model.     

Selecting between batch and zone order picking strategies in a distribution center

An order picking strategy in a distribution center (DC) defines the manner in which pickers navigate the picking area to pick items from storage locations. We focus on the problem of selecting between a batch picking and a zone picking strategy. For this problem, we propose a cost model to estimate the cost of each type of picking strategy. In our cost model we consider the effects of pick-rate, picker blocking, workload-imbalance, and the sorting system requirement. Through an example problem, we show how system throughput, order sizes, item distribution in orders, and wavelength affect the picking strategy selection decision.     

Modeling port competition from a transport chain perspective

This paper considers the competition between two ports involving both hinterland shipments and transhipments. Taking a transport chain perspective including deep-sea, port, feeder and inland transportation, we present a static cost model to examine ports’ relative competitiveness and justify the development of game models. A non-cooperative game model is then formulated for a two-ports-one-ocean carrier system. The optimal ports’ pricing and the carrier’s port-of-call decisions are derived. A centralized supply chain model is then discussed. The game model is further extended to uncertain demand situations. A case study of Southampton and Liverpool ports is provided to illustrate the results.     

Multi-level inventory management decisions with transportation cost consideration

In this article we address specific inventory management decisions with transportation cost consideration in a multi-level environment consisting of a supplier–warehouse–retailers. We develop two models – namely, decentralized ordering model and centralized ordering model to investigate the effect of collective ordering by retailers on the total inventory cost of the system. A numerical study shows that the proposed model is robust and generates reasonable cost savings. The models have potential in several multi-level applications such as fresh or frozen food delivery to stores of different supermarkets or the supply of medicine to a number of hospitals from a wholesaler.     

Sustainable hub location under mixed uncertainty

This paper addresses a novel sustainable hub location problem (SHLP) in which two new environmental-based cost functions accounting for air and noise pollution of vehicles are incorporated. To cope with uncertain data incorporated in the model, a mixed possibilistic–stochastic programming approach is proposed to construct the crisp counterpart. A simulated annealing (SA) and an imperialist competitive algorithm (ICA) with a new solution representation are developed to solve real-sized instances whose performances are compared with a proposed lower bound. Finally, some computational experiments are provided to demonstrate the effectiveness of the proposed model and solution approaches.     

Modeling the joint choice of access modes and flight routes with parallel structure and random heterogeneity

This study focuses on the influence of ground access transportation on air travel. The error components logit model with parallel structure is first adopted to formulate the joint choice behavior of access, airports, and flights. The proposed model proves to be effective in exploring interdependence between choice dimensions and travelers heterogeneity. The empirical results indicate that fare discounting should be introduced at the city airport and the frequency of existing flight routes should be increased. To benefit the hub airport, authorities of the new airport mass rapid transit system should reconsider the tradeoff between time and cost for express trains.     

Dispatching trucks for drayage operations

We propose a novel model for dispatching trucks given the constraints and sources of uncertainty that arise in drayage operations. The proposed model is designed to minimize the expected cost and is generally applicable to cases including different distributions of random parameters. Numerical examples illustrate this robustness of the model, as well as the potential for reducing the drayage cost by increasing the available storage capacity and permitted number of terminal truck entries. Mathematical results derived within this paper (e.g. expected dwell time) can be used more generally in analyzing transfers in transportation networks under stochastic conditions.     

Coordinated capacity and demand management in a redesigned Air Traffic Management value-chain

We present a re-designed European Air Traffic Management value-chain, with a new role for the Network Manager, which coordinates capacity and demand management decisions, using economic instruments for both areas. A conceptual and mathematical model supports decision-making in that process, focusing on capacity management decisions taken at the strategic level. Total costs are minimized by jointly managing sector-opening schemes and trajectory assignments. A large-scale case study demonstrates clear trade-offs between the volume of capacity ordered and the scope of necessary demand management actions. In addition, the comparison against a baseline, which resembles the current system, shows that with the proposed concept less capacity is needed to serve the same demand, resulting in lower total cost for Aircraft Operators.     

Inventory optimization for a customer airline in a Performance Based Contract

The supply of spare parts has a crucial role in the aviation sector, mainly due to the high costs of spare parts and to the strict availability requirements. In a stand-alone scenario, an airline owns the spare parts and manages the maintenance tasks by itself. A new trend consists of not owning the spare parts and delegate the maintenance tasks to an external company, taking advantage of a specific Performance Based Contract (PBC). The PBCs aim to reduce the ownership cost for the customer airline, while ensuring a target system performance. Spare parts become a variable cost for the customer airline and a business income for the maintenance supplier, which is commonly another airline.This paper proposes an innovative model, i.e. the PBC-METRIC, which supports the customer airline manager to minimize the spare parts supply cost, in compliance with the airline availability requirements and with respect to the PBC. In detail, the PBC-METRIC models a multi-echelon, multi-item, single-indenture, multi-transportation network, by an innovative two-steps algorithm, defining the PBC specifications as modelling variables and parameters. A case study on a European airline, with the role of customer in a PBC, illustrates the outcome of the model.     

Cost adjustment for single item pooling models using a dynamic failure rate: A calculation for the aircraft industry

This paper presents an analytical model for cost estimation in a single-item, multi-hub (S-1,S) inventory policy-pooling model for high-value spare parts in the aviation industry. The model extends existing, static pooling models by implementing a dynamic failure rate, using a maintenance free operating period (MFOP) as a measurement technique to increase availability of aircraft components. The gained results through a dynamic failure rate show significant effects for a reduction of total costs of ownership and achieving a better operational stock planning, which is demonstrated in a numerical application.     

Cost adjustment for single item pooling models using a dynamic failure rate: A calculation for the aircraft industry

This paper presents an analytical model for cost estimation in a single-item, multi-hub (S-1,S) inventory policy-pooling model for high-value spare parts in the aviation industry. The model extends existing, static pooling models by implementing a dynamic failure rate, using a maintenance free operating period (MFOP) as a measurement technique to increase availability of aircraft components. The gained results through a dynamic failure rate show significant effects for a reduction of total costs of ownership and achieving a better operational stock planning, which is demonstrated in a numerical application.     

Cost structures of public transit systems: a panel data analysis

Results from numerous cost studies have generated conflicting results on public transit production technologies. Because prior studies have employed various sub-samples of public transit properties, the diverse results may either reflect alternative technologies or sampling differences. Based on a panel of nearly all transit systems reporting for Section 15 (US National Transit Data) purposes from 1986 to 1994, this paper explores whether public transit production technology differs by size and operating characteristics of the system. The results indicate that US transit properties are heterogeneous with different production technologies, which implies that transit cost analyses based upon a set of heterogeneous systems will generate incorrect inferences on public transit cost and production structures.     

Storage pricing strategies for import container terminals under stochastic conditions

This paper presents a model for determining the optimal storage pricing schedule for import containers. A generic schedule which is characterized by a flat rate and a storage time charge is adopted. The model considers analytically the stochastic behavior of the storage yard, as input/output flows are random variables, and includes the migration to an off-dock warehouse. Two objective functions are proposed: maximizing terminal operator profits and minimizing total integrated cost of the system. Some numerical experiments are provided and a sensitivity analysis is performed to investigate the effect of main variables and approaches on the optimal solution.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.     

复杂山区铁路客运站自动选址研究

无备选站址条件下的客运站自动选址是线站协同优化的前提和基础,而不局限于车站选址的代价最小化,同时考虑连接车站间区间线路的代价具有重要意义。构建复杂山区铁路客运站自动选址模型,首先,建立综合地理信息模型,基于线站一体化的设计理念,实现单个车站选址,确定站心位置及站坪方向;其次,以公路线形优化模型的改进算法完成站—站间自动选线设计;最后,将车站选址代价与区间线路代价紧密结合起来,计算综合代价,实现站址方案的优选。以山区铁路为例对该方法进行验证,结果表明,该方法能高效生成山区铁路车站方案集,并辅助设计人员进行方案优化。     

Planning and optimization of intermodal hub-and-spoke network under mixed uncertainty

This paper addresses the planning and optimization of intermodal hub-and-spoke (IH&S) network considering mixed uncertainties in both transportation cost and travel time. Different from previous studies, this paper develops a novel modeling framework for the IH&S network design problem to jointly minimize the expected value of total transportation costs and the maximum travel time requirement in term of critical value. A new hybrid methodology by combining fuzzy random simulation (FRS) technique and multi-start simulated annealing (MSA) algorithm is designed to solve the proposed model. Numerical experiments are implemented to verify the effectiveness of the proposed model and solution approach.