A lexicographic optimization approach to the deviation-flow refueling station location problem on a general network

With the development of alternative fuel (AF) vehicle technologies, studies on finding the potential location of AF refueling stations in transportation networks have received considerable attention. Due to the strong limited driving range, AF vehicles for long-distance intercity trips may require multiple refueling stops at different locations on the way to their destination, which makes the AF refueling station location problem more challenging. In this paper, we consider that AF vehicles requiring multiple refueling stops at different locations during their long-distance intercity trips are capable of making detours from their preplanned paths and selecting return paths that may be different from original paths for their round trips whenever AF refueling stations are not available along the preplanned paths. These options mostly need to be considered when an AF refueling infrastructure is not fully developed on a highway system. To this end, we first propose an algorithm to generate alternative paths that may provide the multiple AF refueling stops between all origin/destination (OD) vertices. Then, a new mixed-integer programming model is proposed to locate AF refueling stations within a preselected set of candidate sites on a directed transportation network by maximizing the coverage of traffic flows along multiple paths. We first test our mathematical model with the proposed algorithm on a classical 25-vertex network with 25 candidate sites through various scenarios that consider a different number of paths for each OD pair, deviation factors, and limited driving ranges of vehicles. Then, we apply our proposed model to locate liquefied natural gas refueling stations in the state of Pennsylvania considering the construction budget. Our results show that the number of alternative paths and deviation distance available significantly affect the coverage of traffic flows at the stations as well as computational time.

Because of the limited range of alternative fuel vehicles (AFVs) and the sparsity of the available alternative refueling stations (AFSs), AFV drivers cooperatively deviate from their paths to refuel. This deviation is bounded by the drivers’ tolerance. Taking this behavior into account, the refueling station location problem with routing (RSLP-R) is defined as maximizing the AFV flow that can be accommodated in a road network by locating a given number of AFSs while respecting the range limitation of the vehicles and the deviation tolerance of the drivers. In this study, we develop a natural model for the RSLP-R based on the notion of length-bounded cuts, analyze the polyhedral properties of this model, and develop a branch-and-cut algorithm as an exact solution approach. Extensive computational experiments show that the algorithm significantly improves the solution times with respect to previously developed exact solution methods and extends the size of the instances solved to optimality. Using our methodology, we investigate the tradeoffs between covered vehicle flow and deviation tolerance of the drivers and present insights on deviation characteristics of drivers in a case study in California.

A continuous network location problem for a single refueling station on a tree

We introduce and analyze greedy equilibria (GE) for the well-known model of selfish network creation by Fabrikant et al. [PODC'03]. GE are interesting for two reasons: (1) they model outcomes found by agents which prefer smooth adaptations over radical strategy-changes, (2) GE are outcomes found by agents which do not have enough computational resources to play optimally. In the model of Fabrikant et al. agents correspond to Internet Service Providers which buy network links to improve their quality of network usage. It is known that computing a best response in this model is NP-hard. Hence, poly-time agents are likely not to play optimally. But how good are networks created by such agents? We answer this question for very simple agents. Quite surprisingly, naive greedy play suffices to create remarkably stable networks. Specifically, we show that in the Sum version, where agents attempt to minimize their average distance to all other agents, GE capture Nash equilibria (NE) on trees and that any GE is in 3-approximate NE on general networks. For the latter we also provide a lower bound of $\tfrac{3}{2}$ on the approximation ratio. For the Max version, where agents attempt to minimize their maximum distance, we show that any GE-star is in 2-approximate NE and any GE-tree having larger diameter is in $\tfrac{6}{5}$-approximate NE. Both bounds are tight. We contrast these positive results by providing a linear lower bound on the approximation ratio for the Max version on general networks in GE. This result implies a locality gap of Ω(n) for the metric min-max facility location problem, where n is the number of clients.

In this paper, we first consider the classical p-median location problem on a network in which the vertex weights and the distances between vertices are uncertain variables. The uncertainty distribution of the optimal objective value of the p-median problem is given and the concepts of the α-p-median, the most p-median and the expected p-median are introduced. Then, it is shown that the uncertain p-median problem is NP-hard on general networks. However, if the underlying network is a tree, an efficient algorithm for the uncertain 1-median problem with linear time complexity is proposed. Finally, we investigate the inverse 1-median problem on a tree with uncertain vertex weights and present a programming model for the problem. Then, it is shown that the proposed model can be reformulated into a deterministic programming model.

The emergency service station (ESS) location problem has been widely studied in the literature since 1970s. There has been a growing interest in the subject especially after 1990s. Various models with different objective functions and constraints have been proposed in the academic literature and efficient solution techniques have been developed to provide good solutions in reasonable times. However, there is not any study that systematically classifies different problem types and methodologies to address them. This paper presents a taxonomic framework for the ESS location problem using an operations research perspective. In this framework, we basically consider the type of the emergency, the objective function, constraints, model assumptions, modeling, and solution techniques. We also analyze a variety of papers related to the literature in order to demonstrate the effectiveness of the taxonomy and to get insights for possible research directions.

Fuel supply shortages that have emerged in recent hurricane events underscore the weakness of existing emergency logistics planning processes. An effective modeling approach is central to refueling station location and supply planning decisions. This paper documents a research effort based on a simulation–optimization framework integrating a mixed integer program formulation and a mesoscopic simulation model. The mesoscopic simulation model was incorporated with decision rules to select refueling stations, methods to model the impact of stalled vehicles on traffic flow, and a formula to accumulate each vehicle's fuel consumption under various running speed conditions. The mixed integer program formulation is aimed at maximizing the served demand by deciding which stations to operate and how much fuel to supply given limited resources. The interplay between the simulation model and the optimization model continues until convergence. The proposed modeling approach is applied to a case study based on the I-45 corridor between Houston and Dallas, Texas, to highlight the characteristics of the proposed modeling approach.

The valve location problem: Minimizing environmental damage of a spill in long oil pipelines

A simulation and optimisation package for emergency medical services

A genetic approach to the pickup/delivery station location problem in segmented flow based material handling systems

Problem definition: Hydrogen fuel-cell vehicles (HFVs) have been proposed as a promising green transportation alternative. For regions experiencing renewable energy curtailment, promoting HFVs can achieve the dual benefit of reducing curtailment and developing sustainable transportation. However, promoting HFVs faces several major hurdles, including uncertain vehicle adoption, the lack of refueling infrastructure, the spatial mismatch between hydrogen demand and renewable sources for hydrogen production, and the strained power transmission infrastructure. In this paper, we address these challenges and study how to promote HFV adoption by deploying HFV infrastructure and utilizing renewable resources. Methodology/results: We formulate a planning model that jointly determines the location and capacities of hydrogen refueling stations (HRSs) and hydrogen plants as well as electricity transmission and grid upgrade. Despite the complexity of explicitly considering drivers’ HFV adoption behavior, the bilevel optimization model can be reformulated as a tractable mixed-integer second-order cone program. We apply our model calibrated with real data to the case of Sichuan, a province in China with abundant hydro resources and a vast amount of hydropower curtailment. Managerial implications: We obtain the following findings. (i) The optimal deployment of HRSs displays vastly different spatial patterns depending on the HFV adoption target. The capital city, a transportation hub, is excluded from the plan under a low target and only emerges as the center of HFV adoption under a high target. (ii) Promoting the HFV adoption can overall help reduce hydropower curtailment, but the effectiveness depends on factors such as the adoption target and the grid upgrade cost. (iii) Being a versatile energy carrier, hydrogen can be transported to various locations, which allows for strategic placement of HRSs in locations distinct from hydrogen plant sites. This flexibility offers HFVs greater potential cost savings and curtailment reduction compared with other alternative fuel vehicles (e.g., electric vehicles) under current cost estimates. Funding: W. Qi acknowledges the support from the National Natural Science Foundation of China [Grants 72242106, 72188101, and 72272014] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2019-04769]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0381 .

The energy transformation in China is picking up speed, thanks to the mass development and utilization of various clean energies. As an emerging clean energy, hydrogen energy has been highly recognized, and gradually introduced to industry and transport. As a result, hydrogen fuel cell vehicles (HFCVs) attract a growing attention from the government and the public. However, the is a severe lack of hydrogen refueling stations (HRSs). Therefore, this paper explores into the determination of HRS location and volume. The HRS investment market was treated as an oligopoly market, due to the huge investment and few investors of HRSs. To ensure the long-term balanced development of hydrogenation, it is assumed that all investors fully analyze the possible investment behaviors of competitors and HFCV driver preference before making any investment decision. On this basis, a multi-agent optimization model was established to determine the location and volume of HRSs. The results show that, to avoid supply-demand imbalance induced by information asymmetry, the HRSs should be constructed along with an intelligent software platform, which provides HFCV drivers with rich information on hydrogenation service. Our model helps investors determine the core issues of the HRS, including location, size, and supply scale.

This paper presents preliminary results on the comparison of binary and integer based representations for the Base Stations Location (BSL) problem. The simplest model of this problem, which is already NP-complete, is dealt with to compare also different crossover operators. Experimental results support the hypothesis that the integer based representation with a specific crossover operator outperforms the more traditional binary one for a very specific set of instances.

Traffic control policies aim at reducing the negative externalities that ever-growing demand is causing on transportation networks, such as congestion and pollutant emissions. To achieve these goals, strategies coordinating and aligning the effects of several individual traffic controllers have received increasing attention in research and development in the past decades. However, a considerable gap still exists between the desired and experienced performance of advanced dynamic traffic management systems, resulting in failure to completely prevent the increasing peak-hour congestion in main urban areas worldwide. In this work we contribute to assessing whether this gap might be tied to inefficient network design, rather than algorithmic prowess. Based upon our earlier work, we investigate whether a trend can be found between determining locations of controllers in a network following control theoretical insights, and try to confirm our earlier intuitions when dealing with dynamic traffic assignment, featuring accurate propagation and spillback dynamics. To achieve these goals, we extend an existing synthetic network generation tool to allow us to test this hypothesis on real-life-like road networks, and extend our previously developed algorithms for the controller location problems allowing for sufficient generalization. Test results are presented on a simpler deterministic scenario and on 240 randomly generated networks, showcasing that placing controllers following controllability-based principles is advantageous from the perspective of model-based dynamic traffic management applications.

Author(s): Acharya, Tri D.; Jenn, Alan T.; Miller, Marshall R.; Fulton, Lew M. | Abstract: Researchers at UC Davis developed “Spatial Transportation Infrastructure, Energy, Vehicles, and Emissions (STIEVE),” an optimization model for hydrogen refueling stations in California. The model uses inputs from the California Statewide Travel Demand Model (CSTDM) and other sources to determine heavy-duty vehicle travel demand across the state, and the corresponding, localized energy demand. The model then determines which of the transportation analysis zones (areas based on census geography used to replicate areas of trip origins and destinations) delineated by the CSTDM are optimal areas for refueling stations and the number of stations needed in each zone to meet demand while minimizing costs. The final step is a suitability analysis that identifies each station’s specific location within a designated transportation analysis zone, based on a determined footprint for the refueling station.