A fuzzy-stochastic optimization model for the intermodal fleet management problem of an international transportation company

ABSTRACT

In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical and operational level decisions simultaneously. Since real-life fleet planning problems may involve different types of uncertainty jointly such as randomness and fuzziness, a hybrid chance-constrained programming and fuzzy interactive resolution-based approach is employed. Therefore, stochastic import/export freight demand and fuzzy transit times, truck/trailer availabilities, the transport capacity of Ro-Ro vessels, bounds on block train services, etc. can also be taken into account concurrently. In addition to minimize overall transportation costs, optimization of total transit times and CO₂ emission values are also incorporated in order to provide sustainable fleet plans by maximizing customer satisfaction and environmental considerations. Computational results show that effective and efficient fleet plans can be produced by making use of the proposed optimization model.     

Flexible strategic planning of transport systems

Abstract

This paper presents a decision support methodology for long-range planning of transport systems that exhibits strategic flexibility and stochastic system parameters. Unlike one-off strategic decisions, flexible decisions should be dynamically reformulated with time. The proposed methodology is based on the construction of a tree structure of multiple interlinked tactical planning problems, each associated with a scenario in the tree, where problems under scenarios at intermediate dates incorporate in their formulation the solution of the corresponding problems associated with past (future) connected scenarios. The resulting tree structure of interconnected planning decisions becomes a strategic-tactical decision support system that allows managers to formulate suitable flexible strategic decisions that mitigate the consequences associated with downside scenarios while taking advantage of the upside opportunities. The methodology is applied to the planning of a fleet deployment through charter contracts where contract prices depend on both market behavior and the duration of the contract itself.     

Excess Commuting: A Critical Review

Abstract

The stringent ship emission regulations under IMO's (International Maritime Organization) MARPOL Annex VI are a main driver to consider liquefied natural gas (LNG) as a ship fuel. Research into LNG as a marine fuel saw a strong growth in recent years, but no study has analyzed in a systematic way the level of convergence among the findings presented in the wide range of studies. In order to fill this gap, this paper seeks to perform a systematic review to synthesize the findings of 33 published studies on the use of LNG as a ship fuel. The aim is not only to obtain a much broader understanding of the current perspectives and challenges for applying LNG as a bunker for ship propulsion, but also to identify the gaps and weak points in the literature which could suggest future research. Moreover, given the output of the synthesis, the paper presents an extensive decision-making framework for shipowners when deciding on a fuel switch for their fleet from conventional oils to LNG in order to achieve a level of conceptual development beyond that attained in individual studies. In addition, the study also identifies the important role of ports in facilitating and encouraging the large-scale adoption of LNG in the maritime industry.     

A joint optimization model for liner container cargo assignment problem using state-augmented shipping network framework

This paper proposes a state-augmented shipping (SAS) network framework to integrate various activities in liner container shipping chain, including container loading/unloading, transshipment, dwelling at visited ports, in-transit waiting and in-sea transport process. Based on the SAS network framework, we develop a chance-constrained optimization model for a joint cargo assignment problem. The model attempts to maximize the carrier’s profit by simultaneously determining optimal ship fleet capacity setting, ship route schedules and cargo allocation scheme. With a few disparities from previous studies, we take into account two differentiated container demands: deterministic contracted basis demand received from large manufacturers and uncertain spot demand collected from the spot market. The economies of scale of ship size are incorporated to examine the scaling effect of ship capacity setting in the cargo assignment problem. Meanwhile, the schedule coordination strategy is introduced to measure the in-transit waiting time and resultant storage cost. Through two numerical studies, it is demonstrated that the proposed chance-constrained joint optimization model can characterize the impact of carrier’s risk preference on decisions of the container cargo assignment. Moreover, considering the scaling effect of large ships can alleviate the concern of cargo overload rejection and consequently help carriers make more promising ship deployment schemes.     

Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas

Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM, to describe the complex behaviors of both passengers and transportation systems in urban cities. BEAM simulates the driving, parking and charging behaviors of the AEV fleet with range constraints and identifies times and locations of their charging demands. Then, based on BEAM simulation outputs, we adopt a hybrid algorithm to site and size charging stations to satisfy the charging demands subject to quality of service requirements. Based on the proposed framework, we estimate the charging infrastructure demands and calculate the corresponding economics and carbon emission impacts of electrifying a ride-hailing AEV fleet in the San Francisco Bay Area. We also investigate the impacts of various AEV and charging system parameters, e.g., fleet size, vehicle battery capacity and rated power of chargers, on the ride-hailing system’s overall costs.     

Routing and fleet deployment in liner shipping with spot voyages

The routing, scheduling and fleet deployment is an important integrated planning problem faced by liner shipping companies which also lift load from the spot market. This paper is concerned with coordinating the decisions of the assignment of ships to contractual and spot voyages, and the determination of ship routes and schedules in order to maximize profit. We propose a new model for representing voyages as nodes of a directed graph which is used to build a mixed integer programming formulation. Besides contractual and spot nodes, another type of node is put forward to represent a combination of a contractual voyage with one or more spot voyages. In addition, the concept of dominated nodes is introduced in order to discard them and reduce the effort of the search for an optimal solution. A set of test problems has been generated taking into account real world assumptions. The test problems are solved by an optimization software and computational results are reported. The results show the potential of the approach to solve test problems of moderate size.     

An optimization model for bus fleet replacement with budgetary and environmental constraints

ABSTRACT

As maintenance and operation costs increase with usage over time, equipment is replaced when the value of new equipment is more attractive. Some methods have been developed to solve this problem. In the public transport sector, such problems are frequently analyzed by fleet managers and determined by bus age restriction regulations. We propose an Integer Programming model that integrates both budgetary and environmental constraints (CO₂ emissions) which, as far as we know, have not previously been studied in conjunction. The study aims to determine the optimal replacement plan for a fleet of diesel buses of different size, age, maintenance costs and emissions rates, with new (less polluting) diesel buses over a time horizon of 50 years. The results indicate that it is possible to reduce emissions with a low annual budget using an optimal replacement policy.     

Making an Informed Vehicle Scrappage Decision

Abstract

According to the US Federal Highway Administration (FHWA), the number of publicly owned vehicles in the USA reached 3 913 999 in 2003. In order to maintain a stable vehicle fleet, government agencies must repeatedly make vehicle scrappage decisions because older vehicles must first retire to make room for newer vehicles. Typically, these decisions are made based on a deterministic ranking evaluation model to select candidate vehicles for replacement. The paper applied an objective and probabilistic method to a vehicle dataset collected by the DuPage County Forest Preserve District ((DCFPD), in the state of Illinois). A Weibull‐form survival model with time‐varying covariate and unobserved heterogeneity was estimated on the dataset. The results suggest that in addition to the fact that vehicle age is negatively related to the vehicle’s survival probability, there are other variables that also appear influential. The survival probabilities of alternative fuel vehicles are similar to those of reformulated unleaded gasoline vehicles. The results suggest that a probabilistic and objective model can benefit government agencies in their vehicle scrappage decisions.     

The effectiveness and costs of speed reductions on emissions from international shipping

Greenhouse gas emissions from international shipping are an increasing concern. The paper evaluates whether vessel speed reduction can be a potentially cost-effective CO₂ mitigation option for ships calling on US ports. By applying a profit-maximizing equation to estimate route-specific, economically-efficient speeds, we explore policy impacts of a fuel tax and a speed reduction mandate on CO₂ emissions. The profit-maximizing function incorporates opportunity costs associated with speed reduction that go unobserved in more traditional marginal abatement cost analyses. We find that a fuel tax of about $150/ton fuel will lead to average speed-related CO₂ reductions of about 20–30%. Moreover, a speed reduction mandate targeted to achieve 20% CO₂ reduction in the container fleet costs between $30 and $200 per ton CO₂ abated, depending on how the fleet responds to a speed reduction mandate.     

Reducing transit fleet emissions through vehicle retrofits,replacements, and usage changes over multiple time periods

Bus transit is often promoted as a green form of transportation, but surprisingly little research has been done on how to run transit systems in a green manner. Both vehicle task assignment and purchase models are generally constructed to minimize financial costs. Integrating vehicle task assignment with purchase decisions is made challenging by the different time scales involved. An integer programming approach is used to combine vehicle purchase, retrofit and aggregated task assignment decisions. The formulation is designed to operate in sequence with traditional vehicle task assignment models, to add emissions and long term financial cost elements to the objective, while maintaining computational tractability and feasible input data requirements. In a case study, a transit agency saves money in the long term by using stimulus money to buy CNG infrastructure instead of purchasing only new buses. Carbon prices up to $400/(ton CO₂ equivalent) do not change vehicle purchase decisions, but higher carbon prices can cause more diesel hybrid purchases, at a high marginal cost. Although the motivation and numerical case study are from the US transit industry, the model is formulated to be widely applicable to green fleet management in multiple contexts.     

A model and optimization-based heuristic for the operational aircraft maintenance routing problem

This paper investigates the Operational Aircraft Maintenance Routing Problem (OAMRP). Given a set of flights for a specific homogeneous fleet type, this short-term planning problem requires building feasible aircraft routes that cover each flight exactly once and that satisfy maintenance requirements. Basically, these requirements enforce an aircraft to undergo a planned maintenance at a specified station before accumulating a maximum number of flying hours. This stage is significant to airline companies as it directly impacts the fleet availability, safety, and profitability. The contribution of this paper is twofold. First, we elucidate the complexity status of the OAMRP and we propose an exact mixed-integer programming model that includes a polynomial number of variables and constraints. Furthermore, we propose a graph reduction procedure and valid inequalities that aim at improving the model solvability. Second, we propose a very large-scale neighborhood search algorithm along with a procedure for computing tight lower bounds. We present the results of extensive computational experiments that were carried out on real-world flight networks and attest to the efficacy of the proposed exact and heuristic approaches. In particular, we provide evidence that the exact model delivers optimal solutions for instances with up to 354 flights and 8 aircraft, and that the heuristic approach consistently delivers high-quality solutions while requiring short CPU times.     

Optimal planning of liquefied natural gas deliveries

We investigate the problem of designing an optimal annual delivery plan for Liquefied Natural Gas (LNG). This problem requires determining the long-term cargo delivery dates and the assignment of vessels to the cargoes while accommodating several constraints, including berth availability, liquefaction terminal inventory, planned maintenance, and bunkering requirements. We describe a novel mixed-integer programming formulation that captures important industry requirements and constraints with the objective of minimizing the vessel fleet size. A peculiar property of the proposed formulation is that it includes a polynomial number of variables and constraints and is, in our experience, computationally tractable for large problem instances using a commercial solver. Extensive computational runs demonstrate the efficacy of the proposed model for real instances provided by a major energy company that involve up to 118 cargoes and a 373-day planning horizon.     

Short-term planning of liquefied natural gas deliveries

The ability of a supplier of liquefied natural gas (LNG) to deliver cargoes at desired times, while effectively managing a fleet of cryogenic vessels can significantly impact its profits. We investigate in this paper an LNG short-term delivery planning problem by considering mandatory cargoes as well as optional cargoes to select, along with the scheduling of a heterogeneous vessel fleet with controllable cruising speeds. Several technical constraints are accommodated including time windows, berth availability, bunkering restrictions, inventory, liquefaction terminal storage capacity, maximum waiting time, and planned maintenance restrictions. The objective is to maximize the net profit.We propose a mixed-integer programming formulation that includes a polynomial number of variables and constraints and accommodates all of the problem features. Also, we describe an optimization-based variable neighborhood search procedure that embeds the proposed compact formulation. To assess the quality of the generated solutions, we propose a second valid formulation with an exponential number of decision variables and we solve its linear programming relaxation using column generation. We provide the results of extensive computational results that were carried out on a set of large-scale set of realistic instances, with up to 62 vessels and 160 cargoes, provided by a major LNG producer. These results provide evidence that the proposed improvement procedure yields high-quality solutions.     

Strategic considerations behind the network–regional airline tie ups – A theoretical and empirical study

The paper examines the strategic vertical relationship between network and regional airlines. We develop a model to illustrate how network airlines can use the contractual relationship with regional airlines as an efficient tool to simultaneously drive out inefficient network airlines and also accommodate other cost efficient network airlines in any specific market. The model is tested on U.S. data using simultaneous and sequential choice models. We find that market size, cost differences between network airlines, as well as cost differences between network and regional airlines, are the chief determinants of the network airlines’ decisions on whether or not to serve a market with their own fleet, as well as how many regional airlines to contract with.     

A statistical analysis of consumer perceptions towards automated vehicles and their intended adoption

ABSTRACT

While automated vehicle (AV) development continues to progress rapidly, how the public will accept and adopt automated vehicles remains an open question. Using extensive survey data, we apply cluster analysis to better understand consumer perceptions toward potential benefits and concerns related to AVs with regard to factors influencing their AV adoption likelihood. Four market segments are identified – ‘benefits-dominated,’ ‘concerns-dominated,’ ‘uncertain,’ and ‘well-informed.’ A random parameters multinomial logit model is then estimated to identify factors influencing the probability of respondents belonging to one of these four market segments. Among other influences (such as socio-economic and current travel characteristics), it is found that ‘Millennials’ have a higher probability of belonging to the well-informed market segment, ‘Gen-Xers’ with a lower probability to the uncertain market segment, and ‘Baby Boomers’ with a higher probability to the concerns-dominated market (relative to the ‘Great Generation’). We also study the individuals’ expressed likelihood of AV adoption using separate random parameters ordered probit estimations for each of the four market segments. The substantial and statistically significant differences across each AV consumer market segment underscore the potentially large impact that different consumer demographics may have on AV adoption and the need for targeted marketing to achieve better market-penetration outcomes.     

Siting charging stations for electric vehicle adoption in shared autonomous fleets

The transportation sector is undergoing three revolutions: shared mobility, autonomous driving, and electrification. When planning the charging infrastructure for electric vehicles, it is critical to consider the potential interactions and synergies among these three emerging systems. This study proposes a framework to optimize charging infrastructure development for increasing electric vehicle (EV) adoption in systems with different levels of autonomous vehicle adoption and ride sharing participation. The proposed model also accounts for the pre-existing charging infrastructure, vehicle queuing at the charging stations, and the trade-offs between building new charging stations and expanding existing ones with more charging ports.Using New York City (NYC) taxis as a case study, we evaluated the optimum charging station configurations for three EV adoption pathways. The pathways include EV adoption in a 1) traditional fleet (non-autonomous vehicles without ride sharing), 2) future fleet (fully autonomous vehicles with ride sharing), and 3) switch-over from traditional to future fleet. Our results show that, EV adoption in a traditional fleet requires charging infrastructure with fewer stations that each has more charging ports, compared to the future fleet which benefits from having more scattered charging stations. Charging will only reduce the service level by 2% for a future fleet with 100% EV adoption. EV adoption can reduce CO₂ emissions of NYC taxis by up to 861 Tones/day for the future fleet and 1100 Tones/day for the traditional fleet.     

Centralized versus distributed feeder ship service: The case of the Maasvlakte harbour area of Rotterdam

Coastal and inland feeder shipping is a critical factor for intercontinental container transport. The question is whether each intercontinental terminal should be equipped with its own service stations for feeder shipping, or whether pooling of the facilities would be more effective. For this paper, the service station examined for the service of feeder ships is equipped with two quay cranes operating in parallel supported by a small active quay stack. The centre for this feeder service consists of several of these stations. Simulation shows that a crane productivity of 96% is feasible with an average vehicle waiting time of 1 min, that a central service requires fewer service stations than a distributed service and that the quay transport for central and distributed transport requires the same number of terminal vehicles. The analysis shows that a centralized service is preferable, attracting 70% of the market potential.     

Economic benefits of introducing LNG-fuelled ships for imported flour in South Korea

As the concerns about air pollution have steadily increased, the perception that ships are the source of pollutants and toxic emissions is also expanding. Thus, the International Maritime Organization (IMO) is tightening maritime regulations to reduce air pollution from ships. Currently, the government and related industries are trying to replace heavy fuel oil with liquefied natural gas (LNG) to counter future IMO regulations. Since the use of LNG is expected to increase costs, it is necessary to estimate the social benefits to determine the legitimacy of the replacement. The purpose of this study is to analyse the public’s willingness to pay (WTP) for products imported in LNG-fuelled ships using the contingent valuation method. Flour, the most of which is currently imported in South Korea, is chosen as the subject of empirical analysis. As a result, the mean additional WTP was KRW 571 (USD 0.51) per kg. This value corresponds to about 36% of the existing flour price. Therefore, South Korean households are willing to pay a considerable premium on the flour imported via LNG-fuelled ships. These results can serve as useful bases for future LNG bunkering-related policies.     

The role of light duty vehicles in future air pollution: a case study of Sacramento

Abstract

On-road light-duty vehicles (LDVs) play an important role in contributing to urban air pollution. Although vehicles are getting cleaner, regional growth in vehicle population and vehicle miles traveled would somewhat offset California's efforts in transportation pollution reduction. To better understand the role of LDVs in future air pollution, we conduct a case study for Sacramento, California, and investigate future trends in urban air pollution attributable to the light-duty fleet. Results indicate that ambient concentrations of CO, NO _x , and total organic gases (TOGs) caused by future light-duty fleets would dramatically decrease over coming years. The resulting concentrations in 2030 might be as low as approximately 20% of the 2005 concentrations. These reflect the improvements in vehicle/fuel technologies and standards in California. However, the future particulate matter (PM₁₀) pollution could be slightly worse than that caused by the 2005 fleet. This is a result of the growing fleet-average emission factors of particulates from 2005 to 2030. For purposes of future particulate control, more attention needs to be paid to LDVs, besides heavy-duty vehicles.     

System for assessing Aviation’s Global Emissions (SAGE), Part 2: Uncertainty assessment

This paper focuses on assessing and applying the Federal Aviation Administration’s System for assessing Aviation’s Global Emissions (SAGE), Version 1.5, to evaluate global aircraft fuel consumption and emissions. The model is capable of computing fuel burn and emissions on a flight-by-flight, fleet and global basis. Here, a parametric study was conducted to rank-order the effects that the modeling uncertainties had on estimates of fuel burn and emissions. Statistical methods were applied to analyze both the random and systematic errors of the model. Also, applying the model to a sample policy analysis case allowed an examination of more stringent engine certification standards for mitigating aviation emissions. Uncertainties of the model were carefully accounted for in the fuel burn and emissions scenarios of the policy options. Results show that for some applications, SAGE may be used to resolve small differences in fleet emissions performance. Although the absolute uncertainty in flight-by-flight NO_x predictions from the model are of the order of 40%, results show that it is well within the current capabilities of the model to distinguish between the fleet average NO_x emissions associated with the typical NO_x stringency options considered in policy analyses.