Airline network structure and the gravity model

In this paper, we determine characteristics of the cost-minimizing airline network under economies of density. Airline demand is asymmetric and governed by the gravity model. Airline networks are restricted to those where each spoke city is assigned to a single hub and where hub cities are fully interconnected. The cost-minimizing network is a mixture of a point-to-point and a single hub network. Multi-hub networks where passengers change planes at more than one airport are found to be suboptimal.     

Airline mergers and their effect on network structure

The Airline Deregulation Act of 1978 introduced high-level competition into the US domestic industry. The resulting network changes have created a deluge of research, largely focused on the hub-and-spoke structure. Over 15 years have passed since deregulation, and the industry still appears somewhat unsettled. Recent concerns of domestic carriers include downsizing of some hubs, and discussions of further consolidation of operations. Mergers may be the answer for airlines with shrinking markets within the US transportation system. This study examines the effect on network structure created by the merger of two carriers from the perspective of hub structures, accessibility and geographic coverage.     

Slot auction in an airport network with demand uncertainty

This paper investigates the effectiveness of airport slot auction by developing a two-stage model, which distributes slots among competing airlines using an ascending-bid multi-unit auction. Airlines assign different values to slots at peak and off-peak periods, and each carrier has its private (subjective) forecast of future demand. Conditional on the slots available to them, airlines compete in frequency, fare, and aircraft size over a congested airport network. The market outcome under such a scheme is benchmarked to those under an ex ante allocation and an ex post allocation by a social planner. Comparison results suggest that the auction-based scheme is inferior to the other two schemes when there is little fluctuation in demand, whereas the auction-based system is more effective when there is substantial demand uncertainty. Auctioning some grandfathered slots can improve social welfare but the marginal effect may diminish quickly.     

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.     

Coordinated aviation network resource allocation under uncertainty

Congestion in the air traffic system, both recurrent and non-recurrent, is typically handled by rationing access rights to individual resources such as airports or important parts of the airspace. Under the planning paradigm employed in the US, this rationing process occurs independently at each resource. The stochastic integer programming model proposed in this paper brings coordination to this process while considering capacity uncertainty. Results of a case study suggest that the model is tractable, and generates capacity allocations that improve efficiency and enable greater responsiveness to changing capacity conditions.     

Airline demand distributions: passenger revenue management and spill

Both revenue management and airline schedule optimization need to characterize the distribution of likely demand outcomes. Sources have proposed both Gamma and Normal shapes for these distributions. Data suggests that a model combining both distributions is appropriate. The model explains when the Gamma shape will dominate and when the Normal will determine the shape. One consequence of this understanding is that Gamma shapes are probably better for revenue management and Normal for spill modeling. However, it takes a compound process combining the two to generate all the observed characteristics of various cases.     

Multi-objective design of an organ transplant network under uncertainty

We propose a novel multi-period location–allocation model for the design of an organ transplant transportation network under uncertainty. The model consists of a bi-objective mathematical programming model that minimizes total cost and time, including waiting time in the queue for the transplant operation, while considering organs’ priorities. A fuzzy multi-objective programming based approach is presented to solve the small and medium size problems to optimality. For larger problems, we propose two meta-heuristics based algorithms. Lower bounds, and several numerical examples with managerial insights are discussed. A real case-study is provided, and the existing and the proposed optimal solutions are compared.     

Dynamic network design for reverse logistics operations under uncertainty

The design of reverse logistics network has attracted growing attention with the stringent pressures from environmental and social requirements. In general, decisions about reverse logistics network configurations are made on a long-term basis and factors influencing such reverse logistics network design may also vary over time. This paper proposes dynamic location and allocation models to cope with such issues. A two-stage stochastic programming model is further developed by which a deterministic model for multiperiod reverse logistics network design can be extended to account for the uncertainties. A solution approach integrating a recently proposed sampling method with a heuristic algorithm is also proposed in this research. A numerical experiment is presented to demonstrate the significance of the developed stochastic model as well as the efficiency of the proposed solution method.     

Stochastic programming approach to re-designing a warehouse network under uncertainty

The warehouse network re-design problem includes integrating or eliminating existing warehouses and establishing new sites. In this paper, we incorporate variability in product demand and operational costs with a two-stage stochastic modeling approach. We use the Sample Average Approximation (SAA) approach together with Benders decomposition to provide a solution method. Our results indicate not only that the stochastic solution is an improvement over the deterministic solution but also that the solutions’ differences grow with increasing uncertainty. The stochastic solutions show more robustness than the deterministic solutions. The computational results show that a change in the type of probability distribution of the stochastic parameters does not significantly affect the value of the stochastic solutions.     

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.     

Global supply chain network dynamics with multicriteria decision-making under risk and uncertainty

In this paper, we consider the dynamics of a global supply chain network economy in the presence of risk and uncertainty in which distinct speeds of adjustment are included. We assume three tiers of decision-makers: manufacturers, distributors, and retailers, who acquire the product in order to satisfy the demand at the demand markets. The manufacturers, distributors, and retailers may be based in the same or in different countries and may transact in different currencies. We allow for electronic transactions in the form of electronic commerce between the manufacturers and the retailers as well as between the distributors and the retailers since the retailers may be physical or virtual. In addition, supply-side risk and demand-side risk are handled in our formulation with the former being expressed as a multicriteria decision-making problem for each manufacturer and distributor (with distinct weights associated with the criteria) and the latter being handled with the use of uncertain demands. The proposed framework allows for the modeling and theoretical analysis of such global supply chain networks, which involve competition within a tier of decision-makers but cooperation between tiers. Numerical examples are provided for illustrative purposes.     

Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty

Considering economic, environmental and social impacts, this paper presents a new sustainable closed-loop location-routing-inventory model under mixed uncertainty. The environmental impacts of CO₂ emissions, fuel consumption, wasted energy and the social impacts of created job opportunities and economic development are considered in this paper. The uncertain nature of the network is handled using a stochastic-possibilistic programming approach. Furthermore, for large-sized problems, a hybrid meta-heuristic algorithm and lower bounds are developed and discussed. Finally, a real case study is provided to demonstrate the applicability of the model in real-world applications, and several in-depth analyses are conducted to develop managerial implications.     

Scenario tree airline fleet planning for demand uncertainty

This paper proposes an innovative multi-period modeling approach to solve the airline fleet planning problem under demand uncertainty. The problem is modeled using a scenario tree approach. The tree is composed of nodes, which represent points of decision in multiple time stages of the planning horizon, and branches, representing demand variation scenarios. The branches link the decision nodes in consequent time stages and compose scenario paths. Fleet decisions are modeled according to these scenario paths, resembling the real-life process in which fleet plans are not defined in a single moment but instead are adjusted according to the demand development. Given that some scenario paths share common decision nodes, decisions among scenarios need to be synchronized. A mixed-integer linear programming model is proposed to determine the ideal fleet composition for each scenario in the tree and to describe this interdependency between scenarios. Considering the probability of a scenario, fleet composition probabilities for each time-period can be determined. Two real-world based case studies are performed to show the validity of the model. Results show that the proposed scenario tree approach can provide flexible multi-period airline fleet plans, which are more robust to future demand scenarios than fleet solutions obtained using the traditional approach of considering a single deterministic demand evolution scenario.     

An analysis of demand uncertainty in the hotel market

This study proposes a two-step approach to investigate the impact of certain environmental variables on demand uncertainty within the hotel sector. Adopting a simultaneous-equation model and using the operation data of international tourist hotels in Taiwan, this study shows that chained hotels face less demand uncertainty than independent hotels. Additionally, hotels that are more distant from the airport or located in metropolitan areas face a significantly higher degree of demand uncertainty.     

A hybrid decomposition algorithm for designing a multi-modal transportation network under biomass supply uncertainty

This study presents a two-stage stochastic programming model for the design and management of a biomass co-firing supply chain network under feedstock supply uncertainty. To represent a more realistic case, we generate scenarios from prediction errors of the historical and forecasted biomass supply availabilities. We solve the model using a hybrid decomposition algorithm that combines Sample average approximation with an enhanced Progressive hedging algorithm. The proposed algorithm is validated via a real-world case study using data from Mississippi and Alabama. Computational results indicate that the proposed algorithm is capable of producing high quality solutions in a reasonable amount of time.     

Design of a bi-objective reliable healthcare network with finite capacity queue under service covering uncertainty

This paper designs a reliable healthcare network. Under limited capacity, queue of patients may deteriorate the condition and leads to risk of death. Consequently, it is vital to investigate a queue system that considers the condition and changes over the time. Besides, treatment units just serve patients that are in their coverage threshold, while this threshold is affected by several factors. This paper considers number of patients and covering threshold under uncertainty. To handle uncertainty, an integrated approach is proposed. Two meta-heuristic algorithms are developed for the given problem. Finally, we carried out experiments to assess proposed model and approaches.     

Refueling-station location problem under uncertainty

In this paper, we introduce a two-stage stochastic refueling station location model, where the first stage locates permanent stations and the second locates portable stations. The portable alternative fuel stations are an innovative feature in transportation network. The models are applied to an intercity network for Arizona. Computational results show that the permanent stations locate in and around heavily populated nodes. In addition, the results obtained for the portable stations can be utilized to set up permanent stations when the investor intends to increase the number of such stations. The computational results of the exact and greedy approach are reported.     

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.     

Valuation of freight transportation contracts under uncertainty

This paper applies concepts from the theory of Real Options to hedge uncertainty in transportation capacity and cost using derivative contracts, called truckload options. We make three contributions. First, we provide a closed-form pricing formula for basic truckload options when the truckload spot price on a given lane follows a simple mean-reverting process. Second, since only monthly statistics about truckload spot prices are currently available, we provide an approach to estimate the parameters needed to value truckload options. Finally, a numerical illustration based on real data shows that truckload options could be valuable to both shippers and carriers.     

Robust optimization for fleet planning under uncertainty

We create a formulation and a solution procedure for fleet sizing under uncertainty in future demands and operating conditions. The formulation focuses on robust optimization, using a partial moment measure of risk. This risk measure is incorporated into the expected recourse function of a two-stage stochastic programming formulation, and stochastic decomposition is used as a solution procedure. A numerical example illustrates the importance of including uncertainty in the fleet sizing problem formulation, and the nature of the fundamental tradeoff between acquiring more vehicles and accepting the risk of potentially high costs if insufficient resources are available.