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.     

Gravity models for dynamic transport planning: Development and implementation in urban networks

This paper describes the development and comparison through a real-life implementation of dynamic extensions of the entropy-based gravity model of trip distribution for dynamic transport planning purposes in urban networks. The single dynamic gravity model (SDGM) accounts only for the inter-period (long-term) evolution of travel demand. The doubly dynamic gravity model (DDGM) takes into account both the inter-period and intra-period (short-term or within-day) evolution of travel demand. The computational analysis and explanation of the differences between the two models are demonstrated in relation to different demand inputs, model parameters and performance measures.     

Dynamic DEA models with network structure: An application for Iranian airlines

Efficiency estimation of interdependent divisions within a company or assessing the interrelated processes in a production system provides insights for improving the operational performance. Recent developments in network data envelopment analysis (NDEA) models enable decision making units (DMUs) to be informed of inefficient processes within the system. The NDEA model assesses the processes of the system in a specific moment and ignores the dynamic effects within the production processes. Thus, without considering the temporal dimension of production processes, biased efficiency measurement will be obtained that provides misleading information to DMUs. For evaluating the performance of a DMU with interrelated processes during specified multiple periods, this paper proposes a relational dynamic NDEA (DNDEA) model which measures the efficiencies of the system and its internal processes over the time, simultaneously. To illustrate the capability of the proposed model, this study for the first time measures the efficiency of eight Iranian airlines in several periods connected to each other by carry over flows. The actual data is gathered in three periods from 2010 to 2012 and the results are compared with the dynamic DEA and network DEA models in the same time span.     

SARIMA damp trend grey forecasting model for airline industry

The aim of this paper is to propose a new model that improves the Damp Trend Grey Model (DTGM) with a dynamic seasonal damping factor to forecast routes passengers demand (pax) in the air transportation industry. The model is called the SARIMA Damp Trend Grey Forecasting Model (SDTGM). In the DTGM, the damp trend factor is a static smoothing factor because it does not change over time, and therefore, it cannot capture the dynamic behavior of time series data. For this reason, the modification consists in using the trend and seasonality effects of time series data to calculate a dynamic damp trend factor as time grows. The DTGM damping factor is based on the forecasted data obtained by the GM(1,1) model; otherwise, the SDTGM calculates a seasonal damping factor based on historical data using a large amount of data points for short lead-times. The SDTGM has less uncertainty than the DTGM. The simulation results show that the SDTGM captures the seasonality effect and does not allow the forecast to exponentially grow. The SDTGM forecasts more reasonable routes pax for short lead-times when having a large amount of data points than the DTGM. The United States domestic air transport market data are used to compare the performance of the DTGM against the proposed SDTGM.     

铁路客运机车动态调度仿真研究

通过对铁路机车运用系统的调研,提出客运机车动态调度模式。考虑机车运用中的设备故障、机破等随机因素,建立机车运用动态调度仿真模型,确定模型约束条件。并以某客运机务段为例,对仿真结果进行分析,指出机车动态调度的实现条件,以及实现机车实时跟踪、减少设备投入和增强机务系统柔性等实施效果。     

Dynamic relief-demand management for emergency logistics operations under large-scale disasters

This paper presents a dynamic relief-demand management model for emergency logistics operations under imperfect information conditions in large-scale natural disasters. The proposed methodology consists of three steps: (1) data fusion to forecast relief demand in multiple areas, (2) fuzzy clustering to classify affected area into groups, and (3) multi-criteria decision making to rank the order of priority of groups. The results of tests accounting for different experimental scenarios indicate that the overall forecast errors are lower than 10% inferring the proposed method’s capability of dynamic relief-demand forecasting and allocation with imperfect information to facilitate emergency logistics operations.     

铁路编组站阶段计划动态调整方法研究

铁路编组站阶段计划执行过程中的不确定事件导致其无法持续最优,阶段计划动态调整方法对于提高编组站作业效率具有重要意义。研究提出包括时间预测、动态车流推算、计划调整、计划实施、实时信息采集反馈等步骤的阶段计划动态调整流程,通过计算残差相关系数进行数据属性相关性分析和降维处理,利用机器学习方法和神经网络模型预测各阶段作业过程用时,建立基于作业过程用时精准预测的动态车流推算模型,对动态车流推算过程进行符号化描述,提出模型的约束条件和目标函数,设计基于蚁群算法的编组站动态车流推算模型求解算法。结果表明,作业过程用时预测误差随训练集样本数量增大而逐渐减小,蚁群算法计算时间满足阶段计划动态调整实际应用的需要。     

Reliability and operations: Keys to lumpy aircraft spare parts demands

This study provides unique new insights into the reasons for lumpy aircraft spare parts demands, and identifies opportunities to improve the regularity of aircraft spares demands. The study develops its unique insights into aircraft spare parts demands by considering the typical failure probability distribution (Weibull Distribution) for aircraft spare parts. The study identifies the range of Weibull model parameters that explain typical aircraft part failure rates, and uses these parameters to perform a Monte Carlo simulation of notional aircraft components in typical aircraft fleet sizes and operations. Each notional component is repeatedly used to failure and replaced, providing a simulated spare part demand rate. The data is evaluated to uncover patterns that allow a deeper understanding of how reliability and operational input factors impact the spare part demand characteristics. The study finds that the aircraft fleet size has the greatest impact on the lumpiness of aircraft spare parts demands. The study also recommends other measures that fleet managers may take to reduce the lumpiness of their spares demands.     

Symmetrical design of strategy-pairs for enplaning and deplaning an airplane

Enplaning and deplaning processes are two main activities that passengers experience in an airplane. They are also the main factors contributing to the airplane turn time. Thus, both processes need to be carefully considered when designing a new strategy. The main contribution of this paper is twofold. Firstly, we propose a symmetrical design of deplaning strategies to match three typical grouped enplaning strategies (back-to-front, windows-to-aisle and reverse pyramid), in which the groups are organized in a LIFO (Last In First Out) manner. Secondly, we present an integrated cellular automaton model to describe the dynamic characteristics of passengers in the enplaning and deplaning processes. Numerical evaluation results indicate that the proposed windows-to-aisle and reverse pyramid strategies perform better in the following aspects: (i) the total operation time decreases; (ii) the two strategies are less sensitive to the load condition, e.g., luggage distribution and cabin occupancy rate; (iii) passengers’ satisfaction is enhanced since both individual waiting time and processing time lower down; (iv) the two strategies are fairer for the passengers since the difference among the groups remarkably shrinks.     

基于不同故障类型的不完全预防维护模型

通过引入受维修次数、预防维护费用共同制约的改善因子,全面考虑Type I和Type II两种不同的故障类型,建立不完全维护模型,求解相应系统装备的维修策略。模型以维修期内的最小单位时间维护成本率为目标,在不同可靠度的条件下,求得最佳的预防维修时间间隔及最佳的预防维修次数,分析了成本率曲线和时间间隔对不同参数的敏感度,并且对各种参数对系统最佳维修次数的影响进行讨论。     

Designing a new supply chain for competition against an existing supply chain

We develop a bi-level model for designing an entrant supply chain (SC) in the presence of a pre-existing competing SC where demand is elastic with respect to price and distance. The model assumes dynamic competition between the new and pre-existing SCs in retailers’ level and probabilistic customers’ behavior. Strategic facility location and flow decisions are made while considering inventory carrying costs incurred on the operational level. We formulate the problem and propose exact and metaheuristic algorithms to solve it. The model is solved using data from a real-life case and also randomly generated test problems to extract managerial insights.     

Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach

This paper investigates an order allocation problem of a manufacturer/buyer among multiple suppliers under the risks of supply disruption. A mixed integer non-linear programming (MINLP) model is developed for order allocation considering different capacity, failure probability and quantity discounts for each supplier. We have shown that the formulated problem is NP-hard in nature and genetic algorithm (GA) approach is used to solve it. The model is illustrated through a numerical study and the result portrays that the cost of supplier has more influence on order quantity allocation rather than supplier’s failure probability.     

A deterministic mathematical model to support temporal and spatial decisions of the soybean supply chain

A novel deterministic mathematical model is presented as part of research into a stochastic optimization model for the soybean supply chain in Brazil. The model was conceived as a tool to aid in the decision-making of any trader involved in this highly complex market. The model is intended to be applied to decisions related to tactical planning over a time span of one year. The major spatial and temporal components of the soybean complex, including transportation mode decisions, are incorporated into the model. The mathematical model is described in detail. Several stochastic parameters are used with fixed values in the deterministic model to construct various scenarios. These parameters are the purchase and sale prices of the grain on the market, the crop failure rate and the volumes of demand. The model was tested using data from a large trade in Brazil with consistent results.     

Dynamic pricing of ancillary services based on passenger choice behavior

Based on passenger choice behavior, a dynamic pricing mechanism of ancillary services has been brought up for analysis in this paper. With the analysis of the passenger choice behavior of ancillary services, this study (1) analyzed the pricing of ancillary services from airlines' websites, (2) launched a survey to investigate passengers’ characteristics and preferences, (3) applied the binary logistic model to analyze passenger choice of ancillary services, and integrated into the dynamic pricing model of ancillary services. The optimal prices can be obtained at the maximum of the revenue. Based on the historical data of a Chinese domestic flight, the optimal prices of ancillary services for different types of passengers can be obtained at the maximum revenue of ancillary services, with a 74.4% increase of revenue. This paper not only created a general pricing model for improving the ancillary revenue but also provided theoretical support for ancillary pricing. The model with enough data can be dynamically applied to various flights and ancillary services.     

Does travellers’ casual attribution affect pre-recovery emotions and behavioural intentions? A field study of airline travellers in Malaysia

This study examines the relationship between airline travellers' casual attribution, pre-recovery emotions, and negative behavioural intentions after a service failure and proposes a model for analysing direct effects of airline travellers' casual attribution of stability (failure frequency) and controllability (the extent to which the airline can control the failure) on negative behavioural intentions as well as its indirect effects through pre-recovery emotions. Data were gathered through a survey of airline travellers who experienced a failure in the past six months. The results indicate that both dimensions of casual attribution influenced pre-recovery emotions and negative behavioural intentions, and that pre-recovery emotions were significantly related to negative behavioural intentions. In addition, the results verify the mediating effect of pre-recovery emotions on the relationship between casual attribution and behavioural intentions. These results have important managerial implications.     

Modelling dependence between tourism demand and exchange rate using the copula-based GARCH model

This paper investigates dependence between tourism demand and exchange rate, using the case of China, and from a new perspective by using copula–GARCH models. The empirical results show that the volatility of exchange rate is not a determinant factor in fluctuation of China's inbound tourism demand from the countries being studied. Furthermore, only Russia exhibits risk-adverse behaviour with extreme SUR depreciation, or CNY appreciation associated with an extreme decline in arrivals. Third, introducing the tail dependence and dynamic dependence between growth rates of tourism demand and exchange rate add much to the explanatory ability of the model. The findings of this study have important implications for destination manager and travel agent as it helps to understand the impact of exchange rates on China inbound tourism demand and provide a complementary academic approach on evaluating the role of exchange rates in the international tourism demand model.     

Space-time dynamics: A modeling approach for commuting departure time on linked datasets

Commuters' departure time related decisions are important in time geography. Analytic tools have been proposed to capture the inherent choice determinants both in time and space. Although the dynamic aspects of the problem have been identified, most of the existing studies are based on static models. In this paper, a dynamic modeling framework is proposed to explore the relationship between commuters' departure time choices and the evolution of en route traffic. A data linkage method is developed to create an integrated dataset that enables the observation of commuters' reaction to changes in travel time and traffic conditions over time. A regional household travel survey is linked to travel information obtained from the Google Maps application program interface (API), creating a synthetic longitudinal dataset. Two decision rules are applied to model commuters' response to the evolution of traffic. The results indicate that travel time, distance to work location, flexibility in working schedule, expected arrival time, and commuters' sociodemographic influence departure time choices. It is also found that accounting for dynamics improves model fit and out-of-sample predictions. Both the dynamic model and the proposed data linkage method contribute to the understanding of human activities in space and time and can be used to enhance transportation demand analysis and urban policy studies.     

The impact of user heterogeneity on road franchising

A model is presented for analyzing Pareto-efficient build-operate-transfer toll road contracts. The formulation simultaneously allows maximizing social welfare and private profit when road users vary in their value-of-time (VOT). The failure rate and mean residual functions of the VOT distribution are used to characterize Pareto-efficient solutions. Service quality, measured in terms of the volume-to-capacity ratio, is shown to be better than, identical to, or lower than the socially optimal level depending on the curvature of the mean residual VOT function. The outcomes of various regulatory regimes are examined as well.     

Comparison of royalty methods for build–operate–transfer projects from a negotiation perspective

This study constructs a royalty negotiation model for the bi-level programming (BLP) problem and develops a heuristic algorithm for solving the BLP problem. Concession rate, learning effect, and the time value discount rate are integrated into the proposed algorithm to reflect an authentic negotiation process. A case study is employed to simulate the negotiation behavior of two parties and alternative royalty strategies are discussed. Analytical results indicate that the two parties acquire the best negotiation result during the fifth negotiation. The operational revenue-based royalty model is more preferred by governments, while concessionaires favor more the operational output-based royalty model.     

A hybrid parallel genetic algorithm for yard crane scheduling

This paper aims at postulating a novel strategy in terms of yard crane scheduling. In this study, a dynamic scheduling model using objective programming for yard cranes is initially developed based on rolling-horizon approach. To resolve the NP-complete problem regarding the yard crane scheduling, a hybrid algorithm, which employs heuristic rules and parallel genetic algorithm (PGA), is then employed. Then a simulation model is developed for evaluating this approach. Finally, numerical experiments on a specific container terminal yard are used for system illustration. Computational results suggest that the proposed method is able to solve the problem efficiently.