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.     

A robust optimization approach for the road network daily maintenance routing problem with uncertain service time

This paper studies the robust optimization approach for the routing problem encountered in daily maintenance operations of a road network. The uncertainty of service time is considered. The robust optimization approach yields routes that minimize total cost while being less sensitive to substantial deviations of service times. A robust optimization model is developed and solved by the branch-and-cut method. In computational experiments, the behavior of the robust solutions and their performance are analyzed using Monte Carlo simulation. The robust optimization model is also compared with a classic chance-constrained programming model. The experimental analysis provides managerial insights for decision makers to determine an appropriate routing strategy.     

基于排队论的车站验证检票口数量优化研究

基于实测数据拟合,验证车站实名制车票的旅客到达分布为泊松分布,运用排队理论建立车站验证检票口数量与客流需求相匹配的优化模型,根据旅客流量变化情况预估验证检票口合理数量。通过实例计算表明,基于排队论方法进行验证检票口数量优化配置,可以降低车站验证检票服务成本和旅客停留费用,提高经济性;提出的验证检票口数量优化方法和服务优化措施,可以协助车站合理设置验证检票口数量,提高服务质量。     

一种基于Hopfield神经网络运输问题的优化方法

针对运输问题的特点,利用连续Hop-field神经网络中能量函数的概念和含义确定网络电路的参数并证明系统的稳定性。将优化运输问题转换成求解网络系统的平衡点,采用制成的神经网络电路优化计算速度快、实时性强,通过计算机仿真计算证明了系统的有效性、可行性。同时也给其他线性、非线性规划问题的优化提供了新的途径。     

单车场大规模车辆路径优化问题研究

针对传统优化技术在解决大规模车辆路径问题中存在的缺陷,提出了一种解决单车场大规模车辆路径优化问题的综合启发式算法。首先,采用Sweep技术将区域分解成几个子区。其次,设计了分区的禁忌搜索算法,并采用相邻区域综合优化技术,提高了算法的全局搜索能力。仿真试验表明,该算法能够有效解决大规模车辆路径优化问题。     

Route optimization for energy efficient airport shuttle operations – A case study from Dallas Fort worth International Airport

The objective of this research is to reduce energy consumption from intra airport shuttle operations by optimizing routes and schedules, without compromising on passenger travel experience. To achieve this objective, we propose an optimization model that generates optimal airport shuttle routes for a given set of constraints and a discrete-event simulator that evaluates the optimal shuttle routes in a stochastic environment to understand the tradeoffs between the amount of time passengers wait for shuttles, and shuttle energy consumption. The proposed optimization model and stochastic simulation are tested using shuttle route data provided by the Dallas Fort Worth International Airport. Results indicate that optimized routes can lead to a 20% energy reduction in shuttle operations with a modest 2-min increase in average shuttle wait times. The optimization model and simulator presented here are designed to be generalizable and can be adapted to optimize shuttle operations at any major airport.     

A comparison of two exact methods for passenger railway rolling stock (re)scheduling

The assignment of rolling stock units to timetable services in passenger railways is an important optimization problem that has been addressed by many papers in different forms. Solution approaches have been proposed for different planning phases: strategic, tactical, operational, and real-time planning. In this paper we compare two approaches within the operational and real-time planning phase. The first exact approach is based on a known Mixed Integer Linear Program (MILP) which is solved using CPLEX. The second approach is a new method that is an extension of a recently introduced MILP, which is solved using a column and row generation approach. In this paper, we benchmark the performance of the methods on networks of two countries (Denmark and The Netherlands). We use the approaches to make daily schedules and we test their real time applicability by performing tests with different disruption scenarios. The computational experiments demonstrate that both models can be used on both networks and are able to find optimal rolling stock circulations in the different planning phases. Furthermore, the results show that both approaches are sufficiently fast to be used in a real-time setting.     

An efficient airline seat reallocation algorithm considering customer dissatisfaction

Nowadays, many airlines tend to provide an advance seat selection service to maximize customer satisfaction to secure market shares in the competitive air transport market. However, the preassigned seat configuration of certain flight legs can be changed often due to aircraft changes for certain reasons, such as maintenance and reservation rates. Then certain customer's seat features, such as row location, position of aisle/window, relative location, and position with the same group member can be different before/after the change, and it can lead to huge customer dissatisfaction. Therefore, an efficient airline seat reallocation algorithm to minimize customer dissatisfaction is introduced in this study, which is based on mathematical model-based optimization with several types of objective functions that reflect customer dissatisfaction levels. A numerical example is performed to validate the developed airline seat reallocation algorithm, and an additional analysis is also executed to derive important insight.     

Optimization models for placement of an energy-aware electric vehicle charging infrastructure

This paper addresses the problem of optimally placing charging stations in urban areas. Two optimization criteria are used: maximizing the number of reachable households and minimizing overall e-transportation energy cost. The decision making models used for both cases are mixed integer programming with linear and nonlinear energy-aware constraints. A multi-objective optimization model that handles both criteria (number of reachable households and transportation energy) simultaneously is also presented. A number of simulation results are provided for two different cities in order to illustrate the proposed methods. Among other insights, these results show that the multi-objective optimization provides improved placement results.     

A dynamic carsharing decision support system

This paper proposes a dynamic optimization–simulation model as a decision support system for one-way carsharing organizations. To reduce the vehicle imbalance in one-way systems, a Vehicle Relocation Optimization model is solved successively in a discrete event simulation. Each event is the arrival of a new user. The model is compared to an a priori benchmark model. Autoshare is chosen as a case study. Results show that increasing the reservation time (time between requesting and picking up a vehicle) from 0 to 30 min reduces fleet size by 86%. The model captures a tradeoff between vehicle relocation hours and fleet size.     

二次指派问题的蚁群算法研究

蚁群算法是受自然界蚂蚁觅食过程中,基于信息素的最短路径搜索食物行为启发,提出的一种智能优化算法。在采用蚁群算法求解二次指派问题中,针对蚁群算法存在的过早收敛问题,使用距离及流量作为启发式信息并引入局部优化,对蚁群算法的结果加以改进,计算机仿真结果表明,蚁群算法对求解二次指派问题有较好的效果。     

基于混合禁忌搜索算法的物流配送路径优化问题研究

在对配送路径优化问题进行描述的基础上,建立物流配送路径优化问题的数学模型,提出了一种求解车辆路径问题的混合禁忌搜索算法。在该混合算法中,通过车辆—任务分配结构的划分,将大规模问题拆分成可并行计算的若干小规模问题,减少了算法的计算时间。并通过理论分析和仿真算例,证明了该混合禁忌搜索算法的有效性。     

The effects of resupply flexibility on the design of service parts supply systems

We apply simulation optimization to determine the optimal design and stock distribution for a single item supply system with a one-for-one replenishment policy. We investigate both the effect of the number of echelons and the degree of resupply flexibility, considering single-echelon and two-echelon systems with either no resupply flexibility, lateral transhipments only or both lateral transhipments and emergency deliveries. We show that the implementation of an emergency delivery option allows an augmentation of the fraction of customers served by lateral transhipments and that increased resupply flexibility enables increasingly important cost savings.     

大型铁路客运站候车层商业布局优化研究

随着出行选择的多样化,铁路旅客对商业服务需求的差异性逐步增大。大型铁路客运站商业设施规模不断扩大,对客运设备发挥其正常功能造成一定干扰,而合理的商业设施布局可以在保证铁路运输功能正常运行的同时,提高客运站商业服务水平。因此以提高大型客运站商业设施布局合理性为优化目标,将其分解为降低商业设施对旅客流线的干扰性、提高商业设施之间的相关性及减少旅客走行距离3个子目标,构建候车层商业设施布局优化模型。以天津站为例,介绍天津站候车层商业布局现状,通过求解0-1规划模型,得到并分析优化结果;并对优化前后的候车层旅客行为进行仿真研究,验证优化后布局方案的合理性。     

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.     

An innovative method to optimize the maintenance policies in an aircraft: General framework and case study

Maintenance policies applied to aircrafts are governed by a mix of airworthiness authorities’ regulations and choices of suppliers and users. This allows airlines to use different strategies to minimize the total costs of maintenance. In this paper, a new approach that integrates the failure and reparation processes, such as modelling, optimization algorithms, and simulation methods, is proposed to define the best maintenance strategies for complex systems.A case study of an airline carrier is presented. In particular, several critical components for the A320 aircraft family are considered. The impact of the spare parts inventory management is discussed. Different preventive maintenance policies are tested and simulated. With the new policies, the average availability of the aircraft is satisfactory and the total annual cost is reduced to a value of approximately 20% in comparison with the previous policies adopted by the company.     

The impacts of Radio Frequency Identification (RFID) technology on supply chain costs

Radio Frequency Identification (RFID) is regarded as a promising technology for the optimization of supply chain processes since it improves manufacturing and retail operations from forecasting demand to planning, managing inventory, and distribution. This study uses a simulation model to calculate the expected benefits of an integrated RFID system on a three-echelon supply chain obtained through performance increases in efficiency, accuracy, visibility, and security level. The study investigates how the product value, lead time, and demand uncertainty affect the performance of the integrated RFID supply chain in terms of cost factors at the echelon level.     

Optimizing and assigning price levels for air traffic management

Pricing policies could encourage airline companies to modify departure times and routes of their flights in order to reach a route-slot allocation target that minimizes the en-route congestion. The problem of restricting the number of price levels and the assignment of one price level to each sector at each time period is studied using a simulation based on a Logit discrete choice model. In this model, an option is defined as the combination of a departure time and a route. For each flight, a utility is associated to each option and takes into account the flying cost, the cost of ground delay and the prices of crossed sectors. The optimization of the pricing policy considers average flows and minimizes the quadratic difference between desired and expected flows on each option. A heuristic algorithm that involves successive iterations of simulated annealing and gradient methods performs this optimization. The simulated annealing assigns a price level to each sector at each time period, and the gradient algorithm computes new values of price levels. The test of the method on constructed examples indicates that the use of only four price levels does not significantly deteriorate the performance of the system with respect to the use of independent prices.     

提速双线的大区间对通过能力的影响

在客货列车速差和站间距增大的情况下,双线自动闭塞区段客货列车运行时分差大于追踪列车间隔时间的大区间将会较多地出现。大区间引起旅客列车扣除系数增加,其增加的幅度与大区间的数量和分布有关。为此提出繁忙干线宜采用旅客列车成组连发的行车组织方法以充分利用通过能力。     

Optimization of loading sequence and rehandling strategy for multi-quay crane operations in container terminals

In this paper, we consider the optimization of loading sequence and rehandling strategy in the terminal operation. We present an optimization strategy to minimize the number of rehandles, and establish a mathematical model to integrate the loading sequence and the rehandling strategy under the parallel operation of multi-quay cranes. Furthermore, we give an improved genetic algorithm to solve the model. We show the efficiency of the optimization strategy and algorithm by comparing them with previous strategies and heuristics.