The impact of fleet size on performance-based incentive management

Performance-based contracting (PBC) is envisioned to lower the asset ownership cost while ensuring desired system performance. System availability, widely used as a performance metric in such contracts, is affected by multiple factors such as equipment reliability, spares stock, fleet size, and service capacity. Prior studies have either focussed on ensuring parts availability or advocating the reliability allocation during design. This paper investigates a single echelon repairable inventory model in PBC. We focus on reliability improvement and its interaction with decisions affecting service time, taking into account the operating fleet size. The study shows that component reliability in a repairable inventory system is a function of the operating fleet size and service rate. A principal-agent model is further developed to evaluate the impact of the fleet size on the incentive mechanism design. The numerical study confirms that the fleet size plays a critical role in determining the penalty and cost sharing rates when the number of backorders is used as the negative incentive scheme.     

基于多目标规划的飞机路径恢复最优化算法研究

本文针对同一机场中同机型的多架飞机受到干扰后, 飞机路径恢复的多目标最优化问题进行研究。首先根据航空公司实际航班调整的常用原则和航班干扰管理的基本思想, 基于连接网络建立多目标规划模型, 其中两个目标按照优先级排列:第一个目标为最小化航班的最大延误时间, 第二个目标为最小化参与交换的飞机数量。然后根据该问题的航班波结构特点, 结合求解多目标规划的分层序列法, 分析优化问题的若干最优性质, 并基于快速排序算法和最小费用路算法设计出多项式算法。最后用算例验证了算法的有效性。该研究结果可以为航空公司减少航班延误提供理论和技术支持。     

Optimizing reliability and service parts logistics for a time-varying installed base

Performance based contracting (PBC) emerges as a new after-sales service practice to support the operation and maintenance of capital equipment or systems. Under the PBC framework, the goal of the study is to increase the system operational availability while minimizing the logistics footprint through the design for reliability. We consider the situation where the number of installed systems randomly increases over the planning horizon, resulting in a non-stationary maintenance and repair demand. Renewal equation and Poisson process are used to estimate the aggregate fleet failures. We propose a dynamic stocking policy that adaptively replenishes the inventory to meet the time-varying parts demand. An optimization model is formulated and solved under a multi-phase adaptive inventory control policy. The study provides theoretical insights into the performance-driven service operation in the context of changing system fleet size due to new installations. Trade-offs between reliability design and inventory level are examined and compared in various shipment scenarios. Numerical examples drawn from semiconductor equipment industry are used to demonstrate the applicability and the performance of the proposed method.     

单架飞机受干扰后飞机路径恢复多项式算法研究

飞机路径恢复是航班调整中保证航班能够运行的必要条件之一,而传统目标下的飞机路径优化问题是NP-hard的。本文针对单架飞机受到干扰后,基于最小最大目标的同机型飞机路径最优化问题,给出了一个新的多项式时间算法。首先基于航空公司调整航班的常用原则,提出把最大航班延误时间最小化作为问题的目标。然后根据问题的一些特点和目标形式,设计出解构造算法,得到飞机路径恢复问题的最优解,并分析出算法的复杂度为O(n²)。相对于一般的最小最大二分图匹配算法(复杂度为O(n³log(n))),该算法具有较小的时间复杂度。最后用实例验证了解构造算法的有效性。该研究结果将为航空公司减少航班延误提供理论和方法支持。     

Machine reliability and preventive maintenance planning for cellular manufacturing systems

The paper proposes a preventive maintenance (PM) planning model for the performance improvement of cellular manufacturing systems (CMS) in terms of machine reliability, and resource utilization. In a CMS, parts are processed by a group of interdependent machines, where machine reliability plays an important role in the performance improvement of the cell. Assuming that machine failure times follow a Weibull distribution, the proposed model determines a PM interval and a schedule for performing PM actions on each machine in the cell by minimizing the total maintenance cost and the overall probability of machine failures. The model uses a combined cost and reliability based approach, and optimizes maintenance costs by administering a group maintenance policy subject to a desirable machine reliability threshold. The study also proposes a CMS design model that integrates the above PM concepts into the design process. Illustrative examples are presented to demonstrate the applicability of the proposed approach.     

Simulation model of multi-compartment distribution in the catering supply chain

An efficient distribution system of high performance is needed to anticipate market developments in the catering supply chain (CSC) in the Netherlands. A simulation model was developed to analyse a multi-compartment distribution system which should satisfy customer demands for shorter lead times, increased delivery frequency and improved quality of process and product. The simulation model quantifies logistic and financial performances in various alternative logistic scenarios for multi-compartment distribution in the catering supply chain. This stochastic simulation model was constructed on the basis of a value chain analysis yielding activities and performance indicators (PIs). The model proved to be accurate in its predictions when applied to a case situation taken from the Dutch CSC. It is concluded that discrete event simulation is an effective tool to evaluate promising logistic scenarios. The new, multi-compartment method of distribution allowed for a 14% decrease in total costs in the Dutch CSC while satisfying customer demands.     

A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model

The fleet assignment model assigns a fleet of aircraft types to the scheduled flight legs in an airline timetable published six to twelve weeks prior to the departure of the aircraft. The objective is to maximize profit. While costs associated with assigning a particular fleet type to a leg are easy to estimate, the revenues are based upon demand, which is realized close to departure. The uncertainty in demand makes it challenging to assign the right type of aircraft to each flight leg based on forecasts taken six to twelve weeks prior to departure. Therefore, in this paper, a two-stage stochastic programming framework has been developed to model the uncertainty in demand, along with the Boeing concept of demand driven dispatch to reallocate aircraft closer to the departure of the aircraft. Traditionally, two-stage stochastic programming problems are solved using the L-shaped method. Due to the slow convergence of the L-shaped method, a novel multivariate adaptive regression splines cutting plane method has been developed. The results obtained from our approach are compared to that of the L-shaped method, and the value of demand-driven dispatch is estimated.     

A large neighbourhood search heuristic for the aircraft and passenger recovery problem

This paper introduces a large neighbourhood search heuristic for an airline recovery problem combining fleet assignment, aircraft routing and passenger assignment. Given an initial schedule, a list of disruptions, and a recovery period, the problem consists in constructing aircraft routes and passenger itineraries for the recovery period that allow the resumption of regular operations and minimize operating costs and impacts on passengers. The heuristic alternates between construction, repair and improvement phases, which iteratively destroy and repair parts of the solution. The aim of the first two phases is to produce an initial solution that satisfies a set of operational and functional constraints. The third phase then attempts to identify an improved solution by considering large schedule changes while retaining feasibility. The whole process is iterated by including some randomness in the construction phase so as to diversify the search. This work was initiated in the context of the 2009 ROADEF Challenge, a competition organized jointly by the French Operational Research and Decision Analysis Society and the Spanish firm Amadeus S.A.S., in which our team won the first prize.     

A benders decomposition approach for an integrated airline schedule design and fleet assignment problem with flight retiming, schedule balance, and demand recapture

The airline’s ability to offer flight schedules that provide service to passengers at desired times in competitive markets, while matching demand with an aircraft fleet of suitable size and composition, can significantly impact its profits. In this spirit, optional flight legs can be considered to construct a profitable schedule by optimally selecting among such alternatives in concert with assigning the available aircraft fleet to all the scheduled legs. Examining itinerary-based demands as well as multiple fare-classes can effectively capture network effects and realistic demand patterns. In addition, allowing flexibility on the departure times of scheduled flight legs can increase connection opportunities for passengers, hence yielding robust schedules while saving fleet assignment costs within the framework of an integrated model. Airlines can also capture an adequate market share by balancing flight schedules throughout the day, and recapture considerations can contribute to more realistic accepted demand realizations. We therefore propose in this paper a model that integrates the schedule design and fleet assignment processes while considering flexible flight times, schedule balance, and recapture issues, along with optional legs, path/itinerary-based demands, and multiple fare-classes. A polyhedral analysis is conducted to generate several classes of valid inequalities, which are used along with suitable separation routines to tighten the model representation. Solution approaches are designed by applying Benders decomposition method to the resulting tightened model, and computational results are presented using real data obtained from United Airlines to demonstrate the efficacy of the proposed procedures.     

An Iteration Method for Reliability Evaluation of a Fleet System

The fleet system considered here consists of n identical units (members) which are operated together under an operational program which specifies the schedule of the operation of the system and maintenance performed on the units in the system. Introduction of this operational program makes the reliability evaluation of the fleet system more realistic but complicated. A conventional Markov approach is effective only when a fleet system consists of a few units. This paper presents a new method for the reliability evaluation of a fleet system. This method reduces the problem of the reliability evaluation of the system to that of each unit by introducing a ‘utilization factor’. Therefore, the size of the problem is irrelevant to the number of the units in the system. An iteration method is used to obtain the unique solution of the problem.     

Joint optimization of fleet size and maintenance capacity in a fork-join cyclical transportation system

This article presents an asset management-oriented multi-criteria methodology for the joint estimation of a mobile equipment fleet size, and the maintenance capacity to be allocated in a productive system. Using a business-centred life-cycle perspective, we propose an integrated analytical model and evaluate it using global cost rate, availability and throughput as performance indicators. The global cost components include: (i) opportunity costs associated with lost production, (ii) vehicle idle time costs, and (iii) maintenance resources idle time costs. This multi-criteria approach allows a balanced scorecard to be built that identifies the main trade-offs in the system. The methodology uses an improved closed network queueing model approach to describe the production and maintenance areas. We test the proposed methodology using an underground mining operation case study. The decision variables are the size of a load-haul-dump fleet and specialized maintenance crew levels. Our model achieves savings of 20.6% in global cost terms with respect to a benchmark case. We also optimize the system to achieve desired targets of vehicle availability and system throughput (based on system utilization). The results show increments of 7.1% in vehicle availability and 13.5% in system throughput with respect to baseline case. For the case studied, these criteria also have a maximum, which allows for further improvement if desired. The results also show the importance of using balanced performance measures in the decision process. A multi-criteria optimization was also performed, showing the Pareto front of considered indicators. We discuss the trade-offs among different criteria, and the implications in finding balanced solutions. The proposed analytical approach is easy to implement and requires low computational effort. It also allows for an easy re-evaluation of resources when the business cycle changes and relevant exogenous factors vary.     

Improvements to a large neighborhood search heuristic for an integrated aircraft and passenger recovery problem

Because most commercial passenger airlines operate on a hub-and-spoke network, small disturbances can cause major disruptions in their planned schedules and have a significant impact on their operational costs and performance. When a disturbance occurs, the airline often applies a recovery policy in order to quickly resume normal operations. We present in this paper a large neighborhood search heuristic to solve an integrated aircraft and passenger recovery problem. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during the recovery period. The method is based on an existing heuristic, developed in the context of the 2009 ROADEF Challenge, which alternates between three phases: construction, repair and improvement. We introduce a number of refinements in each phase so as to perform a more thorough search of the solution space. The resulting heuristic performs very well on the instances introduced for the challenge, obtaining the best known solution for 17 out of 22 instances within five minutes of computing time and 21 out of 22 instances within 10 minutes of computing time.     

基于BP神经网络的航空公司机队可靠性评价模型

机队作为航空公司运输能力的关键,其安全性与可靠性直接影响到航空公司的经济效益.根据航空公司机队可靠性统计和数据采集方式以及实际应用情况,建立了航空公司机队可靠性评价指标体系.鉴于机队可靠性受多种复杂因素影响及各指标体系非线性等特点,结合人工神经网络基本原理和特性,提出了BP神经网络机队可靠性评价模型.最后应用该模型对航空公司机队可靠性进行了实例分析,得出了评价等级.结果表明,基于BP神经网络机队可靠性评价模型是可行的,该方法能够实现动态的评价,对提高航空公司机队可靠性评价技术水平具有现实的意义.     

A spatiotemporal Data Envelopment Analysis (S-T DEA) approach: the need to assess evolving units

We consider a two-stage supply chain with one supplier and one manufacturer. The manufacturer faces a Poisson demand process where the arrival rate depends on the selling price, the announced delivery time, and the delivery reliability defined as the probability of satisfying the announced delivery time. Such a demand model generalizes the works in the literature by simultaneously considering the above three demand sensitivity factors. The main purpose of this paper is to study the equilibrium decisions in the supply chain with an all-unit quantity discount contract. We consider four scenarios regarding whether the leadtime standard, the delivery reliability standard, and the manufacturer’s capacity are endogenous, and whether the manufacturer’s production cost is its private information. We find that an all-unit quantity discount scheme can coordinate the supply chain for most cases. Managerial insights are observed regarding the impact of the three demand sensitivity factors. For example, the breakpoint in an optimal quantity discount contract always increases with the delivery reliability sensitivity under an exogenous delivery reliability, but may decrease under an endogenous delivery reliability; with asymmetric information, a higher variance of the manufacturer’s unit production costs leads to a lower unit wholesale price for the low-cost manufacturer.     

Design of a recovery network for WEEE collection: the case of Galicia,Spain

This research aims to optimize the design of the reverse logistic network for the collection of Waste of Electric and Electronic Equipment (WEEE), in the Spanish region of Galicia. As a basis for our study a three-phase hierarchical approach is proposed. In the first phase a facility location problem is formulated and solved by means of a mixed integer linear programming; in the second phase a new integer programming formulation for the corresponding heterogeneous fleet vehicle routing problem is presented, and a savings-based heuristic algorithm is developed to efficiently solve the related collection routing problems; in the third phase a simulation study is performed on the collection routes in order to assess the overall performance of the recovery system. The results show a good performance of the proposed procedure, and an improved configuration of the recovery network compared to the one currently in use (particularly transportation costs are reduced by 29.2%).     

Performance replication of the Spot Energy Index with optimal equity portfolio selection: Evidence from the UK,US and Brazilian markets

This paper reproduces the performance of a geometric average Spot Energy Index by investing only in a subset of stocks from the Dow Jones Composite Average, the FTSE 100 and Bovespa Composite indexes, and in two pools that include only energy-sector stocks from the US and the UK respectively. Daily data are used and the index-tracking problem for passive investment is addressed with two evolutionary algorithms – the differential evolution algorithm and the genetic algorithm. The performance of the suggested investment strategy is tested under three different scenarios: buy-and-hold, quarterly and monthly rebalancing, accounting for transaction costs where necessary.     

Importance Sampling Simulations of Markovian Reliability Systems Using Cross-Entropy

This paper reports simulation experiments, applying the cross entropy method such as the importance sampling algorithm for efficient estimation of rare event probabilities in Markovian reliability systems. The method is compared to various failure biasing schemes that have been proved to give estimators with bounded relative errors. The results from the experiments indicate a considerable improvement of the performance of the importance sampling estimators, where performance is measured by the relative error of the estimate, by the relative error of the estimator, and by the gain of the importance sampling simulation to the normal simulation.     

Goal Programming Models for Assigning Search and Rescue Aircraft to Bases

This paper develops several variations of a goal programming model for optimally allocating a fleet of search and rescue aircraft to a fixed set of available and potentially available bases. In addition, the model determines the number of aircraft of each type from each base (at which that type has been stationed) to assign to the various search locations. The criterion for optimality is to maximize the probability of locating each distress in a specified time. These models are then modified to include fleet planning issues. Solution procedures relating to the models are discussed.     

The emergence of deontological codes in public administration

We study a model of the evolutionary selection of social standards of behavior in a large public organization, in terms of the propensity to bribery of the organization's rent-seeking officers. We examine several scenarios, allowing for the existence of various types of anti-corruption sanctioning mechanisms, for the role of pro-social, interiorized value systems, for the impact of informational costs on the viability of "informationally sophisticated" players. The conditions under which corruption is eventually eliminated or at least kept under control in the various scenarios are derived and discussed.     

Operational aircraft maintenance routing problem with remaining time consideration

The aircraft maintenance routing problem is one of the most studied problems in the airline industry. Most of the studies focus on finding a unique rotation that will be repeated by each aircraft in the fleet with a certain lag. In practice, using a single rotation for the entire fleet is not applicable due to stochasticity and operational considerations in the airline industry. In this study, our aim is to develop a fast responsive methodology which provides maintenance feasible routes for each aircraft in the fleet over a weekly planning horizon with the objective of maximizing utilization of the total remaining flying time of fleet. For this purpose, we formulate an integer linear programming (ILP) model by modifying the connection network representation. The proposed model is solved by using branch-and-bound under different priority settings for variables to branch on. A heuristic method based on compressed annealing is applied to the same problem and a comparison of exact and heuristic methods are provided. The model and the heuristic method are extended to incorporate maintenance capacity constraints. Additionally, a rolling horizon based procedure is proposed to update the existing routes when some of the maintenance decisions are already fixed.