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.     

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.     

A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district

We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.     

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。     

GIS-based highway maintenance prioritization model: an integrated approach for highway maintenance in Nepal mountains

Roadside slope failures often result in day to week-long traffic disruption and are a major problem in Nepal so that timely maintenance of roadside slopes needs greater priority for smooth operation of traffic. In this paper, we develop a Geographic Information Systems (GIS)-based maintenance model considering pavement and roadside slope stability conditions. A pavement maintenance priority map is prepared based on pavement condition. International Roughness Index (IRI) is used as the pavement condition index. Similarly, a roadside slope maintenance priority map is produced by analyzing slope stability condition. An integrated maintenance priority map is produced by combining both pavement and roadside slope maintenance priority maps with consideration of weighting of each maintenance component. The integrated maintenance priority index developed in the research lies from 1 to 3, and is distributed heterogeneously. Higher index values qualify for higher priority in maintenance planning. Thus, a conventional method of maintenance planning under the constraints of budget, time and resources can be improved by incorporating the actual condition of pavement and roadside slope failure susceptibility, which has the potential to improve significantly the serviceability of the road system. The study shows that GIS, which can manage and visualize different types of data together or separately, can help the decision making process for road maintenance planning in the Himalayan regions.     

Aircraft routing for on-demand air transportation with service upgrade and maintenance events: Compact model and case study

This paper addresses a problem faced by airline companies that offer on-demand flight services. Given a list of flight requests, the company has to assign its aircraft to these requests while minimizing operational costs. The main issue in this planning process involves the positioning of aircraft when they are not available at the airports of customer departure. The cost of this positioning should be as low as possible, as the customers’ expenditures are proportional to the requested flight hours only. We propose a compact optimization model to support decision making in this situation. It takes into account the mandatory aircraft maintenance events and the possibility of flight upgrades according to the their impact on the operational costs. One important and novel feature of this model is that it allows the anticipation or postponement of the beginning of flights and maintenance events within a given tolerance, giving more freedom to the decision making process. This research is motivated by a case study carried out with a fractional management airline company that operates in European and Asian countries. Computational experiments using real-life data collected from the company show that the proposed model can be solved quickly using general-purpose optimization software, including open-source alternatives. The results indicate that the obtained solutions lead to significant reductions in the operational costs and hence can be used in practice for effective decision making.     

Inventory optimization for a customer airline in a Performance Based Contract

The supply of spare parts has a crucial role in the aviation sector, mainly due to the high costs of spare parts and to the strict availability requirements. In a stand-alone scenario, an airline owns the spare parts and manages the maintenance tasks by itself. A new trend consists of not owning the spare parts and delegate the maintenance tasks to an external company, taking advantage of a specific Performance Based Contract (PBC). The PBCs aim to reduce the ownership cost for the customer airline, while ensuring a target system performance. Spare parts become a variable cost for the customer airline and a business income for the maintenance supplier, which is commonly another airline.This paper proposes an innovative model, i.e. the PBC-METRIC, which supports the customer airline manager to minimize the spare parts supply cost, in compliance with the airline availability requirements and with respect to the PBC. In detail, the PBC-METRIC models a multi-echelon, multi-item, single-indenture, multi-transportation network, by an innovative two-steps algorithm, defining the PBC specifications as modelling variables and parameters. A case study on a European airline, with the role of customer in a PBC, illustrates the outcome of the model.     

Multistage optimization of the supply chains of biofuels

In this study, a mathematical model that integrates spatial and temporal dimensions is developed for strategic planning of future bioethanol supply chain systems. The planning objective is to minimize the cost of the entire supply chain of biofuel from biowaste feedstock fields to end users over the entire planning horizon, simultaneously satisfying demand, resource, and technology constraints. This model is used to evaluate the economic potential and infrastructure requirements for bioethanol production from eight waste biomass resources in California as a case study. It is found that, through careful supply chain design, biowaste-based ethanol production can be sustained at a compatible cost around $1.1 per gallon.     

Evaluation of access to health care in rural areas using enhanced two-step floating catchment area (E2SFCA) method

The study concerns with the quantification of accessibility to health facilities which is a prime concern in rural areas. In order to assure access to health care, planning commissions and policy decision makers require definite and reliable measures of accessibility values, thus that appropriate health care shortage areas can be analyzed and a policy decision can be taken accordingly to pacify the problem. Thus in this paper enhanced two step floating catchment area (E2SFCA) method, a special case of gravity model is used to quantify the present accessibility levels to health in rural areas. E2SFCA method which is an enhancement of two step floating catchment area (2SFCA) method differentiates the accessibility of the population within the catchment area by introducing a distance decay function. At the point when calibrating the distance decay from the travel behavior of patients in the study areas, it is revealed that sigmoidal functions are more likely suitable for the high population density regions and also plain terrains and decline functions for low population density regions and difficult desert or hilly areas. The paper yields health care shortage areas.     

An optimization approach to aircraft dispatching strategy with maintenance cost – A case study

This paper presents an optimization approach to identify aircraft dispatching strategy at a flight training school. The strategy adopted by the school was to dispatch the aircraft which is closest to its scheduled maintenance. This strategy was examined and compared with other potential dispatching strategies. The paper presents a mixed integer linear programming model to identify the strategy that minimizes the total cost of scheduled maintenance. The analysis shows that the optimization approach can save 2%–5% on annual maintenance cost compared with other strategies. The model can equally be applied to rental cars or trucking companies.     

Robust planning and disruption management in roll-on roll-off liner shipping

This paper presents different strategies for handling disruptions in fleet deployment in roll-on roll-off liner shipping, which basically consists of assigning a fleet of vessels to predefined voyages at minimum cost. A new mathematical model of the problem is presented, including a set of robust planning strategies, such as adding slack and rewarding early arrivals. To solve real-life instances a rolling horizon heuristic is proposed. A computational study, where we also propose some recovery planning strategies, is conducted, and simulation results show that adding robustness significantly reduces the actual cost of the plan and the total delays of the voyages.     

Predicting transit mode choice of New Jersey workers commuting to New York City from a stated preference survey

This study examines transit mode choice preferences of New Jersey commuters traveling to New York City (NYC) with the objective of assisting trans-Hudson ferry service planning on the basis of variables significantly affecting mode choice. A stated preference survey was conducted to collect data from a total of 2134 respondents who commute from northern New Jersey to NYC. A mixed multinomial logit model (MMLM) with random coefficients was used to analyze the data. The study considered the choice between four transit modes—ferry, PATH train, bus, and commuter rail—all of which can be used to cross the Hudson River to travel from New Jersey to NYC. Essential model results are presented for all four modes, but detailed results are discussed for only the ferry, PATH, and bus modes. Model results indicate that commuters' choices are affected not only by travel time and cost for the segment of the journey that crosses the Hudson River, but also reliability and comfort during that segment, number of access modes required by the journey, and travel time and cost of trips from home to stations/terminals before crossing the river. The results also show that commuters' attachment to the modes they currently use serves as a barrier to switching to other modes. The most important takeaway from this research for transit service planning is that there is a need to consider the attributes of both individual trip segments as well as entire journeys between commuters' home and work when exploring changes to transit service characteristics.     

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.     

A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation

This paper focuses on the planning of road restoration efforts during disaster response and recovery. The primary objective is to maximize network accessibility for all locations in the area during the restoration process so that survivors are evacuated and road side debris is removed as soon as possible. We propose a dynamic path based mathematical model that identifies criticality of blockages and clears them with limited resources. This model is more efficient than link based models and can solve restoration problems for realistic size networks within reasonable time. Algorithm performance is demonstrated using two instances based on districts in Istanbul.     

Comparison of solution approaches for the train load planning problem in seaport terminals

In this paper the train load planning problem arising in a seaport container terminal is considered. This problem consists in determining the optimal assignment of containers to wagon slots in order to maximize the train utilization and, at the same time, to minimize unproductive operations in the terminal. Different solution approaches based on a mathematical programming model are compared. The best solution procedure, satisfactory both in terms of quality of the obtained solutions and for the computational times, is identified through extensive experimental tests. This procedure could be included in a planning tool to be used in real seaport terminals.     

A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach

This paper presents a new multi-objective mathematical model to address a Healthcare Inventory Routing Problem (HIRP) for medicinal drug distribution to healthcare facilities. The first part of objective function minimizes total inventory and transportation costs, while satisfaction is maximized by minimizing forecast error which caused by product shortage and the amount of expired drugs; Greenhouse Gas (GHG) emissions are also minimized. A demand forecast approach has been integrated into the mathematical model to decrease drug shortage risk. A hybridized possibilistic method is applied to cope with uncertainty and an interactive fuzzy approach is considered to solve an auxiliary crisp multi-objective model and find optimized solutions.     

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.     

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.     

The sky is the limit: Assessing aircraft market diffusion with agent-based modeling

This paper presents an agent-based model for the diffusion of new aircraft models. Expanding on the classical economic decision framework, where investment decision-making is entirely based on profitability, our more holistic modeling approach takes into account profitability, flexibility, as well as the environmental impact of new aircraft models in the adoption decision process. Technical parameters, such as the range and passenger number per aircraft model considered, various types of pollutant emissions of the aircraft engine, as well as daily operational data, are used as covariates in the model. In validation for the most common mid-range passenger aircraft models of Airbus and Boeing, our agent-based model produces results that are comparable to observed real-world data on the market diffusion of existing mainstream aircraft models. This result shows the applicability and usefulness of our model, which can subsequently also be applied for studying the diffusion of aircraft models embodying new generations of components. Our simulation shows that a price reduction or a decrease in pollutant emissions of new aircraft models can be expected to lead to more adoption and faster diffusion. Furthermore, our modeling approach demonstrates that a holistic and systematic framework that includes not only profitability (in terms of payback time) but also flexibility (in terms of optimal range and amount of passengers) and environmental impact (in terms of deviation from regulatory standards) can be helpful for modeling the investment decision-making process of airlines.     

A novel mathematical model for a multi-period,multi-product optimal ordering problem considering expiry dates in a FEFO system

One of the main challenges of retail units is to determine the order quantities of different types of products, each with a specific expiry date, so that the system cost including shortage cost is minimized. We study a new multi-product multi-period replenishment problem for a First Expired-First Out (FEFO) based warehouse management system. The proposed nonlinear model is first converted to a linear one and then solved by applying two evolutionary algorithms: the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in which design parameters are set using Taguchi method. Computational results demonstrate the applicability of the proposed model for perishable items and comparing the results shows the efficiency of the proposed metaheuristics as well.