Fleet replacement decisions under demand and fuel price uncertainties

Zheng, Shiyuan; Chen, Shun

doi:10.1016/j.trd.2016.09.001

Cited by 36 publications

(14 citation statements)

References 27 publications

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“…An issue with this model, however, is that in-storage assets are allowed to age as those in-use do. A real option model was proposed by Zheng and Chen (2018) [49] to decide on the replacement timing of a fleet of ships, when the owner deals with uncertain demand on different routes and uncertain prices of the fuel Marine gas oil (MGO) and liquefied natural gas (LNG). They compared the results, taking into account two affecting government policies, i.e.…”

Section: Classical Economic Modelsmentioning

confidence: 99%

“…Traditionally, R/R models rely on life cycle costing (LCC) concepts [47]. Recently, more advanced techniques have been also proposed in this domain to account for technological improvements [48], uncertainty of demand and market parameters [49], technical failure characteristics of assets [50], the effect of unknown parameters on time-to-failure distribution [51], and so forth. Despite all these attempts, however, there are complexities associated with this problem that have not been modelled yet.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable asset and project management

Abdi¹

2021

Preprint

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There is a growing demand for companies to report and demonstrate their environmental credentials and corporate responsibility, which presents an opportunity for them to differentiate and gain a competitive advantage in the marketplace. They are now recognizing the limited capacity of the environment to endure the current level of development and economic growth, depletion of natural resources, increasing problem of waste, worrying carbon dioxide emissions, and other environmental impacts. In fact, for asset and project managers, financial criteria are no longer the sole considerations to achieve success and shareholder value. Therefore, environment is being considered as a future source of risk or opportunity. The present research proposes methodology and mathematical models for a sustainable asset and project management, with the focus on the environmental aspect of sustainable development and more specifically the issue of greenhouse gas (GHG) emissions. The following models have been presented in this dissertation. First, a mathematical fleet optimization model is developed, which incorporates the environmental impacts of a fleet of assets over a finite horizon, in addition to its total cost of ownership. As a unique feature of the model, it allows the assets to be kept in storage over any time period, in which such assets do not deteriorate as in-use assets do. The mathematical model optimizes the number of new, in-use, in-storage, and salvaged assets in each time period, so that the total economic costs and environmental impacts are minimized. The application of this work is illustrated in a fleet of excavators. Second, a hybrid Bayesian network (BN) is proposed for fleet availability analysis, focusing on the uncertainty of assets failure and repair rates. We model the common causes to individual rates, as well as the common causes that affect both failure and repair rates at the same time. The proposed model explicitly quantifies uncertainty in repair and failure rates of a fleet of assets and provides an appropriate method for modeling complex dependencies and factors affecting reliability, maintainability, or both, by considering influencing factors, either technical (such as working temperature, environment, quality, stress, etc.) or organizational (such as staff quality, management policies, etc.). We will then extend the model to consider extremely rare and/or previously unobserved risks (e.g. heavy storms, droughts, floods, etc.) that can significantly weaken reliability or maintainability levels. Third, a deterministic model for equipment repair-replacement (R/R) decision with both economic and environmental considerations is formulated. We converted the model into an algorithm and an automatic R/R Calculator. A probabilistic version of this model is then developed to factor in the quality of preventive maintenance, repair perfection, and risk events. We also model the causal relationship between equipment reliability and its GHG emissions during the operation phase. A plastic shredder case study was used to present the models’ results. Fourth, we aim to capture the uncertainty of carbon price in the Western Climate Initiative (WCI) market, by determining the causality between carbon price and its driving forces. A probabilistic model is developed using BNs to infer the possible ranges of each driving force that could have an escalation/depreciation effect on price as well as the magnitude of this effect. The model is developed and run based on a database of historical and projection on the selected driving factors in all the jurisdictions of the WCI market, providing the most probable price(s) over the next ten years. Finally, we developed two models to estimate and control project GHG emissions. The first model is developed based the earned value management (EVM) technique, a common practice in project cost and schedule performance measurement. The proposed model provides project managers with metrics to measure project GHG performance at any point in time over the life of a project and forecast the final emissions. In addition, we proposed a probabilistic model to quantify the uncertainty of project GHG emissions using Monte Carlo Simulation and BN techniques. The model provides a quantitative risk analysis mechanism to estimate the total emissions of the project as well as prediction of final emissions during the implementation process. The proposed models are applied to a work package of a real construction project.

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Section: Classical Economic Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sustainable asset and project management

Abdi¹

2021

Preprint

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“…The other aspect considered in this research is the variability in feedstock price. Optimization models that consider price uncertainty can be found in the literature applied to a plethora of areas such as electricity [24], metal [25], fleet replacement [26], chemical industry [27], supply chain network [28] or forestry [29] using different approaches. A widely used approach to deal with price volatility is two stage stochastic programming with recourse.…”

Section: Rdfs (Wco)mentioning

confidence: 99%

Planning strategies to address operational and price uncertainty in biodiesel production

et al. 2019

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The use of low-cost feedstocks such as waste cooking oils has gained prominence in biodiesel production due to its potential economic and environmental advantages. Since these feedstocks are derived from multiple sources, its compositional variability has led to quality concerns that may significantly limit its utilization. One potential strategy to address this concern is to use stochastic blending models to optimize the mixing of secondary and primary oils (e.g., palm, canola, or soya).In this paper, we present a stochastic blending model that embeds a second, key source of uncertainty: the future price of feedstocks, a topic of tremendous concern for producers. The stochastic blending model embeds a chance-constrained formulation to account for compositional variability and uses time-series methods to address feedstock price uncertainty. The model was developed to support production-planning decisions to minimize cost and cost variation in biodiesel production. We demonstrate that the proposed approach is useful for determining an optimal planning of feedstock acquisition, blending and storage in order to minimize the risks associated with feedstock price fluctuations. Results show that addressing the compositional uncertainty via the chance-constrained formulation will allow for the use of waste cooking oil in biodiesel blends without compromising their technical performance.

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“…They solve the problem in a rolling horizon fashion using a stochastic programming look-ahead model, and explore the impact of different scenario trees with different recourse functions. Zheng and Chen (2016) propose a real option model to solve a fleet replacement model under demand and fuel price uncertainty. Monte Carlo simulation is used to find replacement probabilities in future years and the net present value of cost savings.…”

Section: Health Care Routing Problemsmentioning

confidence: 99%

A unified framework for rich routing problems with stochastic demands

Markov

Bierlaire

Cordeau

et al. 2018

Transportation Research Part B: Methodological

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We introduce a unified framework for rich vehicle and inventory routing problems with complex physical and temporal constraints. Demands are stochastic, can be non-stationary, and are forecast using any model that provides the expected demands and their error term distribution, which can be any theoretical or empirical distribution. We offer a detailed discussion on the modeling of demand stochasticity, focusing on the probabilities and cost effects of undesirable events, such as stock-outs, breakdowns and route failures, and their associated recourse actions. Tractability is achieved through the ability to pre-compute or at least partially pre-process the stochastic information, which is possible under mild assumptions for a general inventory policy. We integrate the stochastic aspect into a mixed integer non-linear program, illustrate applications to various problem classes, and show how to model specific problems through the lens of inventory routing. The case study is based on two sets of realistic instances, representing a waste collection inventory routing problem and a facility maintenance problem, respectively. We analyze the effects of our assumptions on modeling realism and tractability, and demonstrate that our framework significantly outperforms deterministic policies in its ability to limit the number of undesirable events for the same routing cost.

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Fleet replacement decisions under demand and fuel price uncertainties

Cited by 36 publications

References 27 publications

Sustainable asset and project management

Sustainable asset and project management

Planning strategies to address operational and price uncertainty in biodiesel production

A unified framework for rich routing problems with stochastic demands

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