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International audience; In time of financial crisis, bank loans are often extremely difficult to obtain for many companies. However, companies always need free cash flow to efficiently react against to any uncertainty. This work demonstrates the impact of financial consequences on operational decisions in the single-product, single-level, infinite capacity EOQ model. We propose an operation-related working capital requirement (WCR) model in a tactical planning context. The classic EOQ model is extended by integrating the WCR financing cost with a cost minimization objective and deriving its analytical solution. Compared with the optimal policy of the classic EOQ model, our approach leads to a new policy with a smaller production lot size due to the new cost trade-offs. Furthermore, an analytical analysis with a classic EOQ-based formula that considers the cost of capital demonstrates the sensitivity of approximating financial costs compared to our exact approach. Finally, sensitivity analysis and numerical examples are also provides.

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models e.g., Bayesian networks have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network DBN by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke's method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information.

International audience; This article is within the context of decision models aimed for maintenance of structures and infrastructures in civil engineering. The contribution relies on the construction of a degradation model oriented toward risk analysis. The proposed model can be defined as a meta-model in the sense that it is based on observations while incorporating key features from the degradation process necessary for the maintenance decision. We propose to stimulate the construction of the degradation model based on the crack propagation of a submerged reinforced concrete structure subject to chloride-induced corrosion. Furthermore, a set of numerical illustrations is performed to demonstrate the advantages and applicability of the proposed approach in risk management and maintenance contexts.

Physics-based models are intensively studied in mechanical and civil engineering but their constant increase in complexity makes them harder to use in a maintenance context, especially when degradation model can/should be updated from new inspection data. On the other hand, Markovian cumulative damage approaches such as Gamma processes seem promising; however, they suffer from lack of acceptability by the civil engineering community due to poor physics considerations. In this article, we want to promote an approach for modeling the degradation of structures and infrastructures for maintenance purposes which can be seen as an intermediate approach between physical models and probabilistic models. A new statistical, data-driven state-dependent model is proposed. The construction of the degradation model will be discussed within an application to the cracking of concrete due to chloride-induced corrosion. Numerical experiments will later be conducted to identify preliminary properties of the model in terms of statistical inferences. An estimation algorithm is proposed to estimate the parameters of the model in cases where databases suffer from irregularities.

International audience; In times of crisis, companies need free cash flow to efficiently react against all uncertainty to ensure solvency. However, classical dynamic lot-sizing models only consider the physical flow of goods. In this paper, we introduce a first link between dynamic lot-sizing and the financial aspects of working capital requirements (WCR). We propose a new generic WCR model which allows us to evaluate the company's financial situation throughout the planning horizon. Moreover, a dynamic lot-sizing-based, discounted cash flow model is established for single-site, single-level, single-product and infinite capacity cases. It is shown that the zero-inventory ordering property holds for this case and thus a polynomial-time algorithm may be utilized. Numerical tests are presented in order to show the relevance of our approach compared with the traditional dynamic lot-sizing model.

Cervical cancer is the second most common cancer in women around the world, and the human papillomavirus (HPV) is universally known as the necessary agent for developing this disease. Through early detection of abnormal cells and HPV virus types, cervical cancer incidents can be reduced and disease progression prevented. We propose a finite-horizon Markov decision process model to determine the optimal screening policies for cervical cancer prevention. The optimal decision is given in terms of when and what type of screening test to be performed on a patient based on her current diagnosis, age, HPV contraction risk, and screening test results. The cost function considers the tradeoff between the cost of prevention and treatment procedures and the risk of taking no action while taking into account a cost assigned to loss of life quality in each state. We apply the model to data collected from a representative sample of 1141 affiliates at a health care provider located in Bogota´, Colombia. To track the disease incidence more effectively and avoid higher cancer rates and future costs, the optimal policies recommend more frequent colposcopies and Pap tests for women with riskier profiles. Key words: cervical cancer; screening; Markov decision process.

This article focused on the transition experiences of 20 successful Chinese immigrants, in particular their transition stories and how they accounted for their success (what facilitated and hindered their transition). An enhanced critical incident method was used for data analysis. Four major success categories emerged: having a positive attitude and personality, skills and resource development, education and work experience, and community and family support. There was less emphasis on hindering categories, but some problems were identified. There was specific information on the effectiveness of the employment counseling process from those who had participated in an employment counseling program.

This research aims to contribute knowledge of value creation in service-driven manufacturing based on S-D logic rather than G-D logic. By using case study methodology to gather primary and secondary data, we have addressed four key research questions and identified significant findings which enable managerial discussion. Several research highlights include: 1) a set of synergistic managerial practices, including establishing interactive platforms, defining participating rules and building incentive mechanism, can achieve better use of operant resources of multiple actors; 2) managing the interactive platform and tools is a vital condition for value co-creation with multiple actors; 3) the use-value of customer is determined by means of personalised service based on customers' context; 4) a service ecosystem is built according to the shared institutional logic and clear rules of action. This study will promote the development of the manufacturing servitisation theory towards the S-D logic paradigm.

Statistical process control and maintenance optimization have an intuitive relationship, but the literature has largely treated them as separate entities. We formulate a partially observable Markov decision process (POMDP) that integrates these two fields by jointly optimizing the conditioned-based maintenance policy and dynamic control chart parameters used to observe the system. We present an insightful numerical example that demonstrates the flexibility of the policy as well it promising structural properties and practical implementation.

International audience; —Many models have been recently developed for the optimization of biomass related supply chains. However, models for biopower supply chains powered by animal waste have not received much attention yet. In this paper, we propose a mixed integer linear programming model for supplier selection and procurement planning for a biopower plant. The model integrates time window constraints for the collection of animal waste as well as inventory constraints. We show that the model is intractable with a state-of-the art commercial solver and propose a heuristic approach based on the Adaptive Large Neighbourhood Search (ALNS) framework. We show the efficiency of this approach on a case study in central France.

International audience; —This paper presents a new tactical optimization problem for non-hazardous waste and end-life product supply chain. Waste transport and recycling become crucial in our modern society, with a huge environmental and economic impact for industrials and communities. Operations on products during transport such as grinding or sorting allows companies to densify transports and reduce the overall supply cost. Integrating these new aspects, we introduce a new problem we term the multi-commodity, multi-flow problem with transformations and propose a linear mathematical model to solve it. With an application to a waste transport company and a performance benchmark on a linear solver, we show the pertinence of the model in a real case study and its scalability for more complex situations.

Tactical planning consists of developing production plans to fulfill client demands with a minimal logistic cost. However, plans generated by classical models for tactical planning do not consider a minimum financial need in terms of Working Capital Requirements (WCR) to maintain the activities related to the operating cycle. In this paper, we introduce a first link between tactical planning and the financial aspects of WCR. The concept of WCR is widely used in practice to assess the financial situation at any time. We propose a new generic WCR model which allows us to evaluate the company's financial situation during the planning horizon. In addition, we develop a dynamic lot-sizing-based model with WCR modeling for singlesite, single-level, single-product and infinite capacity cases. An exact algorithm is also presented with numerical tests in order to compare our approach with the traditional dynamic lot-sizing model.

International audience; Bridge lifetime pose an important challenge in terms of maintenance for decision makers or asset managers. In this regard Markov chains have been used successfully in practice as models for bridge deterioration. However, one limitation of Markov chains can be the assessment of the transition probabilities. In this paper, we propose an approach based on Bayesian networks (BNs) to quantify the transition probabilities of the system state. One of the advantages of doing so is that the BN may be quantified through physical variables linked to the underlying degradation process in an intuitive way through expert judgment combined with field measurements. In addition, the possibility of using Bayesian inference allows updating the probabilities when observations become available that could provide different relevant views of the long-term degradation. An application to a hypothetical stock of steel bridges in the Netherlands is presented and illustrates the method.

International audience; Maintenance decisions can be directly affected by the introduction of a new asset on the market, especially when the new asset technology could increase the expected profit. However new technology has a high degree of uncertainty that must be considered such as, e.g., its appearance time on the market, the expected revenue and the purchase cost. In this way, maintenance optimization can be seen as an investment problem where the repair decision is an option for postponing a replacement decision in order to wait for a potential new asset. Technology investment decisions are usually based primarily on strategic parameters such as current probability and expected future benefits while maintenance decisions are based on “functional” parameters such as deterioration levels of the current system and associated maintenance costs. In this paper, we formulate a new combined mathematical optimization framework for taking into account both maintenance and replacement decisions when the new asset is subject to technological improvement. The decision problem is modelled as a non-stationary Markov decision process. Structural properties of the optimal policy and forecast horizon length are then derived in order to guarantee decision optimality and robustness over the infinite horizon. Finally, the performance of our model is highlighted through numerical examples.

The present study was designed to study the mechanisms by which dietary conjugated linoleic acids (CLA) decrease serum cholesterol. Hamsters were fed a semi-synthetic diet containing 1 g cholesterol/kg diet with or without supplementation with 20 g linoleic acid (LA) and 20 g CLA/kg diet. After 8 weeks, serum fasting total cholesterol (TC) and triacylglycerol (TG) were significantly lower in the LA-supplemented and CLA-supplemented groups compared with those of the control (CTL) hamsters. In contrast to LA, CLA significantly lowered hepatic cholesterol but it increased the level of adipose tissue cholesterol, suggesting that the hypocholesterolaemic mechanism of CLA is different from that of LA. CLA decreased the activity of intestinal acyl CoA:cholesterol acyltransferase (ACAT) whereas LA had no effect on this enzyme. Consequently, CLA supplementation increased the faecal excretion of total neutral sterols, but it had no or little effect on the faecal acidic sterols. If the ACAT is associated with cholesterol absorption, the part of mechanisms by which CLA decreases serum cholesterol may involve down-regulation of intestinal ACAT activity.

International audience; The possibility of new technology occurrence has an important impact on the maintenance/ replacement decision. Therefore a challenge in maintenance decision marking is to determine maintenance policy and replacement investment plan under an uncertainty of technology improvement. In each period, we must decide whether to gather additional information on the potential improvement of a new technology, then chose the appropriate action for the asset (Do Nothing, Maintenance or Replacement). To formulate this scenario, we use a non stationary Markov Decision Process (MDP) model and provide some properties of the optimal policy based on a given set of numerical examples.

International audience; Classical results in maintenance optimization are based on the characterization of an average system degradation behaviour. The main reasons of such approaches are the robustness of the decisions with respect to the system state and the time horizon. By cons, the ''smoothing'' effect in existing degradation models and the ''generic'' aspect of the decision affect the decision quality as these models do not take into account potential changes in deterioration modes. Such consideration can be particularly valuable in a safety context. We propose here a condition-based maintenance approach which adapts the maintenance decision according to the degradation behaviour updated using current state observations. First, we propose to extend the system state definition, usually limited to an indicator of observable degradation by adding information related to the speed of deterioration referred hereafter as potential of degradation or deterioration growth rate. Moreover, we propose to take benefit of current observations as well as performed actions to update the degradation laws. More than the model fitness improvement, another advantage of our approach is the proposition of a more realistic modelling of the maintenance impact onto the future behaviour of the system. However, the introduction of the potential of degradation as a new decision parameter does not prejudge an intuitive structure for the decision policy. Moreover, as the classical concept of the failure rate, the potential of degradation is non observable. which implies more efforts in both optimization modelling and solution procedure. We highlight the structural properties of the optimization problem which ensure an optimal control limit policy. We will conclude our communication by the illustration of the results of our maintenance model in a pavement management context.

International audience; A condition-based maintenance optimization approach is developed for the road-cracking problem in order to derive optimal maintenance policies that minimize a total discounted maintenance cost. The approach is based on a Markov decision process that takes into ac- count multiple actions with varying effects on future road performance. Maintaining the road consists of adding a new asphalt layer; however, as resurfacing actions are constrained by a maximum total road thickness, the maintenance decision is not only how thick a layer to apply, but also how much old road to remove. Each combination of these actions leads to different maintenance costs and different future degradation behaviours. The road state is modelled by a dependent bivariate deterioration variable (the longitudinal cracking percentage and the deterioration growth rate), for taking these different changes in the cracking patterns into account. Moreover, the sensitivity to cracking for existing roads can be reduced with the addition of new layers, and thus actions that can lead to states better than good-as-new have to be considered. A numerical analysis is provided to illustrate the benefits of the introduction of the deterioration speed in the decision framework, as well as the belief that initially building a road to its maximum thickness is not optimal. The trade-offs in the design decisions and the exploitation/maintenance costs are also explored.

The formulation of a partially observed Markov decision process (POMDP) model to adaptively schedule testing, minimal repairs and overhauls for a two-state process with age-dependent degradation is presented. Structurally, the optimal maintenance policy is of control-limit type with respect to the repair and overhaul actions in both age and the probability of being out-of-control. A tailored solution algorithm is developed that iteratively determines an upper bound on the truncation age under the optimal policy. Numerical examples highlight the flexibility of the model, possible complexities of the optimal policy, and cost savings in comparison with less flexible models.

International audience; We develop an optimal maintenance policy for a road section to minimize the total maintenance cost over the infinite horizon when some deterioration and decision parameters are not observable. Both perfect and imperfect maintenance actions are possible through the application of various thicknesses of resurfacing layers. We use a two-phase deterioration process based on two parameters: the longitudinal cracking percentage and the deterioration growth rate. Our deterioration model is a state-based model based on the state-dependent Gamma process for the longitudinal cracking percentage and the Bilateral Gamma process for the deterioration growth rate. Moreover the maintenance decision is constrained by a maximum road thickness that makes the maintenance decisions more complex as it becomes how much surface layer to add as well as to remove. Because only one of the two deterioration parameters is observable, we formulate the problem as a partially observed Markov decision process and solve it using a grid-based value iteration algorithm. Numerical examples have shown that our model provides a preventive maintenance policy that slows down the initiation as well as the propagation of longitudinal cracks and that may ameliorate the road state to a better than as-good-as-new one by altering its composition through additive resurfacing layers.

International audience; We propose a maintenance decision framework where the state of the system is based on a percentage of total degrada- tion and the rate of change of the degradation. At each decision interval, the degradation percentage may be observed perfectly as well as the rate of change with respect to the last observation. We consider five possible actions based on these two observable phenomenon: Do nothing, three imperfect maintenance actions, and a complete renewal. Prior imperfect maintenance models do not consider a scenario where imperfect maintenance not only restores the system to a state less than “as good as new” but also under a new deterioration law. In this case, it is necessary to consider that the rate of degradation after imperfect maintenance will be altered based on the action performed and the state of the system prior to the maintenance. We propose to develop an imperfect maintenance optimization model based on a Markov decision process framework to determine the optimal maintenance actions given the state of the system at each decision interval and motivate the problem through road maintenance.

The economic design of control charts and the optimization of preventive maintenance policies have separately received a tremendous amount of attention in the quality and reliability literature over the years in an attempt to reduce the costs associated with operating manufacturing processes. Not until recently has the proposal been made to integrate these two fields and utilize the relationship between quality and equipment performance to improve the productivity of a manufacturing process. In this paper, we extend the initial preliminary investigation of this idea of using an chart in conjunction with an age-replacement preventive maintenance policy. We formulate a partially observable, discrete-time Markov decision process in order to obtain the near-optimal combined preventive maintenance/statistical process control policy that minimizes the costs associated with maintenance, sampling, and poor quality. We develop transition probabilities for the various states of the infinite horizon problem and a so...

International audience; We develop an inspection and maintenance policy to minimize the cost of maintaining a given section of road or highway when there is a great deal of uncertainty in the degradation process. We propose to model the degradation of a section of road based on the proliferation and growth of cracks. We utilize a combination of a Poisson and gamma process to account for the tremendous amount of uncertainty and difficulty in predicting the proliferation of cracks. Our policy defines the optimal inspection interval as well as the minimum threshold at which to perform crack repairs. Furthermore, our policy contains a safety constraint to prevent the probability of a "catastrophic" failure from exceeding a pre-determined reliability value. Numerical calculations have shown that our model will extend the lifecycle of the road by performing preventive, conditioned-based maintenance to slow down the growth of cracks. Classical preventive maintenance policies usually shorten the lifecycle by forcing earlier renewals.

Since the publication of "Sustainable Communities--building for the future", Government attention has focused largely on high-density affordable housing in the four "growth areas": Thames Gateway; Ashford; Milton Keynes--South Midlands, and London--Stansted--Cambridge. In this article, Thomas Yeung and Tony Bates suggest that a greater and more sustainable impact would be achieved if architects, planners, and developers considered the potential for community-based water and waste management and on-site energy generation and distribution right from the start of the project. In particular, they consider that the communal nature of hospitals, universities, and public/community housing provides a great opportunity for on-site renewable CHP and/or distributed heating, which could combine global environmental benefits with improved local amenities. They describe a simple model for prioritising energy management in the built environment, and draw on lessons learnt at ETRCL in Dagenham and BedZED in Surrey to offer a few recommendations for Government and developers. Tony Bates is the business development manager for Scott Wilson in the South East and is responsible for the promotion of sustainable communities through relationships with architects, developers, land owners and local authorities. Thomas Yeung leads the Energy Infrastructure Technologies group in Scott Wilson. This team offers an integrated approach to clean community-based energy generation, energy management, waste and water management, sustainable transport, and sustainable buildings/communities.

Formation of vesicular intermediates in protein transport between the endoplasmic reticulum and the Golgi apparatus involves a mechanism that sorts and packages two classes of molecules into transport vesicles: targeting molecules, which are required for targeting and consumption of vesicular intermediates, and cargo proteins. In order to examine the importance of cargo in this packaging reaction, we developed an in vitro assay that quantifies vesicle formation based on segregation of targeting molecules. Here we document that endoplasmic reticulum devoid of cargo proteins is competent in the formation and release of targeting molecule-containing vesicles in a fashion indistinguishable from its normal counterpart. This observation implies that packaging of cargo proteins may be uncoupled from the recruitment of targeting molecules during vesicle budding from the endoplasmic reticulum. Using the same assay, we demonstrate that the packaging of targeting molecules into vesicles is not dependent on the lumenal chaperone, BiP (Kar2p).

Publisher Summary This chapter describes the purification of functional Sec23p–Sec24p complex using a reconstituted vesicle budding and release assay. The Sec23 gene product is an 85-kDa protein that activates the GTPase activity of Sar1p and is tightly associated with a 105-kDa protein (Sec24p) to form a 400-kDa complex. The Sec23p–Sec24p complex, together with Sar1p and the Sec13p–Sec31p complex, are able to fulfill the cytosolic requirement in stimulating vesicle budding and release from the ER in vitro. The isolation of temperature-sensitive mutants defective in the transit of secretory proteins to their proper destinations has been invaluable in identifying essential components of the yeast secretory pathway. Genetic studies have implicated sec12, 13, 16, 23, 24, 31, and Sar1 in transport vesicle formation from the endoplasmic reticulum (ER). In an effort to elucidate the function of each component, cell-free assays have been developed to reproduce this reaction faithfully in vitro.

COPII vesicle formation requires only three coat assembly subunits: Sar1p, Sec13/31p, and Sec23/24p. PI 4-phosphate or PI 4,5-bisphosphate is required for the binding of these proteins to liposomes. The GTP-bound form of Sar1p recruits Sec23/24p to the liposomes as well as to the ER membranes, and this Sar1p-Sec23/24p complex is required for the binding of Sec13/31p. Ultrastructural analysis shows that the binding of COPII coat proteins to liposomes results in coated patches, coated buds, and coated vesicles of 50–90 nm in diameter. Budding proceeds without rupture of the donor liposome or vesicle product. These observations suggest that the assembly of the COPII coat on the ER occurs by a sequential binding of coat proteins to specific lipids and that this assembly promotes the budding of COPII-coated vesicles.

We develop a condition-based maintenance optimization approach for the road cracking problem in order to derive an optimal action-planning policy from a Markov decision process that minimizes the expected cost. Our model deals with multiple imperfect actions that consist of different resurfacing thicknesses and that have varying effects on the future deterioration law. We model the deterioration after maintenance to be dependent upon both the state of the road before maintenance and the type of maintenance performed. Another new aspect of our model is the possibility for a maintenance action to render the road to a state better than as-good-as-new. The application of new road layers is, however, constrained by a maximum total road thickness. Some maintenance actions may then become infeasible due to the thickness of the road relative to the constraint. As the road is constrained by a maximum total thickness, the maintenance decision is made complex by the determination of not only how thick of a layer to apply, but also how much old road to remove. We model the road state by two deterioration variables: the longitudinal cracking percentage and its associated growth rate. Our degradation model will take into account the changing road composition which is a function of the successive application of new and removal of old layers.

International audience; Classical spare parts inventory models assume that the same vintage of technology will be utilized throughout the planning horizon. However, asset replacement often occurs in the form of a new technology that renders existing spare parts inventories obsolete. This paper aims to study the impact of spare parts inventory on equipment maintenance and replacement decisions under technological change via a Markov decision process formulation. The replacement decision is complex in that one must decide with which technology available on the market to replace the current asset. Under technological change, it is shown that the do nothing and repair options have significantly more value as they allow waiting the appearance of even better technologies in the future.

This work is a part of a research project for the development of new condition-based approaches for road maintenance optimization. The degradation pattern on interest is here the longitudinal cracking process due to cumulative fatigue (the traffic repetition leads to the occurrence of cracks in the road basement which grows up to the road surface). In this context, we have proposed a new theoretical deterioration model based on two dependent indicators -the observable deterioration measurement and the potential deterioration growth. Even if the construction of the model is based on practical considerations, its application in an operational context remains difficult. The objective of this communication is to study the applicability of such a model and propose some improvements. After analyzing the original model to highlight its strengths and limitations, we propose to revisit the definitions of the decision parameters while specifying the construction of the associated functions. A statistical inference procedure is discussed. A numerical example based on the original maintenance model is presented to illustrate the benefits of this approach that we will present as some “best practices” for future road pavement management.