► The aim is to design production networks for second generation synthetic biodiesel. ► A multi-period MIP-model is used for location, capacity and technology planning. ► The approach is applied to ...the region of Niedersachsen, Germany. ► We give recommendations for political decision makers and for potential investors.
In the medium-term, second generation synthetic bio-diesel will make an important contribution to sustainable mobility. However, attributed to political, technical, and market related uncertainties, it is still not clear which interest groups will invest in production capacities and which technologies will be used. Hence, a multi-period MIP-model is presented for integrated location, capacity and technology planning for the design of production networks for second generation synthetic bio-diesel. The approach is applied to the region of Niedersachsen, Germany. Principle network configurations are developed for this region considering different scenarios and different risk attitudes of interest groups. As results of the investigation, recommendations are drawn regarding advantageous plant concepts, as well as strategies for the capacity installation. Finally, recommendations for political decision makers as well as for potential investors are deduced.
In addition to regular retail distribution channels, a firm nowadays can avail themselves of such information technology (IT) as the Internet to distribute products directly “on line” (referred to as ...an “e-tail” distribution channel). The mix of retailing with e-tailing has added a new dimension of competition to the firm's distribution channels. The central issue of this competition is the competitive pricing policies between retail and e-tail distribution channels. In this paper, we consider the price competition between these two channels under two market game settings: the Bertrand and the Stackelberg price competition models. In the Bertrand competition, the manufacturer and retailer simultaneously select e-tail and retail price, respectively, while in Stackelberg competition, the manufacturer as a leader selects the e-tail price, then the retailer selects retail price. We obtain both the Bertrand and Stackelberg equilibrium pricing policies, and compare the profit gains under these two competitions. Based on our results, we propose an appropriate strategy for the manufacturer to adopt when adding an e-tail channel. We also show that an optimal wholesale price exists under a different market structure that could be used to encourage the retailer to accommodate the additional e-tail channel.
This paper deals with the problem of joint production, setup and subcontracting control of unreliable manufacturing systems producing two product types. The production requires setups each time it ...switches from one product type to another. Subcontracting is an integral part of the decision-making process due to limited production capacity in existing facility. The objective is to propose an effective control policy for the considered system which simultaneously manages production, setup and subcontracting activities. The complexity of the problem lies in the interaction between internal manufacturing decisions and subcontracting that outsource a part of the production, in a dynamic and stochastic environment. An experimental optimisation approach is adopted to determine the optimal control parameters which minimise the average total cost. Extensive sensitivity analyses are performed to illustrate the robustness and the usefulness of the adopted approach. An in-depth study comparing five control policies across a wide range of system parameters is also conducted. Extended cases closer to reality are also investigated considering elements such as the preventive maintenance and the production of non-conforming products. The best control policy in terms of economic performance is then obtained. Valuable insights providing a better understanding of interactions involving production, setup, and subcontracting are discussed.
The purpose of this paper is to investigate an one-supplier–one-retailer supply chain that experiences a disruption in demand during the planning horizon. While demand uncertainty has long been a ...central research issue in supply chain management, little attention has been given to disruptions once the production plan has been made. In this paper, we show that changes to the original plan induced by a disruption may impose considerable deviation costs throughout the system. One of our general goals is to analyze these costs.
When the production plan and the supply chain coordination scheme are designed in a static manner, as is most often the case, both will have to be adjusted under a disruption scenario. Using wholesale quantity discount policies, we derive conditions under which the supply chain can be coordinated so that the maximum potential profit is realized. Our results are applicable for both centralized and decentralized decision-making.
This paper addresses the problem of finding robust production and maintenance schedules for a single machine with failure uncertainty. Both production and maintenance activities occupy the machine׳s ...capacity, while production depletes the machine׳s reliability and maintenance restores its reliability. Thus, we propose a proactive joint model which simultaneously determines the production scheduling and maintenance policy to optimize the robustness of schedules. Then, a three-Phase heuristic algorithm is devised to solve the mathematic model. Computational results indicate that the performance of solution can be significantly improved using our algorithm compared with the solutions by the traditional way. Furthermore, the balance of quality robustness and solution robustness and the impact of jobs׳ due dates are explored in detail.
Data-driven methods have been revolutionizing the way physicists and engineers handle complex and challenging problems even when the physics is not fully understood. However, these models very often ...lack interpretability. Physics-aware machine learning (ML) techniques have been used to endow proxy models with features closely related to the ones encountered in nature; examples span from material balance to conservation laws. In this study, we proposed a hybrid-based approach that incorporates physical constraints (physics-based) and yet is driven by input/output data (data-driven), leading to fast, reliable, and interpretable reservoir simulation models. To this end, we built on a recently developed deep learning–based reduced-order modeling framework by adding a new step related to information on the input–output behavior (e.g., well rates) of the reservoir and not only the states (e.g., pressure and saturation) matching. A deep-neural network (DNN) architecture is used to predict the state variables evolution after training an autoencoder coupled with a control system approach (Embed to Control—E2C) along with the addition of some physical components (loss functions) to the neural network training procedure. Here, we extend this idea by adding the simulation model output, for example, well bottom-hole pressure and well flow rates, as data to be used in the training procedure. Additionally, we introduce a new architecture to the E2C transition model by adding a new neural network component to handle the connections between state variables and model outputs. By doing this, it is possible to estimate the evolution in time of both the state and output variables simultaneously. Such a non-intrusive data-driven method does not need to have access to the reservoir simulation internal structure, so it can be easily applied to commercial reservoir simulators. The proposed method is applied to an oil–water model with heterogeneous permeability, including four injectors and five producer wells. We used 300 sampled well control sets to train the autoencoder and another set to validate the obtained autoencoder parameters. We show our proxy’s accuracy and robustness by running two different neural network architectures (propositions 2 and 3), and we compare our results with the original E2C framework developed for reservoir simulation.
The long-standing argument that focused operations outperform others stands in contrast to claims about the benefits of broader operational scope. The performance benefits of focus are typically ...attributed to reduced complexity, lower uncertainty, and the development of specialized expertise; the benefits of greater breadth are linked to the economies of scope achieved by sharing common resources, such as advertising or production capacity, across activities. Within the literature on corporate strategy, this tension between focus and breadth is reconciled by the concept of related diversification (i.e., a firm with multiple operating units, each specializing in distinct but related activities). We consider whether there are similar benefits to related diversification
within
an operating unit and examine the mechanism that generates these benefits. Using the empirical context of cardiovascular care within hospitals, we first examine the relationship between a hospital's level of specialization in cardiovascular care and the quality of its clinical performance on cardiovascular patients. We find that, on average, focus has a positive effect on quality performance. We then distinguish between
positive spillovers
and
complementarities
to examine (1) the extent to which a hospital's specialization in areas related to cardiovascular care directly impacts performance on cardiovascular patients (positive spillovers) and (2) whether the marginal benefit of a hospital's focus in cardiovascular care depends on the degree to which the hospital "cospecializes" in related areas (complementarities). In our setting, we find evidence of such complementarities in specialization.
This paper was accepted by Christian Terwiesch, operations management.
Traditional inventory models focus on risk-neutral decision makers, i.e., characterizing replenishment strategies that maximize expected total profit, or equivalently, minimize expected total cost ...over a planning horizon. In this paper, we propose a framework for incorporating risk aversion in multiperiod inventory models as well as multiperiod models that coordinate inventory and pricing strategies. We show that the structure of the optimal policy for a decision maker with exponential utility functions is almost identical to the structure of the optimal risk-neutral inventory (and pricing) policies. These structural results are extended to models in which the decision maker has access to a (partially) complete financial market and can hedge its operational risk through trading financial securities. Computational results demonstrate that the optimal policy is relatively insensitive to small changes in the decision-maker's level of risk aversion.
This paper aims to apply a hybrid method of supplier selection to a well-known Turkish company operating in the appliance industry. The data envelopment analytic hierarchy process (DEAHP) methodology ...developed by Ramanathan, R., (Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process. Computers and Operations Research,
2006
, 33, 1289-1307) was chosen as the survey method. In this method, the data envelopment analysis (DEA) approach is embedded into analytic hierarchy process (AHP) methodology. This research concluded that the DEAHP method outperforms the AHP method for supplier selection despite the findings that the AHP model suggested supplier 1 to be the best supplier, contradicting the suggestion made by the DEAHP model and the real action taken by BEKO in selecting supplier 2. These findings imply that DEAHP criteria reflect closer to the real optimum of the decision made. Drawing on a real case our study has supported Ramanathan's (
2006
) work confirming the view that the DEAHP method provides a better decision than the AHP method for supplier selection. Because the DEAHP model is relatively more cumbersome to apply, its application will be more appropriate for high-value components where stringent purchasing criteria are required. In contrast, AHP would remain to be an appropriate approach for relatively lower value components (C class). The novelty of this research lies in the application of a hybrid approach to a real industry case-the DEAHP method for supplier selection, where little has been done on this subject. This study has dealt with one of the most important subjects in supply chain management providing a better decision for supplier selection using appropriate quantitative approaches.
► We provide statistical inference for DEA estimators of Directional distances. ► Directional DEA estimator share the known properties of the traditional radial DEA estimators. ► We develop ...consistent bootstrap procedures for statistical inference. ► We present some illustrative empirical examples.
In productivity and efficiency analysis, the technical efficiency of a production unit is measured through its distance to the efficient frontier of the production set. The most familiar non-parametric methods use Farrell–Debreu, Shephard, or hyperbolic radial measures. These approaches require that inputs and outputs be non-negative, which can be problematic when using financial data. Recently, Chambers et al. (1998) have introduced directional distance functions which can be viewed as additive (rather than multiplicative) measures efficiency. Directional distance functions are not restricted to non-negative input and output quantities; in addition, the traditional input and output-oriented measures are nested as special cases of directional distance functions. Consequently, directional distances provide greater flexibility. However, until now, only free disposal hull (FDH) estimators of directional distances (and their conditional and robust extensions) have known statistical properties (Simar and Vanhems, 2012). This paper develops the statistical properties of directional d estimators, which are especially useful when the production set is assumed convex. We first establish that the directional Data Envelopment Analysis (DEA) estimators share the known properties of the traditional radial DEA estimators. We then use these properties to develop consistent bootstrap procedures for statistical inference about directional distance, estimation of confidence intervals, and bias correction. The methods are illustrated in some empirical examples.
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