2,545 research outputs found

    International Manufacturing Networks Supply Strategy Design Aided by Simulation Tools: An Empirical Study in the Wind Sector

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    [ENG] In the last decades, many manufacturing companies are suffering a trend toward multisite location and greater fragmentation of theirs productive and logistic processes. This fact out lights that the open of boundaries in Eastern Europe and the irruptions of countries in the global trade economy, have forced to develop value added activities such as engineering, purchasing, manufacturing and assembly in different places, even in different countries

    Knowledge-based approach for the integration of the planning and scheduling decision-making levels

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    This work aims to integrate the tactical and operational decision making levels. A typical Scheduling mixed integer linear programming (MILP) model has been solved using several demand scenarios. The results have been analyzed and accordingly the operation cost vs. production levels nonlinear equations have been obtained. The aforementioned equations have been included as constraints of the SC planning problem. Hence, production, inventory and distribution variables along the complete SC have been optimized using a NLP model.Postprint (published version

    Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models

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    Crude oil industry very fast became a strategic industry. Then, optimization of the Crude Oil Supply Chain (COSC) models has created new challenges. This fact motivated me to study the COSC mathematical programming models. We start with a systematic literature review to identify promising avenues. Afterwards, we elaborate three concert models to fill identified gaps in the COSC context, which are (i) joint venture formation, (ii) integrated upstream, and (iii) environmentally conscious design

    Petroleum Refining and Petrochemical Industry Integration and Coordination under Uncertainty

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    Petroleum refining and the petrochemical industry account for a major share in the world energy and industrial market. In many situations, they represent the economy back-bone of industrial countries. Today, the volatile environment of the market and the continuous change in customer requirements lead to constant pressure to seek opportunities that properly align and coordinate the different components of the industry. In particular, petroleum refining and petrochemical industry coordination and integration is gaining a great deal of interest. However, previous research in the field either studied the two systems in isolation or assumed limited interactions between them. The aim of this thesis is to provide a framework for the planning, integration and coordination of multisite refinery and petrochemical networks using proper deterministic, stochastic and robust optimization techniques. The contributions of this dissertation fall into three categories; namely, a) Multisite refinery planning, b) Petrochemical industry planning, and c) Integration and coordination of multisite refinery and petrochemical networks. The first part of this thesis tackles the integration and coordination of a multisite refinery network. We first address the design and analysis of multisite integration and coordination strategies within a network of petroleum refineries through a mixed-integer linear programming (MILP) technique. The integrated network design specifically addresses intermediate material transfer between processing units at each site. The proposed model is then extended to account for model uncertainty by means of two-stage stochastic programming. Parameter uncertainty was considered and included coefficients of the objective function and right-hand-side parameters in the inequality constraints. Robustness is analyzed based on both model robustness and solution robustness, where each measure is assigned a scaling factor to analyze the sensitivity of the refinery plan and the integration network due to variations. The proposed technique makes use of the sample average approximation (SAA) method with statistical bounding techniques to give an insight on the sample size required to give adequate approximation of the problem. The second part of the thesis addresses the strategic planning, design and optimization of a network of petrochemical processes. We first set up and give an overview of the deterministic version of the petrochemical industry planning model adopted in this thesis. Then we extend the model to address the strategic planning, design and optimization of a network of petrochemical processes under uncertainty and robust considerations. Similar to the previous part, robustness is analyzed based on both model robustness and solution robustness. Parameter uncertainty considered in this part includes process yield, raw material and product prices, and lower product market demand. The Expected Value of Perfect Information (EVPI) and Value of the Stochastic Solution (VSS) are also investigated to numerically illustrate the value of including the randomness of the different model parameters. The final part of this dissertation addresses the integration between the multisite refinery system and the petrochemical industry. We first develop a framework for the design and analysis of possible integration and coordination strategies of multisite refinery and petrochemical networks to satisfy given petroleum and chemical product demand. The main feature of the work is the development of a methodology for the simultaneous analysis of process network integration within a multisite refinery and petrochemical system. Then we extend the petroleum refinery and petrochemical industry integration problem to consider different sources of uncertainties in model parameters. Parameter uncertainty considered includes imported crude oil price, refinery product price, petrochemical product price, refinery market demand, and petrochemical lower level product demand. We apply the sample average approximation (SAA) method within an iterative scheme to generate the required scenarios and provide solution quality by measuring the optimality gap of the final solution

    A Review of Production Planning Models: Emerging features and limitations compared to practical implementation

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    In the last few decades, thanks to the interest of industry and academia, production planning (PP) models have shown significant growth. Several structured literature reviews highlighted the evolution of PP and guided the work of scholars providing in-depth reviews of optimization models. Building on these works, the contribution of this paper is an update and detailed analysis of PP optimization models. The present review allows to analyze the development of PP models by considering: i) problem type, ii) modeling approach, iii) development tools, iv) industry-specific solutions. Specifically, to this last point, a proposed industrial solution is compared to emerging features and limitations, which shows a practical evolution of such a system

    Long-term-robust adaptation strategies for reservoir operation considering magnitude and timing of climate change: application to Diyala River Basin in Iraq

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    2020 Spring.Includes bibliographical references.Vulnerability assessment due to climate change impacts is of paramount importance for reservoir operation to achieve the goals of water resources management. This requires accurate forcing and basin data to build a valid hydrology model and assessment of the sensitivity of model results to the forcing data and uncertainty of model parameters. The first objective of this study is to construct the model and identify its sensitivity to the model parameters and uncertainty of the forcing data. The second objective is to develop a Parametric Regional Weather Generator (RP-WG) for use in areas with limited data availability that mimics observed characteristics. The third objective is to propose and assess a decision-making framework to evaluate pre-specified reservoir operation plans, determine the theoretical optimal plan, and identify the anticipated best timeframe for implementation by considering all possible climate scenarios. To construct the model, the Variable Infiltration Capacity (VIC) platform was selected to simulate the characteristics of the Diyala River Basin (DRB) in Iraq. Several methods were used to obtain the forcing data and they were validated using the Kling–Gupta efficiency (KGE) metric. Variables considered include precipitation, temperature, and wind speed. Model sensitivity and uncertainty were examined by the Generalized Likelihood Uncertainty Estimation (GLUE) and the Differential Evolution Adaptive Metropolis (DREAM) techniques. The proposed RP-WG was based on (1) a First-order, Two-state Markov Chain to simulate precipitation occurrences; (2) use of Wilks' technique to produce correlated weather variables at multiple sites with conservation of spatial, temporal, and cross correlations; and (3) the capability to produce a wide range of synthetic climate scenarios. A probabilistic decision-making framework under nonstationary hydroclimatic conditions was proposed with four stages: (1) climate exposure generation (2) supply scenario calculations, (3) demand scenario calculations, and (4) multi-objective performance assessment. The framework incorporated a new metric called Maximum Allowable Time to examine the timeframe for robust adaptations. Three synthetic pre-suggested plans were examined to avoid undesirable long-term climate change impacts, while the theoretical-optimal plan was identified by the Non-dominated Sorting Genetic Algorithm II. The multiplicative random cascade and Schaake Shuffle techniques were used to determine daily precipitation data, while a set of correction equations was developed to adjust the daily temperature and wind speed. The depth of the second soil layer caused most sensitivity in the VIC model, and the uncertainty intervals demonstrated the validity of the VIC model to generate reasonable forecasts. The daily VIC outputs were calibrated with a KGE average of 0.743, and they were free from non-normality, heteroscedasticity, and auto-correlation. Results of the PR-WG evaluation show that it exhibited high values of the KGE, preserved the statistical properties of the observed variables, and conserved the spatial, temporal, and cross correlations among the weather variables at all sites. Finally, risk assessment results show that current operational rules are robust for flood protection but vulnerable in drought periods. This implies that the project managers should pay special attention to the drought and spur new technologies to counteract. Precipitation changes were dominant in flood and drought management, and temperature and wind speed changes effects were significant during drought. The results demonstrated the framework's effectiveness to quantify detrimental climate change effects in magnitude and timing with the ability to provide a long-term guide (and timeframe) to avert the negative impacts
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