Modeling the Crude Oil Scheduling Problem with Integration with Lower Level Production Optimization and Uncertainty

This research is focused on the modeling and optimization of the crude oil scheduling problem in order to generate the most appropriate schedule for the unloading, charging, blending, and movement of crude oil in a refinery, which means obtaining the schedule that generates the lowest costs. Uncertainty, which is often present in these types of optimization problems, is also analyzed and taken into account for the resolution of crude oil scheduling problem. A comprehensive novel model is proposed to describe the upper level crude oil scheduling problem, generate an optimal solution for the mentioned problem, and allow integration with the lower level production optimization problem of a refinery. This integration is possible due to the consideration of total flows of the different types of crude oil instead of flows of a particular key component in the crude oil to linearize the upper level problem and generate a less complex model. The proposed approach incorporates all the logistical costs including the sea waiting, unloading and inventory costs together with the costs associated with the transfer of crude oil from one to another entity. Moreover, this model also offers the possibility of considering multiple tank types including storage and blending tanks throughout the supply chain and the incorporation of the capability of storing more than one crude oil type in the storage tanks during the schedule horizon. A comparative analysis is performed against other models proposed and preliminary results of integration with a lower operational level are provided. In order to take into account the possibility of uncertainty or fuzziness in the scheduling problem, for the first time an approach is proposed to face the resolution of this problem in order to obtain a more realistic scheduling of the allocations of crude oil. Fuzzy linear programming theory is used here to represent this uncertainty in order to find an optimal solution that takes into account the lack of precise information on the part of the decision maker without losing the linearity of the original system. Uncertainty in the minimum demand to be satisfied in the distillation unit according to the necessities of the market and the lack of precise information about certain costs involved in the operations throughout the supply chain are separately considered. Among the different approaches utilized in fuzzy linear programming, the flexible programming or Zimmermann method and its extension to fuzziness in objective functions are implemented. A comparison between the two cases studied and the crisp model is performed with the aim of determining the effect of these uncertainties in the schedule of the crude oils movements between the different entities in the supply chain and the total cost generated

Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

[EN] Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.This research was initiated within the framework of the project funded by the Ministerio de Economía y Competitividad [Ref. DPI2011-23597] entitled ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) already finished. After, the project leading to this application has received funding from the European Union’s research and innovation programme under the H2020 Marie Skłodowska-Curie Actions with the grant agreement No 691249, Project entitled ’Enhancing and implementing Knowledge based ICT solutions within high Riskand Uncertain Conditions for Agriculture Production Systems’ (RUC-APS).Mundi, I.; Alemany Díaz, MDM.; Poler, R.; Fuertes-Miquel, VS. (2019). 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Multi-objective optimisation under deep uncertainty

Most of the decisions in real-life problems need to be made in the absence of complete knowledge about the consequences of the decision. Furthermore, in some of these problems, the probability and/or the number of different outcomes are also unknown (named deep uncertainty). Therefore, all the probability-based approaches (such as stochastic programming) are unable to address these problems. On the other hand, involving various stakeholders with different (possibly conflicting) criteria in the problems brings additional complexity. The main aim and primary motivation for writing this thesis have been to deal with deep uncertainty in Multi-Criteria Decision-Making (MCDM) problems, especially with long-term decision-making processes such as strategic planning problems. To achieve these aims, we first introduced a two-stage scenario-based structure for dealing with deep uncertainty in Multi-Objective Optimisation (MOO)/MCDM problems. The proposed method extends the concept of two-stage stochastic programming with recourse to address the capability of dealing with deep uncertainty through the use of scenario planning rather than statistical expectation. In this research, scenarios are used as a dimension of preference (a component of what we term the meta-criteria) to avoid problems relating to the assessment and use of probabilities under deep uncertainty. Such scenario-based thinking involved a multi-objective representation of performance under different future conditions as an alternative to expectation, which fitted naturally into the broader multi-objective problem context. To aggregate these objectives of the problem, the Generalised Goal Programming (GGP) approach is used. Due to the capability of this approach to handle large numbers of objective functions/criteria, the GGP is significantly useful in the proposed framework. Identifying the goals for each criterion is the only action that the Decision Maker (DM) needs to take without needing to investigate the trade-offs between different criteria. Moreover, the proposed two-stage framework has been expanded to a three-stage structure and a moving horizon concept to handle the existing deep uncertainty in more complex problems, such as strategic planning. As strategic planning problems will deal with more than two stages and real processes are continuous, it follows that more scenarios will continuously be unfolded that may or may not be periodic. "Stages", in this study, are artificial constructs to structure thinking of an indefinite future. A suitable length of the planning window and stages in the proposed methodology are also investigated. Philosophically, the proposed two-stage structure always plans and looks one step ahead while the three-stage structure considers the conditions and consequences of two upcoming steps in advance, which fits well with our primary objective. Ignoring long-term consequences of decisions as well as likely conditions could not be a robust strategic approach. Therefore, generally, by utilising the three-stage structure, we may expect a more robust decision than with a two-stage representation. Modelling time preferences in multi-stage problems have also been introduced to solve the fundamental problem of comparability of the two proposed methodologies because of the different time horizon, as the two-stage model is ignorant of the third stage. This concept has been applied by a differential weighting in models. Importance weights, then, are primarily used to make the two- and three-stage models more directly comparable, and only secondarily as a measure of risk preference. Differential weighting can help us apply further preferences in the model and lead it to generate more preferred solutions. Expanding the proposed structure to the problems with more than three stages which usually have too many meta-scenarios may lead us to a computationally expensive model that cannot easily be solved, if it all. Moreover, extension to a planning horizon that too long will not result in an exact plan, as nothing in nature is predictable to this level of detail, and we are always surprised by new events. Therefore, beyond the expensive computation in a multi-stage structure for more than three stages, defining plausible scenarios for far stages is not logical and even impossible. Therefore, the moving horizon models in a T-stage planning window has been introduced. To be able to run and evaluate the proposed two- and three-stage moving horizon frameworks in longer planning horizons, we need to identify all plausible meta-scenarios. However, with the assumption of deep uncertainty, this identification is almost impossible. On the other hand, even with a finite set of plausible meta-scenarios, comparing and computing the results in all plausible meta-scenarios are hardly possible, because the size of the model grows exponentially by raising the length of the planning horizon. Furthermore, analysis of the solutions requires hundreds or thousands of multi-objective comparisons that are not easily conceivable, if it all. These issues motivated us to perform a Simulation-Optimisation study to simulate the reasonable number of meta-scenarios and enable evaluation, comparison and analysis of the proposed methods for the problems with a T-stage planning horizon. In this Simulation-Optimisation study, we started by setting the current scenario, the scenario that we were facing it at the beginning of the period. Then, the optimisation model was run to get the first-stage decisions which can implement immediately. Thereafter, the next scenario was randomly generated by using Monte Carlo simulation methods. In deep uncertainty, we do not have enough knowledge about the likelihood of plausible scenarios nor the probability space; therefore, to simulate the deep uncertainty we shall not use anything of scenario likelihoods in the decision models. The two- and three-stage Simulation-Optimisation algorithms were also proposed. A comparison of these algorithms showed that the solutions to the two-stage moving horizon model are feasible to the other pattern (three-stage). Also, the optimal solution to the three-stage moving horizon model is not dominated by any solutions of the other model. So, with no doubt, it must find better, or at least the same, goal achievement compared to the two-stage moving horizon model. Accordingly, the three-stage moving horizon model evaluates and compares the optimal solution of the corresponding two-stage moving horizon model to the other feasible solutions, then, if it selects anything else it must either be better in goal achievement or be robust in some future scenarios or a combination of both. However, the cost of these supremacies must be considered (as it may lead us to a computationally expensive problem), and the efficiency of applying this structure needs to be approved. Obviously, using the three-stage structure in comparison with the two-stage approach brings more complexity and calculations to the models. It is also shown that the solutions to the three-stage model would be preferred to the solutions provided by the two-stage model under most circumstances. However, by the "efficiency" of the three-stage framework in our context, we want to know that whether utilising this approach and its solutions is worth the expense of the additional complexity and computation. The experiments in this study showed that the three-stage model has advantages under most circumstances(meta-scenarios), but that the gains are quite modest. This issue is frequently observed when comparing these methods in problems with a short-term (say less than five stages) planning window. Nevertheless, analysis of the length of the planning horizon and its effects on the solutions to the proposed frameworks indicate that utilising the three-stage models is more efficient for longer periods because the differences between the solutions of the two proposed structures increase by any iteration of the algorithms in moving horizon models. Moreover, during the long-term calculations, we noticed that the two-stage algorithm failed to find the optimal solutions for some iterations while the three-stage algorithm found the optimal value in all cases. Thus, it seems that for the planning horizons with more than ten stages, the efficiency of the three-stage model be may worth the expenses of the complexity and computation. Nevertheless, if the DM prefers to not use the three-stage structure because of the complexity and/or calculations, the two-stage moving horizon model can provide us with some reasonable solutions, although they might not be as good as the solutions generated by a three-stage framework. Finally, to examine the power of the proposed methodology in real cases, the proposed two-stage structure was applied in the sugarcane industry to analyse the whole infrastructure of the sugar and bioethanol Supply Chain (SC) in such a way that all economics (Max profit), environmental (Min CO₂), and social benefits (Max job-creations) were optimised under six key uncertainties, namely sugarcane yield, ethanol and refined sugar demands and prices, and the exchange rate. Moreover, one of the critical design questions - that is, to design the optimal number and technologies as well as the best place(s) for setting up the ethanol plant(s) - was also addressed in this study. The general model for the strategic planning of sugar- bioethanol supply chains (SC) under deep uncertainty was formulated and also examined in a case study based on the South African Sugar Industry. This problem is formulated as a Scenario-Based Mixed-Integer Two-Stage Multi-Objective Optimisation problem and solved by utilising the Generalised Goal Programming Approach. To sum up, the proposed methodology is, to the best of our knowledge, a novel approach that can successfully handle the deep uncertainty in MCDM/MOO problems with both short- and long-term planning horizons. It is generic enough to use in all MCDM problems under deep uncertainty. However, in this thesis, the proposed structure only applied in Linear Problems (LP). Non-linear problems would be an important direction for future research. Different solution methods may also need to be examined to solve the non-linear problems. Moreover, many other real-world optimisation and decision-making applications can be considered to examine the proposed method in the future

Petroleum Refining and Petrochemical Industry Integration and Coordination under Uncertainty

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

Strategic development in the petrochemical industry

Imperial Users onl

Petroleum refinery scheduling with consideration for uncertainty

Scheduling refinery operation promises a big cut in logistics cost, maximizes efficiency, organizes allocation of material and resources, and ensures that production meets targets set by planning team. Obtaining accurate and reliable schedules for execution in refinery plants under different scenarios has been a serious challenge. This research was undertaken with the aim to develop robust methodologies and solution procedures to address refinery scheduling problems with uncertainties in process parameters. The research goal was achieved by first developing a methodology for short-term crude oil unloading and transfer, as an extension to a scheduling model reported by Lee et al. (1996). The extended model considers real life technical issues not captured in the original model and has shown to be more reliable through case studies. Uncertainties due to disruptive events and low inventory at the end of scheduling horizon were addressed. With the extended model, crude oil scheduling problem was formulated under receding horizon control framework to address demand uncertainty. This work proposed a strategy called fixed end horizon whose efficiency in terms of performance was investigated and found out to be better in comparison with an existing approach. In the main refinery production area, a novel scheduling model was developed. A large scale refinery problem was used as a case study to test the model with scheduling horizon discretized into a number of time periods of variable length. An equivalent formulation with equal interval lengths was also presented and compared with the variable length formulation. The results obtained clearly show the advantage of using variable timing. A methodology under self-optimizing control (SOC) framework was then developed to address uncertainty in problems involving mixed integer formulation. Through case study and scenarios, the approach has proven to be efficient in dealing with uncertainty in crude oil composition

A CHANCE-CONSTRAINED APPROACH FOR OPTIMIZATION OF GAS PROCESSING PLANT OPERATION UNDER UNCERTAIN CONDITIONS

Natural gas plant operations contribute hugely to the economies of many developed nations that depend on hydrocarbon resources. The plant operation is usually subjected to continuous variations in upstream conditions, such as flow rate, composition, temperature and pressure, which propagate through the plant and affect its stable operations. As a result, decision making for optimal operating conditions of an in-operation plant is a complex problem and it is exacerbated with the changing product specifications and variations in energy supplies. This work presents a new solution method to the problem, which is based on chance constrained optimization method. A deterministic model is initially developed from process simulation using Aspen HYSYS and later converted to a chance constrained model. The probabilistic model is then relaxed to its equivalent deterministic form and solved for optimum solution using GAMS. The optimum solution is determined probabilistically using chance constraints that are held at a user-defined confidence level. Optimal solution is represented graphically as a trade-off between reliability of holding the process constraints and profitability of the plant. Three case studies are presented to demonstrate the new method. Optimization results show that uncertainty of plant parameters significantly affect the economic performance of the plant operation. The solution approach developed in this work is able to increase the reliability of maintaining the profit by more than 95% confidence level. As a result, the risk of constraints violation is reduced from more than 50% using the typical deterministic optimization to less than 5% with the chance constrained optimization approach. In addition, the results from this study indicate that the variation of material flow from the plant inlet has greater impact by more than 85.5% on profit compared to variation from the plant outlet, which is less than 2%. The variations of energy flow affect on profit is mainly changes with confidence level measurement higher than 95%, although material flow uncertainty is more sensitive to profit changes than uncertainty in energy flow. Final computational results also highlight the advantage of the developed chance constrained approach, which combines both the profit and the vi reliability of the process constraints, over “worst case” and two-stage programming approaches. Decisions from the “worst case” approach may reach to more than 99% confidence level which can drastically decrease the profit while the optimal decision from the two-stage programming does not clearly show to how much extent that the profit has been held. The developed solution approach in this work can aid as guidelines to flexible plant operation decision making for the in-operating plant by satisfying all the process constraints at certain confidence level