Multi-period Scenarios Research Articles

Design of closed-loop manufacturing-remanufacturing system network considering uncertain defect and waste rate: Scenario-based chance-constrained programming

To facilitate sustainability by extending the life-cycle and residual value of production materials, a collaborate scheduling scheme for closed-loop manufacturing system with disturbance uncertainty is studied. Based on the features of the closed-loop manufacturing system under multi-product and multi-period scenarios, a mixed-integer linear optimization model is developed, where manufacturing and remanufacturing units are coordinated by recycling the production materials from work-in-process and final products. Based on this, a novel closed-loop manufacturing-remanufacturing scheduling framework is proposed. Additionally, in response to variations in product quality and processing capabilities, a scenario-based chance-constrained programming is introduced to address potential sources of uncertainty. Subsequently, a sequential optimization method is devised to reduce computational complexity. Finally, an industrial case from the electronic factory is investigated to demonstrate its applicability by implementing the proposed approach with comparison to several other strategies.

The Efficacy of Multi-Period Long-Term Power Transmission Network Expansion Model with Penetration of Renewable Sources

The electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialisation, population growth, urbanisation and, of course, the evolution of modern technologies in this 4th industrial revolution era. Such a rapid increase in energy demand introduces a huge challenge into the power system, which has paved way for network operators to seek alternative energy resources other than the conventional fossil fuel system. Hence, the penetration of renewable energy into the electricity supply mix has evolved rapidly in the past three decades. However, the grid system has to be well planned ahead to accommodate such an increase in energy demand in the long run. Transmission Network Expansion Planning (TNEP) is a well ordered and profitable expansion of power facilities that meets the expected electric energy demand with an allowable degree of reliability. This paper proposes a DC TNEP model that minimises the capital costs of additional transmission lines, network reinforcements, generator operation costs and the costs of renewable energy penetration, while satisfying the increase in demand. The problem is formulated as a mixed integer linear programming (MILP) problem. The developed model was tested in several IEEE test systems in multi-period scenarios. We also carried out a detailed derivation of the new non-negative variables in terms of the power flow magnitudes, the bus voltage phase angles and the lines’ phase angles for proper mixed integer variable decomposition techniques. Moreover, we intend to provide additional recommendations in terms of in which particular year (within a 20 year planning period) can the network operators install new line(s), new corridor(s) and/or additional generation capacity to the respective existing power networks. This is achieved by running incremental period simulations from the base year through the planning horizon. The results show the efficacy of the developed model in solving the TNEP problem with a reduced and acceptable computation time, even for large power grid system.

Dynamic Optimal Power Flow of Active Distribution Network Based on LSOCR and Its Application Scenarios

Optimal power flow (OPF) is a crucial aspect of distribution network planning and operation. Conventional heuristic algorithms fail to meet the system requirements for speed and accuracy, while linearized OPF approaches are inadequate for distribution networks with high R/X ratios. To address these issues and cater to multi-period scenarios, this study proposes a dynamic linearized second-order cone programming-based (SOCP) OPF model. The model is built by first establishing a dynamic OPF model based on linearized second-order conic relaxation (LSOCR-DOPF). The components of the active distribution network, such as renewable energy power generation units, energy storage units, on-load-tap-changers, static var compensators, and capacitor banks, are then separately modeled. The model is implemented in MATLAB and solved by YALMIP and GUROBI. Finally, three representative scenarios are used to evaluate the model accuracy and effectiveness. The results show that the proposed LSOCR-DOPF model can ensure calculation time within 3 min, voltage stability, and error control within 10−6 for all three applications. This method has strong practical value in the fields of active distribution network day-ahead dispatch, accurate modeling of ZIP load, and real-time operation.

An integrated methodology for the selection of sustainable suppliers and order allocation problem with quantity discounts, lost sales and varying supplier availabilities

In this study, selection of sustainable suppliers (SSS) and order allocation (OA) problem is generalized by considering multi-product, multi-supplier and multi-period scenarios with quantity discounts, lost sales and varying supplier availabilities. The study is designed in four phases. In the first phase, suppliers are ranked by using integrated the Fuzzy Analytic Hierarchy Process (F-AHP) and fuzzy Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE) considering social and environmental factors as well as economic factors. The output of the first phase is used in the second phase of the method. In the second phase, a fuzzy multi-objective optimization (FMOO) model is proposed to find quantities to be purchased from suppliers. In the third phase, both the Augmented ε-constraint (AUGMECON) method and LP-metrics are used to obtain the Pareto front of the problem. Finally, in the fourth phase, a solution is selected by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) from the non-dominated solutions. The effectiveness of the proposed method is shown on real data from a firm selling medical devices.

Strategic Scheduling of Discrete Control Devices in Active Distribution Systems

The frequent actuation of discrete control devices dcds, e.g., on-load tap changers, drastically reduces their lifetime. This, in turn, imposes a huge replacement cost. Simultaneous scheduling of these \textsc{dcd}s and continuous control devices, e.g., distributed energy resources, is imperative for reducing the operating cost. This also increases the lifetime of \textsc{dcd}s and helps to avoid the sub-optimal/infeasible solutions. Considering the high cost of discrete control actions (dcas), they may never be justified against the other options in a short scheduling horizon (sh). With a longer sh, their benefits over a long period justify dcas. However, a shorter \textsc{sh} helps to hedge against the risk impelled by uncertainties. Here, the system's future is modeled as a set of multi-period scenarios. The operator exploits a long sh, but solely applies the decisions made for the first period and waits for updated data to make the next decisions. This enables cost reduction by strategically applying dcas prior to the time that they are inevitable while avoiding them when unneeded. The proposed branch-and-cut-based solution methodology accurately deals with dcas while applying some expediting heuristics. During the branching process, a globally convergent trust-region algorithm solves the integer relaxed problems.

Optimal sourcing policies for single and multiple period scenarios

Determining the optimum number of suppliers and the optimum quantities to order from each of them is a critical problem for any supply chain. The objective of this paper is to identify the optimal sourcing policy of a retailer for the single and multi-period context when the firm can source its order to multiple suppliers along with a back-up supplier for the emergency situations. The expected total profit is mathematically modelled for single and multi-period scenarios. The optimal sourcing policy is obtained by maximising the expected total profit with respect to the order quantities. Closed form solution is obtained for uniformly distributed demand for both single and multi-period scenarios. It is observed that the multi-period solution is less sensitive compared to the single-period solution. Also it is found that it is optimal for the firm to lessen the amount of supplier diversification in case of planning for multiple periods.

Optimal sourcing policies for single and multiple period scenarios

Determining the optimum number of suppliers and the optimum quantities to order from each of them is a critical problem for any supply chain. The objective of this paper is to identify the optimal sourcing policy of a retailer for the single and multi-period context when the firm can source its order to multiple suppliers along with a back-up supplier for the emergency situations. The expected total profit is mathematically modelled for single and multi-period scenarios. The optimal sourcing policy is obtained by maximising the expected total profit with respect to the order quantities. Closed form solution is obtained for uniformly distributed demand for both single and multi-period scenarios. It is observed that the multi-period solution is less sensitive compared to the single-period solution. Also it is found that it is optimal for the firm to lessen the amount of supplier diversification in case of planning for multiple periods.

Deterministic approach for active distribution networks planning with high penetration of wind and solar power

In this paper, a novel deterministic approach for the planning of active distribution networks within a distribution market environment considering multi-configuration of wind turbines (WTs) and photovoltaic (PV) cells is proposed. Multi-configuration multi-period market-based optimal power flow is utilized for maximizing social welfare taking into account uncertainties associated with wind speed, solar irradiance and load demand as well as different operational status of WTs and PVs. Multi-period scenarios method is exploited to model the aforementioned uncertainties. The proposed approach assesses the effect of multiple-configuration of PVs and WTs on the amount of wind and solar power that can be produced, the distribution locational marginal prices all over the network and on the social welfare. The application of the proposed approach is examined on a 30-bus radial distribution network.

Evaluating Modular Node Architectures With Limited Shelf Interconnection [Invited

In order to simplify and speed up optical network dimensioning, the client-to-line ports interconnection restrictions imposed by the node architecture deployed are typically disregarded. However, as some node architectures can impose restrictions in the client-to-line ports interconnection, some grooming optimizations may be precluded. This is particularly relevant in multi-period scenarios where client traffic is churned between periods. In this case, depending on the node architecture deployed, hitless client traffic churn can be impossible to perform and manual intervention is required. In this work, we present an integer linear programming-based framework that minimizes the number of modules required considering the client-to-line interconnection restrictions. Moreover, the model also computes the client traffic churn blocking probability. The four node architectures considered are nonblocking electrical cross connect (EXC), standalone EXCs, EXCs with extendable backplanes, and EXCs with interconnection via high bit rate client ports. Using the proposed models, we evaluate the impact of node architecture flexibility on the probability of blocking traffic. The results show that EXCs with interconnection via high bit rate client ports tend to reduce the percentage of blocked traffic compared to the remaining modular architectures when the number of required shelves is higher than 2.

Traffic Grooming Policies Under Switching Constraints in Next-Generation Transport Networks

Network planning evolves along with the developments in optical transport technology. In the current ecosystem, network elements are required to be increasingly flexible in order to respond to changing requirements more rapidly. Transmission modules such as the sliceable bandwidth-variable transponder (SBVT) are a step forward in this direction, enabling us to adapt line-rates at the same time that multi-directionality is assured. These modules are applicable to multiple networking scenarios, which may have very diverse constraints on the type of client-layer flexibility that supports the SBVTs. In particular, the switching capacity available for traffic grooming operations can have a significant impact on the overall efficiency of this type of deployment. This paper proposes a framework to optimize multi-period planning scenarios for SBVT-enabled optical networks, which is utilized to study the interaction between the transport and client layers under different grooming and switching conditions. The analysis reveals that encouraging multi-hop grooming at the expense of further usage of client interfaces is beneficial in scenarios where switching capacity is most constrained.

Optimizing Gasoline Recipes and Blending Operations Using Nonlinear Blend Models

Gasoline is one of the largest-volume products of the oil industry that yields 60%–70% of the total refinery revenues. This work presents a novel continuous-time mixed-integer nonlinear programming (MINLP) formulation for the gasoline blend scheduling problem. It incorporates nonlinear blending correlations for an improved prediction of key blend properties, and nonlinear constraints for precisely tracking the inventory level in product tanks when multiple blenders are operated. The approach handles nonidentical blenders, multipurpose tanks, sequence-dependent changeovers, limited amounts of gasoline components, and multiperiod scenarios with component flow rates changing with the period. Operating rules for blenders and product/component tanks are also considered. A special model feature is the use of floating slots dynamically allocated to time periods while solving the problem. An approximate mixed-integer linear programming (MILP) formulation assuming ideal mixing provides a good initial point. By fix...

Systematic Multi-Period Stress Scenarios with an Application to CCP Risk Management

Systematic Multi-Period Stress Scenarios with an Application to CCP Risk Management

Deriving three-way decisions from intuitionistic fuzzy decision-theoretic rough sets

Three-way decisions with decision-theoretic rough sets (DTRSs) provide a new methodology to confront risk decision problems. The risk is associated with the loss function of DTRSs. Under the intuitionistic fuzzy environment, we combine the loss functions of DTRSs with intuitionistic fuzzy sets (IFSs). Considering the new evaluation format of loss function with intuitionistic fuzzy numbers (IFNs), we propose a naive model of intuitionistic fuzzy decision-theoretic rough sets (IFDTRSs) and elaborate its relevant properties in advance. At this point, a critical issue is the determination of three-way decisions. In the frame of IFDTRSs, we then explore deriving three-way decisions for single-period decision making. Based on the positive and negative characteristics of IFNs, we design three strategies to address IFNs and derive corresponding three-way decisions. Meanwhile, we compare the three strategies and summarize their own applicabilities. In order to accommodate multi-period scenarios, we further extend IFDTRSs to the multi-period situation. With the aid of the results of the single period decision making, we analyze three aggregation operations of IFDTRSs for multi-period information, which are DIFWA, DIFPA and DIFOA, respectively. By comparing these operations, an algorithm for deriving three-way decisions in multi-period decision making is designed. These results help us to make a reasonable decision in the intuitionistic fuzzy environment. Finally, an example is presented to elaborate on three-way decisions with IFDTRSs.

Planning Active Distribution Networks Considering Multi-DG Configurations

Planning distribution systems without considering the operation status of multiple distributed generation (DG) units could result in constraining the network, lowering the utilization of its assets and minimizing the total DG capacity that can be accommodated. In this paper, the impact of multiple DG configurations on the potential of active network management (ANM) schemes is firstly investigated. Secondly, the paper proposes a multi-configuration multi-period optimal power flow (OPF)-based technique (MMOPF) for assessing the maximum DG capacity under ANM schemes considering 1) variability of demand and generation profiles (multi-period scenarios), and 2) different operational status of DG units (multi-configurations). The results show that the availability of DGs at certain locations could critically impact the amount of DG capacity at other locations. If DGs are properly allocated and sized at certain locations up to the optimal limits, even with a “fit-and-forget” approach, the total connected DG capacity can be maximized, with minimum utilization of ANM schemes. However, exceeding these optimal limits may lead to minimizing the total DG penetration in the long term, impacting the system reliability due to the operational status of multiple DG units, and consequently, imposing more investments on ANM schemes to increase the amount of connected DG capacity.

A methodology for creating sequential multi-period base-case scenarios for large data sets

Key performance indicators in engineering problems include but are not limited to financial, operational, management and environmental factors, which are significantly affected by aspects such as seasonality, fouling, economic climate, production rates, supply and demand. The search for an optimal solution to a problem must take into consideration this variability, otherwise running the risk of critical dimensioning or cost estimation errors. Testing solutions using full data sets covering large periods of time can be a computational challenge, and the analysis of results complicated. For the feasibility of such a study, it is therefore necessary to reduce the large data sets to a number of base case scenarios, which simultaneously reduce the number of data points to be handled while still representing the variability of the system. A novel method is therefore developed to address this problem. This method offers a way of designing an index of sequential periods common to each production level, which when averaged accurately represent periods of nominal values for each level. The method exploits a multi-objective evolutionary algorithm, minimising the standard deviation of the base cases compared to the real data as well as respecting crucial null value periods. Null value periods are typically found in turnarounds or supply and demand problems and are usually incorrectly represented in other methods. Lastly, the resulting base cases are sequential periods, which is important when dealing with scheduling, shutdown or storage problems. The method is tested using anonymised data and is compared to previously existing methods, with results showing improvement in the performance of the base cases with respect to the objective functions.

Targeting for carbon sequestration retrofit planning in the power generation sector for multi-period problems

Carbon constrained energy planning (CCEP) is useful to ensure that the CO2 emissions limit for a region is met through deployment of low-carbon technologies. The increased demand in energy consumption due to economic growth requires additional energy supply and generation which would subsequently increase the carbon emissions. Nevertheless, most countries are now committed to reduce carbon emission to achieve long term sustainability goals. However, the development of alternative energy sources or carbon capture and storage (CCS) initiatives for power plants entails major capital investments. This paper demonstrates how these issues may be handled using CCEP with insight- and optimisation-based targeting techniques for multi-period scenarios. Both approaches were developed recently for CCEP problems, but previous techniques were limited to single-period planning. The extensions to multi-period scenarios are demonstrated in this work with hypothetical illustrative examples, as well as a Malaysian case study.

A Mixed-Integer Linear Approach for Assessing the Impact of Bilateral Contracts in a Combined Energy Market Operating Under Payment Minimization

A combined market model of energy and reserve services operating under Payment Minimization (PM) is formulated through a mixed-integer linear model. The combined market model considers the simultaneous presence of long-term bilateral contracts and the short-term market of energy and several reserve services. In this context, a common standard framework allows establishing comparisons between the PM model and the usual bid cost minimization model in terms of obtained allocated levels, prices of services, payments, revenues, bid costs and agent financial portfolios. The suggested extended PM model has several important characteristics such as considering the system transmission network, generation and transmission capacity limits, generator ramp limits and multi-period short-term markets. The behavior of the system operation and resulting economic indexes are evaluated and compared using operation scenarios. The model is tested in the IEEE 30-Bus system including multi-period scenarios.

On process optimization considering LCA methodology

The goal of this work is to research the state-of-the-art in process optimization techniques and tools based on LCA, focused in the process engineering field. A collection of methods, approaches, applications, specific software packages, and insights regarding experiences and progress made in applying the LCA methodology coupled to optimization frameworks is provided, and general trends are identified. The "cradle-to-gate" concept to define the system boundaries is the most used approach in practice, instead of the "cradle-to-grave" approach. Normally, the relationship between inventory data and impact category indicators is linearly expressed by the characterization factors; then, synergic effects of the contaminants are neglected. Among the LCIA methods, the eco-indicator 99, which is based on the endpoint category and the panel method, is the most used in practice. A single environmental impact function, resulting from the aggregation of environmental impacts, is formulated as the environmental objective in most analyzed cases. SimaPro is the most used software for LCA applications in literature analyzed. The multi-objective optimization is the most used approach for dealing with this kind of problems, where the ε-constraint method for generating the Pareto set is the most applied technique. However, a renewed interest in formulating a single economic objective function in optimization frameworks can be observed, favored by the development of life cycle cost software and progress made in assessing costs of environmental externalities. Finally, a trend to deal with multi-period scenarios into integrated LCA-optimization frameworks can be distinguished providing more accurate results upon data availability.

Operational optimization of the utility system of an oil refinery

The objective of this work is to develop a mathematical programming model applied to the operational planning of the utility plant of the RECAP Refinery (São Paulo, Brazil), as well as its interconnections with the process units. The problem is formulated as a mixed-integer linear programming (MILP) model where the mass and energy balances, the operational status of each unit, and the demand satisfaction are defined in multiple time periods. The model determines the operational configuration of the plant by minimizing utility costs, and identifies steam losses as well as inefficient units by comparing the optimal solutions with the current operation. The MILP is able to accurately represent the topology and optimize the operation of the real-world system under different utility demands and abnormal situations in single and multiperiod scenarios, achieving up to 10% cost reduction. The MILP is currently integrated with the plant database and used for the planning of the refinery utility system.

Managing New and Remanufactured Products

We study a firm that makes new products in the first period and uses returned cores to offer remanufactured products, along with new products, in future periods. We introduce the monopoly environment in two-period and multiperiod scenarios to identify thresholds in remanufacturing operations. Next, we focus our attention on the duopoly environment where an independent operator (IO) may intercept cores of products made by the original equipment manufacturer (OEM) to sell remanufactured products in future periods. We characterize the production quantities associated with self-selection and explore the effect of various parameters in the Nash equilibrium. Among other results, we find that if remanufacturing is very profitable, the original-equipment manufacturer may forgo some of the first-period margin by lowering the price and selling additional units to increase the number of cores available for remanufacturing in future periods. Further, as the threat of competition increases, the OEM is more likely to completely utilize all available cores, offering the remanufactured products at a lower price.