An Integrated Stochastic Optimization and Simulation Approach to SERU vs. Assembly Line Manufacturing Systems

Day-Ahead scheduling of centralized energy storage system in electrical networks by proposed stochastic MILP-Based bi-objective optimization approach

Summary form only given. This paper presents a stochastic mixed-integer linear programming (SMILP) approach to maximize total expected profit of one price-maker hydro producer in a pool-based electricity market. Head dependence, commitment decisions, discharge ramping, startup costs and forbidden zones are all effectively handled in our approach. Uncertainty about the competitors' offers is adequately represented by residual demand curves (RDCs) scenarios. The management of risk is suitably addressed by conditional value-at-risk (CVaR) to provide the efficient frontier, i.e, the solutions set for which the expected profit may not be augmented without enlarging the variance of profit. Appropriate offering strategies to the pool are also developed, consisting of supply functions built for different risk levels. A representative cascaded hydro system with 7 reservoirs is considered to analyze and compare risk-neutral vs. risk-averse results.

This paper proposes a stochastic optimization approach for disaster preparation in distribution systems. For an upcoming storm, utilities should have a preparation plan that includes warehousing restoration supplies, securing staging sites (depots), and prepositioning crews and equipment. Pre-storm planning enables faster and more efficient post-disaster deployment of crews and equipment resources to damage locations. To assist utilities in making this important preparation, this paper develops a two-stage stochastic mixed integer linear program. The first stage determines the depots, number of crews in each site, and the amount of equipment. The second stage is the recourse action that deals with acquiring new equipment and assigning crews to repair damages in realized scenarios. The objective of the developed model is to minimize the costs of depots, crews, equipment, and penalty costs associated with delays in obtaining equipment and restoration. We consider the uncertainties of damaged lines, number and type of equipment required, and expected repair times. The model is validated on modified IEEE 123-bus distribution test system.

To mitigate outpatient care delivery inefficiencies induced by resource shortages and demand heterogeneity, this paper focuses on the problem of allocating and sequencing multiple medical resources so that patients scheduled for clinical care can experience efficient and coordinated care with minimum total waiting time. We leverage highly granular location data on people and medical resources collected via Real-Time Location System technologies to identify dominant patient care pathways. A novel two-stage Stochastic Mixed Integer Linear Programming model is proposed to determine the optimal patient sequence based on the available resources according to the care pathways that minimize patients’ expected total waiting time. The model incorporates the uncertainty in care activity duration via sample average approximation.We employ a Monte Carlo Optimization procedure to determine the appropriate sample size to obtain solutions that provide a good trade-off between approximation accuracy and computational time. Compared to the conventional deterministic model, our proposed model would significantly reduce waiting time for patients in the clinic by 60%, on average, with acceptable computational resource requirements and time complexity. In summary, this paper proposes a computationally efficient formulation for the multi-resource allocation and care sequence assignment optimization problem under uncertainty. It uses continuous assignment decision variables without timestamp and position indices, enabling the data-driven solution of problems with real-time allocation adjustment in a dynamic outpatient environment with complex clinical coordination constraints.

In recent years, the analysis and evaluation of manufacturing systems’ behaviour and their performance became essential. Discrete event simulation (DES) as a digital enterprise technology is an effective tool both in production related decision support processes and in structure or performance analysis of manufacturing systems. Building a DES model of a manufacturing system is a difficult task and requires special competence. Reducing the efforts spent on draft simulation is the aim of an ongoing research which is presented in the paper. The developed framework simplifies and accelerates the process of model building. A production oriented implementation of the ANSI/ISA-95 standard was used by the proposed modelling methodology to define a generic data structure supporting the creation of models without specific knowledge related to the simulation software applied. The data structure enables the development and application of proprietary simulation engines tailored for specific problems. On the other hand, numerous objective tests containing simulation model validation methods are also announced through a well-described example problem. By this method, several validating key performance indicators (KPIs) were tested, and it was indirectly determined whether the simulation model adequately represents the behaviour of the real system. The automatic simulation model generation in our approach enables using different simulation tools and presented through the examples of both commercial and self-developed software.

This chapter presents the formulation and resolution of a stochastic mixed integer linear programming model for pig production planning. The aim of the model is to optimize the entire pig supply chain according to the number of farms operating for the same company or cooperative. The model maximizes the total revenue calculated from the income of sales to the abattoir and the production costs. Production cost depends on each type of farm involved in the process. Factors like farm capacity, trucks available, reproduction management policies and transportation costs are taken into account. The proposed model considers a medium-term planning horizon and specifically provides optimal transport planning in terms of animals to be transported and number of trucks needed. Uncertainty in sale prices is explicitly incorporated via a finite set of scenarios. The algebraic modelling software OPL Studio, in combination with the solver CPLEX both from IBM ILOG are used to solve the different instances considered. The results of an averaged single scenario (deterministic instance) are useful in assessing the suitability of the stochastic approach. Finally, the conclusions drawn from the study including an outlook are presented.

This paper proposes a stochastic adaptive robust optimization approach to build the bidding curves of an aggregator managing a fleet of electric vehicles (EVs) participating in the day-ahead and intraday electricity markets. These bidding decisions are made hourly, one day in advance, within an uncertain environment. In this context, uncertainties comprise market prices, as well as driving requirements of EV users. These uncertainties are accounted for by using a set of scenarios and confidence bounds, respectively. In this way, this paper combines classic stochastic optimization techniques with adaptive robust optimization, realistically modeling multiple sources of uncertainty. EVs are equipped with vehicle-to-grid technology so that they can both buy and sell energy to the market. The resulting stochastic adaptive robust optimization problem is solved by using the column-and-constraint generation algorithm, which ensures the attainment of the optimal solution in a finite number of steps. Simulations are run by applying CPLEX under GAMS. A case study demonstrates the effectiveness of the proposed approach. Results show that the bidding decisions of the EV aggregator are sensitive to the uncertainty in driving requirements of EVs, which can be controlled through the uncertainty budget. This highlights the usefulness of the proposed approach to prevent the attainment of suboptimal bidding decisions. Moreover, the good performance of the algorithm in terms of obtaining the optimal solution with computational times lower than 6 min suggests potential for model expansion and increased complexity in future works.

In this paper, the two level stochastic optimisation approach has been suggested. In the lower level, the probability distribution functions (pdfs) for bus voltages and branch currents have been determined using the Monte Carlo simulation (MCS) to be employed in chance‐constrained probabilistic optimisation by taking into account solar radiation and power consumption uncertainties in the distribution networks (DNs). In the upper level, artificial hummingbird algorithm (AHA) handles the expected power loss minimisation subjected to chance constraints, which are related to bus voltages and branch currents, by optimising photovoltaic (PV) system capacities. This research examines the effect of uncertainties in PV system performing under diverse solar radiation and varying PV penetration level scenarios on expected power losses with stochastic DN limits. The stochastic optimisation approach has been compared with the deterministic method for observing the efficiency with optimal power usage. This research improves the knowledge base for optimal PV installation in DN by combining AHA with MCS and emphasising chance‐constrained methods. To indicate the efficacy of proposed strategy, the optimisation outcomes are tested utilising MCS under various uncertainty circumstances and DN parameters are assessed in terms of probabilities of exceeding limitations. The results are compared with the application of firefly algorithm (FA) using stochastic assessment and simulations. The simulation results show that the AHA technique outperforms the FA method in terms of effectively minimising power losses with less simulation time.

This paper describes the development of an optimization model that considers the travel times of buses and traffic patterns to minimize deviations from planned schedules for routes across a network in a European city. Traffic congestion is a significant challenge which negatively affects the on-time performance and reliability of bus services. To address this issue, smart traffic management systems such as SCATS (Sydney Coordinated Adaptive Traffic System) are implemented to enhance traffic flow. While SCATS allows buses to request priority at road junctions controlled by traffic signals, conflicts may arise when different buses have opposing requests. To address this, we use a stochastic optimization approach to determine an optimal time-specific traffic interference solution that network owners can impose, such as signal phasing, to minimize schedule deviations for buses throughout the entire network.

A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Kraft pulping mill

Robust optimization of a reactive semibatch distillation process under uncertainty

Improving quality control through chance constrained programming: A case study using Bailey method

The integration of fluctuating renewable energies leads to higher price fluctuations in day-ahead markets, consequently the incentives for the activation of flexible loads increase. Even if relative forecasting errors decrease, the absolute forecasting error of renewable power production is expected to increase, therefore the demand for reserve power will rise. To fully exploit the economic potential of evolving energy markets through the utilization of production process flexibility, multiple markets need to be considered at the same time. Production planning and the participation in the reserve markets can be formulated as a multistage Stochastic Mixed-Integer Linear Programming (SMILP) problem that minimizes the expected total costs, which consists of cost for purchasing power subtracted by the revenues from offering reserve energy. The presented approach incorporates a production process model which considers uncertainties of spot and reserve market prices in terms of a stochastic process.

The unique and complex challenges of sustainable interplanetary travel necessitate a novel space logistics paradigm, which is hereafter referred to as the interplanetary supply chain. To lay the groundwork for this anticipated paradigm shift, this work aims to introduce principles and methodologies of supply chain (SC) planning to the field of space logistics. To this end, a stochastic multistage mixed integer linear programming model is proposed that explores the vision of a permanent human presence on Mars from a logistics standpoint. The model optimizes material flows, propellant production, and in-space infrastructure allocations to identify the optimal SC network design. Notably, demand uncertainty at the Mars base is considered, which makes it the first model to investigate the effects of stochasticity in space logistics planning. Several case studies are investigated, which incorporate data on Earth–Mars transfer windows and flight trajectories obtained through the National Aeronautics and Space Administration’s trajectory browser. The results reveal numerous interdependencies and, thus, highlight the need for holistic SC modeling approaches that go beyond the isolated mission planning of the extant literature. For instance, the findings show frequent interactions among spacecraft as they regularly engage in division of labor regarding cargo and propellant transportation. Moreover, complex propellant supply networks that involve strategic in-space infrastructure allocation and propellant production on celestial bodies are established. Overall, the developed model provides valuable insights into the planning of a sustainable interplanetary travel, offers a new SC perspective, and lays the foundation for future research in this area.

Emergency departments (EDs) are frequently challenged by fluctuating patient volumes and resource constraints, leading to overcrowding and operational inefficiencies. This study presents a framework that combines design thinking and discrete event simulation (DES) to optimize ED processes and improve patient outcomes. By using both methods in conjunction with the design of experiments, the digital twin evaluates the effects of various resource configurations on key performance indicators, such as length of stay (LOS). The model highlights critical resource bottlenecks and explores alternative scenarios to improve ED performance. A case study demonstrates that targeted adjustments in nurse staffing and bed management significantly improve operational productivity and reduce patient LOS. The proposed approach provides hospital administrators with practical information to optimize resource allocation and streamline ED operations, contributing to better patient experience. This methodology is particularly suited for healthcare systems that face different patient demands and limited resources.