The utility system is a crucial power source for chemical production processes, and the sustainable utility system is one of the key research topics in the field of energy and chemical engineering. To reduce the carbo...
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The utility system is a crucial power source for chemical production processes, and the sustainable utility system is one of the key research topics in the field of energy and chemical engineering. To reduce the carbon emissions of the system, this study integrates the utility system with renewable energy and energy storage devices, transforming it into a sustainable utility system. To address the impact of multiscale uncertainties in renewable energy supply and steam demand on system decision optimization, this study develops a two-stage hybrid interval-stochastic programming method using stochastic intervals to model multiscale uncertainties to enhance modeling flexibility and reduce computational time. In the first stage, the capacities of renewable energy and storage systems are planned. The second stage involves solving an optimization problem under the uncertainties of renewable energy. The stochastic behavior of wind speed, solar irradiance, and steam demand is captured using scenario trees in the stochastic programming framework. In constructing the scenario tree, uncertainties are modeled by combining stochastic intervals. A risk coefficient is defined for the approximate representation of stochastic intervals to address the challenge of solving interval uncertainties while ensuring the flexibility of steam and power generation among the utility system, renewable energy system, and storage devices. Finally, a case study of a utility system in an actual ethylene chemical process validated the economic and environmental benefits of sustainable retrofitting, as well as the effectiveness of the proposed method in handling uncertainties. The optimization results indicate that the proposed model reduces carbon emissions by 5.2%, and the proposed method decreases computational time by 91% compared to stochastic programming.
We propose a decomposition algorithm for multistage stochastic programming that resembles the progressive hedging method of Rockafellar and Wets but is provably capable of several forms of asynchronous operation. We d...
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We propose a decomposition algorithm for multistage stochastic programming that resembles the progressive hedging method of Rockafellar and Wets but is provably capable of several forms of asynchronous operation. We derive the method from a class of projective operator splitting methods fairly recently proposed by Combettes and Eckstein, significantly expanding the known applications of those methods. Our derivation assures convergence for convex problems whose feasible set is compact, subject to some standard regularity conditions and a mild "fairness" condition on subproblem selection. The meth-od's convergence guarantees are deterministic and do not require randomization, in con-trast to other proposed asynchronous variations of progressive hedging. Computational experiments described in an online appendix show the method to outperform progressive hedging on large-scale problems in a highly parallel computing environment.
We present a stochastic programming model for informing the deployment of ad hoc flood mitigation measures to protect electric substations prior to an imminent and uncertain hurricane. The first stage captures the dep...
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We present a stochastic programming model for informing the deployment of ad hoc flood mitigation measures to protect electric substations prior to an imminent and uncertain hurricane. The first stage captures the deployment of a fixed number of mitigation resources, and the second stage captures grid operation in response to a contingency. The primary objective is to minimize expected load shed. We develop methods for simulating flooding induced by extreme rainfall and construct two geographically realistic case studies, one based on Tropical Storm Imelda and the other on Hurricane Harvey. Applying our model to those case studies, we investigate the effect of the mitigation budget on the optimal objective value and solutions. Our results highlight the sensitivity of the optimal mitigation to the budget, a consequence of those decisions being discrete. We additionally assess the value of having better mitigation options and the spatial features of the optimal mitigation.
The network that ensures the delivery of electricity to end customers, and consists of actors such as transformer centers, distribution centers, distribution transformers and field distribution boxes is called electri...
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The network that ensures the delivery of electricity to end customers, and consists of actors such as transformer centers, distribution centers, distribution transformers and field distribution boxes is called electricity distribution network. Effective design of electricity distribution networks plays an important role in terms of ensuring the continuous supply of electricity and decreasing the costs of electricity distribution companies. Motivated by this fact, in this study, we focus on an electricity distribution network consisting of transformer centers, distribution centers, distribution transformers and field distribution boxes, and propose a two-stage stochastic programing model for the location, cable and flow decisions under demand uncertainty. The proposed model is tested on a real-life case study regarding Eski & scedil;ehir, which on one hand shows the applicability of the proposed model on real-life instances, and on the other hand brings important managerial insights. As an example, computational results reveal that ignoring the uncertainties in the electricity distribution networks may bring substantial additional costs.
Effective project risk management is critical in environments where both micro-level and macro-level risks are present. Traditional models often focus on micro-level risks, neglecting broader macroeconomic uncertainti...
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Effective project risk management is critical in environments where both micro-level and macro-level risks are present. Traditional models often focus on micro-level risks, neglecting broader macroeconomic uncertainties such as geopolitical instability and supply chain disruptions. This research introduces a two-stage stochastic programming model designed to optimize the selection of Risk Response Actions (RRAs) under uncertainty while addressing both types of risk. The model incorporates "here-and-now" decisions at the planning stage and "waitand-see" decisions as uncertainties unfold, enabling adaptive risk management throughout the project lifecycle. To solve the model efficiently, we employ an evolutionary algorithm combined with Sample Average Approximation (SAA) to handle the computational complexity of multiple scenarios. The model is applied to a real-world case study involving the integration of IoT and ERP systems in a smart factory in Iran, a project characterized by significant macroeconomic and geopolitical risks. Our key contribution lies in providing a comprehensive risk response strategy selection model that simultaneously addresses micro- and macro-level risks while incorporating strategic flexibility through outsourcing decisions. The results demonstrate that our model outperforms traditional deterministic models, offering enhanced resilience against macro-level risks and improved project performance under uncertainty. These findings provide valuable insights for project managers aiming to increase resilience and adaptability in volatile environments. By integrating both internal and external risk factors, our model offers a robust tool for managing complex projects, enhancing decision-making and project outcomes in uncertain conditions.
Multistage stochastic programming is a powerful tool allowing decision-makers to revise their decisions at each stage based on the realized uncertainty. However, organizations are not able to be fully flexible, as dec...
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Multistage stochastic programming is a powerful tool allowing decision-makers to revise their decisions at each stage based on the realized uncertainty. However, organizations are not able to be fully flexible, as decisions cannot be revised too frequently in practice. Consequently, decision commitment becomes crucial to ensure that initially made decisions remain unchanged for a certain period of time. This paper introduces partially adaptive multistage stochastic programming, a new optimization paradigm that strikes an optimal balance between decision flexibility and commitment by determining the best stages to revise decisions depending on the allowed level of flexibility. We introduce a novel mathematical formulation and theoretical properties eliminating certain constraint sets. Furthermore, we develop a decomposition method that effectively handles mixed-integer partially adaptive multistage programs by adapting the integer L-shaped method and Benders decomposition. Computational experiments on stochastic lot-sizing and generation expansion planning problems show substantial advantages attained through optimal selections of revision times when flexibility is limited, while demonstrating computational efficiency attained by employing the proposed properties and solution methodology. By adhering to these optimal revision times, organizations can achieve performance levels comparable to fully flexible settings.
This research introduces a novel selective maintenance model in the case of systems undergoing multiple consecutive missions. The model considers uncertainties related to future operating conditions during each missio...
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This research introduces a novel selective maintenance model in the case of systems undergoing multiple consecutive missions. The model considers uncertainties related to future operating conditions during each mission. Within each maintenance break, various optional actions ranging from replacements which are perfect to imperfect and also minimal repairs can be chosen for individual components. Evaluating the probabilities of successful future mission accounts for uncertainties associated with component operational conditions. The selective maintenance problem is formulated as a nonlinear mixed-integer model for optimization, and computational challenges are addressed using the progressive hedging algorithm. Numerical examples validate the new proposed model and illustrate the benefits of the model by estimating a more realistic reliability level and lower maintenance cost.
This paper introduces the operational fleet composition problem with stochastic demands (OFCP-SD), which is commonly faced by e-commerce companies in the context of short-term capacity planning for freight transportat...
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This paper introduces the operational fleet composition problem with stochastic demands (OFCP-SD), which is commonly faced by e-commerce companies in the context of short-term capacity planning for freight transportation. We propose a two-stage stochastic programming formulation, in which the planned fleet size and mix decisions constitute the first stage, while recourse actions are taken in the second stage to hire extra vehicles or to make vehicle cancellations according to the observed demands. Hiring extra vehicles incurs additional costs, while vehicle cancellations involve financial restitutions. The objective is to minimize the fleet overall cost, which comprises the first-stage planned cost as well as the expected cost or restitution stemming from the fleet adjustments made in the second stage. A scenario generation procedure is devised, and a variable-fixing matheuristic is suggested for the OFCP-SD. A case study conducted within the Brazilian middle-mile operation of the leading e-commerce company in Latin America shows the advantages of explicitly modeling the stochastic demands and underlines the benefits of the proposed approach for the business. Compared to a simplified deterministic approach, the use of the OFCP-SD demonstrated a yearly potential cost avoidance of more than USD 2.5 million as well as an annual reduction of more than 20 thousand pallets transported by means of extra vehicles.
Two-stage stochastic programming (2SP) is an effective framework for decision-making and modeling under uncertainty. Some 2SP problems are challenging due to their high dimensionality and nonlinearity. Machine learnin...
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Two-stage stochastic programming (2SP) is an effective framework for decision-making and modeling under uncertainty. Some 2SP problems are challenging due to their high dimensionality and nonlinearity. Machine learning can assist in solving 2SP problems by providing data-driven insights and approximations. Evolutionary algorithms are more general and effective methods for handling various 2SP problems by exploiting their structures and features. However, there is still a research gap in combining machine learning and evolutionary algorithms for solving 2SP problems. Therefore, this paper proposes for the first time a Machine Learning-enabled Evolutionary 2SP framework (MLE2SP), which uses machine learning to construct surrogate-assisted evolutionary optimization frameworks for 2SP. It constructs a novel multi-output 2SP surrogate model that considers scenarios and decision variables of both stages for the first time and proposes a data conversion method to handle the high-dimensional decision variables and scenarios. It also proposes a Machine Learning-enabled Differential Evolution Sampling (MLDES) method to update candidate solutions, which extracts knowledge from dominant candidate solutions to guide the evolutionary direction. Moreover, this work provides open sources of linear and nonlinear two-stage stochastic mixed-integer programming problem instances as benchmark test functions. The effectiveness and generality of the proposed algorithm and framework are verified by the test results on the benchmark test functions and a disaster relief logistics problem, which provide a new research direction for designing general and effective two-stage stochastic programming solving frameworks.
We investigate the stochastic transfer synchronization problem, which seeks to synchronize the timetables of different routes in a transit network to reduce transfer waiting times, delay times, and unnecessary in-vehi...
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We investigate the stochastic transfer synchronization problem, which seeks to synchronize the timetables of different routes in a transit network to reduce transfer waiting times, delay times, and unnecessary in-vehicle times. We present a sophisticated two-stage stochastic mixed-integer programming model that takes into account variability in passenger walking times between bus stops, bus running times, dwell times, and demand uncertainty. Our model incorporates new features related to dwell time determination by considering passenger arrival patterns at bus stops which have been neglected in the literature on transfer synchronization and timetabling. We solve a sample average approximation of our model using a problem-based scenario reduction approach, and the progressive hedging algorithm. As a proof of concept, our computational experiments on instances using transfer nodes in the City of Toronto, with a mixture of low- and high-frequency routes, demonstrate the potential advantages of the proposed model. Our findings highlight the necessity and value of incorporating stochasticity in transfer-based timetabling models.
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