Article
Engineering, Chemical
Renfu Tu, Qi Liao, Liqiao Huang, Yingqi Jiao, Xuemei Wei, Yongtu Liang
Summary: Accurate estimation of remaining capacity is crucial for pipeline companies to improve their service quality and economic benefits. This study develops a mathematical model for multiproduct pipelines to obtain the optimal remaining capacity at different injection nodes during different periods. The model is validated and sensitivity analysis is conducted to identify the driving factors of the optimal remaining capacity. The comparison reveals that the tightness of delivery time has a major impact on the optimal remaining capacity, especially at downstream nodes of the pipeline. Relevant suggestions are provided to help pipeline operators make decisions based on the results.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2023)
Article
Engineering, Chemical
Ning Xu, Qi Liao, Zhengbing Li, Yongtu Liang, Rui Qiu, Haoran Zhang
Summary: This paper introduces a hybrid-time mixed integer linear programming model for low-energy scheduling of single-source multiproduct pipelines, which efficiently divides the time horizon into slots and initializes all time points' sequence for seeking satisfactory solutions. The model's superiority is validated by two real-world cases.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2021)
Article
Computer Science, Interdisciplinary Applications
Zhengbing Li, Yongtu Liang, Qi Liao, Ning Xu, Jianqin Zheng, Haoran Zhang
Summary: This study focuses on the scheduling problem of branched multiproduct pipeline system under market-oriented mode, proposing a scheduling method that considers market demand uncertainty and robust inventory management. This method can effectively reduce operational costs and adjustment frequency caused by demand uncertainty, ensuring that the schedule remains feasible within a certain fluctuation range.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Management
Hossein Mostafaei, Pedro M. Castro, Susana Relvas, Iiro Harjunkoski
Summary: This paper introduces a new optimal scheduling model for an oil transportation system, which can avoid forbidden product sequences, consider filler batch constraints, and include inventory management constraints. The model shows excellent performance in computational efficiency and linear programming, making a significant contribution to the existing technology.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Christos Koulamas, George J. Kyparisis
Summary: This paper investigates flow shop scheduling problems with two distinct job due dates and explores their solvability under different job processing times. The study provides algorithms and time complexities for these problems. Moreover, it offers solutions and time complexities for hierarchical bi-criteria proportionate flow shop scheduling problems.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Sergio Ackermann, Yanina Fumero, Jorge M. Montagna
Summary: This study examines the impact of order consolidation on optimizing production schemes and improving production efficiency in multiproduct multistage batch plants. By using a discrete-time mixed-integer linear programming model, simultaneous solutions to batching and scheduling problems were achieved, showing that order consolidation can reduce batch requirements and improve the utilization efficiency of plant production capacity.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Xueli Yan, Xingsheng Gu
Summary: This paper proposes an algorithm to solve the multi-product multi-stage production scheduling problem by combining improved differential evolution algorithm and memetic algorithm, effectively improving the scheduling performance.
COMPUTERS & OPERATIONS RESEARCH
(2022)
Article
Engineering, Industrial
D. Strachotova, J. Dyntar
Summary: This paper describes an efficient discrete modeling approach for multiproduct pipeline systems in the Witness simulation software environment, utilizing a simulation model consisting of six blocks and logical elements instead of physical elements. This innovative method allows for efficient simulation of bidirectional product flow in pipelines and supports scheduling in complex pipeline networks.
INTERNATIONAL JOURNAL OF SIMULATION MODELLING
(2021)
Article
Environmental Sciences
Maziar Yazdani, Kamyar Kabirifar, Amir M. Fathollahi-Fard, Mohammad Mojtahedi
Summary: The study proposes a new insight to the theory of scheduling dealing with sequence-dependent due dates to address uncertainties and complexities associated with production problems in the construction industry. Integrated DE-simulation approach is shown to provide better results compared to other approaches through a series of computational tests.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Management
Maurizio Boccia, Adriano Masone, Claudio Sterle, Teresa Murino
Summary: Automated guided vehicles (AGVs) are widely used in AGV-based transportation systems for the movement of goods and materials. The scheduling of transfer jobs on AGVs is crucial to overcome delays in production and material handling processes. However, the issue of AGV battery depletion and recharge has been neglected in previous research. This study focuses on the AGV scheduling problem with battery constraints and proposes novel solution methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Pedram Beldar, Milad Moghtader, Adriana Giret, Amir Hossein Ansaripoor
Summary: The combination of job scheduling and maintenance activity is investigated in this paper. A new mixed integer linear programming model is proposed, and two meta-heuristic approaches based on Simulated Annealing and Variable Neighborhood Search are developed. The results indicate that the proposed methods have a competitive behavior and outperform other algorithms in most cases.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Management
Jan-Erik Justkowiak, Sergey Kovalev, Mikhail Y. Kovalyov, Erwin Pesch
Summary: This study introduces a single machine scheduling problem with assignable job due dates to minimize total late work. The problem is proved to be NP-hard and no solution algorithm is proposed. Two pseudo-polynomial dynamic programming algorithms and an FPTAS are presented to solve this problem. In addition, a new single machine scheduling problem to minimize maximum late work of jobs with assignable due dates is introduced. An O(n log n) time algorithm is developed for this problem, where n is the number of jobs. The optimal solution value of this new problem serves as a lower bound for the optimal value of the total late work minimization problem and is utilized in the FPTAS.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Management
Gur Mosheiov, Daniel Oron, Dvir Shabtay
Summary: This study focuses on single machine scheduling problems with generalized due-dates, examining scenarios like job rejection and machine unavailability. The research proves the NP-hardness of these problems and proposes dynamic programming algorithms. It concludes that scheduling problems under certain conditions remain NP-hard.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Funing Li, Sebastian Lang, Bingyuan Hong, Tobias Reggelin
Summary: This article proposes a deep reinforcement learning approach to solve the parallel machine scheduling problem with family setups constraints, aiming to minimize the total tardiness. By designing a novel variable-length representation of states and actions, the method can calculate a comprehensive priority for each job at each decision time point and select the next job directly according to these priorities. The experimental results demonstrate the strong generalization capability of the trained agent and validate its superiority compared to three dispatching rules and two metaheuristics.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Management
Dries Bredael, Mario Vanhoucke
Summary: This paper provides a review of ten existing metaheuristic solution procedures for the resource-constrained multi-project scheduling problem. Algorithmic implementations are constructed and verified on original test instances. An extensive benchmark analysis is performed on a novel dataset, resulting in an overall ranking of the metaheuristic solution methods and key insights into competitive solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Vanina G. Cafaro, Pedro C. Pautasso, Jaime Cerda, Diego C. Cafaro
COMPUTERS & CHEMICAL ENGINEERING
(2019)
Article
Engineering, Chemical
Pedro C. Pautasso, Diego C. Cafaro, Jaime Cerda
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2019)
Article
Engineering, Chemical
Victoria G. Achkar, Vanina G. Cafaro, Carlos A. Mendez, Diego C. Cafaro
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2019)
Article
Green & Sustainable Science & Technology
Betzabet Morero, Agustin F. Montagna, Enrique A. Campanella, Diego C. Cafaro
Article
Engineering, Chemical
Jaime Cerda, Vanina G. Cafaro, Diego C. Cafaro
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2020)
Article
Engineering, Chemical
Diego C. Cafaro, Ignacio Grossmann
Summary: This work addresses the issue of water management in shale gas production, proposing an optimization plan to maximize the reuse of impaired water resources. By using a multiperiod mixed-integer linear programming model, the challenging stay-or-mobilize trade-off is solved, achieving effective optimization of both wellpad shale gas operations and water distribution networks.
Article
Engineering, Chemical
Victoria G. Achkar, Vanina G. Cafaro, Carlos A. Mendez, Diego C. Cafaro
Summary: Lifting operations are crucial for sustaining shale gas wells productivity, with Artificial Lift Systems (ALS) being a new focus for enhancing production. A Mixed Integer Linear Programming (MILP) formulation has been developed to manage multiple ALS in a multiwell pad, optimizing selection, investment, and operational decisions. Detailed piecewise functions have been introduced to account for ALS installation and removal times, with the performance of the MILP evaluated through five case studies.
Article
Computer Science, Interdisciplinary Applications
Diego C. Cafaro, Ignacio E. Grossmann
COMPUTERS & CHEMICAL ENGINEERING
(2020)
Article
Computer Science, Interdisciplinary Applications
Nelida B. Camussi, Jaime Cerda, Diego C. Cafaro
Summary: This study proposes an optimization approach based on a mixed-integer nonlinear mathematical programming model to optimize the operational cost of multi-product synchronous assembly lines. A two-stage solution strategy is introduced to improve the convergence and effectiveness of the model.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Chemical
Demian J. Presser, Vanina G. Cafaro, Diego C. Cafaro
Summary: Polymer flooding is widely used in enhanced oil recovery, aiming to increase oil recovery by improving fluid viscosity and sweep efficiency. A mixed-integer nonlinear optimization approach has been developed to optimize polymer injection strategies, maximizing the net present value of the project.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2021)
Article
Engineering, Chemical
Jaime Cerda, Vanina G. Cafaro, Diego C. Cafaro
Summary: This study introduces new systematic approaches for the synthesis of heat exchanger networks using efficient optimization models. The methods aim to minimize either the utility usage or total cost of the HENs, and utilize mixed-integer linear programming and mixed-integer non-linear programming to achieve this goal. By validating with benchmark examples, significant savings in total costs, up to 37%, have been achieved compared to previous contributions.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2022)
Article
Engineering, Multidisciplinary
Agustin F. Montagna, Diego C. Cafaro, Ignacio E. Grossmann, Damian Burch, Yufen Shao, Xiao-Hui Wu, Kevin Furman
Summary: The optimal design of gathering networks for unconventional oil and gas production is crucial for improving the economics of unconventional projects. This study proposes a complex multiperiod formulation and an optimization framework to obtain efficient solutions within reasonable computational times, leading to near optimal solutions for real-world instances.
OPTIMIZATION AND ENGINEERING
(2023)
Article
Operations Research & Management Science
Diego C. Cafaro, Demian J. Presser, Ignacio E. Grossmann
Summary: This overview paper presents a systematic description of mathematical models proposed in recent years for the optimal design of pipeline networks in the energy industry. It provides a general framework to address these problems based on both the network topology and the physical properties of the fluids. Computational challenges are illustrated through examples from industry collaboration projects.
Article
Engineering, Multidisciplinary
Agustin F. Montagna, Diego C. Cafaro, Ignacio E. Grossmann, Ozgur Ozen, Yufen Shao, Ti Zhang, Yuanyuan Guo, Xiao-Hui Wu, Kevin C. Furman
Summary: In the context of a global energy transition, this study presents a generalized optimization framework for designing oil and gas gathering networks considering shale oil and gas development strategies. The models developed allow for optimal determination of the network components and consideration of uncertain scenarios. The potential of the proposed formulations is assessed through solving four case studies in the unconventional industry.
OPTIMIZATION AND ENGINEERING
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Victoria G. Achkar, Vanina G. Cafiaro, Carlos A. Mendez, Diego C. Cafaro
29TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PT A
(2019)
Article
Computer Science, Interdisciplinary Applications
Nohan Joemon, Melpakkam Pradeep, Lokesh K. Rajulapati, Raghunathan Rengaswamy
Summary: This paper introduces a smoothing-based approach for discovering partial differential equations from noisy measurements. The method is data-driven and improves performance by incorporating first principles knowledge. The effectiveness of the algorithm is demonstrated in a real system using a new benchmark metric.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhibin Lu, Yimeng Li, Chang He, Jingzheng Ren, Haoshui Yu, Bingjian Zhang, Qinglin Chen
Summary: This study proposes a new inverse design method using a physics-informed neural network to identify optimal heat sink designs. A hybrid PINN accurately approximates the governing equations of heat transfer processes, and a surrogate model is constructed for integration with optimization algorithms. The proposed method accelerates the search for Pareto-optimal designs and reduces search time. Comparing different scenarios facilitates real-time observation of multiphysics field changes, improving understanding of optimal designs.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Luca Gasparini, Antonio Benedetti, Giulia Marchese, Connor Gallagher, Pierantonio Facco, Massimiliano Barolo
Summary: In this paper, a method for batch process monitoring with limited historical data is investigated. The methodology utilizes machine learning algorithms to generate virtual data and combines it with real data to build a process monitoring model. Automatic procedures are developed to optimize parameters, and indicators and metrics are proposed to assist virtual data generation activities.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Julia Jimenez-Romero, Adisa Azapagic, Robin Smith
Summary: Energy transition is a significant and complex challenge for the industry, and developing cost-effective solutions for synthesizing utility systems is crucial. The research combines mathematical formulation with realistic configurations and conditions to represent utility systems and provides a basis for synthesizing energy-efficient utility systems for the future.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Samuel Adeyemo, Debangsu Bhattacharyya
Summary: This work develops algorithms for estimating sparse interpretable data-driven models. The algorithms select the optimal basis functions and estimate the model parameters using Bayesian inferencing. The algorithms estimate the noise characteristics and model parameters simultaneously. The algorithms also exploit prior analysis and special properties for efficient pruning, and use a modified Akaike information criterion for model selection.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Abbasali Jafari-Nodoushan, Mohammad Hossein Dehghani Sadrabadi, Maryam Nili, Ahmad Makui, Rouzbeh Ghousi
Summary: This study presents a three-objective model to design a forward supply chain network considering interrelated operational and disruptive risks. Several strategies are implemented to cope with these risks, and a joint pricing strategy is used to enhance the profitability of the supply chain. The results show that managing risks and uncertainties simultaneously can improve sustainability goals and reduce associated costs.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
T. A. Espaas, V. S. Vassiliadis
Summary: This paper extends the concept of higher-order search directions in interior point methods to convex nonlinear programming. It provides the mathematical framework for computing higher-order derivatives and highlights simplified computation for special cases. The paper also introduces a dimensional lifting procedure for transforming general nonlinear problems into more efficient forms and describes the algorithmic development required to employ these higher-order search directions.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
David A. Linan, Gabriel Contreras-Zarazua, Eduardo Sanhez-Ramirez, Juan Gabriel Segovia-Hernandez, Luis A. Ricardez-Sandoval
Summary: This study proposes a parallel hybrid algorithm for optimal design of process flowsheets, which combines stochastic method with deterministic algorithm to achieve faster and improved convergence.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaoyong Lin, Zihui Li, Yongming Han, Zhiwei Chen, Zhiqiang Geng
Summary: A novel GAT-LSTM model is proposed for the production prediction and energy structure optimization of propylene production processes. It outperforms other models and can provide the optimal raw material scheme for actual production processes.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, Bhushan Gopaluni, Artur M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangoz, Ali Mesbah, Fani Boukouvala, Fernando Lima, Antonio del Rio Chanona, Christos Georgakis
Summary: This paper provides a concise perspective on the potential of machine learning in the PSE domain, based on discussions and talks during the FIPSE 5 conference. It highlights the need for domain-specific techniques in molecular/material design, data analytics, optimization, and control.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hesam Hassanpour, Prashant Mhaskar, Brandon Corbett
Summary: This work addresses the problem of designing an offset-free implementable reinforcement learning (RL) controller for nonlinear processes. A pre-training strategy is proposed to provide a secure platform for online implementations of the RL controller. The efficacy of the proposed approach is demonstrated through simulations on a chemical reactor example.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Hunggi Lee, Donghyeon Lee, Jaewook Lee, Dongil Shin
Summary: This study introduces an innovative framework that utilizes a limited number of sensors to detect chemical leaks early, mitigating the risk of major industrial disasters, and providing faster and higher-resolution results.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Sibel Uygun Batgi, Ibrahim Dincer
Summary: This study examines the environmental impacts of three alternative hydrogen-generating processes and determines the best environmentally friendly option for hydrogen production by comparing different impact categories. The results show that the solar-based HyS cycle options perform the best in terms of global warming potential, abiotic depletion, acidification potential, ozone layer depletion, and human toxicity potential.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
LaGrande Gunnell, Bethany Nicholson, John D. Hedengren
Summary: A review of current trends in scientific computing shows a shift towards open-source and higher-level programming languages like Python, with increasing career opportunities in the next decade. Open-source modeling tools contribute to innovation in equation-based and data-driven applications, and the integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to significantly accelerate progress, but long-term support mechanisms are still necessary.
COMPUTERS & CHEMICAL ENGINEERING
(2024)
Article
Computer Science, Interdisciplinary Applications
Daniel Cristiu, Federico d'Amore, Fabrizio Bezzo
Summary: This study presents a multi-objective mixed integer linear programming framework to optimize the supply chain for mixed plastic waste in Northern Italy. Results offer quantitative insights into economic and environmental performance, balancing trade-offs between maximizing gross profit and minimizing greenhouse gas emissions.
COMPUTERS & CHEMICAL ENGINEERING
(2024)