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Single-Machine Rework Rescheduling to Minimize Total Waiting Time With Fixed Sequence of Jobs and Release Times

IEEE ACCESS 2021年 9卷 1205-1218页

作者： Guo, Yandong Huang, Min Wang, Qing Jorge Leon, V. Bohai Univ Coll Math & Phys Jinzhou 121013 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Texas A&M Univ Dept Engn Technol & Ind Distribut College Stn TX 77843 USA Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA

Motivated by energy-saving efforts in the quartz manufacturing industry, this study investigates a single-machine rescheduling problem for a set of newly arrived rework jobs. The original jobs have release times, and the rework jobs need to be separated in a schedule already in progress. The objective is to achieve energy savings by minimizing the total waiting time. A mixed-integer linear programming model is formulated and its NP-hardness is proved. Properties of the optimal solution are derived and used to design the subsequent algorithms. A pseudo-polynomial optimal algorithm for a special case, a heuristic algorithm and a genetic algorithm with an adaptive local search mechanism for general problems are developed and tested. Numerical simulations show that the genetic algorithm yields high-quality solutions under various conditions. A case study suggests that significant energy savings can be achieved with the proposed rescheduling methodology.

关键词： Rescheduling waiting time mixed-integer linear programming heuristics rule-guided adaptive genetic algorithm NP-hard

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Constraint programming approach for multi-objective two-sided assembly line balancing problem with multi-operator stations

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ENGINEERING OPTIMIZATION 2021年第8期53卷 1315-1330页

作者： Kizilay, Damla Cil, Zeynel Abidin Izmir Democracy Univ Dept Ind Engn Izmir Turkey

In recent years, the two-sided assembly line (TAL) has become popular since it offers several advantages, such as fewer workstations and movement of fewer workers inside the line. TAL assumes that each mated workstation contains two workstations in parallel, and it can process tasks on the left, on the right or on either side. However, in a traditional TAL, only one worker is responsible for completing the task at each workstation. Dealing with large-size products and massive operations may require more than one operator to perform tasks. This study proposes mixed-integer linear programming and constraint programming (CP) models for the two-sided assembly line balancing problem with multi-operator stations for the purpose of solving small- and large-size problems. Computational results indicate that the CP model finds optimal solutions for all instances. It is useful to highlight that the CP model is highly concise and solved by a black-box, commercial solver.

关键词： Two-sided assembly line multi-operator line balancing constraint programming mixed-integer linear programming

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Particle swarm optimization for integrated scheduling problem with batch additive manufacturing and batch direct-shipping delivery

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COMPUTERS & OPERATIONS RESEARCH 2024年 161卷

作者： Sugianto, Willy Chandra Kim, Byung Soo Incheon Natl Univ Dept Ind & Management Engn 119 Acad Ro Incheon 22012 South Korea

This study addresses a novel proRblem for the integrated scheduling of additive manufacturing and delivery processes with the objective of minimizing the total weighted completion time of the ordered parts. In the manufacturing process, batch production on non-identical parallel additive manufacturing machines is considered. A single batch of production, which is referred to as a build, consists of parts requiring the same material. Sequence-dependent setup times occur between two adjacent builds. Once the production run is completed, the parts in the builds are regrouped into delivery batches within the same customer. In the delivery process, parts are delivered by homogeneous vehicles to the corresponding customers through direct-shipping method. To optimally solve the small problem, a mixed-integer linear programming model is formulated. For solving largescale problems, three rule-based heuristics that are embedded in particle swarm optimization (PSO) are proposed. A tight lower bound is developed to evaluate the performance of the proposed algorithms. The computational experiment shows that the proposed PSO with delivery waiting time minimization rule is effective to solve small-scale problems, while in large-scale problems, capacity usage maximization rule performs the best and both outperform another existing metaheuristic. Several sensitivity analyses are also performed to provide additional managerial insights.

关键词： Scheduling Additive manufacturing mixed-integer linear programming Particle swarm optimization Meta-heuristic Local search

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Home energy management system for residential fuel cell-based combined heat and power system

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JOURNAL OF BUILDING ENGINEERING 2024年 98卷

作者： Teo, J. C. Yao, Leehter Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan

The goal of this paper is to propose an optimization scheme for enhancing power dispatch and load scheduling for residential fuel cell-based combined heat and power systems (FC-CHPS) using mixed integer linear programming (MILP), considering the lifetime degradation of both the fuel cell system (FCS) and battery energy storage systems (BESS). The scheme is applied in the home energy management system that oversees the electric and thermal power of residential FC-CHPS. First, the nonlinearity in the relationship between the natural gas consumption and output electric power, and between the residual thermal power and output electric power of the fuel cell system (FCS) in the FC-CHPS is approximated using piecewise linearization. Then, the piecewise linearization is integrated with MILP to derive optimal power dispatch and load scheduling solutions, while accounting for the nonlinearity of the FCS. Next, cost functions representing the lifetime degradation of FCS and BESS are formulated. These cost functions result in nonlinear optimization constraints. Therefore, innovative methods are proposed to linearize these constraints, allowing the continued use of MILP as the optimization method and factoring in the lifetime degradation of FCS and BESS. Compared to the mixed-integer non-linear programming (MINLP) method that does not linearize the non-linearity in the FCS or the lifetime degradation of the FCS and BESS, the proposed MILP scheme achieves the identical objective function value. Furthermore, the computation time for the proposed MILP scheme is 99.94 % lower than that required by the MINLP method. The proposed scheme provides accurate results in short computation times.

关键词： Combined heat and power Fuel cell systems Home energy management system mixed-integer linear programming Piecewise linearization

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Management of a district heating network using model predictive control with and without thermal storage

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OPTIMIZATION AND ENGINEERING 2021年第3期22卷 1897-1919页

作者： Quaggiotto, Davide Vivian, Jacopo Zarrella, Angelo Univ Padua Dept Ind Engn Padua Italy

District heating and cooling networks are a key infrastructure to decarbonise the heating and cooling sector. Besides the design of new networks according to the principles of the 4th and 5th generation, operational aspects may significantly contribute to improve the efficiency of existing networks from both economic and environmental standpoints. This article is the second step of a work that aims to exploit the flexibility of existing networks and improve their economic and environmental performance, using the district heating network of Verona as a case study. In particular, the first part of the research demonstrated through numerical simulations that the thermal inertia of the water contained in the pipes can be used to shift the heat production of the generators over time by acting on the flow rate circulating in the network. This article shifts the focus from the heat distribution side to the heat supply. A model predictive control strategy was formulated as a MILP optimization problem to schedule the heat supply of the cogeneration plants, heat pump and gas boilers as a function of heat load, waste heat production and electricity price forecasts. Computer simulations of considered district heating network were carried out executing the optimization with a rolling-horizon scheme over two typical weeks. Results show that the proposed look-ahead control achieves a reduction in the operational costs of about 12.5% and 5.8%, respectively in a middle season and a winter representative week. Increasing the flexibility of the system with a centralized heat storage tank connected to the CHP and HP units, these percentage rise to respectively 20% and 6.3%. In the warmest periods, when the total installed power of the CHP and HP plants is sufficient to supply the entire heat demand during the peak, and the modulation of these plants has a higher impact, the cost reduction related to the additional thermal energy storage is more relevant.

关键词： Model predictive control mixed-integer linear programming District heating simulation

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Demand response with load-block shifting in the day ahead scheduling of predominantly hydro systems: Impact on the large-scale Brazilian case

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2025年 43卷

作者： Longhi, Gabriel M. Borges, Carmen L. T. Diniz, Andre L. UFRJ Fed Univ Rio De Janeiro Dept Elect Engn COPPE Rio De Janeiro Brazil UERJ State Univ Rio De Janeiro CEPEL Brazilian Elect Energy Res Ctr Rio De Janeiro Brazil

Operational planning and scheduling of electrical systems aims to meet the system load in the most economical way, managing not only the risk of load curtailment but also to ensure ramping capability. Flexibility is also pursued by exploring the large penetration of variable renewable generation such as wind/solar plants. Demand response (DR) programs have emerged as an alternative to accommodate such variability by stimulating or paying customers to curtail or shift their load, yielding a reduction in peak prices and smoother hourly price curves. Even though the advantages of DR for originally pure thermal systems have been widely shown in the literature, quantification of the effects of DR optimization in predominantly hydro systems is yet to be better explored, since such systems already have a large flexibility in operation. We present a mixed integer linear programming application of DR with load-block shifting for the day ahead scheduling model of predominantly hydro systems, based on the official model that is used to dispatch and set system hourly prices for the Brazilian system. The model comprises load curtailment based on price offer by consumers, as well as load-block shifting, which consists in a more precise representation of loads that have time-shifting flexibility but not load profile flexibility. We show that the performance of the model is computationally feasible and assess its impact on marginal/total operation costs. We also evaluate some aspects such as: competition among offers and optimal bidding points from the customers point of view. Results are presented for the IEEE 14-bus system and the actual Brazilian hydrothermal-wind system.

关键词： Hydro Systems Day ahead scheduling Demand response Load shifting mixed-integer linear programming

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Constructing Uncertainty Sets From Covariates in Power Systems

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IEEE Transactions on Power Systems 2025年

作者： Bertsimas, Dimitris Koukouvinos, Thodoris Koulouras, Angelos Georgios Massachusetts Institute of Technology Operations Research Center United States

Robust optimization (RO) immunizes against uncertainties in power systems through uncertainty sets that control the robustness and conservativeness of the underlying optimization problem. Despite earlier work in their structure and properties, there are few suggestions on calibrating their size. In this paper, we propose a method to determine (predict) the uncertainty set size using machine learning models and mixed- integer optimization (MIO), leveraging historical data that consist of covariates or features. In essence, we utilize covariates to simultaneously predict the uncertain parameters and construct an uncertainty set around the nominal prediction based on the confidence in the prediction itself. In addition, we introduce optional amendments to our framework so that the uncertainty set bounds are covariate-dependent and also develop an outer approximation scheme for efficiently solving the underlying MIO problem in larger datasets. We apply our framework to uncertainty sets for available wind resource capacity in the Adaptive Robust Unit Commitment (ARUC) problem. We show that our approach gives lower probabilities of constraint violation than commonly used statistical approaches, without necessarily exhibiting an increase in the cost. © 1969-2012 IEEE.

关键词： mixed-integer linear programming

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Feature selection in linear support vector machines via a hard cardinality constraint: A scalable conic decomposition approach

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European Journal of Operational Research 2025年

作者： Bomze, Immanuel D'Onofrio, Federico Palagi, Laura Peng, Bo Faculty of Mathematics and Research Network Data Science University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Rome00185 Italy VGSCO University of Vienna Oskar-Morgenstern-Platz 1 Wien1090 Austria

In this paper, we study the embedded feature selection problem in linear Support Vector Machines (SVMs), in which a cardinality constraint is employed, leading to an interpretable classification model. The problem is NP-hard due to the presence of the cardinality constraint, even though the original linear SVM amounts to a problem solvable in polynomial time. To handle the hard problem, we first introduce two mixed-integer formulations for which novel semidefinite relaxations are proposed. Exploiting the sparsity pattern of the relaxations, we decompose the problems and obtain equivalent relaxations in a much smaller cone, making the conic approaches scalable. To make the best usage of the decomposed relaxations, we propose heuristics using the information of its optimal solution. Moreover, an exact procedure is proposed by solving a sequence of mixed-integer decomposed semidefinite optimization problems. Numerical results on classical benchmarking datasets are reported, showing the efficiency and effectiveness of our approach. © 2025

关键词： mixed-integer linear programming

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Distributionally robust facility location problem under decision-dependent stochastic demand

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第2期292卷 548-561页

作者： Basciftci, Beste Ahmed, Shabbir Shen, Siqian Sabanci Univ Ind Engn Program Istanbul Turkey Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

While the traditional facility location problem considers exogenous demand, in some applications, locations of facilities could affect the willingness of customers to use certain types of services, e.g., carsharing, and therefore they also affect realizations of random demand. Moreover, a decision maker may not know the exact distribution of such endogenous demand and how it is affected by location choices. In this paper, we consider a distributionally robust facility location problem, in which we interpret the moments of stochastic demand as functions of facility-location decisions. We reformulate a two-stage decision-dependent distributionally robust optimization model as a monolithic formulation, and then derive exact mixed-integer linear programming reformulation as well as valid inequalities when the means and variances of demand are piecewise linear functions of location solutions. We conduct extensive computational studies, in which we compare our model with a decision-dependent deterministic model, as well as stochastic programming and distributionally robust models without the decision-dependent assumption. The results show superior performance of our approach with remarkable improvement in profit and quality of service under various settings, in addition to computational speed-ups given by formulation enhancements. These results draw attention to the need of considering the impact of location decisions on customer demand within this strategic-level planning problem. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Uncertainty modeling Facility location Distributionally robust optimization Decision-dependent uncertainty mixed-integer linear programming

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Generation Maintenance Scheduling for Power Systems Considering the Risk Quantification of Hybrid Uncertainty

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IEEE Transactions on Power Systems 2025年

作者： Yang, Xiao Zhao, Yong Li, Yuanzheng Huang, Cheng Ding, Qiang Key Laboratory of Image Information Processing and Intelligent Control of Ministry of Education of China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Power Science Research Institute of State Grid Jiangsu Electric Power Company China China Electric Power Research Institute Company China

The accurate quantification of risk caused by uncertainty forms a crucial foundation for formulating the generation maintenance scheduling (GMS) of power systems. However, the probability distribution functions (PDFs) of uncertain variables such as wind power and load are challenging to model accurately or are unknown, which makes it difficult to measure their economic risk and formulate appropriate GMS for power systems. To address this issue, we consider hybrid uncertainty from wind power and load, and propose a novel interval-probabilistic worst conditional value-at-risk (IP-WCVaR)-based generation maintenance scheduling method. Firstly, a novel IP-WCVaR method is proposed to measure the risk of the interval and probabilistic hybrid uncertainty, and the analytical mathematical model of the IP-WCVaR is derived through typical scenarios of probability correction. On this basis, the positive and negative spinning reserve models are established using the IP-WCVaR and then integrated into the GMS model, which enhances the resilience of the power system. Finally, the new risk-averse GMS model is formulated as the lower and upper boundary optimal models, which are transformed into tractable mixed integer linear programming problems based on the interval extreme value theory. The effectiveness and superiority of the proposed IP-WCVaR method are verified on the modified IEEE 24-bus and IEEE 118-bus power systems. © 1969-2012 IEEE.

关键词： mixed-integer linear programming

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