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Integrated remanufacturing scheduling of disassembly, reprocessing and reassembly considering energy efficiency and stochasticity through group teaching optimization and simulation approaches

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ENGINEERING OPTIMIZATION 2024年第12期56卷 2018-2039页

作者： Fu, Yaping Zhang, Zhengpei Liang, Pei Tian, Guangdong Zhang, Chaoyong Qingdao Univ Sch Business Qingdao Peoples R China Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing Peoples R China Shandong Univ Key Lab High Efficiency & Clean Mech Manufacture Minist Educ Jinan Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

The energy crisis and environmental pollution are receiving increasing attention from governments and communities. This study researches energy-aware remanufacturing systems. Remanufacturing aims to reuse valuable resources from end-of-life products and produce as-new products. Since remanufacturing systems involve a series of disassembly, processing and assembly operations, remanufacturing schedule integrates disassembly, processing and assembly shops. A multi-objective scheduling of remanufacturing systems is proposed, considering workstation use, energy consumption and customer satisfaction simultaneously. A chance-constrained programming model is established to minimize makespan and energy consumption while satisfying total tardiness requirements. A hybrid method is developed, using group teaching optimization and a discrete event simulation system, which can seek and evaluate potentially favourable solutions. The approach is validated on a group of test instances using well-known methods. The results reveal that this method can find non-dominated solutions with well-converged and well-diversified performance, verifying its advantages in providing informed decisions for managers and engineers.

关键词： Remanufacturing scheduling multi-objective optimization group teaching optimization chance-constrained programming discrete event simulation

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An efficient stochastic programming approach for solving integrated multi-objective transportation and inventory management problem using goodness of fit

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KYBERNETES 2022年第2期51卷 768-803页

作者： Gupta, Srikant Chaudhary, Sachin Chatterjee, Prasenjit Yazdani, Morteza Jaipuria Inst Management Dept Decis Sci Jaipur Rajasthan India Govt Med Coll Dept Community Med Kannauj India MCKV Inst Engn Dept Mech Engn Howrah India Thu Dau Mot Univ Inst Engn & Technol Thu Dau Mot Vietnam ESIC Business & Mkt Sch Madrid Spain

Purpose Logistics is the part of the supply chain (SC) that plans, executes and handles forward and reverse movement and storage of products, services and related information, in order to respond to customers' needs effectively and efficiently. The main concern for logistics is to ensure that the correct product is placed at the right time. This paper introduces a linear model of shipping focused on decision-making, which includes configuration of shipping network, choosing of transport means and transfer of individual customer shipments through a particular transport system. Design/methodology/approach In this study, authors try to address the problem of supply chain network (SCN) where the primary goal is to determine the appropriate order allocation of products from different sources to different destinations. They also seek to minimize total transportation cost and inventory cost by simultaneously determining optimal locations, flows and shipment composition. The formulated problem of getting optimal allocation turns out to be a problem of multi-objective programming, and it is solved by using the max-addition fuzzy goal programming approach, for obtaining optimal order allocation of products. Furthermore, the problem demand and supply parameters have been considered random in nature, and the maximum likelihood estimation approach has been used to assess the unknown probabilistic distribution parameters with a specified probability level (SPL). Findings A case study has also been applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. Results of this study are very relevant for the manufacturing sector in particular, for those facing logistics issues in SCN. It enables researchers and managers to cope with various types of uncertainty and logistics risks associated with SCN. Research limitations/implications The principal contribution of the proposed model is the improved modelling of tr

关键词： Multi-objective optimization Transportation problem Inventory problem chance-constrained programming Fuzzy set theory

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A Novel Approach for Efficiency Evaluation in Data Envelopment Analysis Framework with Fuzzy Stochastic Variables

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2025年第3期27卷 866-881页

作者： Huang, Lizhen Chen, Lei Fuzhou Univ Sch Econ & Management Fuzhou 350108 Fujian Peoples R China

Data envelopment analysis (DEA) is a widely used approach for evaluating the relative efficiency of decision-making units (DMUs) in multiple-input and multiple-output situations. Although traditional DEA models use precise input-output data, real-world problems often involve mixed uncertainties, including fuzziness and stochasticity. This paper focuses on dealing with situations where inputs and outputs have both fuzzy and stochastic characteristics, using DEA models for efficiency evaluation. Through the integration of the alpha-level approach and chance-constrained programming, novel DEA models with fuzzy stochastic variables (FSVs) are proposed, and deterministic equivalent interval DEA models with linear constraints are provided to address this problem. The main contributions and advantages of the proposed model over existing DEA models with FSVs are fourfold: (1) linear and always-feasible models are proposed;(2) a fixed and uniform production boundary (i.e., the same set of constraints) is used to measure the efficiency of DMUs with fuzzy stochastic input and output;(3) the obtained results can distinguish between efficient and inefficient DMUs;(4) Equivalent interval DEA models were obtained to provide a more comprehensive assessment of the efficiency of the DMUs. Finally, a numerical example is presented to demonstrate the applicability of the proposed models and the feasibility of the obtained solutions.

关键词： Data envelopment analysis Fuzzy stochastic variables alpha-Level approach chance-constrained programming Interval efficiency

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A Novel Interval Power Flow Method Based on Hybrid Box-Ellipsoid Uncertain Sets

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第4期39卷 6111-6114页

作者： Wang, Dapeng Zhang, Cong Li, Jiayong Zhu, Lipeng Zhou, Bin Zhou, Quan Cheng, Long Shuai, Zhikang Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Grid Hunan Elect Power Co Ltd Changsha Power Supply Branch State Changsha 410082 Peoples R China

To account for the probability distribution information of uncertain scenarios, this letter proposes a novel interval power flow (NIPF) method based on hybrid uncertain sets. Firstly, a NIPF model is established by integrating the hybrid box-ellipsoid sets characterizing uncertainties in interval power flow (IPF), which is solved by the optimizing-scenario method (OSM). Then, a multivariate statistical analysis method (MSAM) and a chance-constrained programming-based method (CCPM) are proposed for determining the optimal radii of the ellipsoid set and side length of the box set under two scenarios, respectively, namely, the normal distributions without correlations and the arbitrary distributions with correlations. Finally, case studies validate the effectiveness and advantage of the proposed method compared with traditional IPF methods based on Monte Carlo simulation, box sets, and ellipsoid sets.

关键词： Ellipsoids Correlation Uncertainty Load flow Gaussian distribution Hybrid power systems Generators Interval power flow chance-constrained programming hybrid box-ellipsoid sets optimizing-scenario method

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Value of risk aversion in perishable products supply chain management

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2024年第2期89卷 517-552页

作者： Pathy, Soumya Ranjan Rahimian, Hamed Clemson Univ Dept Ind Engn Clemson SC 29634 USA

In this paper, we study optimal procurement and inventory decisions for a supply chain with a single perishable product under demand uncertainty. To control risk, on the one hand, we use a risk-averse objective, and on the other hand, we utilize a chance constraint to satisfy demand with a high probability. We formulate the problem as a two-stage stochastic program with a chance constraint and risk-averse objective, where long-term decisions on pre-positioning products are made in the first stage, while recourse decisions on reallocation and emergency procurement are made in the second stage. To allow for different risk preferences, we incorporate conditional value-at-risk into the objective function and study its combination with the expectation or worst-case of the second-stage costs. To solve the resulting models, we develop various variants of the L-shaped method, based on dual and primal decomposition, and by leveraging the connection between the optimization of coherent risk measures and distributionally robust optimization. Through extensive numerical experiments, we demonstrate the value of risk aversion and present a comparative computational study on the performance of different algorithms.

关键词： Perishable supply chain chance-constrained programming Two-stage stochastic programming Risk-averse optimization Decomposition algorithm Cutting planes

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Efficient Scenario Generation for chance-constrained Economic Dispatch Considering Ambient Wind Conditions

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第4期39卷 5969-5980页

作者： Zhang, Qian Shukla, Apurv Xie, Le Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

Scenario approach is an effective data-driven method for solving chance-constrained optimization while ensuring desired risk guarantees with a finite number of samples. Crucial challenges in deploying this technique in the real world arise due to non-stationarity environments and the absence of appropriate risk-tuning models tailored for the desired application. In this article, we focus on designing efficient scenario generation schemes for economic dispatch in power systems. We propose a novel scenario generation method based on filtering scenarios using ambient wind conditions. These filtered scenarios are deployed incrementally in order to meet desired risk levels while using minimum resources. In order to study the performance of the proposed scheme, we illustrate the procedure on case studies performed for both 24-bus and 118-bus systems with real-world wind power forecasting data. Numerical results suggest that the proposed filter-and-increment scenario generation model leads to a precise and efficient solution for the chance-constrained economic dispatch problem.

关键词： Scenario generation chance-constrained programming economic dispatch wind power forecasting error scenario approach

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A chance-constrained optimization approach integrating project scheduling and material ordering to manage the uncertain material supply

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COMPUTERS & OPERATIONS RESEARCH 2024年 166卷

作者： Tian, Baofeng Zhang, Jingwen Demeulemeester, Erik Liu, Hao Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China Katholieke Univ Leuven Fac Econ & Business Res Ctr Operat Management B-3000 Leuven Belgium

To deal with the impact of uncertain material supply on the implementation of projects, we investigate a problem of concurrently finding a robust baseline schedule and a material ordering plan under different levels of uncertainty. Assuming the material warehouses are restricted, a chance-constrained model of the integrated resource-constrained project scheduling and material ordering problem with a confidence level (1 - theta) is thus formulated. To tackle the difficult joint chance constraints, we firstly transform the chance-constrained model into a scenario-based integer programming model by the sample average approximation approach. Based on dominance relationship among scenarios, we then derive an exact branch-and-bound algorithm and a sampling-based genetic algorithm (SGA) to solve the model. For the branch-and-bound algorithm, a tailor-made branching scheme and four pruning rules are developed to accelerate the search process. For the SGA, two sampling methods and a local search procedure are presented to boost solution quality. Furthermore, extensive numerical experiments are carried out to test the performance of the proposed algorithms. Specifically, 4608 instances with four different confidence levels and six different sample sizes are generated. Then the Taguchi method is employed to calibrate the parameters of the SGA. Besides, the numerical results demonstrate that the proposed branch-and-bound algorithm can solve more instances with shorter CPU times than the CPLEX solver, and the solution quality of the proposed SGA is far better than that of two existing meta-heuristics and two variants of SGA. Finally, a sensitivity analysis for the key parameters of the model is conducted. By considering the uncertain material supply, limited warehouse capacity and various confidence levels, our research contributes to the effective decision on project scheduling and material ordering in real-world contexts.

关键词： Project scheduling Integrated optimization chance-constrained programming Branch and bound Uncertainty Limited storage space

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MU-MIMO Beamforming With Limited Channel Data Samples

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第11期42卷 3032-3047页

作者： Li, Shaoran Jiang, Nan Chen, Yongce Xie, Weijun Lou, Wenjing Hou, Y. Thomas NVIDIA Corp Santa Clara CA 95051 USA Georgia Tech Atlanta GA 30332 USA Virginia Tech Blacksburg VA 24061 USA

Channel State Information (CSI) is a critical piece of information for MU-MIMO beamforming. However, CSI estimation errors are inevitable in practice. The random and uncertain nature of CSI estimation errors poses significant challenges to MU-MIMO beamforming. State-of-the-art works addressing such a CSI uncertainty can be categorized into model-based and data-driven works, both of which have limitations when providing a performance guarantee to the users. In contrast, this paper presents Limited Sample-based Beamforming (LSBF)-a novel approach to MU-MIMO beamforming that only uses a limited number of CSI data samples (without assuming any knowledge of channel distributions). Thanks to the use of CSI data samples, LSBF enjoys flexibility similar to data-driven approaches and can provide a theoretical guarantee to the users-a major strength of model-based approaches. To achieve both, LSBF employs chance-constrained programming (CCP) and utilizes the $\infty $ -Wasserstein ambiguity set to bridge the unknown CSI distribution with limited CSI samples. Through problem decomposition and a novel bilevel formulation for each subproblem based on limited CSI data samples, LSBF solves each subproblem with a binary search and convex approximation. We show that LSBF significantly improves the network performance while providing a probabilistic data rate guarantee to the users.

关键词： Channel uncertainty chance-constrained programming data samples 5G MU-MIMO beamforming Channel uncertainty chance-constrained programming data samples 5G MU-MIMO beamforming

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Operation optimization of Combined Heat and Power microgrid in buildings consider renewable energy, electric vehicles and hydrogen fuel

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ENERGY 2025年 319卷

作者： Huang, Yongyi Ahmed, Shoaib Ueda, Soichiro Liang, Xunyu Howlader, Harun Or Rashid Lotfy, Mohammed Elsayed Senjyu, Tomonobu Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn 337 Jinhai Sandao St Wenzhou 325025 Peoples R China Univ Ryukyus Fac Engn 1 Senbaru Okinawa 9030213 Japan Anhui Xinghao Engn Project Management Consulting C Fuyang 232007 Anhui Peoples R China Univ Hawaii Hawaii Nat Energy Inst Res Corp Hawaii HI 96822 USA Zagazig Univ Elect Power & Machines Dept Zagazig 44519 Egypt

This paper introduces a forward-thinking framework that integrates renewable energy, Electric Vehicles (EVs), and hydrogen within Combined Heat and Power (CHP) microgrids (MGs) for effective building energy management. By utilizing Particle Swarm Optimization (PSO) to find the optimal solution and incorporating chance-constrained programming (CCP) to handle uncertainties in renewable energy generation and EV loads, this framework addresses the complexities of modern energy systems. The study employs Monte Carlo (MC) to simulate the EV load profile, applies K-means clustering to categorize load and renewable generation patterns, and uses a Sigmoid function-based model for Real-Time Pricing (RTP). The combination of PSO and CCP is used to optimize the system's operating strategy. This evaluates the system's economic benefits and impact on carbon emissions by analyzing different scenarios, such as weekdays versus weekends and various weather conditions (sunny, cloudy, rainy). The results show that due to the high price of hydrogen, it is currently costly to replace hydrogen completely. However, this integrated approach not only improves energy efficiency and reduces carbon footprint but also ensures system reliability under uncertain conditions, contributing to broader environmental sustainability.

关键词： Microgrid Renewable energy Electric vehicles Monte Carlo simulation K-means Real-time pricing Particle swarm optimization chance-constrained programming Combined heat and power

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Distributionally robust chance-constrained optimization with Gaussian mixture ambiguity set

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COMPUTERS & CHEMICAL ENGINEERING 2024年 186卷

作者： Kammammettu, Sanjula Yang, Shu-Bo Li, Zukui Univ Alberta Dept Chem & Mat Engn 9211 116 St Edmonton AB T6G1H9 Canada

Conventional chance -constrained programming methods suffer from the inexactness of the estimated probability distribution of the underlying uncertainty from data. To this end, a distributionally robust approach to the problem allows for a level of ambiguity considered around a reference distribution. In this work, we propose a novel formulation for the distributionally robust chance -constrained programming problem using an ambiguity set constructed from a variant of optimal transport distance that was developed for Gaussian Mixture Models. We show that for multimodal process uncertainty, our proposed method provides an effective way to incorporate statistical moment information into the ambiguity set construction step, thus leading to improved optimal solutions. We illustrate the performance of our method on a numerical example as well as a chemical process case study. We show that our proposed methodology leverages the multimodal characteristics from the uncertainty data to give superior performance over the traditional Wasserstein distance -based method.

关键词： chance-constrained programming Distributionally robust Ambiguity set Wasserstein distance Gaussian mixture models (GMMs) Optimal transport

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