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Fuzzy mixed integer non-linear programming model for the design of an algae-based eco-industrial park with prospective selection of support tenants under product price variability

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JOURNAL OF CLEANER PRODUCTION 2016年第PartB期136卷 183-196页

作者： Ubando, Aristotle T. Culaba, Alvin B. Aviso, Kathleen B. Tan, Raymond R. Cuello, Joel L. Ng, Denny K. S. El-Halwagi, Mahmoud M. De La Salle Univ Dept Mech Engn 2401 Taft Ave Manila 0922 Philippines De La Salle Univ Ctr Engn & Sustainable Dev Res 2401 Taft Ave Manila 0922 Philippines De La Salle Univ Dept Chem Engn 2401 Taft Ave Manila 0922 Philippines Univ Arizona Agr & Biosyst Engn Dept Tucson AZ USA Univ Nottingham Ctr Excellence Green Technol Dept Chem & Environm Engn Malaysia Campus Semenyih Malaysia Texas A&M Univ Dept Chem Engn College Stn TX 77843 USA

Eco-industrial parks provide a platform for the application of industrial symbiosis where the synergistic net work of companies reuse portions of their by-products to reduce disposed waste, reduce environmental emissions, and improve plant efficiency. However, designing a complex network of material and energy exchanges between companies in an industrial park while satisfying multiple conflicting objectives require a systematic design methodology. In addition, strategic decision-making in an eco-industrial park involves the selection of prospective companies (i.e., support tenants), which complement the existing companies (i.e., anchor tenants). In this study, a fuzzy mixed-integer non-linear programming model is proposed to select prospective support tenants in an eco-industrial park while satisfying the product demand, minimizing the environmental footprint of the eco-industrial park, and also maximizing the annualized profit of each company in the eco-industrial park. A hypothetical but realistic case study involving an algae-based eco-industrial park is used to demonstrate the application of the model. The results demonstrate the selection of the appropriate support tenants for the algae-based eco-industrial park together with the optimal plant configuration. Sensitivity analysis is used to assess the performance of the algae-based eco-industrial park with respect to the changes in prices of the by-products. The developed model thus aid the planners of an eco-industrial park in assessing which among the prospective support tenants would best complements an existing anchor tenant. Furthermore, the model can also identify price negotiation points between tenants for some product streams which may show sensitivity on the plant capacity of each tenant. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Eco-industrial parks Fuzzy set theory mixed-integer non-linear programming Microalgae Bioenergy

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A mixed-integer non-linear programming with surrogate model for optimal remediation design of NAPLs contaminated aquifer

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INTERNATIONAL JOURNAL OF ENVIRONMENT AND POLLUTION 2014年第1期54卷 1-16页

作者： Luo, Jiannan Lu, Wenxi Jilin Univ Minist Educ Key Lab Groundwater Resources & Environm Changchun 130021 Peoples R China Jilin Univ Coll Environm & Resources Changchun 130021 Peoples R China

A mixed-integer non-linear programming (MINLP) with surrogate model was introduced to derive the optimal surfactant enhanced aquifer remediation (SEAR) process (remediation cost minimisation and removal rate maximisation) at a nitrobenzene-contaminated site. First, a 3D multi-phase flow simulation model was developed to simulate the SEAR process;using a radial basis function artificial neural network (RBFANN), the surrogate model was built which was an approximation of the simulation model;a MINLP was built to identify the optimal remediation strategies and genetic algorithm (GA) and penalty function were combined to solve the model;at last, the optimal remediation strategies were obtained. The approximation result of RBFANN was compared with that of back-propagation artificial neural network (BPANN), mean absolute error, mean relative error and coefficient of determination of the developed RBFANN model were 0.01, 2.27% and 0.85 respectively, which indicated much higher approximation accuracy than BPANN. The MINLP with surrogate model is a powerful tool for non-aqueous phase liquids (NAPLs) contaminated site remediation optimisation problem and it can greatly improve computational efficiency.

关键词： groundwater remediation mixed-integer non-linear programming non-aqueous phase liquid NAPL surrogate model

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Dynamic bus bridging strategy in response to metro disruptions integrated with routing, timetabling and vehicle dispatching

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 134卷

作者： Yuan, Yin Li, Shukai Liu, Shi Qiang D'Ariano, Andrea Yang, Lixing Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Roma Tre Univ Dept Civil Comp Sci & Aeronaut Technol Engn I-00146 Rome Italy

Unplanned metro disruptions always result in severe confusion and delays, while bus bridging can provide a promising resolution by efficient evacuating stranded passengers. This article investigates the dynamic bus bridging problem under metro disruptions to generate the routing, timetabling and vehicle dispatching schemes for bus bridging services in an online fashion. Specifically, we formulate a mixed-integer non-linear programming model for each decision stage, with the objective of minimizing passenger travel times and operational costs. This model focuses on the role of multimodal transportation in improving the overall urban public transportation network's responses to metro disruption emergencies, which involves the utilization of temporary bus bridging services and the spare capacity of unaffected metro lines, passenger transfers and path choices. To address the model complexity, we propose a two-level decomposition approach to split the original problem into the master problem and subproblem. The approach can ensure the optimal solution in finite iterations. To further improve the performance of the solution approach, we design multiple acceleration techniques (i.e., customizing integer cuts supporting parallel computation, solution adjustment, domain reduction for the master problem, warm start and bound contraction for the subproblem) without compromising optimality. Extensive experiments verify that the proposed method can effectively evacuate stranded passengers, improving passenger satisfaction and meanwhile reducing operational costs. The proposed two-level decomposition approach with multiple acceleration techniques demonstrates higher computational efficiency than the common commercial solver and standard two-level decomposition approach, facilitating timely disruption responses. Additionally, according to the computational results, we derive a series of managerial insights for decision-makers.

关键词： Metro disruptions Bus bridging mixed-integer non-linear programming Two-level decomposition

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Modelling reservation strategies for managing peak-hour stranding on an oversaturated metro line

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 167卷

作者： Tang, Jie Wu, Jianjun Zhang, Ping Zhang, Yufeng Cao, Jinxin Beijing Jiaotong Univ Inst Transportat Syst Sci & Engn Beijing Peoples R China Inner Mongolia Univ Inner Mongolia Acad Sci & Technol Hohhot Peoples R China

Metro is the main travel model for urban commuters in many metropolises around the world. During peak hours, large numbers of passengers pour into metro stations for rail services, but some are unable to board the trains in time and left stranded on the platform or even queuing outside the stations. The trip reservation (TR) strategy, where passengers preplan their trips and reserve their entry time to the stations. This paper develops an entry reservation strategy (ERS) to optimize the commuter flow during peak hours, and construct a multi-objective passenger flow joint optimization model based on many-to-many passenger demand to minimize the total trip cost of passengers at reservation station and the number of stranded passengers at intermediate stations. The passenger flow optimization problem is formulated as a mixed-integer non-linear programming (MINLP) model. We design an iterative sequential search algorithm combined with the GUROBI solver to obtain the parameters of the optimal ERS and the passenger flow distribution in the metro system after disaggregated reformulation of the complex constraints of the model. We also demonstrate the accuracy and effectiveness of the proposed method with two experiments - an illustrative example and a large-scale case study of Beijing Metro. The results of Beijing Metro experiment show that the joint optimization model with entry reservation strategy (JO-ERS) reduces the number of stranded passengers by 88.46 % compared with the original passenger flow from the AFC.

关键词： Reservation strategy Passenger flow control Metro system Multi-objective optimization mixed-integer non-linear programming

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Optimal scheduling method and fast-solving algorithm for large-scale virtual power plants communication networks

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APPLIED ENERGY 2024年 371卷

作者： Li, Jiaxin Xu, Zhanbo Zhou, Yuzhou Li, Yuting Wu, Jiang Guan, Xiaohong Xi An Jiao Tong Univ Syst Engn Inst MOEKLINNS Xian 710049 Peoples R China Tsinghua Univ Dept Automat CFINS TNLIST Beijing 100084 Peoples R China

As the scale of virtual power plants (VPPs) continues to expand, the communication demands between VPPs and the management center are increasing. To maintain the communication of the entire system, VPPs operators must pay high costs, and then, how to reduce communication costs as much as possible while ensuring VPPs communication requirements has become an important and difficult issue. However, in the existing literature, there are few scheduling methods for large-scale VPPs communications. To this end, this paper proposes an optimal scheduling method based on software-defined wide area network (SD-WAN) to reduce communication costs. First, the communication network architecture of large-scale VPPs is analyzed in detail, and communication services are categorized according to delay requirements. Second, for the most expensive wide area network layer, a communication network control structure based on SD-WAN is designed, and an optimal scheduling model is established to minimize communication costs while ensuring communication service quality. This model is formulated as a mixed-integer nonlinear programming problem, and then linearized and constraint-relaxed to enable solved by the state-of-the-art solver (i.e., Gurobi). Third, to further overcome the challenge of solving large-scale problems, such as low computation efficiency and memory overflow, a two-stage fast-solving algorithm is proposed, which sorts and categorizes VPPs branch sites and optimizes the problem in two stages, enabling the expedited resolution of the problem. Numerical tests verify the effectiveness of the proposed method. Especially for large-scale VPPs, the proposed algorithm improves computation efficiency by a thousand times without perceivable degradation in performance, compared to the state-of-the-art solver.

关键词： Virtual power plants Software-defined wide area network mixed-integer non-linear programming Fast-solving algorithm

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Multi-Objective Multi-Dimensional Resource Allocation for Categorized QoS Provisioning in Beyond 5G and 6G Radio Access Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第3期72卷 1790-1803页

作者： Fu, Yongqin Wang, Xianbin Fang, Fang Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada Western Univ Dept Comp Sci London ON N6A 5B9 Canada

To effectively meet the diverse Quality of Service (QoS) requirements from proliferating applications, a widely-adopted practical solution in radio access network (RAN) is categorized QoS provisioning, which utilizes virtual networks (i.e., tenants) to support a limited number of service categories. Apparently, one critical issue is RAN resource allocation among coexisting tenants. However, conventional single objective-based approaches cannot ensure fairness among different service categories. Moreover, except for radio resource, computing and storage resources also need to be considered. Besides, appropriate allocation of computing and storage resources could help mitigating backhaul network congestion. Hence, we aim to optimize the key QoS indicators of three main service categories and reduce backhaul bandwidth consumption simultaneously. We formulate the problem of multi-dimensional resource allocation from RAN to tenants as a multi-objective mixed-integer non-linear programming (MINLP) problem, which is challenging to solve directly due to the competing objectives and the mutual-influenced resources. For guaranteeing fairness, this problem is reformulated as a single-objective optimization problem using weighted sum approach. Moreover, a decoupling-based iterative optimization (DBIO) algorithm is proposed to decompose it into three subproblems to solve iteratively. Simulation results demonstrate that DBIO algorithm can achieve superior performance with much less time consumption, compared with three metaheuristic algorithms.

关键词： Multi-objective multi-dimensional resource allocation categorized QoS provisioning radio access network mixed-integer non-linear programming decoupling-based iterative optimization

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Imitation Learning for Adaptive Video Streaming With Future Adversarial Information Bottleneck Principle

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 13670-13683页

作者： Wang, Shuoyao Lin, Jiawei Ye, Fangwei Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210095 Peoples R China

Adaptive video streaming plays a crucial role in ensuring high-quality video streaming services. Despite extensive research efforts devoted to Adaptive BitRate (ABR) techniques, the current reinforcement learning (RL)-based ABR algorithms may benefit the average Quality of Experience (QoE) but suffers from fluctuating performance in individual video sessions. In this paper, we present a novel approach that combines imitation learning with the information bottleneck technique, to learn from the complex offline optimal scenario rather than inefficient exploration. In particular, we leverage the deterministic offline bitrate optimization problem with the future throughput realization as the expert and formulate it as a mixed-integer non-linear programming (MINLP) problem. To enable large-scale training for improved performance, we propose an alternative optimization algorithm that efficiently solves the formulated MINLP problem. To address the overfitting issues due to the future information leakage in MINLP, we incorporate an adversarial information bottleneck framework. By compressing the video streaming state into a latent space, we retain only action-relevant information. Additionally, we introduce a future adversarial term to mitigate the influence of future information leakage, where Model Prediction Control (MPC) policy without any future information is employed as the adverse expert. Experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing the quality of adaptive video streaming, providing a 7.30% average QoE improvement and a 30.01% average ranking reduction.

关键词： Adaptive video streaming imitation learning information bottleneck mixed-integer non-linear programming Adaptive video streaming imitation learning information bottleneck mixed-integer non-linear programming

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Optimization of a two-echelon supply chain with random demand and random defect rate under strict carbon cap policy

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BENCHMARKING-AN INTERNATIONAL JOURNAL 2022年第3期29卷 793-816页

作者： Ghosh, Arindam IFHE Deemed Univ Dept Operat & IT ICFAI Business Sch IBS Hyderabad India

Purpose The yield of defective items and emissions of greenhouse gases in supply chains are areas of concern. Organizations try to reduce the yield defective items and emissions. In this paper, a constrained optimization model is developed with consideration of the yield of defective items and strict carbon cap policy simultaneously and then optimized. Further, sensitivity analyses have been carried out to draw different managerial insights. Precisely, we have tried to address the following research questions: (1) how to optimize the cost for a two-echelon supply chain considering yield of defective items and strict carbon cap policy, (2) how the total expected cost and total expected emissions act with changing parameters. Design/methodology/approach The mathematical modeling approach has been adopted to develop a model and further optimized it with optimization software. Costs and emissions from different areas of a supply chain have been derived and then the total cost and total emissions have been formulated mathematically. One constrained mixed-integer nonlinear programming (MINLP) problem has been formulated and solved considering emissions-related, velocity and production related-constraints. Further, different sensitivity analyses have been derived to draw some managerial insights. Findings In this paper, many decision variables have been calculated with a set of basic values of other parameters. It has been found that both cost and emissions can be controlled by controlling different parameters. It has been also found that some parameters have very little or no influence either on cost or emissions. In most cases, originations may exhaust the given limit of carbon cap to optimize their costs. Originality/value In spite of my sincere efforts, no paper has been found that has considered the yield of defective items and strict carbon cap policy simultaneously. In this paper, it is assumed that both demand and defect rates are random in nature. The model, prese

关键词： Supply chain Defective items Strict carbon cap policy mixed-integer non-linear programming

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Bi-Criteria Optimisation for Energy Harvesting Relaying: Formulation and Algorithm Implementation

Bi-Criteria Optimisation for Energy Harvesting Relaying: For...

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IEEE Global Communications Conference (GLOBECOM)

作者： AlQahtani, Dokhyl Chen, Yunfei Univ Warwick Sch Engn Coventry W Midlands England

ISBN: (纸本)9781665435406

This paper proposes a bi-criteria optimisation framework that maximises both the network rate and the harvested energy, which are contradictory objectives. Using the practical non-linear energy harvesting (EH) model, we jointly optimise relay selection (RS), power splitting (PS) and power allocation (PA). We decouple the relay selection variables from the other resource allocation variables to convert the original mixed-integer non-linear programming (MINLP) problem into a tractable problem. For PS and PA, the well-known epsilon-constraint method is applied to convert the bi-criteria problem into a convex problem. For RS, we propose a sub-optimal algorithm based on a selection order function with linear complexity. The simulation results indicate that the proposed schemes perform better than the benchmarks, drastically reducing computational complexity from exponential to polynomial.

关键词： Bi-Criteria optimisation epsilon-constraint method mixed-integer non-linear programming relay selection non-linear energy harvesting model

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Airline Network Planning: mixed-integer non-convex optimization with demand-supply interactions

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2021年 154卷 100-124页

作者： Birolini, Sebastian Jacquillat, Alexandre Cattaneo, Mattia Antunes, Antonio Pais Univ Bergamo Dept Management Informat & Prod Engn Bergamo Italy MIT Sloan Sch Management 77 Massachusetts Ave Cambridge MA 02139 USA Univ Coimbra Dept Civil Engn CITTA Coimbra Portugal

Airlines routinely use analytics tools to support flight scheduling, fleet assignment, revenue management, crew scheduling, and many other operational decisions. However, decision support systems are less prevalent to support strategic planning. This paper fills that gap with an original mixed-integer non-convex optimization model, named Airline Network Planning with Supply and Demand interactions (ANPSD). The ANPSD optimizes network planning (including route selection, flight frequencies and fleet composition), while capturing interdependencies between airline supply and passenger demand. We first estimate a demand model as a function of flight frequencies and network configuration, using a two-stage least-squares procedure fitted to historical data, and then formalize the ANPSD by integrating the empirical demand function into an optimization model. The model is formulated as a non-convex mixed-integer program. To solve it, we develop an exact cutting plane algorithm, named 2..ECP, which iteratively generates hyperplanes to develop an outer approximation of the non-linear demand functions. Computational results show that the 2.. ECP algorithm outperforms state-of-the-art benchmarks and generates tight solution quality guarantees. A case study based on the network of a major European carrier shows that the ANPSD provides much stronger solutions than baselines that ignore - fully or partially - demand-supply interactions.

关键词： Airline strategic planning demand-supply interactions mixed-integer non-linear programming Outer approximation

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