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Joint stochastic optimisation of open-pit mine production scheduling with ramp design

INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2024年第6期38卷 480-495页

作者： Cutler, Jacob Dimitrakopoulos, Roussos McGill Univ Dept Min & Mat Engn Montreal PQ Canada

stochastic optimisation of open-pit mine production scheduling maximises the NPV of a mine's extraction sequence while satisfying production constraints and minimising deviations from targets. Past approaches consider mining block access based on slope constraints, thus providing no guarantee that the infrastructure required for equipment access is feasible. To address this issue, a joint stochastic optimisation of life-of-mine production scheduling and ramp design is proposed, ensuring feasible equipment access to the related mining blocks. An application at a gold mine demonstrates the value of considering the complex relationship that exists between ramp design, equipment access and production scheduling during optimisation.

关键词： Open-pit mine production scheduling optimisation stochastic integer programming ramp design

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stochastic Resource Optimization for Wireless Powered Hybrid Coded Edge Computing Networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第3期23卷 2022-2038页

作者： Ng, Wei Chong Lim, Wei Yang Bryan Xiong, Zehui Niyato, Dusit Poor, H. Vincent Shen, Xuemin Sherman Miao, Chunyan Nanyang Technol Univ NTU Alibaba Grp Singapore 639798 Singapore Nanyang Technol Univ NTU Alibaba NTU Joint Res Inst Singapore 639798 Singapore Singapore Univ Technol Design Pillar Informat Syst Technol & Design Singapore 487372 Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

To enable ubiquitous Artificial Intelligence (AI) in the next-generation wireless communications networks, computation-intensive tasks such as data processing and model training have to be performed by energy-constrained end users. In this paper, we present a hybrid coded edge computing network whereby users can choose to complete their computation task through: i) local computation with the wireless power transfer derived from base stations, ii) coded edge offloading, or iii) hybrid computation involving edge offloading and local computation. To minimize the overall network cost, we propose a stochastic resource optimization approach. Given the stochastic nature of wireless charging efficiency and edge servers computation capacities, which can only be observed ex-post , a computation strategy for each user is determined using the two-stage stochastic integer programming (SIP). To address the complexity of the SIP problem which scales with the size of the network, we introduce the efficient computation methods of Benders' decomposition and sample average approximation. Besides, we present a special case of z -stage stochastic offloading optimization that is applicable when the corrective edge offloading action can be executed in multiple stages, e.g., for non-time-sensitive tasks that do not need to be completed by stage two. Finally, we provide extensive sensitivity analyses to evaluate the performance of the proposed cost minimization approach amid varying network parameters. We demonstrate that our approach outperforms deterministic optimization approaches for in-network cost minimization.

关键词： Task analysis Servers Internet of Things Wireless communication stochastic processes Computational modeling Encoding Edge computing resource management network economics multi-tier computing stochastic integer programming

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Heterogeneous Multi-resource Planning and Allocation Under stochastic Demand

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INFORMS JOURNAL ON COMPUTING 2023年第5期35卷 929-951页

作者： Baxter, Arden Keskinocak, Pinar Singh, Mohit Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Georgia Inst Technol Ctr Hlth & Humanitarian Syst Atlanta GA 30332 USA

We study the capacity planning and allocation decisions for multiple heterogeneous resources, considering potential demand scenarios, where each demand requests a subset of the available resource types simultaneously at a specified time, location, and duration (smRmD). We model this problem as a two-stage stochastic integer program and consider two variants for the objective function: (a) maximize the expected reward of demands met over all scenarios, subject to a budget B for resources, and (b) maximize the expected reward of demands met over all scenarios minus the cost of resources. Contributions of this work include (i) a thorough complexity analysis of smRmD and its variants, (ii) analysis of structural properties, (iii) development of various approximation algorithms using the unique structural properties of smRmD and its variants, and (iv) an extensive computational study to explore the ease with which exact and approximate solutions may be found.

关键词： stochastic integer programming resource allocation resource planning approximation algorithms

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stochastic Resource Allocation for Semantic Communication-aided Virtual Transportation Networks in the Metaverse 25

Stochastic Resource Allocation for Semantic Communication-ai...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Ng, Wei Chong Du, Hongyang Lim, Wei Yang Bryan Xiong, Zehui Niyato, Dusit Miao, Chunyan Alibaba Grp Hangzhou Peoples R China Alibaba NTU Joint Res Inst Hangzhou Peoples R China Energy Res Inst NTU Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9798350303582;9798350303599

The physical-virtual world synchronization to develop the Metaverse will require a massive transmission and exchange of data. In this paper, we introduce semantic communication for the development of virtual transportation networks in the Metaverse. Leveraging the perception capabilities of edge devices, virtual service providers (VSPs) can subscribe to their preferred edge devices to receive the semantic data of interest. However, the demands of the VSPs are highly dependent on the users that they are serving. To address the resource allocation problem amid stochastic user demand, we propose a stochastic semantic transmission scheme (SSTS) based on two-stage stochastic integer programming. Using real data captured by edge devices we deploy in Singapore, the simulation results show that SSTS can minimize the transmission cost of the VSPs while accounting for the users' demand uncertainties.

关键词： Metaverse Semantic Communication Resource Allocation stochastic integer programming

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stochastic stope design optimisation under grade uncertainty and dynamic development costs

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2022年第2期36卷 81-103页

作者： Faria, Matheus Furtado e Dimitrakopoulos, Roussos Pinto, Claudio McGill Univ Dept Min & Mat Engn Cosmo Stochast Mine Planning Lab Montreal PQ Canada Univ Fed Minas Gerais Dept Min Engn Belo Horizonte MG Brazil

Stope design optimisation defines three-dimensional extraction volumes aiming to maximise cashflows, subject to geotechnical and operational constraints. Available stope layout methods are deterministic, failing to account for grade uncertainty and variability that affect stope locations and sizes, as well as value. A two-stage stochastic integer programming model for stope design optimisation is proposed, integrating grade uncertainty quantified through geostatistical simulations, level allocation, variable stope and pillar sizes for different geotechnical zones, and development costs. An application at an underground gold mine employing sublevel open stoping highlights the integration and management of grade uncertainty to define risk-resilient stope design.

关键词： Stope design optimisation stochastic integer programming underground mine planning geological uncertainty

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UAV-assisted Wireless Power Charging for Efficient Hybrid Coded Edge Computing Network

UAV-assisted Wireless Power Charging for Efficient Hybrid Co...

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IEEE International Conference on Communications (ICC)

作者： Ng, Jer Shyuan Ng, Wei Chong Lim, Wei Yang Bryan Xiong, Zehui Niyato, Dusit Leung, Cyril Miao, Chunyan Alibaba Grp Singapore Singapore Alibaba NTU JRI Singapore Singapore SUTD Singapore Singapore Nanyang Technol Univ SCSE Singapore Singapore Nanyang Technol Univ LILY Res Ctr Singapore Singapore Univ British Columbia ECE Vancouver BC Canada

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

With the ubiquitous sensing enabled by the Internet-of-Things (IoT), massive amount of data is generated every second, transforming the way we interact with the world. To manage big data and enable analytics at the edge of the network, large amount of computation power is required to perform the computation intensive tasks. However, the energy-constrained IoT devices are not able to perform the computation tasks without compromising the quality-of-service of the applications. In this paper, we propose a hybrid network in which users can offload their computation tasks to edge servers through coded edge offloading or perform local computation with the wireless power transfer derived from coalitions of unmanned aerial vehicles (UAVs) serving as mobile charging stations. We consider a two-level optimization approach where an optimal UAV coalitional structure that minimizes the network cost is formed. In the performance evaluation, we provide extensive sensitivity analyses to study the performance of the cost minimization approach amid varying network parameters.

关键词： Edge computing coded computation unmanned aerial vehicles stochastic integer programming coalition

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Underground mine scheduling under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期294卷 340-352页

作者： Nesbitt, Peter Blake, Lewis R. Lamas, Patricio Goycoolea, Marcos Pagnoncelli, Bernardo K. Newman, Alexandra Brickey, Andrea Naval Postgrad Sch Operat Res Dept Monterey CA 93943 USA Colorado Sch Mines Dept Appl Math & Stat Golden CO 80401 USA Univ Adolfo Ibanez Sch Business Santiago Chile Colorado Sch Mines Dept Mech Engn Golden CO 80401 USA South Dakota Sch Mines & Technol Dept Min Engn & Management Rapid City SD 57701 USA

Underground mine schedules seek to determine start dates for activities related to the extraction of ore, often with an objective of maximizing net present value;constraints enforce geotechnical precedence be-tween activities, and restrict resource consumption on a per-time-period basis, e.g., development footage and extracted tons. Strategic schedules address these start dates at a coarse level, whereas tactical sched-ules must account for the day-to-day variability of underground mine operations, such as unanticipated equipment breakdowns and ground conditions, both of which might slow production. At the time of this writing, the underground mine scheduling literature is dominated by a deterministic treatment of the problem, usually modeled as a Resource Constrained Project Scheduling Problem (RCPSP), which pre-cludes mine operators from reacting to unforeseen circumstances. Therefore, we propose a stochastic integer programming framework that: (i) characterizes uncertainty in duration and economic value for each underground mining activity;(ii) formulates a new stochastic variant of the RCPSP;(iii) suggests an optimization-based heuristic;and, (iv) produces implementable, tactical schedules in a practical amount of time and provides corresponding managerial insights. (c) 2021 Elsevier B.V. All rights reserved.

关键词： OR in natural resources stochastic integer programming Optimization-based heuristics Project scheduling Underground mining

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Logic-Based Benders Decomposition and Binary Decision Diagram Based Approaches for stochastic Distributed Operating Room Scheduling

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INFORMS JOURNAL ON COMPUTING 2021年第4期33卷 1551-1569页

作者： Guo, Cheng Bodur, Merve Aleman, Dionne M. Urbach, David R. Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Univ Toronto Inst Hlth Policy Management & Evaluat Toronto ON M5S 3E3 Canada Univ Hlth Network Techna Inst Toronto ON M5G 1P5 Canada Univ Toronto Dept Surg Toronto ON M5S 3E3 Canada Womens Coll Hosp Dept Surg Toronto ON M5S 1B2 Canada

The distributed operating room (OR) scheduling problem aims to find an assignment of surgeries to ORs across collaborating hospitals that share their waiting lists and ORs. We propose a stochastic extension of this problem where surgery durations are considered to be uncertain. In order to obtain solutions for the challenging stochastic model, we use sample average approximation and develop two enhanced decomposition frameworks that use logic-based Benders (LBBD) optimality cuts and binary decision diagram based Benders cuts. Specifically, to the best of our knowledge, deriving LBBD optimality cuts in a stochastic programming context is new to the literature. Our computational experiments on a hospital data set illustrate that the stochastic formulation generates robust schedules and that our algorithms improve the computational efficiency. Summary of Contribution: We propose a new model for an important problem in healthcare scheduling, namely, stochastic distributed operating room scheduling, which is inspired by a current practice in Toronto, Ontario, Canada. We develop two decomposition methods that are computationally faster than solving the model directly via a state-of-the-art solver. We present both some theoretical results for our algorithms and numerical results for the evaluation of the model and algorithms. Compared with its deterministic counterpart in the literature, our model shows improvement in relevant evaluation metrics for the underlying scheduling problem. In addition, our algorithms exploit the structure of the model and improve its solvability. Those algorithms also have the potential to be used to tackle other planning and scheduling problems with a similar structure.

关键词： distributed operating room scheduling stochastic integer programming logic-based Benders decomposition binary decision diagram

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On the Values of Vehicle-to-Grid Electricity Selling in Electric Vehicle Sharing

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2021年第2期23卷 488-507页

作者： Zhang, Yiling Lu, Mengshi Shen, Siqian Univ Minnesota Dept Ind & Syst Engn Minneapolis MN 55455 USA Purdue Univ Krannert Sch Management W Lafayette IN 47907 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

Problem definition: We study electric vehicle (EV) sharing systems and explore the opportunity for incorporating vehicle-to-grid (V2G) electricity selling in EV sharing. Academic/practical relevance: The problem involves complex planning and operational decisions, as well as multiple sources of uncertainties. The related optimization models impose significant computational challenges. The potential value of V2G integration may have far-reaching impacts on EV sharing and sustainability. Methodology: We formulate the problem as a two-stage stochastic integer linear program. In the first stage, we optimize decisions related to service planning, the capacity of parking and charging facilities, EV battery capacities, and EV allocation in each zone under uncertain time-dependent trip demand and electricity prices. In the second stage, for a realized demand-price scenario, we construct a time-and-charging-status expanded transportation network and optimize operations of the shared vehicle fleet, EV battery charging, and V2G selling. We develop Benders decomposition and scenario decomposition approaches to improve computational efficiency. A linear-decision-rule-based approximation approach is also provided to model dynamic operations. Results: Via testing instances based on real-world and synthetic data, we demonstrate the computational efficacy of our approaches and study the benefits of integrating V2G in EV sharing from the service provider, consumer, and socioenvironmental aspects. Managerial implications: V2G integration can significantly increase the profitability of EV sharing and the quality of service. It results in the preference of larger EV fleets and battery capacities, which further leads to various socioenvironmental benefits. The benefit of V2G can still prevail, even with more severe battery degradation and can be more significant when combined with (i) more stringent service levels, (ii) more traffic congestion, or (iii) urban spatial structures with conc

关键词： sustainable transportation energy systems electric vehicle sharing vehicle-to-grid stochastic integer programming decomposition algorithms

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Resource distribution under spatiotemporal uncertainty of disease spread: stochastic versus robust approaches

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COMPUTERS & OPERATIONS RESEARCH 2023年 149卷

作者： Basciftci, Beste Yu, Xian Shen, Siqian Univ Iowa Tippie Coll Business Dept Business Analyt Iowa City IA 52242 USA Ohio State Univ Dept Integrated Syst Engn Columbus OH USA Univ Michigan Ann Arbor Dept Ind & Operat Engn Ann Arbor MI USA

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the "demand-driven"properties of the SP and DRO solutions. Our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage.

关键词： COVID-19 pandemic Vaccine distribution Resource allocation stochastic integer programming Distributionally robust optimization Multi-objective optimization

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