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An integrated chance-constrained stochastic model for a preemptive multi-skilled multi-mode resource-constrained project scheduling problem: A case study of building a sports center

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期126卷

作者： Mirnezami, Seyed-Ali Tavakkoli-Moghaddam, Reza Shahabi-Shahmiri, Reza Ghasemi, Mohammad Shahed Univ Fac Engn Dept Ind Engn Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

A multi-mode resource-constrained project scheduling problem (MRCPSP) with multiple skills is investigated in this paper. Unlike the traditional form of this problem, and considering the real-world project circumstances, project activities can be preempted. In this paper, a new multi-objective mixed-integer linear programming (MILP) model with three objective functions is extended. These objectives are: (1) minimizing the project makespan, (2) minimizing the total resource costs, and (3) minimizing the total project risk. Based on real-life projects, non-renewable resources are represented as an uncertain stochastic parameter. To cope with the uncertain environment, chance-constrained programming with a confidence level is considered. A real-world construction project of a sports center in Tehran is utilized to demonstrate the applicability of the presented formulation. A well-known lexicographic optimization method, namely AUGMECON2, is applied to solve the proposed formulation with three objectives. Ultimately, for the case study and two datasets J30 and MM50, the proposed lexicographic optimization algorithm is compared with an efficient multi-objective mathematical programming technique known as the AUGMECON method. The comparison is based on performance metrics (i.e., IGD and HV) commonly used in multi-objective optimization. The results show the relative dominance of the proposed lexicographic optimization algorithm over the AUGMECON method in all sizes of the problem instances.

关键词： Project scheduling problem Multiple skills Activity preemption chance-constrained programming Uncertainty

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Two-stage data-driven dispatch for integrated power and natural gas systems by using stochastic model predictive control

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APPLIED ENERGY 2023年第1期343卷

作者： Zhao, Yuehao Li, Zhiyi Ju, Ping Zhou, Yue Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China Cardiff Univ Sch Engn Cardiff CF24 3AA Wales

The optimal dispatch of the integrated power and natural gas systems can increase the utilization rate of renewable energy and energy efficiency while decreasing operation costs. The common prediction errors of wind power and electric load have the potential to negatively impact the normal operation of the integrated power and natural gas systems. A two-stage data-driven dispatch strategy is proposed to reduce this effect, consisting of the day-ahead dispatch stage and the intraday rolling dispatch stage using stochastic model predictive control (MPC). In the day-ahead dispatch stage, the data-driven chance constraints of tie-line power and reserve of gas -fired generators are built, and the day-ahead tie-line power is obtained and regarded as input parameters to the intraday dispatch stage. In the intraday dispatch stage, the data-driven chance constraints of tie-line power and reserve of gas-fired generators with the latest rolling prediction data are built, and the remaining control vari-ables are obtained. The distribution characteristics of the stochastic prediction errors of wind power and electric load are captured and described by the variational Bayesian Gaussian mixture model with massive historical data. Then the original stochastic mixed-integer nonlinear programming problem is converted to a tractable deterministic one by the quantile-based analytical reformulation and convex relaxation technique. Finally, the proposed strategy is verified by the numerical experiments based on a modified IEEE 33-bus system integrated with a 10-node natural gas system and a micro hydrogen system. The numerical results demonstrate that the proposed strategy reduces the actual costs and decreases the violation rate caused by the stochastic prediction errors of wind power and electric load.

关键词： Two-stage dispatch chance-constrained programming Data-driven Stochastic model predictive control Integrated power and natural gas systems

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A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

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IISE TRANSACTIONS 2023年第10期55卷 971-984页

作者： Moug, Kati Jia, Huiwen Shen, Siqian Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. We optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

关键词： Stochastic programming evacuation planning ridesharing chance-constrained programming conditional value at risk (CVaR)

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Sustainable transportation planning considering traffic congestion and uncertain conditions

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 227卷

作者： Babaei, Ardavan Khedmati, Majid Jokar, Mohammad Reza Akbari Tirkolaee, Erfan Babaee Sharif Univ Technol Dept Ind Engn Tehran Iran Istinye Univ Dept Ind Engn Istanbul Turkiye

Transportation activities, especially road transportation, have a great impact on economic growth. On the other hand, sustainability is a major concern for transportation planning. In this work, a data-oriented network is developed to evaluate the sustainability of vehicle types. Then, this network is integrated with a multi-objective optimization model in order to provide the planning of a three-stage transportation problem, according to traffic congestion. Some criteria including total profit, efficiency of different vehicle types, relationship among the customers supplied by a specified retailer, risk of underestimating unmet demand, and selling price are used to determine the objective functions. The chance-constrained programming (CCP) and Chebyshev Goal Pro-gramming (CGP) approaches are applied to solve the proposed integrated model. To the best of the authors' knowledge, it is the first time that traffic congestion under the conditions of simultaneous fuzzy and stochastic uncertainty has been integrated into sustainable transportation planning. In addition, the applicability and validity of the developed model are assessed on a case study. The results are then analyzed and appraised by Data Envelopment Analysis (DEA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. The findings prove that the components of the proposed model have a very beneficial effect on the solution, and also perform much better than the competing approaches in the literature. Two important points from the results of this paper are that (a) traffic congestion is more effective in the initial levels of the supply chain, and (b) transportation planning using efficient vehicles may reduce the desirability of the objective function values.

关键词： Sustainable transportation planning Three-stage transportation problem Traffic congestion Data-oriented network Multi-objective optimization chance-constrained programming

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Unveiling environmental implications of Canadian electricity system's low-carbon transitions: A multi-regional stochastic optimization-driven input-output model

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JOURNAL OF CLEANER PRODUCTION 2023年第1期420卷

作者： Chen, Leian Huang, Guohe Luo, Bin Liu, Lirong Univ Regina Environm Syst Engn Program Regina SK S4S 0A2 Canada Univ Surrey Ctr Environm & Sustainabil Guildford GU2 7XH England

Low-carbon transition of the electricity system is essential for achieving greenhouse gas (GHG) emission mitigation goals. Nevertheless, such a transition involves various complexities and the related environmental impacts on various sectors need to be explored. In this study, a multi-regional stochastic optimization-driven input-output model (STIO) has been developed to unveil the environmental implications of the low-carbon transitions under various policy scenarios from a sectoral perspective and applied to Canada. STIO can (1) explore optimal decarbonization pathways under uncertainties through a stochastic electricity system optimization module;(2) link the outputs to a power generation technology-focused input-output module to examine sectoral-level environmental impacts;(3) reflect the interactive effects of multiple policy factors on system response through an interaction analysis module. Results find that higher carbon taxes would reduce direct and indirect emissions of the electricity sector by 38.3% and 33.1%, respectively, in 2050. Specifically, the significance of indirect emissions from the natural gas-fired power sector in Alberta is highlighted;its reduced production would indirectly curtail emissions in the oil and gas extraction (Canada's largest GHG emissions source) and services (the highest GDP contributor to Canada's economy) sectors by 15.9% and 16.1%, respectively. Further analysis shows that the interaction among a high-level carbon tax and increasing capital cost of natural gas-fired power technology would contribute to the electricity sector's indirect emission reductions. The findings can help policymakers gain insights into the environmental impacts of various decarbonization pathways on the whole economy.

关键词： Energy system optimization model Environmentally-extended input-output analysis Factorial analysis chance-constrained programming Carbon tax GHG emission mitigation

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Development of a multi-regional factorial optimization model for supporting electric power system's low-carbon transition-A case study of Canada

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RESOURCES CONSERVATION AND RECYCLING 2023年第1期194卷

作者： Chen, Leian Huang, Guohe Chen, Jiapei Luo, Bin Univ Regina Environm Syst Engn Program Regina SK S4S 0A2 Canada

In this study, a multi-regional factorial optimization model (MRFO) has been developed for supporting nation-wide transitions to low-carbon electric power systems (EPS) under the commitment to reduce greenhouse gas emissions. Through integrating non-deterministic optimization methods (interval linear, chance-constrained, and mixed-integer linear programming) with factorial analysis, MRFO can address multiple uncertainties stated as intervals and probability distribution in system parameters and objectives;it can also unveil the effects of multiple uncertain parameters and their interactions on system performance. A Canadian case study is provided to demonstrate the applicability of the proposed approach. Optimal schemes of electricity generation, capacity expansion, and inter-regional trades at different risk levels are examined with the objective of minimizing the total system costs. Results indicate that renewable energy (i.e., wind and solar) would play an important role in facilitating the transitions to low-carbon EPS, which would contribute to approximately 13% of the total national electricity generation by 2050. In addition, increasing the collaboration among regional EPS would have positive effects on the national penetration of low-carbon power generation in the light of the diversities in regional generation mixes. The findings can support the national efforts in formulating desired long-term power gener-ation expansion plans and emission reduction policies.

关键词： Optimization Factorial analysis Net-zero carbon emission Low-carbon technology Inter-regional electricity trades chance-constrained programming

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Data-driven distributionally robust chance-constrained optimization with Wasserstein metric

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JOURNAL OF GLOBAL OPTIMIZATION 2021年第4期79卷 779-811页

作者： Ji, Ran Lejeune, Miguel A. George Mason Univ Dept Syst Engn & Operat Res Fairfax VA 22030 USA George Washington Univ Dept Decis Sci Washington DC USA

We study distributionally robust chance-constrained programming (DRCCP) optimization problems with data-driven Wasserstein ambiguity sets. The proposed algorithmic and reformulation framework applies to all types of distributionally robust chance-constrained optimization problems subjected to individual as well as joint chance constraints, with random right-hand side and technology vector, and under two types of uncertainties, called uncertain probabilities and continuum of realizations. For the uncertain probabilities (UP) case, we provide new mixed-integer linear programming reformulations for DRCCP problems. For the continuum of realizations case with random right-hand side, we propose an exact mixed-integer second-order cone programming (MISOCP) reformulation and a linear programming (LP) outer approximation. For the continuum of realizations (CR) case with random technology vector, we propose two MISOCP and LP outer approximations. We show that all proposed relaxations become exact reformulations when the decision variables are binary or bounded general integers. For DRCCP with individual chance constraint and random right-hand side under both the UP and CR cases, we also propose linear programming reformulations which need the ex-ante derivation of the worst-case value-at-risk via the solution of a finite series of linear programs determined via a bisection-type procedure. We evaluate the scalability and tightness of the proposed MISOCP and (MI)LP formulations on a distributionally robust chance-constrained knapsack problem.

关键词： Distributionally robust optimization chance-constrained programming Wasserstein metric Mixed-integer programming

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Water Distribution Network Optimization Model with Reliability Considerations in Water Flow (Debit)

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WATER 2023年第17期15卷 3119页

作者： Mawengkang, Herman Syahputra, Muhammad Romi Sutarman, Sutarman Weber, Gerhard Wilhelm Univ Sumatera Utara Dept Math Medan 20155 Indonesia Poznan Univ Tech Fac Engn Management PL-60965 Poznan Poland Middle East Tech Univ Inst Appl Math TR-06800 Ankara Turkiye

Water distribution networks (WDNs) are defined as the planning for the development, distribution, and utilization of water resources. The main challenge of WDNs is to preserve limited water resources while providing effective benefits from these resources in accordance with environmental considerations. Water distribution networks use hydraulic components to connect water resources to consumers. The diameter of each pipe, the layout of the pipe network, and the total length of pipes all contribute to the most effective layout for a water distribution system. This study considers the assurance that the flow (discharge) of water is in accordance with what is expected, with such aspects apt to be described as a particular form of reliability. As a result, this study proposes a stochastic optimization model with non-linear probability constraints for overcoming the challenges of water distribution networks while taking water flow reliability into account. The pressure drop equation causes the non-linear shape. The stochastic model of the opportunity constraint is changed to a deterministic multi-objective model using an approach based on integer programming and sample averaging to solve the resulting model. The direct search approach (neighbourhood search) is then applied to tackle the integer part.

关键词： water network problem chance-constrained programming modelling neighbourhood search operation research for development

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On the Egalitarian-Utilitarian spectrum in stochastic capacitated resource allocation problems

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2023年第1期262卷

作者： Li, Linda Firouz, Mohammad Ahmed, Abdulaziz Delen, Dursun Missouri State Univ Coll Business Dept Mkt Springfield MO 65897 USA Univ Alabama Birmingham Collat Sch Business Dept Management Informat Syst & Quantitat Methods Birmingham AL 35294 USA Univ Alabama Birmingham Sch Hlth Profess Dept Hlth Serv Adm Birmingham AL 35233 USA Oklahoma State Univ Spears Sch Business Dept Management Sci & Informat Syst Tulsa OK 74106 USA Istinye Univ Fac Engn & Nat Sci Dept Ind Engn Istanbul Turkiye

In this paper, we study a generalized resource allocation problem where a limited resource is to be allocated to a set of agencies with stochastic receiving capacities while taking into account the decision-maker's preference towards equity in allocation. Taking the empiric distributional nature of the capacities into account, we formulate the problem in a chance-constrained based Mixed-Integer programming framework with user -specified reliability and tolerance levels as well as equity preference level. We show that the problem can be conveniently reduced to a linear equivalent with adjusted capacity constraints. Deriving the tightest lower and upper bounds corresponding to the utilitarian and egalitarian perspectives, respectively, we give the closed -form optimal solutions for such cases. Using real data, we test the behavior of our model with a view towards the inherent equity-efficiency as well as reliability-efficiency trade-offs. We further characterize the cost and value of information in a detailed analysis.

关键词： chance-constrained programming Efficient frontier Egalitarian Resource allocation Stochastic capacities Utilitarian

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Optimization of maintenance for complex manufacturing systems using stochastic Remaining Useful Life prognostics

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期182卷

作者： He, Junkai Khebbache, Selma Anjos, Miguel F. Hadji, Makhlouf Inst Mines Telecom Telecom SudParis 9 Rue Charles Fourier F-91000 Evry France Technol Res Inst SystemX 8 Ave Vauve F-91120 Palaiseau France Univ Edinburgh Sch Math Peter Guthrie Tait Rd Edinburgh EH9 3FD Scotland

This paper leverages Remaining Useful Life (RUL) prognostic information for preventive maintenance planning in complex manufacturing factories. Such a factory consists of multiple complex systems that use redundant components as backups to ensure system availability. The purpose is to minimize production fluctuations in the factory due to maintenance or system breakdown. To achieve this, we propose a Mixed-Integer Linear programming (MILP) model that minimizes the overall production loss. Furthermore, we incorporate random RUL decrease rates according to reliability theory and extend the MILP model using chance-constrained programming. A novel approximation method for dealing with chance constraints is proposed to solve the stochastic model and approximation properties are analyzed. Computational results show that (i) our MILP model can provide more reasonable maintenance decisions compared to the literature. (ii) Sensitivity analysis reports that RUL prognostics and thresholds significantly impact maintenance decisions. (iii) The analysis of the impacts of confidence levels in chance constraints provides decision-makers with the flexibility to adjust maintenance planning under different levels of uncertainty.

关键词： Maintenance Remaining useful life prognostic Complex manufacturing system Mixed integer linear programming chance-constrained programming

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