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Integrating environmental considerations and resilience in material sourcing for construction projects: A two-stage stochastic programming model

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Chen, Zixuan Hammad, Ahmed W. A. Alyami, Mana Haddad, Assed N. Univ New South Wales UNSW Sydney Sch Built Environm Sydney Australia Macroview Projects Sydney Australia Najran Univ Coll Engn Dept Civil Engn Najran Saudi Arabia Univ Fed Rio De Janeiro Environm Engn Program Rio De Janeiro Brazil

This study presents a multi-objective two-stage stochastic model to optimise supplier selections, material ordering, inventory management, and material trans-shipments within a construction project portfolio. The model is designed to address challenges faced by general contractors when managing a project portfolio, particularly in controlling financial costs and minimising the environmental impact associated with material transportation and storage. Material back-up sourcing is integrated into the model as a strategy to mitigate supply chain disruptions. The model focuses on assisting the general contractor's resilient material management decisions in response to supply disruptions influenced by multiple uncertain factors. To achieve this, disruption probabilities are estimated using a Bayesian network based on primary suppliers' reliabilities and the macro market conditions. An analysis is undertaken to assess the impact of the Bayesian network-based scenario modelling approach on model results. Additionally, sensitivity analysis reveals that higher penalties for material shortages or storage fees have a minimal effect on total supply chain costs due to the contractor's capacity to utilise back-up sourcing and trans-shipments. Similarly, while higher costs for back-up suppliers increase overall expenses, the total carbon dioxide equivalent emissions are reduced by approximately 20% due to decreasing deliveries and trans-shipment activities. This study uses a novel Bayesian network-based approach to model CSC disruption scenarios, and further contributes to construction supply chain resilience studies by integrating backup sourcing decisions and project portfolio material trans-shipments within a two-stage stochastic programming framework.

关键词： Construction supply chains Resilience Material trans-shipments Back-up sourcing two-stage stochastic programming Bayesian network

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two-stage stochastic programming problems involving multi-choice parameters

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APPLIED MATHEMATICS AND COMPUTATION 2014年 240卷 109-114页

作者： Barik, S. K. Biswal, M. P. Chakravarty, D. Indian Inst Technol Dept Math Kharagpur 721302 W Bengal India Indian Inst Technol Dept Min Engn Kharagpur 721302 W Bengal India

In this paper, we propose a two-stage stochastic linear programming model considering some of the right hand side parameters of the first stage constraints as multi-choice parameters and rest of the right hand side parameters of the constraints as exponential random variables with known means. Both the randomness and multi-choiceness are simultaneously considered for the model parameters. Randomness is characterized by some random variables with its distribution and multi-choiceness is handled by using interpolating polynomials. To solve the proposed problem, first we remove the fuzziness and then for multi-choice parameters interpolating polynomials are established. After establishing the deterministic equivalent of the model, standard mathematical programming technique is applied to solve the problem. A numerical example is presented to demonstrate the usefulness of the proposed methodology. (C) 2014 Elsevier Inc. All rights reserved.

关键词： stochastic programming two-stage stochastic programming Exponential random variables Multi-choice parameter Lagrange interpolating polynomials

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two-stage stochastic programming for the Refined Oil Secondary Distribution With Uncertain Demand and Limited Inventory Capacity

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IEEE ACCESS 2020年 8卷 119487-119500页

作者： Li, Zhenping Zhang, Yuwei Zhang, Guowei Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China Capital Univ Econ & Business Sch Management Engn Beijing 100070 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

In recent years, more and more oil companies adopt initiative delivery mode to make the refined oil secondary distribution scheme. In this work, we focus on the optimization problem of refined oil secondary distribution based on the initiative distribution mode considering stochastic demand and the limited inventory capacity of each petrol station. We present a two-stage stochastic programming model that determines the replenishment quantity of each petrol station based on its existing stock and the available supply quantity of each oil depot, as well as transportation schedule. When the uncertainty in demand can be captured via a finite set of scenarios, the two-stage stochastic programming model is transformed into an equivalent deterministic mixed integer programming model that can be efficiently solved by CPLEX solver. The effectiveness of the two-stage stochastic programming model is verified by simulation on extensive computer-generated instances. To solve practical problems with a large number of scenarios, we propose a method to reduce the problem scale by merging similar scenarios. We demonstrate that compared to the optimal solution obtained from the model with all scenarios, the gap corresponding to the model with merged scenarios is always less than 1%. The results of the sensitivity analysis show that an increase in the inventory capacity leads to a decrease in the total cost within a certain range. The results of this study can help companies making refined oil secondary distribution plan.

关键词： Refined oil secondary distribution initiative distribution mode transportation stochastic demand limited inventory capacity two-stage stochastic programming

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two-stage stochastic programming WITH LINEARLY BI-PARAMETERIZED QUADRATIC RECOURSE

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SIAM JOURNAL ON OPTIMIZATION 2020年第3期30卷 2530-2558页

作者： Liu, Junyi Cui, Ying Pang, Jong-Shi Sen, Suvrajeet Univ Southern Calif Daniel J Epstein Dept Ind & Syst Engn Los Angeles CA 90089 USA Univ Minnesota Dept Ind & Syst Engn Minneapolis MN 55455 USA

This paper studies the class of two-stage stochastic programs with a linearly bi-parameterized recourse function defined by a convex quadratic program. A distinguishing feature of this new class of nonconvex stochastic programs is that the objective function in the second stage is linearly parameterized by the first-stage decision variable, in addition to the standard linear parameterization in the constraints. While a recent result has established that the resulting recourse function is of the difference-of-convex (dc) kind, the associated dc decomposition of the recourse function does not provide an easy way to compute a directional stationary solution of the two-stage stochastic program. Based on an implicit convex-concave property of the bi-parameterized recourse function, we introduce the concept of a generalized critical point of such a recourse function and provide a sufficient condition for such a point to be a directional stationary point of the stochastic program. We describe an iterative algorithm that combines regularization, convexification, and sampling and establish the subsequential convergence of the algorithm to a generalized critical point, with probability

关键词： two-stage stochastic programming difference-of-convex directional stationarity

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two-stage stochastic programming of a hybrid SOFC-renewable integrated energy system considering time sequence correlation

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2023年第1期20卷 1-14页

作者： Zhang, Yisong Chen, Ke Jiang, Jingjing Sun, Li Southeast Univ Sch Energy & Environm Key Lab Energy Thermal Convers & Control Minist Educ Nanjing 210096 Peoples R China Demand Side Multichina Elect Power Res Inst CEPR Beijing Key Lab Beijing Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough Leics England

In the face of the ever-increasing pressure on climate change, recent decades witnessed booming interests in the integrated energy systems (IES) consisting of intermittent renewable energy and dispatchable power sources, such as reformed gas-fed solid oxide fuel cells (SOFC). However, optimization of system design and operation is challenging due to the system complexity, inevitable couplings, and various constraints. To this end, this paper proposes a comprehensive model to describe integrated energy systems with combined cooling, heating, and power (CCHP). Optimal economic performance is formulated as the objective function. Constraints are derived based on the safety and operation requirements. The problem is solved by developing a two-stage stochastic programming framework to achieve the optimized results in terms of both design and operation. The second stage concerns the operation scheduling given the operation plan and stochastic characteristics, based on which the first stage concerns the design planning to realize the prescribed CCHP capacity. Considering the computation complexity of the second stage, the stochastic characteristics are represented by selected scenarios. And to present the mutual influence of energy demands and climate conditions, the time sequence correlation among energy demands and renewable energy availability is considered in the clustering-based scenario selection technique. To solve the proposed two-stage framework, the real-coded genetic algorithm and mixed integer linear programming method are applied in the first and second stages, respectively. A case study in San Francisco is carried out to verify the effectiveness of the proposed method, providing some intuitive guidance for future IES operation and system planning.

关键词： Integrated energy system SOFC two-stage stochastic programming time sequence correlation

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two-stage stochastic programming Based on Particle Swarm Optimization for Aircraft Sequencing and Scheduling

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第4期20卷 1365-1377页

作者： Hong, Youkyung Choi, Byunghun Kim, Youdan Seoul Natl Univ Inst Adv Aerosp Technol Dept Mech & Aerosp Engn Seoul 08826 South Korea Agcy Def Dev Deajeon 34186 South Korea

A two-stage stochastic programming algorithm based on particle swarm optimization is proposed for an optimal and robust design in the aircraft sequencing and scheduling problem. The first and second stage decision problems are defined as aircraft sequencing and scheduling, respectively, and the uncertainties that arise from the aircraft flight time between two successive fixes are considered. Particle swarm optimization is utilized for a randomized search to make the first stage decision under incomplete information about uncertain parameters. In addition, the random key representation is adopted to apply particle swarm optimization to a discrete aircraft sequencing problem. Then, the second stage decision is made using mixed integer linear programming after the realization of the uncertain parameters. The performance of the proposed algorithm is verified by numerical simulations.

关键词： Aircraft sequencing and scheduling uncertainty two-stage stochastic programming particle swarm optimization

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two-stage stochastic programming with fixed recourse via scenario planning with economic and operational risk management for petroleum refinery planning under uncertainty

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CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2008年第9-10期47卷 1744-1764页

作者： Khor, Cheng Seong Elkamel, Ali Ponnambalam, Kumaraswamy Douglas, Peter L. Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Univ Teknol PETRONAS Chem Engn Programme Tronoh Malaysia

This work proposes a hybrid of stochastic programming (SP) approaches for an optimal midterm refinery planning that addresses three sources of uncertainties: prices of crude oil and saleable products, demands, and yields. An SP technique that utilizes compensating slack variables is employed to explicitly account for constraints' violations to increase model tractability. Four approaches are considered to ensure solution and model robustness: (1) the Markowitz's mean-variance (MV) model to handle randomness in the objective function coefficients by minimizing the variance (economic risk) of the expected value of the random coefficients;(2): the two-stage SP with fixed recourse approach to deal with randomness in the RHS and LHS coefficients of the constraints by minimizing the expected recourse costs due to constraints' violations;(3) incorporation of the MV model within the framework developed in (2) to formulate a mean-risk model that minimizes both the expectation and the operational risk measure of variance of the recourse costs;and (4) reformulation of the model in (3) by adopting mean-absolute deviation (MAD) as the measure of operational risk imposed by the recourse costs for a novel refinery planning application. A representative numerical example is illustrated. (c) 2007 Elsevier B. V. All rights reserved.

关键词： two-stage stochastic programming refinery planning optimization under uncertainty scenario analysis mean-variance mean-absolute deviation (MAD)

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two-stage stochastic programming approach for the medical drug inventory routing problem under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 128卷 358-370页

作者： Nikzad, Erfaneh Bashiri, Mahdi Oliveira, Fabricio Shahed Univ Dept Ind Engn Tehran Iran Aalto Univ Sch Sci Dept Math & Syst Anal Syst Anal Lab FI-00076 Aalto Finland

Medical drug shortages are an important issue in health care, since they can significantly affect patients' health. Thus, selecting the appropriate distribution and inventory policies plays an important role in decreasing drug shortages. In this context, inventory routing models can be used to determine optimal policies in the context of medical drug distribution. However, in real-world conditions, some parameters in these models are subject to uncertainty. This paper examines the effects of uncertainty in the demand by relying on a two-stage stochastic programming approach to incorporate it into the optimization model. A two-stage model is then proposed and two different approaches based on chance constraints are used to assess the validity of the proposed model. In the first model, a scenario-based two-stage stochastic programming model without probabilistic constraint is proposed, while in the other two models, proposed for validation of the first model, probabilistic constraints are considered. A mathematical-programming based algorithm (a matheuristic) is proposed for solving the models. Moreover, the Latin hypercube sampling method is employed to generate scenarios for the scenario-based models. Numerical examples show the necessity of considering the stochastic nature of the problem and the accuracy of the proposed models and solution method.

关键词： two-stage stochastic programming stochastic inventory routing problem Medical drug distribution Latin hypercube sampling method Chance constraints Matheuristic algorithm

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two-stage stochastic programming for the railroad blocking problem with uncertain demand and supply resources

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 106卷 275-286页

作者： Hasany, Reza Mohammad Shafahi, Yousef Sharif Univ Technol Dept Civil Engn Azadi AvePOB 11155-8639 Tehran Iran

The railroad blocking problem is classified in the tactical level of freight rail transportation problems. The objective of this problem is to determine the optimal paths for each shipment such that the railway limitations are satisfied. In this problem, the quantities of both demand and supply resource indicators are often assumed to be certain and known, but because a blocking solution is designed for a relatively long period of time, this assumption is not reasonable. In this paper, we have developed a two-stage stochastic program for this problem to consider the uncertainty inherent in demand and supply resource indicators. Due to the size and complexity of the stochastic program and the impossibility of using commercial software in even the simplest instances, two solution methods have been proposed. The first method developed is based on the L-Shaped method, and the second method is a modification of the first one that uses a new initial solution (which is obtained by adapting a side optimization model) together with the 1 Shaped method. Extensive experiments on test networks show that the two methods outperform the commercial software and that the second method is superior to the first one. We finally present the application of the uncertain model and the computational results of the second method for the Railways of Iran as a real-size example, and we show that the application of the stochastic model could reduce total cost by more than 12 million dollars per three-month horizon compared with the deterministic solution. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Freight rail transportation Uncertainty two-stage stochastic programming Railroad blocking problem

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two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty

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ENERGY 2016年第Part1期116卷 1145-1157页

作者： Huang, Yun-Hsun Wu, Jung-Hua Hsu, Yu-Ju Ind Technol Res Inst Ind Econ & Knowledge Ctr Hsinchu 310 Taiwan Natl Cheng Kung Univ Dept Resources Engn Tainan 701 Taiwan

Traditional electricity supply planning models regard the electricity demand as a deterministic parameter and require the total power output to satisfy the aggregate electricity demand. But in today's world, the electric system planners are facing tremendously complex environments full of uncertainties, where electricity demand is a key source of uncertainty. In addition, electricity demand patterns are considerably different for different regions. This paper developed a multi-region optimization model based on two-stage stochastic programming framework to incorporate the demand uncertainty. Furthermore, the decision tree method and Monte Carlo simulation approach are integrated into the model to simplify electricity demands in the form of nodes and determine the values and probabilities. The proposed model was successfully applied to a real case study (i.e. Taiwan's electricity sector) to show its applicability. Detail simulation results were presented and compared with those generated by a deterministic model. Finally, the long-term electricity development roadmap at a regional level could be provided on the basis of our simulation results. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Multi-region optimization model two-stage stochastic programming Demand uncertainty Monte Carlo simulation

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