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Integrating lean, resilient, and sustainable practices in supply chain network: mathematical modelling and the AUGMECON2 approach

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2022年第4期9卷 451-471页

作者： Shafiee, Mohammad Mehrjerdi, Yahia Zare Keshavarz, Marzieh Yazd Univ Dept Ind Engn Yazd Iran

Nowadays, organisations try to find a proper method to increase their competitiveness. Lean, resilient, and sustainable are top-hole managerial practices in supply chain management. This paper tries to study the effects of integrating lean, resilient, and sustainable practices in the supply chain network (SCN). To this end, a new multi-objective mixed-integer programming is developed in such a way that lean manufacturing is implemented in a resilient-sustainable SCN. The objective functions try to maximise job opportunities and minimise costs, environmental effects, and delivery time. Resilience has been taken into account to tackle the lack of raw materials during disruptions by implementing multiple sourcing in the model. Besides, to devote more attention to sustainable practices, backup suppliers have been prioritised based on sustainability criteria. The academics were enquired to rate sustainability criteria for selecting a backup supplier. Finally, a fuzzy TOPSIS method was used to rank the backup suppliers based on sustainability criteria. The improved version of augmented epsilon-constraint (AUGMECON2) was applied to cope with multi-objectivity of the problem. The results indicated a synergistic effect among leanness, resilience, and sustainability in the supply chain. Also, it turned out that sustainable backup supplier selection boosts the synergistic effect among these three concepts.

关键词： Lean supply chain resilience sustainability stochastic programming supplier selection ε -constraint method

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The value of coordinated hydropower bidding in the Nordic day-ahead and balancing market

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2022年第1期13卷 53-77页

作者： Aasgard, Ellen Krohn Norwegian Univ Sci & Technol Alfred Getz Vei 3 N-7491 Trondheim Norway

Modern power markets often consist of a series of sequential markets where power is traded. Market agents must coordinate their trading strategy across all markets. In this paper, we formulate the mult-market bidding problem for power producers as a stochastic program. This formulation is implemented within the framework of optimization models used by the Nordic hydropower industry. We also present how input to the stochastic program may be generated by using a forecast-based scenario generation method combined with time-series models that predicts future prices and turnovers in the markets. The model is applied in a case study to investigate the value of participating in the Nordic day-ahead and balancing market. A producer may participate in the balancing market either by considering the markets sequentially or coordinated. Using cases with limited and perfect information about the balancing market to calculate lower and upper bounds, we find that the value of participating in the balancing market using the sequential approach is between 0.8 and 2.6%. Using the coordinated approach, the producer may gain between 1.4 and 2.9%. The value of coordination, i.e. the value of using the coordinated over the sequential approach, is found to be higher in the limited information case (1.7%) than in the perfect information case (1.1%). This indicates that, the more uncertainty, the higher is the value of coordination.

关键词： Electricity markets stochastic programming

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A stochastic look-ahead approach for hurricane relief logistics operations planning under uncertainty

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ANNALS OF OPERATIONS RESEARCH 2022年第1期319卷 1231-1263页

作者： Chang, Yanbin Song, Yongjia Eksioglu, Burak Clemson Univ Clemson SC 29634 USA Univ Arkansas Fayetteville AR 72701 USA

In the aftermath of a hurricane, humanitarian logistics plays a critical role in delivering relief items to the affected areas in a timely fashion. This paper proposes a novel stochastic look-ahead framework that implements a two-stage stochastic programming model in a rolling horizon approach to address the evolving uncertain logistics system state during the post-hurricane humanitarian logistics operations. The two-stage stochastic programming model that executes in this rolling horizon approach is formulated as a mixed-integer programming problem. The model aims to minimize the total cost incurred in the logistics operations, which consist of transportation cost and social cost. The social cost is measured as a function of deprivation for unsatisfied demand. Our extensive numerical results and sensitivity analysis demonstrate the effectiveness of the proposed approach in reducing the total cost incurred during the post-hurricane relief logistics operations compared to the two-stage stochastic programming model implemented in a static fashion.

关键词： stochastic programming Rolling horizon Disaster relief logistics Social cost

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PPE Supply Optimization Under Risks of Disruption from the COVID-19 Pandemic

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Operations Research Forum 2023年第2期4卷 23页

作者： Ash, Cecil Venkatadri, Uday Diallo, Claver Vanberkel, Peter Saif, Ahmed Department of Industrial Engineering Dalhousie University 5269 Morris Street Halifax B3H 4R2 NS Canada

The COVID-19 pandemic has struck health service providers around the world with dire shortages, inflated prices, and volatile demand of personal protective equipment (PPE). This paper discusses supply chain resilience in the context of a Canadian provincial healthcare provider during the COVID-19 pandemic. A multi-period multi-objective mixed-integer programming model is presented for PPE supply planning under disruption risk. The deterministic formulation is extended to consider both two-stage and multi-stage uncertainty in the supply, price, and demand of PPE using stochastic programming (SP) and chance-constrained programming (CCP). The first objective is to minimize a risk measure of the stochastic total cost, either its Expected Value (EV) or its Value-at-Risk (VaR), and the second objective is to minimize the maximum shortage of any product in any time period. The ϵ-constraint method is used to generate sets of Pareto-optimal solutions and analyze the trade-off between these two competing objectives. Numerical experiments are conducted to analyze the efficacy of emergency inventory and increased inventory levels as risk mitigation strategies. We consider uncertainty scenarios based on plausible and actual pandemic trajectories seen around the world during the COVID-19 pandemic including single-wave, two-wave, and exponential growth. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Canadian healthcare Chance-constrained optimization COVID-19 PPE stochastic programming Supply chain resilience

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Investing in Wind Energy Using Bi-Level Linear Fractional programming

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ENERGIES 2023年第13期16卷 4952页

作者： Alrasheedi, Adel F. F. Alshamrani, Ahmad M. M. Alnowibet, Khalid A. A. King Saud Univ Coll Sci Stat & Operat Res Dept Riyadh 11451 Saudi Arabia

Investing in wind energy is a tool to reduce greenhouse gas emissions without negatively impacting the environment to accelerate progress towards global net zero. The objective of this study is to present a methodology for efficiently solving the wind energy investment problem, which aims to identify an optimal wind farm placement and capacity based on fractional programming (FP). This study adopts a bi-level approach whereby a private price-taker investor seeks to maximize its profit at the upper level. Given the optimal placement and capacity of the wind farm, the lower level aims to optimize a fractional objective function defined as the ratio of total generation cost to total wind power output. To solve this problem, the Charnes-Cooper transformation is applied to reformulate the initial bi-level problem with a fractional objective function in the lower-level problem as a bi-level problem with a fractional objective function in the upper-level problem. Afterward, using the primal-dual formulation, a single-level linear FP model is created, which can be solved via a sequence of mixed-integer linear programming (MILP). The presented technique is implemented on the IEEE 118-bus power system, where the results show the model can achieve the best performance in terms of wind power output.

关键词： bi-level optimization fractional programming primal-dual formulation stochastic programming wind energy investment

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An energy system optimization model accounting for the interrelations of multiple stochastic energy prices

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ANNALS OF OPERATIONS RESEARCH 2022年第1期316卷 555-579页

作者： Ren, Hongtao Zhou, Wenji Wang, Hangzhou Zhang, Bo Ma, Tieju East China Univ Sci & Technol Sch Business Meilong Rd 130 Shanghai 200237 Peoples R China Renmin Univ China Sch Appl Econ Beijing 100872 Peoples R China China Natl Petr Corp China Petr Planning & Engn Inst CPPEI Beijing 100083 Peoples R China SINOPEC Beihai Refining & Chem Co Ltd South 4 Rd Beihai 536016 Guangxi Peoples R China Int Inst Appl Syst Anal Schlosspl 1 A-2361 Laxenburg Austria

The variation of and the interrelation between different energy markets significantly affect the competitiveness of various energy technologies, therefore complicate the decision-making problem for a complex energy system consisting of multiple competing technologies, especially in a long-term time frame. The interrelations between these markets have not been accounted for in the existing energy system modelling efforts, leading to a distortion of understanding of the market impact on the technological choices and operations in the real world. This study investigates the strategic and operational decision-making problem for such an energy system characterized by three competing technologies from crude oil, natural gas, and coal. A stochastic programming model is constructed by incorporating multiple volatile energy prices interrelated with each other. Oil price is modelled by the mean-reverting Ornstein-Uhlenbeck process and serves as the exogenous variable in the ARIMAX models for natural gas and downstream plastic prices. The K-means clustering method is employed to extract a handful of distinctive patterns from a large number of simulated price projections to enhance the computing efficiency without losing retaining critical information and insights from the price co-movement. The model results suggest that the high volatility of the energy market weakens the possibility of selecting the corresponding technology. The oil-based route, for example, gradually loses its market share to the coal approach, attributed to a higher volatile oil market. The proposed method is applicable to other problems of the same kind with high-dimensional stochastic variables.

关键词： Energy system modelling stochastic programming Oil market k-means clustering Energy price volatility

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Optimization of active surveillance strategies for heterogeneous patients with prostate cancer

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PRODUCTION AND OPERATIONS MANAGEMENT 2022年第11期31卷 4021-4037页

作者： Zhang, Zheng Denton, Brian T. Morgan, Todd M. Zhejiang Univ Dept Serv Sci & Operat Management Hangzhou Zhejiang Peoples R China Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA Univ Michigan Div Urol Oncol Urol Ann Arbor MI 48109 USA

Prostate cancer (PCa) is common in American men with long latent periods, during which the disease is asymptomatic. Active surveillance is a monitoring strategy commonly used for patients diagnosed with low-risk PCa who may harbor latent high-risk PCa. The optimal monitoring strategy attempts to minimize the disutility of testing while ensuring that the patient is detected at the earliest time when the disease progresses. Unfortunately, guidelines for the active surveillance of PCa are often one-size-fits-all strategies that ignore the heterogeneity among multiple patient types. In contrast, personalized strategies based on partially observable Markov decision process (POMDP) models are challenging to implement in practice given the large number of possible strategies that can be used. This article presents a two-stage stochastic programming approach that selects a set of strategies for predefined cardinality based on patients' disease risks. The first-stage decision variables include binary variables for the selection of periods at which to test patients in each strategy and the assignment of multiple patient types to strategies. The objective is to maximize a weighted reward function that considers the need for cancer detection, missed detection, and cost of monitoring patients. We discuss the structure and complexity of the model and reformulate a logic-based Bender's decomposition formulation that can solve realistic instances to optimality. We present a case study for the active surveillance of PCa and show that our model results in strategies that vary in intensity according to patient disease risk. Finally, we show that our model can generate a small number of strategies that can significantly improve the existing "one-size-fits-all" guideline strategies used in practice.

关键词： active surveillance health policy optimization stochastic programming

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Optimal pricing for electricity retailers based on data-driven consumers' price-response

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TOP 2022年第3期30卷 430-464页

作者： Perez-Santalla, Roman Carrion, Miguel Ruiz, Carlos Univ Carlos III Madrid Avda Univ 30 Leganes 28911 Spain Univ Castilla La Mancha Escuela Ingn Ind & Aeroespacial Toledo Avda Carlos III Toledo 45071 Spain Univ Carlos III Madrid Dept Stat Avda Univ 30 Leganes 28911 Spain Univ Carlos III Madrid UC3M BS Inst Financial Big Data IFiBiD Avda Univ 30 Leganes 28911 Spain

In the present work, we tackle the problem of finding the optimal price tariff to be set by a risk-averse electric retailer participating in the pool and whose customers are price sensitive. We assume that the retailer has access to a sufficiently large smart-meter dataset from which it can statistically characterize the relationship between the tariff price and the demand load of its clients. Three different models are analyzed to predict the aggregated load as a function of the electricity prices and other parameters, as humidity or temperature. More specifically, we train linear regression (predictive) models to forecast the resulting demand load as a function of the retail price. Then, we will insert this model in a quadratic optimization problem which evaluates the optimal price to be offered. This optimization problem accounts for different sources of uncertainty including consumer's response, pool prices and renewable source availability, and relies on a stochastic and risk-averse formulation. In particular, one important contribution of this work is to base the scenario generation and reduction procedure on the statistical properties of the resulting predictive model. This allows us to properly quantify (data-driven) not only the expected value but the level of uncertainty associated with the main problem parameters. Moreover, we consider both standard forward-based contracts and the recently introduced power purchase agreement contracts as risk-hedging tools for the retailer. The results are promising as profits are found for the retailer with highly competitive prices and some possible improvements are shown if richer datasets could be available in the future. A realistic case study and multiple sensitivity analyses have been performed to characterize the risk-aversion behavior of the retailer considering price-sensitive consumers. It has been assumed that the energy procurement of the retailer can be satisfied from the pool and different types of contract

关键词： Electricity retailer Price-sensitive consumers Risk aversion Smart meter data stochastic programming Time-of-use rate

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A forward modelling approach to optimise the portfolio of projects among oil companies under uncertainty: a reactive two-stage stochastic model

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International Journal of Industrial and Systems Engineering 2022年第4期42卷 427-451页

作者： Fayyazi, Mohsen Yakhchali, Siamak Haji Pishvaee, Mir Saman Jolai, Fariborz Department of Industrial Engineering Kish International Campus University of Tehran Tehran Iran School of Industrial Engineering College of Engineering University of Tehran Tehran Iran School of Industrial Engineering Iran University of Science and Technology Tehran Iran

Because of high volatility in oil price, oil companies should change their strategies along with changing oil prices. Thus, dynamic portfolio management is strongly recommended to increase the rate of oil production and determine resource allocation for projects in each period of the planning horizon. To achieving the objective, a two-stage stochastic mathematical model is developed to optimise the portfolio of oil projects. To make the model more realistic, splitting the projects and their resumption are permitted. To solve the model, a robust optimisation approach is designed, and the results of the robust and two-stage stochastic designs are compared. These comparisons are based on a realisation algorithm developed by this study. To illustrate the capability and power of the stochastic model in handling the uncertainty, a case study on an oil company is presented. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： stochastic programming

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A stochastic program for biomass contract selection under demand uncertainty

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2022年第4期13卷 1011-1029页

作者： Guericke, Daniela Madsen, Henrik Tech Univ Denmark Dept Appl Math & Comp Sci Richard Petersens Pl DK-2800 Lyngby Denmark

Due to the carbon neutral goals in many countries, a shift from traditional fuels to biomass is currently taking place in the energy sector. In this publication, we are looking at the long-term biomass contracting decisions for combined heat and power plants and power producers. A major share of biomass contracts are long-term contracts with runtimes of around 1 year, so the actual biomass demand is still uncertain when the contracts are negotiated. The operators can select different types of contracts ranging from fixed contracts with fixed amounts and deliveries to more flexible contracts and call options that allow for some flexibility in terms of amount and delivery times. We propose a stochastic program to optimize the contract selection including amounts and deliveries taking the biomass storage and uncertain demand into account. We present results of a case study from industry and show how the model utilizes the contracts for flexibility to adapt to different demand scenarios. Furthermore, the model is used for investigating the tradeoff between storage restrictions and fulfilling the demand in all scenarios. We show why it is important to model this problem as a stochastic program and why considering an expected demand is not enough.

关键词： Biomass supply planning Biomass contracts stochastic programming Flexibility Value of stochastic solution

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