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Order consolidation for the last-mile split delivery problem with green labels

ANNALS OF OPERATIONS RESEARCH 2023年 1-25页

作者： Zhang, Yuankai Hu, Xiangpei Tian, Qiong Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Minist Educ Key Lab Complex Syst Anal & Management Decis Beijing 100191 Peoples R China Dalian Univ Technol Sch Econ & Management Dalian 116024 Liaoning Peoples R China

Green labels can be utilized to steer customers towards selecting eco-friendly delivery options, such as extending the due time of shipments, which provides excellent opportunities for order consolidation to address the last-mile split delivery problem. Order consolidation involves postponing certain shipments at delivery stations and combining them with other incoming shipments, resulting in fewer trips to deliver the consolidated shipments to customers. To address the tradeoff of whether to offer green label choices to customers, we propose a two-stage stochastic programming model. This model integrates the first-stage decisions of presenting green label choices and the second-stage decisions of generating order consolidation schemes, taking into account uncertain green label choices (labels A, B, and C). The complexity of the deterministic equivalent formulation of the model increases significantly with the number of scenarios, which motivates us to develop a scenario-based order consolidation algorithm. By decomposing the problem based on scenarios, the proposed algorithm first generates a pool of order consolidation schemes for each customer with different types of labels. Then, for each scenario, it combines the solutions for different customers while considering inventory capacity. Numerical experiments have been conducted to demonstrate that the average advantage of the order consolidation approach with green labels is 2.35% compared to the approach without green labels. Additionally, managerial insights for the practical application of green labels are provided.

关键词： Order consolidation Last-mile split delivery Green labels Online retailing two-stage stochastic programming

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Spare Parts Management Strategy for Seismic Resilience Improvement of Ultra-high Voltage Converter Station 3

Spare Parts Management Strategy for Seismic Resilience Impro...

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3rd Asian Conference on Frontiers of Power and Energy

作者： Xue, Zhihang Wang, Hongjun Zhang, Zongxi Zhang, Changhua Elect Power Res Inst State Grid Sichuan Chengdu Peoples R China Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Peoples R China NR Elect Co Ltd Nanjing Peoples R China

ISBN: (纸本)9798350378467;9798350367676

Improving resilience of power system is an important aspect of smart grid research. Ultra-high voltage (UHV) converter station serves as core node for power grid and appropriate spare parts can significantly shorten post-earthquake repair time of the converter station and therefore enhance its seismic resilience. This article takes spare parts strategy of the UHV converter station under budget constraint as its research object. Firstly, a functional component model of the UHV converter station is established, and post-earthquake transmission function indicator and seismic resilience index R with per-unit characteristics are defined. Subsequently, the two-stage stochastic programming (TSSP) method was employed to construct a two-stage optimization model for the seismic resilience improvement of the UHV converter station. The sample average approximation (SAA) method was used to solve the model. Lastly, the effectiveness of the proposed method was validated using data from the +/- 800kV UHV converter station.

关键词： Ultra-high voltage converter station earthquake resilience spare parts management strategy two-stage stochastic programming sample average approximation

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two-stage Production Planning Under stochastic Demand:Case Study of Fertilizer Manufacturing

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Computers, Materials & Continua 2022年第1期70卷 1195-1207页

作者： Chia-Nan Wang Shao-Dong Syu Chien-Chang Chou Viet Tinh Nguyen Dang Van Thuy Cuc Department of Industrial Engineering and Management National Kaohsiung University of Science and TechnologyKaohsiung80778Taiwan Sunline NP Telecommunications Co.Ltd Kaohsiung80778Taiwan Department of Shipping Technology National Kaohsiung University of Science and TechnologyKaohsiung80778Taiwan Faculty of Commerce Van Lang UniversityHo Chi Minh City70000Vietnam Faculty of Industrial Engineering and Management International UniversityHo Chi Minh City70000Vietnam

Agriculture is a key facilitator of economic prosperity and nourishes the huge global *** achieve sustainable agriculture,several factors should be considered,such as increasing nutrient and water efficiency and/or improving soil health and *** fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop *** supplies most of the necessary nutrients for plants,and it is estimated that at least 30%-50%of crop yields is attributable to commercial fertilizer nutrient *** is always a major concern in achieving sustainable and efficient *** reasonable and customized fertilizerswill require a significant increase in the number of formulae,involving increasing costs and the accurate forecasting of the right time to apply the suitable *** alternative solution is given by two-stage production planning under stochastic demand,which divides a planning schedule into two *** primary stage has non-existing demand information,the inputs of which are the proportion of raw materials needed for producing fertilizer products,the cost for purchasing materials,and the production *** total quantity of purchased material and produced products to be used in the blending process must be defined to meet as small as possible a paid *** the second stage,demand appears under multiple scenarios and their respective *** stage will provide a solution for each occurring scenario to achieve the best *** two-stage approach is presented in this paper,the mathematical model of which is based on linear integer *** the diversity of fertilizer types,themathematicalmodel can advise manufacturers about which products will generate as much as profit as ***,two objectives are taken into ***,the paper’s thesis focuses on minimizing overall system costs,e.g.,including inventory cost,pur

关键词： two-stage stochastic programming demand uncertainty planning blending fertilizer

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A stochastic simulation-based risk assessment method for water allocation under the uncertainty

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WATER SUPPLY 2022年第5期22卷 5638-5649页

作者： Chen, Shu Yuan, Zhe Lei, Caixiu Li, Qingqing Wang, Yongqiang Changjiang River Sci Res Inst Water Resources Dept Wuhan Peoples R China Hubei Key Lab River Basin Water Resources & Ecol Wuhan Peoples R China Hubei Prov Water Resources & Hydropower Planning Wuhan Peoples R China

There are a lot of uncertainties in the water resources system, which makes the water allocation plan have very risky. In order to analyze the risks of water resources allocation under uncertain conditions, a new methodology called the stochastic simulation-based risk assessment approach is developed in this paper. First, the main hydrological stochastic variable is fitted by a proper probability distribution. Second, a suitable two-stage stochastic programming is constructed to obtain the expected benefit and optimized water allocation targets. Third, the Monte Carlo method is used to obtain a suitable stochastic sample of the hydrological variable. Fourth, a pre-allocated water optimization model is proposed to obtain optimized actual benefit. The methodology can give a way for risk analysis of water allocation plans obtained by uncertain optimization models, which provides reliable assistance to water managers in decision-making. The proposed methodology is applied to the Zhanghe Irrigation District and the risk of water allocation plan obtained under the randomness of annual inflow is assessed. In addition, three different division methods of the annual inflow are applied in the first step, namely three levels, five levels and seven levels, respectively. From the results, the risk of water allocation scheme obtained by TSP model is 0.372-0.411 and decreases with the increase of number of hydrological levels. Considering both the risk and model complexity, seven hydrological levels are recommended when using TSP model to optimize water allocation under stochastic uncertainty.

关键词： changing environment Monte Carlo stochastic simulation two-stage stochastic programming water allocation risk analysis

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stochastic RWA and Lightpath Rerouting in WDM Networks

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INFORMS JOURNAL ON COMPUTING 2022年第5期34卷 2700-2719页

作者： Daryalal, Maryam Bodur, Merve HEC Montreal Dept Decis Sci Montreal PQ H3T 2A7 Canada Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3GH Canada

In a telecommunication network, routing and wavelength assignment (RWA) is the problem of finding lightpaths for incoming connection requests. When facing a dynamic traffic, greedy assignment of lightpaths to incoming requests based on predefined deterministic policies leads to a fragmented network that cannot make use of its full capacity because of stranded bandwidth. At this point, service providers try to recover the capacity via a defragmentation process. We study this setting from two perspectives: (i) while granting the connection requests via the RWA problem and (ii) during the defragmentation process by lightpath rerouting. For both problems, we present the first two-stage stochastic integer programming model incorporating incoming request uncertainty to maximize the expected grade of service. We develop a decomposition-based solution approach, which uses various relaxations of the problem and a newly developed problem-specific cut family. Simulation of two-stage policies for a variety of instances in a rolling-horizon framework of 52 stages shows that our stochastic models provide high-quality solutions when compared with traditionally used deterministic ones. Specifically, the proposed provisioning policies yield improvements of up to 19% in overall grade of service and 20% in spectrum saving, while the stochastic lightpath rerouting policies grant up to 36% more requests, using up to just 4% more bandwidth spectrum.

关键词： two-stage stochastic programming routing and wavelength assignment lightpath rerouting optical networks

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Driver guidance and rebalancing in ride-hailing systems through mixture density networks and stochastic programming

Driver guidance and rebalancing in ride-hailing systems thro...

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IEEE International Smart Cities Conference (ISC2)

作者： Li, Xiaoming Gao, Jie Wang, Chun Huang, Xiao Nie, Yimin Concordia Univ Informat Syst Engn Montreal PQ Canada Concordia Univ John Molson Sch Business Montreal PQ Canada Ericsson Inc Global Artificial Intelligence Accelerat Montreal PQ Canada

ISBN: (纸本)9781665449199

We propose a data-driven optimization model to reduce riders' wait time for vehicle guidance and rebalancing operations, considering the rider demands are under uncertainty. Instead of assuming a pre-defined rider demand distribution, we propose a data-driven framework that integrates Mixture Density Networks (MDNs) and a two-stage stochastic programming model. The integrated framework can compute high-quality guidance and rebalancing solutions that benefit drivers and riders in the ride-hailing system by leveraging the time-series historical data from real data sets. To prove the performance and effectiveness of our approach, we conduct a group of simulations based on the New York High Volume For-Hire Vehicle (HVFHV) trip records. The validation results show that the proposed method outperforms the data-driven deterministic models using GRU and moving average methods. Most significantly, the riders' average wait time using our proposed approach can be reduced by 75.9% compared to the batched matching mechanism.

关键词： Data-driven optimization two-stage stochastic programming mixture density networks ride-hailing systems

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Whole-lifetime Coordinated Service Strategy for Battery Energy Storage System Considering Multi-stage Battery Aging Characteristics

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Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 689-699页

作者： Feilong Fan Yan Xu Rui Zhang Tong Wan IEEE Energy Research Institute@NTU Nanyang Technological UniversitySingapore College of Smart Energy Shanghai Jiao Tong UniversityShanghaiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore The University of New South Wales SydneyAustralia University of Sydney SydneyAustralia

One battery energy storage system(BESS)can be used to provide different services,such as energy arbitrage(EA)and frequency regulation(FR)support,etc.,which have different revenues and lead to different battery degradation *** paper proposes a whole-lifetime coordinated service strategy to maximize the total operation profit of BESS.A multi-stage battery aging model is developed to characterize the battery aging rates during the whole *** the uncertainty of electricity price in EA service and frequency deviation in FR service,the whole problem is formulated as a twostage stochastic programming *** the first stage,the optimal service switching scheme between the EA and FR services are formulated to maximize the expected value of the whole-lifetime operation *** the second stage,the output power of BESS in EA service is optimized according to the electricity price in the hourly timescale,whereas the output power of BESS in FR service is directly determined according to the frequency deviation in the second *** above optimization problem is then converted as a deterministic mixed-integer nonlinear programming(MINLP)model with bilinear *** Cormick envelopes and a bound tightening algorithm are used to solve *** simulation is carried out to validate the effectiveness and advantages of the proposed strategy.

关键词： Battery energy storage system(BESS) wholelifetime coordinated service multi-stage battery aging model two-stage stochastic programming mixed-integer nonlinear programming(MINLP)

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two-stage Risk Adjusted Cross-Training stochastic programming Model for Improving Daily Operations Under Staff Absenteeism

Two-Stage Risk Adjusted Cross-Training Stochastic Programmin...

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作者： Ammar, Dania University of Michigan

During daily operation, workers are assigned to jobs on an assembly line. This assignment requires that each worker be skillfully qualified to operate the position he is appointed to. Nonetheless, several circumstances may arise at time of operation, of which workers’ absenteeism is considered most critical. Absenteeism leads to a deficiency in the daily available skills keeping some jobs at the mercy of being filled by unqualified workers. To compensate for deficiencies in the obtainable skills under the uncertainty of absenteeism, managements have recourse to cross-training its workers. However, cross-training is costly and requires strategic decisions to identify key regions, workers, and jobs within the assembly line that are to be involved with the cross-training plan while coping with uncertainties. The aim of this study is to develop a cross-training process that resolves the issue of the uncertain daily shortage in skills while preserving the workplace requirements. It presents a two-stage stochastic cross-training model in which specific workers are suggested to be trained to specific jobs for the purpose of maximizing the future expected assignment of skillful workers to jobs under workers’ indefinite inclination to absenteeism and under the varying level of this imposed risk. Research is presented on an assembly line that integrates cross-training plan in its labor-force planning. Results illustrate a reasonable improvement in the level of positions’ occupancy varying according to available resources, target degree of protection against absenteeism and selective parameters that guide the assignment process.

关键词： Cross-training risk-adjusted one-to-one assignment skills job occupancy two-stage stochastic programming assembly line staff absenteeism Thesis

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two-stage stochastic Stable Matching 25th

Two-Stage Stochastic Stable Matching

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25th International Conference on Integer programming and Combinatorial Optimization (IPCO)

作者： Faenza, Yuri Foussoul, Ayoub He, Chengyue Columbia Univ IEOR New York NY 10027 USA

ISBN: (纸本)9783031598340;9783031598357

We introduce and study a two-stage stochastic stable matching problem between students and schools. A decision maker chooses a stable matching in a marriage instance;then, after some agents enter or leave the market following a probability distribution D, chooses a stable matching in the new instance. The goal is, roughly speaking, to maximize the expected quality of the matchings across the two stages and minimize the expected students' discontent for being downgraded to a less preferred school in the second-stage. We consider both the case when D is given explicitly and when it is accessed via a sampling oracle. In the former case, we give a polynomial time algorithm. In the latter case, we show that, unless P = NP, no algorithm can find the optimal value or the optimal solution of the problem in polynomial-time. On the positive side, we give a pseudopolynomial algorithm that computes a solution of arbitrarily small additive error. Our techniques include the use of a newly defined poset of stable pairs, which may be of independent interest.

关键词： Stable Matching two-stage stochastic programming Poset

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A two-stage joint chance-constrained programming considering compound uncertainty of interval, random and fuzzy: a case study for agricultural water planning in an arid area

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stochastic ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第10期36卷 3281-3293页

作者： Guo, Shanshan Guo, Ping China Agr Univ Ctr Agr Water Res China Beijing 100083 Peoples R China Minist Agr & Rural Affairs Wuwei Expt Stn Efficient Water Use Agr Wuwei 733000 Peoples R China

A two-stage joint chance-constrained programming is proposed based on the two-stage stochastic programming, the robust interval programming and the joint chance-constrained programming. In this study, the previous method combining interval order relations and interval possibility degrees is interpreted from the view of robust programming and then is rewritten according to the frame of robust stochastic programming and robust fuzzy programming. Based on the concept of interval possibility degree, the interval chance constrained programming is proposed and a joint chance-constrained method is generated by combining interval with the random and fuzzy chance-constrained programming. The established model is applied in agricultural water planning in an arid region. The results show that the model can provide decision makers with various planting schemes under different risk levels. According to the response of the results in different risk scenarios, decision makers can get the general impression on the influence degree of uncertainties. The analysis on the results can also show the advantage of the established model in handling multiple uncertainties and making tradeoff among different factors like the unit net benefits, penalty and water demand of crops as well as water supply. According to the comparison with the traditional two-step-method-based interval two-stage chance-constrained method, the optimality robustness and the high reference value of the established model can be proved.

关键词： two-stage stochastic programming Joint chance-constrained programming Robust programming Agricultural water planning

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