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Dynamic pooled capacity deployment for urban parcel logistics

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期303卷 650-667页

作者： Faugere, Louis Klibi, Walid White, Chelsea, III Montreuil, Benoit Georgia Inst Technol Phys Internet Ctr Sch Ind & Syst Engn Atlanta GA 30313 USA KEDGE Business Sch Ctr Excellence Supply Chain CESIT Bordeaux France Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA USA

Last-mile logistics is regarded as an essential yet highly expensive component of parcel logistics. In dense urban environments, this is partially caused by inherent inefficiencies due to traffic congestion and the disparity and accessibility of customer locations. In parcel logistics, access hubs are facilities supporting relay-based last-mile activities by offering tem porary storage locations enabling the decoupling of last-mile activities from the rest of the urban distribution chain. This paper focuses on a novel tactical problem: the dynamic deployment of pooled storage capacity modules in an urban parcel network. It formulates the problem as a two-stage stochastic optimization model with recourse, and incorporates the synchronization of underlying operations through continuum approximation-based travel time estimations. It also develops a solution approach based on a rolling horizon algorithm with lookahead, sample average approximation and Benders decomposition enhanced by acceleration methods, able to solve large scale instances of a real-sized megacity. Numerical results, inspired by the case of a large parcel express carrier, are provided to evaluate the computational performance of the proposed approach and suggest up to 28% last-mile cost savings and 26% capacity savings compared to a static capacity deployment strategy. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Supply chain management stochastic programming Urban parcel networks Capacity pooling Dynamic deployment

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A genetic algorithm optimization approach for smart energy management of microgrid

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RENEWABLE ENERGY 2022年 197卷 852-863页

作者： Torkan, Ramin Ilinca, Adrian Ghorbanzadeh, Milad Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Univ Quebec Rimouski Wind Energy Res Lab WERL Rimouski PQ G5L 3A1 Canada

Optimal management and planning of microgrids (MG) are the most important goals for operators. In this study, a Multiobjective Genetic Algorithm (MOGA) is applied to the technical and economic problems of the MG. This stochastic programming considers demand response (DR) programs, reactive loads, and uncertainties due to renewable energies. Demand-side management (DSM) is how to manage and schedule the generation and con-sumption with the objective of cost and greenhouse gases (GHG) emissions minimization. In this work, with the contribution of various customers to demand response programs and reserve schedules, a reduction in the operation cost of the microgrid has resulted. This method facilitates obtaining a complete and comprehensive microgrid model for energy management in the power system, and the results demonstrate that participation in demand response programs and reactive loads can reduce generation, reservation, startup costs, and the amount of pollution. Regarding reservation costs, a 16% reduction was obtained in the presence of the load response, and wind power is a good compromise between cost and pollution among various resources.

关键词： Microgrid Renewable energy Energy management Genetic algorithm Demand response Distributed generation stochastic programming GHG emissions

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Effective Online Order Acceptance Policies for Omnichannel Fulfillment

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2022年第3期24卷 1650-1663页

作者： Jia, Su Karp, Jeremy Ravi, R. Tayur, Sridhar Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA Lyft Inc Marketpl Optimizat San Francisco CA USA

Problem definition: Omnichannel retailing has led to the use of traditional stores as fulfillment centers for online orders. Omnichannel fulfillment problems have two components: (1) accepting a certain number of online orders prior to seeing store demands and (2) satisfying (or filling) some of these accepted online demands as efficiently as possible with any leftover inventory after store demands have been met. Hence, there is a fundamental trade-off between store cancellations of accepted online orders and potentially increased profits because of more acceptances of online orders. We study this joint problem of online order acceptance and fulfillment (including cancellations) to minimize total costs, including shipping charges and cancellation penalties in single-period and limited multiperiod settings. Academic/practical relevance: Despite the growing importance of omnichannel fulfillment via online orders, our work provides the first study incorporating cancellation penalties along with fulfillment costs. Methodology: We build a two-stage stochastic model. In the first stage, the retailer sets a policy specifying which online orders it will accept. The second stage represents the process of fulfilling online orders after the uncertain quantities of in-store purchases are revealed. We analyze threshold policies that accept online orders as long as the inventories are above a global threshold, a local threshold per region, or a hybrid. Results: For a single period, total costs are unimodal as a function of the global threshold and unimodal as a function of a single local threshold holding all other local thresholds at constant values, motivating a gradient search algorithm. Reformulating as an appropriate linear program with network flow structure, we estimate the derivative (using infinitesimal perturbation analysis) of the total cost as a function of the thresholds. We validate the performance of the threshold policies empirically using data from a high-end Nor

关键词： omnichannel retail stochastic programming infinitesimal perturbation analysis dynamic programming

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Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake case study in Iran

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第37期29卷 56323-56340页

作者： Foroughi, Amin Moghaddam, Babak Farhang Behzadi, Mohammad Hassan Sobhani, Farzad Movahedi Islamic Azad Univ Dept Ind Engn Sci & Res Branch Tehran Iran Inst Management & Planning Studies IMPS Tehran Iran Islamic Azad Univ Dept Stat Sci & Res Branch Tehran Iran

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided.

关键词： stochastic programming Resilience supplier Relief logistic Operational and disruptive risks Metaheuristic algorithms

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A Planning Method for Distribution System with Integration of Renewable Energy Generation Considering Optimal Distribution Switching

A Planning Method for Distribution System with Integration o...

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International Conference on Energy Engineering, ICEE 2023

作者： Zhang, Tianyu Luo, Fengzhang Lei, Zheng Yan, Dawei Luo, Tao Li, Yuanyuan Song, Jia Zhang, Denzheng Dong, Yichao Chen, Yiwen Xiao, Qian Economic and Technological Research Institute of State Grid Tianjin Electric Power Company Tianjin300171 China Tianjin University Tianjin300072 China

ISBN: (纸本)9789819771455

To enhance the integration and utilization of high-penetration renewable energy sources, the network structure of future power systems must be rationalized, and operational flexibility must be increased. This paper introduces a planning methodology that incorporates optimal distribution switching for distribution systems integrating renewable energy generation. The approach comprehensively factors in the uncertainties associated with renewable energy generation and preconceived accidents, while maximizing the utilization of optimal distribution switching (ODS). The methodology involves the construction of new lines and the updating of distribution switches to align with ODS requirements. During normal operations, ODS and optimal power generation dispatching are determined considering the uncertainties in renewable energy generation and load demands. Additionally, to minimize the impact of faults, power generation re-dispatching and ODS are adjusted based on the established schemes for the second stage and preconceived accidents. The efficacy of the method is demonstrated through validation on the enhanced IEEE RTS-24 system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： distribution system planning Optimal distribution switching Renewable energy generation stochastic programming

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Surgery Sequencing Coordination with Recovery Resource Constraints

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INFORMS JOURNAL ON COMPUTING 2022年第2期34卷 1207-1223页

作者： Bai, Miao Storer, Robert H. Tonkay, Gregory L. Univ Connecticut Dept Operat & Informat Management Storrs CT 06269 USA Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA

Surgical practice administrators need to determine the sequence of surgeries and reserved operating room (OR) time for each surgery in the surgery scheduling process. Both decisions require coordination among multiple ORs and the recovery resource in the postanesthesia care unit (PACU) in a surgical suite. Although existing studies have addressed OR time reservation, surgery sequencing coordination is an open challenge in the stochastic surgical environment. In this paper, we propose an algorithmic solution to this problem based on stochastic optimization. The proposed methodology involves the development of a surrogate objective function that is highly correlated with the original one. The resulting surrogate model has network-structured subproblems after Lagrangian relaxation and decomposition, which makes it easier to solve than the impractically difficult original problem. We show that our proposed approach finds near-optimal solutions in small instances and outperforms benchmark methods by 13%-51% or equivalently an estimated saving of $760-$7,420 per day in surgical suites with 4-10 ORs. Our results illustrate a mechanism to alleviate congestion in the PACU. We also recommend that practice administrators prioritize sequencing coordination over the optimization of OR time reservation in an effort for performance improvement. Furthermore, we demonstrate how administrators should consider the impact of sequencing decisions when making strategic capacity adjustments for the PACU. Summary of Contribution: Our work provides an algorithmic solution to an open question in the field of healthcare operations management. This solution approach involves formulating a surrogate optimization model and exploiting its decomposability and network-structure. In computational experiments, we quantitatively benchmark its performance and assess its benefits. Our numerical results provide unique managerial insights

关键词： approval unless otherwise noted For more information contact permissions@informs org Keywords stochastic programming healthcare surgery sequencing recovery resource

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A Novel stochastic Optimization Model for Reverse Logistics Network Design of End-of-Life Vehicles: A Case Study of Istanbul

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ENVIRONMENTAL MODELING & ASSESSMENT 2022年第4期27卷 599-619页

作者： Karagoz, Selman Aydin, Nezir Simic, Vladimir Nottingham Trent Univ Nottingham Business Sch Nottingham NG1 4FQ England Coventry Univ Fac Business & Law Sch Strategy & Leadership Priory St Coventry CV1 5FB W Midlands England Yildiz Tech Univ Dept Ind Engn TR-34349 Istanbul Turkey Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11010 Serbia

Waste management is gaining crucial importance as recycling aims at transforming produced waste into value for the economy. As the automotive industry is growing fast worldwide, recycling end-of-life vehicles (ELVs) attracts great research attention. Due to the promulgated regulations, multiple players like the last owners, manufacturers, treatment centres, and municipalities require a more cooperative engagement. The participation of multiple actors in the recycling process of ELVs brings various uncertainties. Additionally, parameters of the recycling process, like the number of vehicles withdrawn per year, cost items, and material composition tend to change due to technological, social, and economic developments. The automotive industry has crucial importance in the Turkish economy, which is highly affected by socio-political and economic issues. Furthermore, the Istanbul metropolitan area has the highest rate of vehicle ownership in Turkey. For that purpose, this paper proposes a scenario-based real-life stochastic optimization model to improve ELV supply chain network management in Istanbul. Sensitivity analyses to changes in scenario occurrence probabilities and changes in the amount of collected ELVs are performed to question the consistency of the study. The results of the mathematical model highlight that the operational cost items have the greatest ratio comparing the other cost items in the model. Furthermore, the results of the sensitivity analysis underline that the operational costs and selling prices of the materials from the ELVs have a significant impact on the profitability of ELVs' recycling process. In addition, uncertainty in the number of ELVs has a significant effect on both operational and strategical decision-making processes. This research can be extended in the direction of examining the effectiveness of ELV management in Turkey since Istanbul could represent the whole of Turkey with its economic and cultural characteristics. Further works

关键词： End-of-life vehicle Decision-making stochastic programming Scenario-based optimization Uncertainty

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Generation Capacity Expansion Problem Considering Different Operation Modes of Combined-Cycle Gas Turbines

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IEEE ACCESS 2022年 10卷 67276-67288页

作者： Gomez-Villarreal, Hernan Canas-Carreton, Miguel Carrion, Miguel Univ Castilla La Mancha Dept Elect Engn Escuela Ingn Ind & Aerosp Toledo Toledo 45071 Spain Univ Castilla La Mancha Dept Elect Elect & Control Engn Campus Toledo Toledo 45071 Spain

The design of future decarbonized power systems is one of the most relevant and challenging problems that power system planners are facing nowadays. It is expected that Combined-Cycle Gas Turbines (CCGTs) play a relevant role in these systems to provide peak power and reserve capacity when intermittent power units be unavailable. However, the large computational size of planning problems has prevented so far from modeling the actual operation of CCGTs accurately. This paper intends to quantify the effect of the modeling of CCGTs in the generation capacity expansion problem. For doing that, the operation of CCGTs is modeled using a mixed-integer linear formulation that considers different operation modes. Afterwards, generation expansion decisions in a realistic case study are analyzed using different accuracy degrees for modeling the operation of CCGTs. The numerical results suggest that the simplification of the modeling of the operation of CCGTs overestimates the flexibility provided by these units, which increases the capacity installed from renewable units between 15 and 25%.

关键词： Costs Turbines Computational modeling Power systems Analytical models Numerical models Generators Combined-cycle gas turbines generation capacity expansion reserve provision stochastic programming storages

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Power grid reconfiguration method applying mobile energy storage vehicles for disaster scenarios 9

Power grid reconfiguration method applying mobile energy sto...

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2024 9th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2024

作者： Jiang, Bin Mei, Zhongxiong Lu, Ming Jiang, Pinfeng Changzhou Power Supply Company State Grid Jiangsu Electric Power Co. Ltd. Jiangsu Changzhou21300 China

This paper proposes an optimization technology of grid restructuring based on sequential decision-making, aimed at enhancing the resilience of microgrids in damaged scenarios. Through a two-stage restructuring optimization model, the first stage determines the topology of the microgrid and the configuration locations of the mobile energy storage vehicles, while the second stage establishes the transportation routes and output of these vehicles. The research utilizes the IEEE-30 bus system for simulation experiments and addresses mixed-integer stochastic programming problems using an improved L-shaped algorithm based on Benders’ decomposition. The experimental results demonstrate that the model effectively reduces the power outage time for critical loads and enhances the resilience of the power grid. Furthermore, the study considers the characteristics of both the power system and the transportation network, providing a theoretical foundation for future research. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： stochastic programming

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Scenario-based stochastic shelter location-allocation problem with vulnerabilities for disaster relief network design

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2022年第5期16卷 507-530页

作者： Hansuwa, Sweety Mohan, Usha Ganesan, Viswanath Kumar Indian Inst Technol Madras Dept Management Studies Chennai 600036 Tamil Nadu India Tata Consultancy Serv Ltd TCS Res & Innovat IIT Madras Res Pk Chennai 600113 Tamil Nadu India

We formulate the shelter location-allocation problem considering the vulnerability of the demand locations and their network connectivities with the shelter locations for disaster management's preparedness and response phase. We propose a scenario-based stochastic model that assigns the set of candidate locations evaluating operational, budgetary limitations, and service level expectations. The solution presents an evacuee-allocation plan considering the best collection of less vulnerable network connectivities between the demand areas and the shelter locations. We present a linear relaxation heuristic and compare the heuristic performance with the scenario-based formulation solved using CPLEX 12.8 optimisation solver for various problem sizes. We finally apply and solve the problem using real-life case data obtained during the major flooding event in and around the Chennai Metropolitan Development Area during 2015 to present our model's applicability and emergency response requirements. [Submitted: 12 November 2020;Accepted: 19 April 2021]

关键词： disaster management shelter location-allocation stochastic programming linear relaxation heuristic

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