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A fast-response mathematical programming approach for delivering disaster relief goods: an earthquake case study

TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2024年第9期16卷 1091-1114页

作者： Qezelbash-Chamak, Jaber Badamchizadeh, Saeid Seifi, Abbas Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

This paper tackles a complex logistics challenge of disaster management, encompassing warehouse location, pre-disaster inventory planning, routing, and post-disaster relief supply delivery. We establish an iterative process for optimizing relief distribution to shelters. Adaptable warehouse inventory reallocation responds to fluctuating demands, guided by a two-phase mathematical programming approach. In the first phase, a two-stage stochastic programming (TSSP) model determines optimal warehouse and shelter locations and inventory levels. In the subsequent phase, we introduce a mixed-integer programming (MIP) model to minimize the overall delivery time by making routing decisions. To streamline the process, we introduce a novel enumeration algorithm that trims down route options by considering unavailable links, effectively transforming the MIP model into an assignment-based model. This innovation results in a noticeable 74% reduction in solution time. Further efficiency is achieved by developing a branch-and-cut algorithm for swift MIP resolution. A real-world case study confirms the practicality of our approach.

关键词： Disaster management humanitarian logistics location two-stage stochastic programming branch and cut Benders decomposition

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Optimization of degree of conformance for multi-response systems considering model imprecision: A data-driven metaheuristic approach

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第2期40卷 783-802页

作者： Hejazi, Taha-Hossein Amirkabir Univ Technol Tehran Polytech Coll Garmsar Dept Ind Engn Tehran Iran

Quality-based design optimization is a process consisting of factor identification, finding an empirical/physical model between responses and factors, and finding a factor setting leading to the best quality levels. Response surface methodology is a well-grounded data-driven approach that applies a collection of statistical-mathematical techniques to achieve the design requirements. Today's complex systems are usually affected by nuisance factors and have more than one output characteristic with some degree of correlation. This study aims to develop two-stage stochastic programming for a multi-response optimization model based on the degree of conformance as the most comprehensive metric for such design problems. The proposed approach can include the model imprecision in the optimization procedure using the probabilistic properties of the estimated parameters. This method would be applied in highly sensitive processes or costly operations that require a more accurate setting to avoid extra reworks or scraps. A simulated annealing algorithm with a boosted scenario checking modification has been applied to cope with the expensive computations for problem instances with a large sample size of uncertain parameters. The proposed model has also analyzed two cases from the literature, and the results support its superiority compared to the existing approaches.

关键词： data-driven optimization metaheuristics multi-response systems statistical modeling two-stage stochastic programming

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Computational Performance Enhancement Strategies for Risk-Averse two-stage stochastic Generation and Transmission Network Expansion Planning

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第1期39卷 273-286页

作者： Garcia-Cerezo, Alvaro Garcia-Bertrand, Raquel Baringo, Luis Univ Castilla La Mancha ETS Ingn Ind Dept Ingn Elect Elect Automat & Comunicac E-13071 Ciudad Real Spain

This paper proposes a new acceleration technique and a representative day aggregation procedure for the risk-averse two-stage stochastic generation and transmission network expansion planning problem, in which the conditional value-at-risk is used. We use a finite set of scenarios to model uncertainty in the peak demand level of loads, along with the capacity and marginal production cost of generating units. Moreover, we use representative days to model the operational variability of the electrical demand and renewable generation. The combination of scenarios and representative days involves many variables and constraints, which may lead to computationally intractable problems. Therefore, we propose a new relaxed version of the constraint generation-based algorithm that reduces the computational time of the problem. We additionally present a two-stage aggregation procedure that combines the modified maximum dissimilarity algorithm and the priority chronological time-period clustering in order to reduce the resolution of the representative days and to pay attention to extreme conditions. The numerical results of modified versions of the IEEE 24-bus Reliability Test System and the IEEE 118-bus Test System show reductions in the computational time of more than 89% for the relaxed constraint generation-based algorithm, and of more than 94% for the two-stage aggregation procedure.

关键词： Constraint generation-based algorithm generation and transmission network expansion planning operational variability representative days risk aversion two-stage stochastic programming uncertainty

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Optimal sizing of hybrid renewable energy systems using quasi-optimal control

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RENEWABLE ENERGY 2024年 226卷

作者： Ali, M. Montaz Mohammed, Nouralden Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa

This paper addresses the critical challenge of sizing hybrid renewable energy systems over their lifespan while accounting for uncertainties in energy sources and load demands. By leveraging stochastic programming, we introduce a novel modeling technique that ensures robust optimization without compromising numerical tractability. Unlike conventional methods, which determine system parameters for the entire project duration upfront, our approach enables annual adjustments to the renewable components' sizing. This dynamic strategy alleviates early -stage under -utilization, aligning system capacity with evolving demand patterns. Central to our approach is the reformulation of the two -stage stochastic program as a quasi -optimal control. This reduces the number of optimization variables significantly, simplifying the solution process. Moreover, thousands of constraints are replaced with a system of differential equations, enhancing computational efficiency. By minimizing capital costs and dynamic operating expenses, we achieve an optimal system size. Through a realworld application in a rural area of South Africa, we demonstrate the effectiveness of our approach. The results showed a remarkable improvement in problem -solving efficiency, reduced under -utilization, and heightened adaptability to unpredictable weather conditions. This innovative framework represents a substantial leap forward in hybrid renewable energy systems design, offering a more agile and efficient solution for sustainable energy generation.

关键词： two-stage stochastic programming Renewable energy Hybrid system Derivative-free optimization Quasi-optimal control Pattern search

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Strategic planning for hub resilience: a stochastic location-routing perspective

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OPERATIONAL RESEARCH 2024年第4期24卷 66页

作者： Musavi, Mir Mohammad Torabi, S. Ali Jolai, Fariborz Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This study addresses the Reliable Capacitated Hub Location-Routing Problem in the presence of probabilistic disruptive events leading to unavailability of hub facilities. The problem is mathematically formulated as a two-stage stochastic mixed-integer linear programming model. In the initial stage, the objective is to minimize the fixed establishment costs of hubs. In the subsequent stage, emphasis is placed on minimizing the comprehensive expected cost, encompassing costs related to transportation, vehicle usage, vehicle routing, and service loss incurred due to disruptions. The proposed solution methodology hinges on a scenario reduction strategy, complemented by a two-stage improvement procedure. To tackle instances of large scale, a hybrid meta-heuristic algorithm is introduced. This algorithm leverages variable neighborhood search and simulated annealing meta-heuristics. Furthermore, parameters of the algorithm are rigorously fine-tuned using the Taguchi method. To validate the proposed approach, extensive computational experiments are conducted utilizing CAB and AP datasets. The findings produce invaluable managerial insights. Numerical experiments affirm the adeptness of the solution methodology in effectively managing failure costs. Additionally, the outcomes underscore the effectiveness of the scenario reduction technique.

关键词： Reliable hub location-routing problem Disruption two-stage stochastic programming Hybrid meta-heuristics

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Consistent routing for local same-day delivery via micro-hubs

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OR SPECTRUM 2024年第2期46卷 375-409页

作者： Ackva, Charlotte Ulmer, Marlin W. Otto von Guericke Univ Fak Wirtschaftswissensch Management Sci Magdeburg Germany

An increasing number of local shops offer same-day delivery in order to compete with the online giants. However, the distribution of parcels from individual shops to customers reduces the rare consolidation opportunities in the last mile even further. Thus, shops start collaborating on urban same-day delivery by using shared vehicles and micro-depots for consolidated transportation of parcels. At this, many stakeholders (storekeepers, drivers, and customers) need to be coordinated. Consistent routes between micro-hubs simplify the distribution process and increase reliability for all stakeholders involved. The shared vehicles thus conduct consistent daily routes between micro-hubs in the city, serving as transshipment and consolidation centres. This allows stores to bring orders to the next micro-hub, where the parcel is picked up by a vehicle and delivered to the micro-hub closest to its destination-if it is feasible with respect to the vehicle's consistent daily schedule. Creating effective schedules is therefore very important. The difficulty of finding an effective consistent route is amplified by the daily uncertainty in order placements. We model the problem as a two-stage stochastic program. While the first stage determines the vehicle schedules, the second stage optimises the flow of realised orders. The goal is to satisfy as many orders per day as possible with the shared vehicles. We propose a time-expanded network formulation of the problem which is solved to optimality using commercial MIP-software. We assess our model against a non-consistent upper bound and a practically-inspired heuristic to evaluate the cost of consistency and the consolidation of goods. We analyse the performance of our method for a variety of instance settings. We observe that collaborative delivery via micro-hubs is worthwhile for delivery time promises of two hours or more. Noticeably, for these service promises, the costs of consistency are surprisingly low.

关键词： Micro-hubs Same-day delivery Routing consistency two-stage stochastic programming

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Value of risk aversion in perishable products supply chain management

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2024年第2期89卷 517-552页

作者： Pathy, Soumya Ranjan Rahimian, Hamed Clemson Univ Dept Ind Engn Clemson SC 29634 USA

In this paper, we study optimal procurement and inventory decisions for a supply chain with a single perishable product under demand uncertainty. To control risk, on the one hand, we use a risk-averse objective, and on the other hand, we utilize a chance constraint to satisfy demand with a high probability. We formulate the problem as a two-stage stochastic program with a chance constraint and risk-averse objective, where long-term decisions on pre-positioning products are made in the first stage, while recourse decisions on reallocation and emergency procurement are made in the second stage. To allow for different risk preferences, we incorporate conditional value-at-risk into the objective function and study its combination with the expectation or worst-case of the second-stage costs. To solve the resulting models, we develop various variants of the L-shaped method, based on dual and primal decomposition, and by leveraging the connection between the optimization of coherent risk measures and distributionally robust optimization. Through extensive numerical experiments, we demonstrate the value of risk aversion and present a comparative computational study on the performance of different algorithms.

关键词： Perishable supply chain Chance-constrained programming two-stage stochastic programming Risk-averse optimization Decomposition algorithm Cutting planes

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A multi-period vaccines supply chain network design with capacity expansion and different replenishment cycles under uncertain demand

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COMPUTERS & OPERATIONS RESEARCH 2024年 167卷

作者： Ariningsih, Paulina Kus Irawan, Chandra Ade Paulraj, Antony Dai, Jing Univ Nottingham Nottingham Univ Business Sch China Ningbo Peoples R China Nottingham Ningbo China Beacons Excellence Res & I Ningbo Peoples R China NEOMA Business Sch Reims France

The World Health Organization (WHO) proposes guidelines for vaccine distribution networks that incorporate economic order quantity and risk pooling principles, specifically through implementing different replenishment cycles between different levels of the distribution network. The substantial impact of different replenishment cycles on inventory levels is demonstrated in the existing literature. However, current optimization network design research has paid limited attention to this aspect. This article aims to address this research gap by investigating a two -stage stochastic programming for a multi -period perishable vaccine network design problem, taking into account the different replenishment cycles characteristic and capacity expansion under uncertain demand conditions. Given the inherent difficulty of solving the proposed problem, a matheuristic approach based on Variable Neighborhood Search (VNS) is developed. To illustrate the practical application and analyze the impact of uncertain demand, we apply the proposed model and its solution method using the Indonesia dataset in the COVID-19 situation. The computational results indicate that lower replenishment cycles leads to higher inventory levels, total costs, and capacity requirements for the top-level distribution center. Optimum replenishment cycles and further managerial implications and future potential research are also presented.

关键词： Vaccine supply chain network Combinatorial optimization Variable Neighborhood Search two-stage stochastic programming

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A stochastic programming Approach for Locating and Dispatching two Types of Ambulances

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TRANSPORTATION SCIENCE 2021年第2期55卷 275-296页

作者： Yoon, Soovin Albert, Laura A. White, Veronica M. Amazon Seattle WA 98109 USA Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA

Emergency Medical Service systems aim to respond to emergency calls in a timely manner and provide prehospital care to patients. This paper addresses the problem of locating multiple types of emergency vehicles to stations while taking into account that vehicles are dispatched to prioritized patients with different health needs. We propose a two-stage stochastic-programming model that determines how to locate two types of ambulances in the first stage and dispatch them to prioritized emergency patients in the second stage after call-arrival scenarios are disclosed. We demonstrate how the base model can be adapted to include nontransport vehicles. A model formulation generalizes the base model to consider probabilistic travel times and general utilities for dispatching ambulances to prioritized patients. We evaluate the benefit of the model using two case studies, a value of the stochastic solution approach, and a simulation analysis. The case study is extended to study how to locate vehicles in the model extension with nontransport vehicles. stochastic-programming models are computationally challenging for large-scale problem instances, and, therefore, we propose a solution technique based on Benders cuts.

关键词： two-stage stochastic programming ambulance location ambulance dispatch emergency medical services

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Resilience Enhancement for Multistate Interdependent Infrastructure Networks: From a Preparedness Perspective

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IEEE TRANSACTIONS ON RELIABILITY 2023年第1期72卷 190-203页

作者： Wang, Kai Xu, Zhaoping Liu, Yu Fang, Yiping Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu 611731 Peoples R China Univ Paris Saclay Lab Genie Ind Chaire Risk & Resilience Complex Sy F-91190 Gif Sur Yvette France

Reducing vulnerability and enhancing resilience of infrastructure networks subject to uncertain disruptions is a challenging task as these networks become increasingly interconnected and interdependent. This article devotes to addressing the preparedness planning problem of interdependent infrastructure networks. The interdependency between infrastructure networks is characterized by a two-way physical interdependency, that is, the state of one infrastructure network is dependent on that of another infrastructure network, and vice versa. Furthermore, the multistate characteristics of infrastructure networks are taken into account and accommodated to the studied physical interdependency. A tailored two-stage stochastic programming framework, which is able to cope with the uncertainty associated with disruption scenarios, is put forth to facilitate an effective resilience enhancement strategy under a limited budget. In this framework, the first-stage problem selects the optimal protection levels of network components in advance, whereas the second-stage problem optimizes the network operation after a disruption scenario occurs. The proposed framework is implemented to an illustrative system composed of interdependent power-water distribution networks to demonstrate the effectiveness of the resilience enhancement strategy.

关键词： Resilience Planning programming Uncertainty stochastic processes Optimization Hazards Interdependent infrastructure networks multistate network resilience two-stage stochastic programming vulnerability

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