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Multimodal Vaccine Distribution Network Design with Drones

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TRANSPORTATION SCIENCE 2023年第4期57卷 1068-1095页

作者： Enayati, Shakiba Li, Haitao Campbell, James F. Pan, Deng Univ Missouri St Louis Coll Business Adm Supply Chain & Analyt Dept St Louis MO 63121 USA

Childhood vaccines play a vital role in social welfare, but in hard-to-reach regions, poor transportation, and a weak cold chain limit vaccine availability. This opens the door for the use of vaccine delivery by drones (uncrewed aerial vehicles, or UAVs) with their fast transportation and reliance on little or no infrastructure. In this paper, we study the problem of strategic multimodal vaccine distribution, which simultaneously determines the locations of local distribution centers, drone bases, and drone relay stations, while obeying the cold chain time limit and drone range. Two mathematical optimization models with complementary strengths are developed. The first model considers the vaccine travel time at the aggregate level with a compact formulation, but it can be too conservative in meeting the cold chain time limit. The second model is based on the layered network framework to track the vaccine flow and travel time associated with each origin destination (OD) pair. It allows the number of transshipments and the number of drone stops in a vaccine flow path to be limited, which reflects practical operations and can be computationally advantageous. Both models are applied for vaccine distribution network design with two types of drones in Vanuatu as a case study. Solutions with drones using our parameter settings are shown to generate large savings, with differentiated roles for large and small drones. To generalize the empirical findings and examine the performance of our models, we conduct comprehensive computational experiments to assess the sensitivity of optimal solutions and performance metrics to key problem parameters.

关键词： vaccine distribution drone delivery cold chain limit network design multimodal and intermodal network design mixed-integer linear programming

Optimizing the COVID-19 cold chain vaccine distribution network with medical waste management: A robust optimization approach

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartA期229卷 120510页

作者： Isik, Eyuep Ensar Yildiz, Seyda Topaloglu Dokuz Eylul Univ Dept Ind Engn TR-35397 Izmir Turkiye Yildiz Tech Univ Dept Ind Engn TR-34349 Istanbul Turkiye

This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vac-cines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model in-cludes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncer-tainty in supply, and in the worst case, approximately 30% of the demand cannot be met.

关键词： Healthcare supply chain Cold chain COVID-19 vaccine distribution Waste management mixed-integer linear programming Robust optimization

Line-haul feeder location-routeing problem: mathematical model and heuristic algorithm

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TRANSPORTMETRICA A-TRANSPORT SCIENCE 2023年第3期19卷

作者： Nadizadeh, Ali Zadeh, Ali Sabzevari Bashiri, Mahdi Ardakan Univ Fac Engn Ind Engn Dept POB 184 Ardakan Iran Univ Alabama Dept Informat Syst Stat & Management Sci Tuscaloosa AL USA Coventry Univ Fac Business & Law Sch Strategy & Leadership Coventry W Midlands England

This study proposes a mathematical formulation and solution approach for a novel extension of the location-routeing problem (LRP), namely line-haul feeder LRP (LFLRP), where large vehicles (trucks) are synchronised with small vehicles (motorcycles) throughout delivery process. Customers are visited by site-dependent vehicles such that those not accessible by trucks must be served by motorcycles. The LFLRP is formulated as a mixed-integer linear programming model, and two efficient heuristic algorithms called EHA and Enhanced-EHA are developed to solve the problem. Experimental results show that the proposed algorithms can provide near-optimal solutions for 18 randomly generated small-scale LFLRP test instances and best-known solutions for 12 out of 19 large-scale standard LRP test instances in reasonable computation time. A cost-benefit analysis also indicates that the LFLRP model can considerably reduce total costs compared to equivalent standard LRP formulations. To provide managerial insights, a case study and sensitivity analysis of key parameters are conducted.

关键词： Location-routeing problem feeder routeing mixed-integer linear programming efficient heuristic algorithm

Optimal operating scheme of neighborhood energy storage communities to improve power grid performance in smart cities

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APPLIED ENERGY 2023年 331卷

作者： Cerna, Fernando, V Pourakbari-Kasmaei, Mahdi Barros, Raone G. Naderi, Ehsan Lehtonen, Matti Contreras, Javier Univ Fed Roraima Dept Elect Engn BR-69310000 Boa Vista Brazil Univ Fed Roraima Postgrad Program Nat Resources Res Area Management & Dynam Nat Resources Renewabl Ave Nova Iorque Aeroporto BR-69310000 Boa Vista Brazil Aalto Univ Dept Elect Engn & Automation Espoo 02150 Finland Southern Illinois Univ Sch Elect Comp & Biomed Engn Carbondale IL 62901 USA Univ Castilla La Mancha Sch Ind Engn Ciudad Real 13071 Spain

In Smart Cities (SC), the efficient management of services such as health, transport, public safety, and especially the electricity ensures the welfare of citizens. In recent years, the insertion of renewable sources (RSs) (e.g., solar and wind) in the power grid (PG) of SCs has contributed to meeting the electricity needs of the various consumer units. However, the large-scale integration of these RSs can fatigue the assets, leading to their premature aging and, consequently, compromising the quality of electricity supply. To overcome these challenges, the implementation of Neighboring Energy Storage Communities (NESCs) employing demand response (DR) strategies along with efficient coordination of storage batteries (SBs) could be a promising alternative. In this sense, the present work proposes a mixed-integer linear programming (MILP) model to efficiently manage SBs and the set of household appliances, including charging electric vehicles (EVs), in an NESC provided solely by PG. The proposed model aims to minimize: the total costs related to energy consumption, the peak rebound effect on the total consumption profile, energy wastage through load factor (LF) improvement, and the deep discharges in the SBs during their daily operational cycle. Operational constraints related to the home appliances, such as average usage time, the number of times that the appliance is used daily, etc., are taking into account. The EV state-of-charge (SOC), EV charging rate limits, and initial and final SOC of the SBs, are also considered. A Monte Carlo Algorithm (MCA) is used to simulate the habitual consumption patterns of each customer. The proposed model was implemented in AMPL and solved using CPLEX. The performance of this proposed model is evaluated considering two NESCs differentiated by the number of consumer communities. A first NESC (small-scale) is analyzed considering only two consumer communities. In this NESC, two case studies (Case 1 and 2) are discussed. Next, the sec

关键词： Demand response Home appliances mixed-integer linear programming Neighborhood energy storage communities Storage batteries Smart cities

Towards energy transition: A novel day-ahead operation scheduling strategy for demand response and hybrid energy storage systems in smart grid

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ENERGY 2024年 293卷

作者： Elsir, Mohamed Al-Sumaiti, Ameena Saad Moursi, Mohamed Shawky El Khalifa Univ Adv Power & Energy Ctr Elect Engn & Comp Sci Dept Abu Dhabi U Arab Emirates

Fossil fuel power plants continue to contribute significantly to carbon emissions, necessitating a transition towards cleaner energy sources. Despite the growing presence of renewables within the power systems, the incorporation of carbon capture technologies into the traditional thermal power plants holds great potential in emissions reduction. In this paper, the integration of renewable energy sources (RES) and coal-fired power generation units outfitted with carbon capture schemes is addressed. Multiple demand response (DR) programs and hydropower plants are strategically utilized to increase the power system flexibility. To effectively plan the day-ahead (DA) operation of the power system, a presumed market-clearing framework is adopted and modelled as a risk-constrained two-objective stochastic mixed-integer linear programming problem. The proposed framework helps to tackle the uncertainties related to RES and demand variations by employing a hidden Markovian process (HMP) technique. To simultaneously minimize the system's operational costs and CO2 emissions, an enhanced version of the augmented..-constraint method is employed. To prove its value, the proposed framework is devoted to the 24-bus IEEE reliability test system (IEEE-RTS). The system features substantial penetration of RES (exceeding 87% of peak load) and standard DR options capacities (less than 25% of peak load). The results show a 24% reduction in load peaks, an over 63% decrease in emissions, and a 17% reduction in the overall operation costs.

关键词： Energy transition Demand response Network-constrained unit commitment Hidden Markovian process Risk-constrained two-objective stochastic mixed-integer linear programming

Faces of maximal plane graphs without short cycles

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arXiv 2024年

作者： Axenovich, Maria Kießle, Leon Sagdeev, Arsenii Karlsruhe Institute of Technology Karlsruhe Germany

For a positive integer g, we study a family of plane graphs G without cycles of length less than g that are maximal in a sense that adding any new edge to G either makes it non-plane or creates a cycle of length less than g. We show that the largest face length fmax(g) of a 2-connected graph from this family satisfies 3g − 12 ≤ fmax(g) ≤ 2(g − 2)2 + *** Codes 05C10, 05C35, 05C12 Copyright © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

Approximating the Gomory mixed-integer Cut Closure Using Historical Data

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arXiv 2024年

作者： Becu, Berkay Dey, Santanu S. Qiu, Feng Xavier, Álinson S. Georgia Institute of Technology AtlantaGA United States Argonne National Laboratory LemontIL United States

Many operations related optimization problems involve repeatedly solving similar mixed integer linear programming (MILP) instances with the same constraint matrix but differing objective coefficients and right-hand-side values. The goal of this paper is to generate good cutting-planes for such instances using historical data. Gomory mixed integer cuts (GMIC) for a general MILP can be parameterized by a vector of weights to aggregate the constraints into a single equality constraint, where each such equality constraint in turn yields a unique GMIC. In this paper, we prove that for a family of MILP instances, where the right-hand-side of the instances belongs to a lattice, the GMIC closure for every instance in this infinite family can be obtained using the same finite list of aggregation weights. This result motivates us to build a simple heuristic to efficiently select aggregations for generating GMICs from historical data of similar instances with varying right-hand-sides and objective function coefficients. For testing our method, we generated families of instances by perturbing the right-hand-side and objective functions of MIPLIB 2017 instances. The proposed heuristic can significantly accelerate the performance of Gurobi for many benchmark instances, even when taking into account the time required to predict aggregation multipliers and compute the cut coefficients. To the best of our knowledge, this is the first work in the literature of data-driven cutting plane generation that is able to significantly accelerate the performance of a commercial state-of-the-art MILP solver, using default solver settings, on large-scale benchmark instances. Copyright © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

The Differentiable Feasibility Pump

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arXiv 2024年

作者： Cacciola, Matteo Forel, Alexandre Frangioni, Antonio Lodi, Andrea Polytechnique Montréal MontréalQC Canada Dipartimento di Informatica Universita di Pisa Pisa Italy Jacobs Technion-Cornell Institute Cornell Tech and Technion-IIT New YorkNY United States

Although nearly 20 years have passed since its conception, the feasibility pump algorithm remains a widely used heuristic to find feasible primal solutions to mixed-integer linear problems. Many extensions of the initial algorithm have been proposed. Yet, its core algorithm remains centered around two key steps: solving the linear relaxation of the original problem to obtain a solution that respects the constraints, and rounding it to obtain an integer solution. This paper shows that the traditional feasibility pump and many of its follow-ups can be seen as gradient-descent algorithms with specific parameters. A central aspect of this reinterpretation is observing that the traditional algorithm differentiates the solution of the linear relaxation with respect to its cost. This reinterpretation opens many opportunities for improving the performance of the original algorithm. We study how to modify the gradient-update step as well as extending its loss function. We perform extensive experiments on MIPLIB instances and show that these modifications can substantially reduce the number of iterations needed to find a solution. Copyright © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

Joint Optimization of Completion Ratio and Latency of Offloaded Tasks with Multiple Priority Levels in 5G Edge

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arXiv 2024年

作者： Moshiri, Parisa Fard Simsek, Murat Kantarci, Burak School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N 6N5 Canada

Multi-Access Edge Computing (MEC) is widely recognized as an essential enabler for applications that necessitate minimal latency. However, the dropped task ratio metric has not been studied thoroughly in literature. Neglecting this metric can potentially reduce the system’s capability to effectively manage tasks, leading to an increase in the number of eliminated or unprocessed tasks. This paper presents a 5G-MEC task offloading scenario with a focus on minimizing the dropped task ratio, computational latency, and communication latency. We employ mixed integer linear programming (MILP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to optimize the latency and dropped task ratio. We conduct an analysis on how the quantity of tasks and User Equipment (UE) impacts the ratio of dropped tasks and the latency. The tasks that are generated by UEs are classified into two categories: urgent tasks and non-urgent tasks. The UEs with urgent tasks are prioritized in processing to ensure a zero-dropped task ratio. Our proposed method improves the performance of the baseline methods, First Come First Serve (FCFS) and Shortest Task First (STF), in the context of 5G-MEC task offloading. Under the MILP-based approach, the latency is reduced by approximately 55% compared to GA and 35% compared to PSO. The dropped task ratio under the MILP-based approach is reduced by approximately 70% compared to GA and by 40% compared to PSO. © 2024, CC BY-NC-ND.

关键词： mixed-integer linear programming