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Innovative Applications of Unsupervised Learning in Uncertainty-Aware Pharmaceutical Supply Chain Planning

IEEE ACCESS 2024年 12卷 107984-107999页

作者： Kochakkashani, Farid Kayvanfar, Vahid Baldacci, Roberto George Washington Univ Dept Elect & Comp Engn Washington DC USA Hamad Bin Khalifa Univ Coll Sci & Engn Div Engn Management & Decis Sci Doha Qatar

The significance of resiliency, reliability, and equity in the pharmaceutical supply chain is often overlooked but becomes evident in the wake of disastrous events. Disruptive incidents underscore the critical importance of these concepts, necessitating the development of innovative frameworks to effectively address the challenges that emerge in their aftermath. This paper introduces a framework specifically designed to address the issues arising from disruptions within the pharmaceutical supply chain. A novel mixed-integer nonlinear programming (MINLP) model is proposed to formulate the pharmaceutical supply chain that encompasses the distribution of both cold and non-cold pharmaceuticals and vaccines. The abundance of diverse pharmaceuticals and vaccines, each with its distinct characteristics, presents a formidable planning obstacle. A noteworthy contribution of this study lies in innovatively applying AI-driven methodologies to pharmaceutical supply chain, employing five pioneering unsupervised learning algorithms for improved inventory management and control. The model's uncertainty is effectively addressed through an innovative joint chance constraint (JCC) formulation. By employing JCC, the model ensures a high level of reliability in satisfying uncertain patient demands. The MINLP formulation with JCCs presents significant computational complexities and intractability. To alleviate these issues, state-of-the-art reformulation algorithms are provided to transform the model into its equivalent mixed-integer linear programming form. The results indicate the efficiency of the equivalent reformulation techniques and illustrate the capabilities of the model to alleviate the resiliency, reliability, and equity concerns.

关键词： Pharmaceutical supply chain equity resiliency mathematical optimization stochastic programming unsupervised learning Pharmaceutical supply chain equity resiliency mathematical optimization stochastic programming unsupervised learning

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A stochastic optimisation model to support cybersecurity within the UK national health service

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年

作者： Grass, Emilia Pagel, Christina Crowe, Sonya Ghafur, Saira Karlsruhe Inst Technol Karlsruhe Germany UCL London England Imperial Coll London London England

Over the past decade, the adoption of new digital technologies in healthcare has surged, significantly enhancing care delivery and accessibility. However, this digital transformation has been accompanied by a sharp increase in cyber-attacks, posing severe risks to hospital functionality and patient safety. To address the challenge of planning for uncertain future cyber incidents, we propose a two-stage stochastic model designed to bolster the cyber resilience of healthcare providers by selecting optimal countermeasures in preparation for upcoming cyber incidents. Numerical tests demonstrate the model's effectiveness, with the Value of the stochastic Solution showing a 21% improvement over a deterministic approach. To be optimally equipped even for low-probability high-impact attacks we incorporate the risk measure Conditional Value-at-Risk. The corresponding countermeasure solution led to 44% fewer rejected patients in a worst-case scenario. The robustness of the proposed solution is underscored by its consistent performance across various scenarios, budget levels, and risk preferences, making it a reliable tool for enhancing cybersecurity in healthcare. These results highlight the importance of tailored, robust cybersecurity strategies in healthcare, ensuring preparedness for a wide range of potential threats.

关键词： stochastic programming cybersecurity risk measure CVaR health services NHS

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Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 181卷

作者： Hu, Zhangchen Chen, Heng Lyons, Eric Solak, Senay Zink, Michael Calif State Univ Coll Business & Econ Fullerton CA 92831 USA Univ Massachusetts Amherst Isenberg Sch Management Amherst MA USA Univ Nebraska Lincoln Coll Business Lincoln NE USA Univ Massachusetts Amherst Coll Engn Amherst MA USA

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of UAVs. To this end, we categorize all relevant costs into three types, namely, economic, environmental, and social costs, and formulate a nonlinear two-stage stochastic programming model to establish optimal paths for UAV missions under weather uncertainty. We then discretize the nonlinear model and propose a tight linear approximation for the discretized problem to allow for a near real-time implementation. To quantify weather uncertainty, we propose a weather scenario generation algorithm to map ensemble-based weather forecast information to airspace blockage maps. With comprehensive computational studies through simulations, we show that our proposed stochastic approach can lower operating costs by an average of around 6%, where the savings increase as weather conditions become more severe and complex. We also find that, for missions operated by small UAVs, it is not sufficient to determine a path solely based on economic cost minimization, but it should rather be through total cost minimization, which involves environmental and social costs. Considering only the economic cost in the optimization may lead to much higher non-economic costs. However, for missions operated by large UAVs, it is sufficient to determine paths through economic cost optimization, as including environmental and social costs in the optimization process does not result in solutions that are much different from those obtained by considering only the economic costs. For both small and large UAVs, a path established solely through environmental or social cost minimization may not be economically susta

关键词： Drone UAV path planning Weather evolution Emissions UAV noise stochastic programming

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A disaggregated integer L-shaped method for stochastic vehicle routing problems with monotonic recourse

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期318卷 520-533页

作者： Parada, Lucas Legault, Robin Cote, Jean-Francois Gendreau, Michel CIRRELT 2500 Chem Polytech Montreal Montreal PQ H3T 1J4 Canada Polytech Montreal 2500 Chem Polytech Montreal Montreal PQ H3T 1J4 Canada Operat Res Ctr Muckley Bldg 1 Amherst St Cambridge MA 02142 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Laval 2325 Rue Univ Quebec City PQ G1V 0A6 Canada

This paper proposes a new integer L-shaped method for solving two-stage stochastic integer programs whose first-stage solutions can decompose into disjoint components, each one having a monotonic recourse function. In a minimization problem, the monotonicity property stipulates that the recourse cost of a component must always be higher or equal to that of any of its subcomponents. The method exploits new types of optimality cuts and lower bounding functionals that are valid under this property. The stochastic vehicle routing problem is particularly well suited to be solved by this approach, as its solutions can be decomposed into a set of routes. We consider the variant with stochastic demands in which the recourse policy consists of performing a return trip to the depot whenever a vehicle does not have sufficient capacity to accommodate a newly realized customer demand. This work shows that this policy can lead to a non-monotonic recourse function, but that the monotonicity holds when the customer demands are modeled by several commonly used families of probability distributions. We also present new problem-specific lower bounds on the recourse that strengthen the lower bounding functionals and significantly speed up the solving process. Computational experiments on instances from the literature show that the new approach achieves state-of-the-art results.

关键词： stochastic programming Integer L-shaped method Monotonic recourse Vehicle routing problem

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A parking incentive allocation problem for ridesharing systems

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 166卷

作者： Nguyen, Ngoc-Dai Gendron, Bernard Lahrichi, Nadia Univ Montreal Dept Comp Sci & Operat Res Montreal ON Canada CIRRELT Montreal ON Canada Polytech Montreal Dept Math & Ind Engn Montreal ON Canada

Ridesharing occurs when people with similar schedules and itineraries travel together to reduce their commuting costs. In this paper, we study how parking spaces can be used to incentivize drivers to participate in ridesharing systems. We develop a Parking Incentive Allocation (PIA) system to promote and allocate parking lots to ridesharing drivers in a stochastic and dynamic environment. The optimization problem is formulated at each period as a multi-stage stochastic decision-dependent program. To overcome the complexity of the model, we propose one greedy policy, and three approximations including two stochastic policies and an expected-value policy. We evaluate the effectiveness of the four policies on the data generated from GPS information collected by the MTL Trajet project, which studies residents' travel patterns throughout the city of Montreal. The computational results indicate that on average, the approximate policies can improve the total distance saving by more than 20% over various problem settings. Additionally, the results show that the performance of the PIA system is significantly influenced by the attractiveness of the parking incentive to drivers.

关键词： Ridesharing Parking incentive stochastic programming

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Joint optimization of recyclable inventory routing problem under uncertainties in an incentive-based recycling system

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 198卷

作者： Zheng, Meimei Li, Yuan Du, Ningxin Wang, Qingyi Huang, Edward Jiang, Peng Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Sichuan Univ Business Sch Dept Ind Engn & Management Chengdu 610064 Peoples R China Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA

Due to the value of resource recovery and the development of a circular economy, waste recycling has gathered global attention. Recently, many emerging cities designed new systems like an incentive-based recycling system (IBRS). In such systems, recyclables are collected through community recycling nodes by offering incentives, then transported to street recycling stations and sorted before being finally recycled. The increased recycling nodes and the incentives enhance the convenience and residents' enthusiasm for waste recycling, but also intensify the uncertainty of recycling quantities and the complexity of the recycling operation management. Poor recycling operation management may result in increased recycling costs or greater loss of recyclables, which discourages residents from participating in recycling. Based on an existing IBRS, this study investigates the joint optimization problem of the recyclable inventory management at each community recycling node and the vehicle routing from the recycling nodes to the recycling station. A two-stage dual-objective multi-period stochastic programming model is established to minimize the loss of recyclables and logistics costs, which is further reformulated using the weighting method and transportation cost approximation parameters. To solve the reformulated model, a three-phase iterative algorithm is designed by combining the progressive hedging algorithm and route splitting algorithm based on the Lin-Kernighan heuristic. A case study is conducted using data from Shanghai's IBRS. The proposed joint decision model is superior to separate decisions and the three-phase iterative algorithm can reduce the average total cost by up to 42.12% compared to the genetic algorithm and the Iteration-Move-Search method in the literature. Additionally, a sensitivity analysis is conducted to provide managerial insights.

关键词： Waste management Incentive-based recycling stochastic programming Inventory routing problem Circular economy

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A target-time-windows technique for project scheduling under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期314卷 792-806页

作者： Lamas, Patricio Goycoolea, Marcos Pagnoncelli, Bernardo Newman, Alexandra Alicanto Labs Santiago Chile Pontificia Univ Catolica Chile Escuela Adm Santiago Chile Univ Cote Azur SKEMA Business Sch Lille France Colorado Sch Mines Dept Mech Engn Golden CO 80401 USA

We address the problem of determining the start times of activities in order to maximize the expected net present value of a project given precedence constraints. We assume that each activity has a random duration and profit with a known probability distribution. Most approaches generate either: a baseline schedule that is robust to uncertainty (using proactive approaches), or a policy that reacts to the revelation of uncertainty (using reactive approaches). We propose an integrated proactive-reactive technique that generates both a baseline time window for each activity's start time, and a policy that indicates how the schedule should be adapted for each realization of uncertainty. The time window explicitly constrains the extent to which the realized start times vary. An important feature of our approach is that, once computed, it can easily be communicated and implemented in practice. Numerical experiments show that the objective value of the solutions generated by our technique can be within 4%, on average, of the optimal value obtained with perfect information, and up to 50% better when compared to an earliest-start policy. Moreover, the variability of activities' start times can be 10 times smaller when compared to those generated by other policies. We solve an instance with 300 scenarios and 357 activities in 30 min, illustrating the scalability of our technique on a real-world problem that produces out-of-sample feasible solutions with a desired probability.

关键词： OR in natural resources Project scheduling Net present value stochastic programming

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A RANDOMIZED PROGRESSIVE HEDGING ALGORITHM FOR stochastic VARIATIONAL INEQUALITY

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第3期20卷 1142-1155页

作者： Mu, Zhenguo Nanjing Univ Posts & Telecommun Coll Sci Nanjing Peoples R China

Recently, stochastic variational inequality (SVI) has been extended from single stage to multistage by Rockafellar and Wets (Math. Program., 165:331-360, 2017) and progressive hedging algorithm (PHA) has also been extended from stochastic programming to multistage stochastic linear comple-mentarity and SVI by Rockafellar and Sun (Math. Program., 174:453-471, 2019). However, the per-iteration cost of PHA can be prohibitively high when the scenario set is large, despite the decomposition and parallelizable nature of the algorithm. To address this issue, we propose a randomized PHA that allows us to control the per-iteration cost by randomly selecting a small subset of scenarios and updating only the corresponding variable components while freezing the variables corresponding to the unselected scenarios at the current iteration. By measuring the quality of an approximate solution using a re-stricted merit function, we demonstrate that despite the significant reduction in per-iteration cost, the randomized PHA converges in expectation and in an ergodic sense at the same sublinear rate as the original PHA.

关键词： stochastic variational inequality progressive hedging algorithm nonanticipativity stochastic programming

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Resilient Resource Allocations for Multi-Stage Transportation-Power Distribution System Operations in Hurricanes

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IEEE TRANSACTIONS ON SMART GRID 2024年第4期15卷 3994-4009页

作者： Li, Jiaqi Xu, Xiaoyuan Yan, Zheng Wang, Han Shahidehpour, Mohammad Xie, Bangpeng Luo, Xiao Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Noncarbon Energy Convers & Utilizat Inst Shanghai 200240 Peoples R China IIT Galvin Ctr Elect Innovat Chicago IL 60616 USA State Grid Shanghai Pudong Power Supply Co Shanghai 200240 Peoples R China State Grid Shanghai Municipal Elect Power Co Lingang Power Serv Ctr Shanghai 200120 Peoples R China

The resilience of distribution networks (DNs) or transportation networks (TNs) has attracted a wide attention due to the frequent occurrence of extreme natural disasters. However, the existing works usually investigate the resilience of two systems independently, which neglects their coordinated operations. This paper models the interactions of DNs and TNs and coordinates multiple resilience enhancement strategies to make a more cost-effective resource allocation scheme for the operation of transportation-power distribution networks (TDNs) in hurricanes. Specifically, resource allocations and TDN operations are designed as a two-stage mixed-integer stochastic programming (TMISP) model, where the first stage is to make resource allocation decisions before the hurricane strikes, and the second stage is to perform multi-stage resilience enhancement strategies to minimize the expected TDN operational losses. The uncertain TDN outages with defensive resources in hurricanes are modeled as decision-dependent uncertainties, which are decoupled into decision-independent uncertainties for generating TDN outage scenarios. A comprehensive scenario reduction method is utilized to reduce the redundancies in simulated scenarios. The penalty-based Gauss-Seidel approach is combined with a scenario-skip technique to solve the TMISP problem with binary variables in both stages. Numerical simulations show that the proposed solution methods provide superior performances in computational efficiency and accuracy, over the traditional scenario reduction and scenario-wise decomposition algorithms in solving the TMISP problem. The investment efficiency is increased with the coordination of various DN and TN resources and multiple resilience enhancement strategies, compared with those of separate allocation schemes.

关键词： Resource management Roads Resilience Hurricanes Maintenance engineering Disasters Emergency services Transportation-power distribution networks resource allocation resilience enhancement stochastic programming decision-dependent uncertainty

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A multi-objective two-stage stochastic unit commitment model for wind and battery-integrated power systems

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JOURNAL OF ENERGY STORAGE 2024年 89卷

作者： Mena, R. Godoy, D. R. Kristjanpoller, F. Viveros, P. Univ Tecn Federico Santa Maria Dept Ind Engn Santiago Chile

This paper presents a mathematical programming framework for modeling the operations of power systems with high wind power penetration and BESS, accounting for wind and power demand uncertainties. For this, we introduce a multi-objective two-stage stochastic unit commitment model aimed at constructing on/off schedules that balance operating costs and CO2 emissions. Under the realistic limitations imposed by BESS high variable operating cost, we focus on assessing and analyzing the operational impact of BESS for two different integration schemes: wind farm-coupled and stand-alone. The proposed method of solution consists in an augmented epsilon-constrained algorithm with an integrated normalized euclidean distance-based bisection routine to produce more equispaced Pareto optimal solutions. We applied the proposed framework to a modified version of the New England IEEE-39 bus test system for three disjoint cases of analysis: without BESS integration, with BESS integrated into a wind farm, and with a stand-alone BESS integrated into the grid. The results demonstrate the efficacy of the proposed framework and show substantial benefits in terms of CO2 emissions reductions, particularly in the stand-alone configuration, which, although limited in extent of use, promotes the direct injection of wind power into the grid, the decommitment of polluting thermal units, enhance the use of free or low emissions technologies, and contributes as a provider of spinning reserves.

关键词： Power systems Wind energy Battery energy storage systems Unit commitment Multi-objective optimization stochastic programming

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