Planning Horizon Research Articles

A comparative analysis of reinforcement learning algorithms for earth-observing satellite scheduling

Deep reinforcement learning (DRL) has shown promise for spacecraft planning and scheduling due to the lack of constraints on model representation, the ability of trained policies to achieve optimal performance with respect to a reward function, and fast execution times of the policies after training. Past work investigates various problem formulations, algorithms, and safety methodologies, but a comprehensive comparison between different DRL methods and problem formulations has not been performed for spacecraft scheduling problems. This work formulates two Earth-observing satellite (EOS) scheduling problems with resource constraints regarding power, reaction wheel speeds, and on-board data storage. The environments provide both simple and complex scheduling challenges for benchmarking DRL performance. Policy gradient and value-based reinforcement learning algorithms are trained for each environment and are compared on the basis of performance, performance variance between different seeds, and wall clock time. Advantage actor-critic (A2C), deep Q-networks (DQN), proximal policy optimization (PPO), shielded proximal policy optimization (SPPO) and a Monte Carlo tree search based training-pipeline (MCTS-Train) are applied to each EOS scheduling problem. Hyperparameter tuning is performed for each method, and the best performing hyperparameters are selected for comparison. Each DRL algorithm is also compared to a genetic algorithm, which provides a point of comparison outside the field of DRL. PPO and SPPO are shown to be the most stable algorithms, converging quickly to high-performing policies between different experiments. A2C and DQN are typically able to produce high-performing policies, but with relatively high variance across the selected hyperparameters. MCTS-Train is capable of producing high-performing policies for most problems, but struggles when long planning horizons are utilized. The results of this work provide a basis for selecting reinforcement learning algorithms for spacecraft planning and scheduling problems. The algorithms and environments used in this work are provided in a Python package called bsk_rl to facilitate future research in this area.

Efficient expansion planning of modern multi-energy distribution networks with electric vehicle charging stations: A stochastic MILP model

This paper presents a stochastic Mixed-Integer Linear Programming (MILP) model developed to address the collaborative expansion planning of modern multi-energy distribution networks, taking into account the integration of Electric Vehicle Charging Stations (EVCSs) in the presence of uncertainties. The model is comprehensively formulated to encompass Distributed Generation (DG) resources, Electric Vehicles (EVs), and Capacitor Banks (CBs), alongside traditional expansion options like substations and circuit construction/reinforcement. The essence of this Distribution System Expansion Planning (DSEP) problem formulation lies in its stochastic approach, featuring chance constraints to effectively manage and address uncertainties. The central objective of the model is to minimize the total investment and operational expenditures associated with the DSEP problem over a designated planning horizon. The rigorous application of optimization techniques ensures the convergence of the MILP model, thereby delivering an efficient and reliable solution. The model's effectiveness is initially assessed using an 18-bus system, followed by comprehensive testing on a larger 123- bus system to gauge its robustness. Thorough performance evaluations are conducted across various case studies, encompassing both the 18- bus and 123- bus systems, culminating in the validation of the model's resilience, scalability, and replicability. These case studies encompass deterministic and stochastic methodologies across six distinct scenarios. The numerical outcomes conclusively demonstrate that the DSEP solution, when utilizing the stochastic approach without considering EVCSs, is associated with elevated costs. However, with a strategic allocation strategy for EVCSs, noteworthy reductions in investment expenditures related to substations, lines, and overall costs are notably achieved. The incorporation of uncertainty management within the DSEP problem underscores the flexibility and adaptability intrinsic to the proposed stochastic MILP model. These attributes render it ideally suited to effectively orchestrating the expansion of multi-energy distribution networks in the presence of EVCSs, thereby enhancing its utility and relevance in addressing modern energy distribution challenges.

Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Radial Power Distribution Networks (PDNs) often suffer from limited reliability, flexibility, and efficiency, leading to service interruptions. Planning for radial PDNs is essential to enhance redundancy resilience, reduce disruptions, and improve overall efficiency. However, traditional PDN planning methods have become obsolete due to the proliferation of Distributed Generation (DG) resources and energy storage systems. Additionally, the rise of Electric Vehicles (EVs) demands sophisticated charging infrastructure planning. This article presents a Mixed-Integer Linear Programming (MILP) model for joint expansion planning of PDN and Electric Vehicle Charging Stations (EVCSs). The model takes into account the construction or reinforcement of substations and circuits, along with the integration of EVs, the installation of DGs, and the placement of capacitor banks, all regarded as traditional conventional expansion options alternatives. To address uncertainties associated with DG generation, conventional loads, and EV demand, our model identifies optimal installation and asset locations. We formulate this as a stochastic scenario-based program with chance constraints for Power Distribution Network Expansion Planning (PDNEP), minimizing investment, operational, and energy loss cost costs over a planning horizon. Through two deterministic and stochastic approaches, encompassing six case studies on an 18-node test system, we evaluate the effectiveness of our model. Results are further validated on a 54-node system, confirming the model’s robustness. Notably, the numerical findings underscore the substantial cost reduction achieved by including EVCSs in the stochastic expansion planning approach, demonstrating its cost-effectiveness. In case study I, where all EVs charge at home during peak hours, it’s the worst case for the PDN. The 54-node system, more complex, demands longer computational time. In the 18-node system, costs improve from 9.97% (case study II) to 3.96% (case study VI) versus the worst-case (case I). In the 54-node system, improvements range from 10.47% (case study II) to 1.40% (case study VI). As a result, In comparative analyses against deterministic and stochastic approaches, our model consistently outperforms in diverse test case studies. The proposed model’s adaptability to address uncertainties underscores its suitability for solving the PDNEP problem in PND.

Comparing Different Replacement Policies for Logging Machines in Brazil

A dynamic programing algorithm to identify schedules that minimize the discounted cost (DC) of logging machines over a planning horizon including gains from technological progress was used. The identified schedules were also compared with three alternative replacement policies derived from the literature and Brazilian forestry companies. The case study used a harvester and a forwarder and a 100-year planning horizon, where the maximum replacement limit was 8 years. To apply the dynamic programing algorithm, it was necessary to generate lists from cash flows, which incorporated the possible replacement combinations of a series of machines according to the length of the planning horizon and the maximum replacement limit. The lists were formed by three descriptors: predecessor node (moment of purchase of the machine), future node (point of sale for the acquisition of a new machine), and arc value (DC information, the mean production cost and mean production). The results show that the DC identified for the series of harvester replacements was higher compared to the forwarder. It was also identified that the harvester's economic life is shorter, and with technological progress, there was a reduction in the economic life of both machines. Technological progress was also responsible for reducing the average production cost and increasing the average production of machines. When comparing the alternative schedules (AS), it was found that, although AS had a higher DC value and mean production costs, there was very little difference between them. In the harvester's case, AS01 had the highest DC value ($4.36 million). By choosing it, the decision maker would bear a DC boost of $54,000, while AS02 and AS03 would trigger an increase of $43,000 and $32,000, respectively. For the forwarder, the schedule with the highest DC value was AS03 ($3.69 million). The postponement of the replacements made in alternative schedule 01 and alternative schedule 02 resulted in an increase in the DC of $5000, while the anticipation of the replacements made in the alternative schedule 03 resulted in an increase of $48,000. The aspect that stood out the most, in relation to the results presented, was the small variation that the alternative schedules presented in relation to the schedules obtained using the dynamic programing algorithm. With a DC variation of less than 1.4%, the results lead us to conclude that the decision maker will not suffer much harm in choosing any of the alternative schedules tested.

Methods for Predicting the Costs of Maintaining Nuclear Heritage Facilities in а Safe State

The relevance of the conducted research is determined by the need to control and forecast the costs of enterprises to maintain shutdown nuclear energy facilities in a save condition in order to solve budget planning problems. In the near future, an increase in operating expenses is expected due to an increase in the number of such facilities. To solve the problem econometric forecasting methods are used.Purpose of the research. This study is aimed to develop a methodology for estimating the costs of maintaining nuclear and radiation hazardous facilities in a safe condition and the safe storage of radioactive waste, considering the specifics of accounting at nuclear industry enterprises and forecasting such costs, taking into account plans for shutting down and decommissioning of facilities and removing accumulated radioactive waste from storage facilities in the future.Materials and methods. In the research there was developed the methodology of isolation from the overall structure costs, associated with maintaining shutdown facilities and storage facilities for radioactive waste of nuclear industry enterprises. A cumulative estimation of such costs has been carried out for facilities whose operation for the intended purpose has currently been discontinued. Based on obtained retrospective data, a correlation-regression model with structural changes was developed to predict the costs of maintaining nuclear energy facilities in a safe condition which are expected to be shut down in the future. The developed model allows considering the specifics of facilities, their level of radiation hazard and overall characteristics, the average level of wages at enterprises and the cost of resources.Results. Based on the plans for the shutdown and decommissioning of nuclear energy facilities and the disposal of radioactive waste from storage facilities, the costs of maintaining such facilities in a safe condition with a planning horizon up to 2050 have been forecasted. The results of calculations show that the costs for nuclear energy facilities (except for nuclear power plant units) will increase to 10 billion rubles per year by 2035 (in 2022 prices) and will significantly decrease after 2035 - up to 7.5 billion rubles per year. The reduction will be due to the transfer to a nuclear and radiation safe state of large facilities of radiochemical plants, as well as the conservation of sites for the placement of industrial uranium-graphite reactors. The costs of maintaining the storage facilities of accumulated radioactive waste in a safe condition will be reduced annually (almost linearly) as they are transferred to the disposal of the removed radioactive waste and the conservation of special radioactive waste facilities. By 2035, costs will be reduced from the current level of 5 billion rubles to 4.3 billion rubles per year.Conclusion. The results of the study confirm the high importance of the implementation of the Federal Target Program “Ensuring Nuclear and Radiation Safety for 2016-2020 and for the period up to 2035”, under which it is planned to decommission about 75 stopped nuclear and radiation hazardous facilities and transfer about 155 thousand cubic meters of radioactive waste to disposal.

A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

PurposeDistribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.Design/methodology/approachThe problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.FindingsFor the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.Originality/valueThe proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

A new mathematical model integrating the carbon tax and horizon planning to optimize the textile and clothing industry supply chain

Energy costs have surged significantly in recent years across various regions of the world. This inflation has a direct impact on households and businesses, by increasing production costs. It risks slowing the global economic recovery by reducing purchasing power and consumption. This study presents a new mixed-integer non-linear programming (MINLP) model with the objective of cost minimization within the direct supply chain of the textile and clothing industry. To consider different aspects in our study, the cost function is decomposed to the supply, production, energy, transportation, storage, and carbon tax emissions costs. It deals also with the management of multiple energy sources (specifically, electricity for production and fuel for transportation). This model simultaneously incorporates several critical factors, including life cycle assessment, carbon tax emissions, and planning horizon. To do this, a product’s carbon emissions are quantified from raw material extraction through to the finished product, and three different planning horizons as long term t=∞, medium term t=3 month, and short term t=1 month are considered to evaluate their impacts on the costs and emissions values. A shorter planning horizon leads to increased costs and reduced emissions reductions compared to a medium or long-term planning horizon. Additionally, the study introduces a new benchmark based on real-world data available in the literature, which serves as a valuable tool for evaluating strategic and tactical planning issues within textile supply chains. To evaluate the performance of our proposed model, we defined three different size of the problem by changing the number of factories, suppliers, production sites, warehouses and customers.The study’s findings reveal that, over a medium and long-term planning horizon, there is an average cost increase of about 2% for different problem sizes, when considering a carbon tax ranging from 44.6€ to 86.2€ per tonne of emissions. When a higher carbon tax is applied, the total cost increases, while the carbone emissions and subcontracting activities will be decrease.

Current and Future Role of Natural Gas Supply Chains in the Transition to a Low-Carbon Hydrogen Economy: A Comprehensive Review on Integrated Natural Gas Supply Chain Optimisation Models

Natural gas is the most growing fossil fuel due to its environmental advantages. For the economical transportation of natural gas to distant markets, physical (i.e., liquefaction and compression) or chemical (i.e., direct and indirect) monetisation options must be considered to reduce volume and meet the demand of different markets. Planning natural gas supply chains is a complex problem in today’s turbulent markets, especially considering the uncertainties associated with final market demand and competition with emerging renewable and hydrogen energies. This review study evaluates the latest research on mathematical programming (i.e., MILP and MINLP) as a decision-making tool for designing and planning natural gas supply chains under different planning horizons. The first part of this study assesses the status of existing natural gas infrastructures by addressing readily available natural monetisation options, quantitative tools for selecting monetisation options, and single-state and multistate natural gas supply chain optimisation models. The second part investigates hydrogen as a potential energy carrier for integration with natural gas supply chains, carbon capture utilisation, and storage technologies. This integration is foreseen to decarbonise systems, diversify the product portfolio, and fill the gap between current supply chains and the future market need of cleaner energy commodities. Since natural gas markets are turbulent and hydrogen energy has the potential to replace fossil fuels in the future, addressing stochastic conditions and demand uncertainty is vital to hedge against risks through designing a responsive supply chain in the project’s early design stages. Hence, hydrogen supply chain optimisation studies and the latest works on hydrogen–natural gas supply chain optimisation were reviewed under deterministic and stochastic conditions. Only quantitative mathematical models for supply chain optimisation, including linear and nonlinear programming models, were considered in this study to evaluate the effectiveness of each proposed approach.

A large-scale heuristic approach to integrate on-demand warehousing into dynamic distribution network designs

A recent business model, on-demand warehousing, enables warehouse owners with extra distribution capacity to rent it out for short periods, providing firms needing flexible network designs a new type of distribution capacity. In this paper, a heuristic is created to solve large scale instances of dynamic facility location models that optimize distribution networks over a multi-period planning horizon, simultaneously considering the selection of different warehouse types with varying capacity granularity, commitment granularity, access to scale, and cost structures. The heuristic iteratively solves selected single-period problems, creating a set of smaller subproblems that are then solved for multiple periods. Their decisions are combined to achieve feasible low-cost solutions, ensuring each customer’s demand point is covered for each period. A set of computational experiments recommends how heuristic settings should be set by industrial decision makers and illustrates the heuristic can generate high-quality solutions for large scale networks during long planning horizons and many decision periods. The heuristic can solve national-level instances with many customer demand points, candidate locations, different warehouse types and capacity levels and many periods.

A heuristic method for production scheduling of an open pit mining operation

ABSTRACT A mathematical model for production scheduling of open pit mines maximises the net present value and satisfies the reserves, pit slope angle, and production capacity constraints. A solution to this model aims to deliver an extraction sequence and the movement of materials across various stages within the operation over a defined planning horizon. However, given that the mineral reserves delineated into thousands of mining blocks form the geological input, the model falls in the category of large-scale optimisation problems, i.e. it is computationally intractable, and exact methods cannot solve realistic scenarios of the problem. Therefore, this paper contributes an alternative to the conventional mathematical model along with a corresponding heuristic method to solve this proposed model. An implementation of the proposed method at various realistic instances reveals better performance in terms of net present value, computation time and optimality gap as compared to the traditional methods.

Make-up strategies with finite planning horizons but infinitely forward-looking asset prices

How effective are forward-guidance and make-up strategies? Standard models find them extremely effective, but by assuming households’ inflation expectations respond much more strongly than in the data. Models where households discount the future find them much less effective and match the small reaction of inflation expectations, but not the actual large reaction of asset prices. We build a model that rationalizes both. Households cognitively discount the future, but more forward-looking financial market professionals incorporate their expectations of future policy into the long-term nominal rates faced by all. We find that make-up strategies have sizably better stabilization properties than inflation targeting.

Multiyear stochastic wind generation investment planning with demand response in distribution system using improved water evaporation optimization

ABSTRACT This article proposes a multiyear distributed generation (DG) investment planning model with the coordination of demand response for making the decision of investment under uncertainty. The aim is to reduce the net present value related to the energy purchasing cost from the grid, investment cost including operation, and maintenance cost, emission penalty cost and energy losses cost. In this article, two investment planning models are developed with a total planning horizon of 20 years: (i) static investment planning model and (ii) dynamic investment planning model. In the static investment, DG is installed in first year, whereas for dynamic investment model, time period of DG installation is considered flexible. The impact of real-time demand response is also analyzed for both cases. The optimal distribution network investment planning problem, which includes multiple DG and demand response, is a complicated combinatorial optimization problem that traditional optimization methods are unable to tackle efficiently. Therefore, an improved water evaporation optimization algorithm is proposed and compared with the genetic algorithm, basic water evaporation algorithm, and particle swarm optimizationin this article. Both DG investment models are applied to the standard 33-bus radial distribution network.

An in-depth metaheuristic approach to design a sustainable closed-loop agri-food supply chain network

As an essential component of human life, agricultural products play a very important role in guaranteeing that individuals get all the essential nutrition. Governments and industries spend great financial resources, define short- and long-term goals, and organize their policies to develop a steady agri-food supply chain and provide fresh, healthy products to their societies. This work proposes a new mixed-integer linear programming model to propose an agri-food supply chain network design for the coconut industry under sustainable terms. This study mainly aims to solve a multi-objective closed-loop supply chain, considering both forward and reverse product movements. The model attempts to manage the net present value of total cost for specific planning horizons while monitoring environmental pollution and job opportunities within the network. Given the NP-hard nature of the network, the solution approach embraces a set of recently developed metaheuristics to overcome its complexity effectively. To this end, six multi-objective optimizers and three hybrid algorithms are utilized, among which the multi-objective artificial rabbit optimizer is first developed and applied in this study. Hence, the model's compatibility with real conditions is investigated using fifteen practical tests. The results of interval plots and the Friedman statistical test emphasize that optimizers can solve all sizes of problems. However, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) outperforms solving practical tests according to both the results of statistical tests and the novel hybrid Multi-Criteria Decision Making (MCDM) framework.

A novel mathematical model and a hybrid grouping evolution strategy algorithm for an automated last mile delivery system considering wind effect

Efficient and timely delivery of goods in last-mile logistics is a key challenge for e-commerce companies. With the rise of drone technology, drone-based delivery has emerged as a promising solution to address the challenges of traditional last-mile logistics. In light of the environmental and social challenges faced by last-mile delivery, integrating sustainability into logistics planning has become a vital requirement rather than a mere option. Drones have been identified as a potential solution to reduce greenhouse gas emissions and improve the sustainability of last-mile logistics. In this study, we present a mathematical model for the multi-depot multi-period drone delivery problem, which considers wind patterns and multi-periodicity to optimize the routing of multiple drones from multiple depots over a planning horizon of one day or shift. We included the wind profiles in our modeling to achieve a more realistic drone route planning. To address the complexity of the problem, we employ the Dantzig-Wolfe decomposition method to obtain a lower bound for the problem. Since we have a grouping problem, a novel metaheuristic algorithm, based on a grouping evolution strategy algorithm is developed for efficient drone routing. The algorithm uses the structural information along with the problem with the aid of a proposed constructive heuristic based on the well-known best fit strategy. Results show that proposed approach, achieved a solution with a gap of 2.99% to the lower bound on average, demonstrating its effectiveness in addressing the complexity of the multi-depot multi-period drone delivery problem while considering the impact of wind patterns.

Freight railcar-to-train assignment and departure scheduling in a railyard

Rail freight distribution operations require assembling trains consisting of multiple freight cars with a common destination, and assigning departure times to the assembled trains. Limits on railyard train assembly capacity restrict the number of trains that may be assembled and, therefore, depart the railyard, per unit time. Each individual car, or unit, in a railyard typically has an associated subsequent destination and preferred due date, as well as an acceptable time window within which the destination location will accept delivery. Railyard planners thus face the recurring problem of determining the assignment of cars to assembled trains, as well as the schedule of train departures within a planning horizon. We propose a 0–1 integer linear program for solving this assignment and scheduling problem under the two objectives of minimizing of the number of required train departures within the week, and maximizing on-time performance. We show that this problem is NP-hard and propose constructive, Lagrangian-based, and neighborhood interchange heuristics for obtaining near-optimal solutions. We also provide computational results that characterize the tradeoff between on-time performance and weekly workload, as measured by the number of weekly train departures.

41 Genetic Selection for Improved Profit Conditioned on Enterprise-Specific Circumstances

Abstract Circumstances of producers relative to objectives, economic constraints, and production environments differ substantially. Consequently, the specific path of sustainability differs among beef enterprises. Such a path must take a supply-chain approach, contemplating market (consumer) demands, and firm-level economic costs and returns in both the short- and long- term. Long-term economic returns imply longer-term stewardship of natural resources. Genetic selection is uniquely poised to aid in the pursuit of greater economic and biological efficiency, given it represents a cumulative and permanent source of change. However, it is critical that such change be consistent with the producer-specific sustainability path. Given there are multiple traits that impact economic outcomes economic selection indexes are the appropriate tool. The general form of such indexes has been around for about 80 years, albeit with a less than desirable adoption rate in beef selection decisions. The process of index development can account for known sensitivities to differences in marketing endpoints and current levels of phenotypic performance. Moreover, the time horizon used to determine the economic consequences of a selection decision could differ among enterprises. The choice of planning horizon (PH) is an important, yet often overlooked component of breeding objective and index construction. Shorter PH inherently emphasizes traits that can be fully expressed in the near term whilst longer PH allow more substantial weighting of traits expressed later in life. Misalignment of breeding objectives and selection indexes, as might be the case in using a terminal index to choose sires in a self-replacing herd, or incorrect choice of PH could lead to less profitable genetic selection decisions by increasing mature cow weight and/or de-emphasizing reproductive longevity. Consequently, our objective was to provide examples of how trait emphasis might be altered under different assumptions during selection index development using the web-based tool iGENDEC. Indexes assumed replacement females were retained and terminal animals were retained through harvest. We then compared changes in PH, annual cow variable costs, and phenotypic means of hot carcass weight (HCW). The combined relative emphasis (RE) of mature cow weight (MWT) and stayability (STAY) in the breeding objective was 0, 10, and 22% when planning horizon was 2, 10, and 20 years, respectively. As HCW increased from 318 to 408 kg the RE of STAY increased from 10.9% to 18.7% while the RE of HCW decreased from 43.8% to 28.9%. When annual cow variable costs were $250, $500, and $750 the RE of MWT was 6, 14.9, and 22.5%, respectively, assuming the value of calves sold stayed the same. Collectively, these examples illustrate that incorrect definition of breeding objectives and parametrization of indexes can lead to inappropriate weighting of traits that are drivers of long-term resource use (i.e., MWT and STAY).

A novel approach for multi-stage investment decisions and dynamic variations in medium-term energy planning for multi-energy carriers community

So far, energy planning methods faced several challenges in achieving a proper assessment due to the extreme variability of input conditions. To considerably decrease the computational efforts in achieving solutions, most of the analyses reported in the scientific literature assume constant costs and demand over the entire planning horizon in a typical year. However, results might not be perfectly aligned with real ones because of the incapability of some models to address several aspects like energy demand/production variability, energy and technology costs, efficiency degradation, and use of different energy carriers. This paper proposes a novel methodology that optimises both the long-term planning and short-term scheduling decisions in the management of a multi-energy carries community by means of a Mixed Integer Linear Programming model that also considers the modular design of technologies (e.g., technological devices selection from a discrete set of variants). Such a model has been applied to the case study of a University campus in Italy, whose historical demand data were used for its energy planning with a time horizon of 30 years. Three scenarios have been analysed: (i) the Business As Usual, (ii) the Sector-coupling scenario, and, finally, (iii) the Hydrogen deployment one. The results are obtained under different energy scenarios, showing the effectiveness of the methodology in dealing with multi-investment stages at different planning levels in a reasonable computational time. In particular, they showed that deploying more sustainable technologies would increase the cost of the electricity (between 43%–89%), while reducing other energy carriers’ cost (about 60%) and lowering all energy carriers’ carbon footprint (between 50%–80%). From a long-term perspective, (i) the use of sector-coupling technologies is beneficial from both economic and environmental points of view, and ii) dynamic variations of some parameters can strongly affect the deployment of high-cost technologies to be installed beyond 2030.

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

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 vaccines. 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 includes 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 uncertainty in supply, and in the worst case, approximately 30% of the demand cannot be met.

Equitable anesthesiologist scheduling under demand uncertainty using multiobjective programming

This work addresses the practical anesthesiologist scheduling (AS) problem motivated by the needs of an academic anesthesiology department. The AS problem requires the department to plan and deploy providers to adequately meet clinical demand and institutional protocols of various clinical units over a planning horizon of up to several weeks. A data‐driven two‐step AS framework is developed by exploiting the historical demand data of anesthesia cases. The first step is a shift design which obtains the optimal shifts considering clinical demand under uncertainty using conditional value‐at‐risk constraints, and the second step is provider assignments that generate the schedule considering optimal and equitable workload distribution and provider availability using multiobjective mixed‐integer programming models. Moreover, the AS framework incorporates the provider specialties, and clinical and lifestyle preferences and aligns with the existing scheduling practices. An ɛ‐constraint solution method is applied for multiobjective optimization, and an iterative solution method is developed to improve solution quality for workload equity in clinical applications. Computational experiments are performed to evaluate the performance of three alternative forms of the workload equity objective function, and the results show that the minimization of the sum of the absolute deviations of provider workloads best balances solution runtime and quality. In the concerned academic anesthesiology department, two clinical problems, the budget and hiring planning and the monthly scheduling, are addressed via the application of the proposed AS framework. For budget and hiring, decision‐makers can make trade‐offs based on their preference using the nondominated frontiers obtained via the ɛ‐constraint method. For monthly scheduling, the iterative solution method can accommodate preassigned shifts capturing institutional requirements while improving workload equity. The workload variance has been substantially reduced from 2.92 to 1.39 after the implementation based on the historical schedule data. The provider schedule satisfaction is improved from 3.13/5 to 3.44/5, and at least 82% of scheduling burden on department leaders is relieved. The developed AS framework is generic and can be extended to the scheduling of other types of care providers, including nurses and residents.

Understanding the Contributions of Paleo‐Informed Natural Variability and Climate Changes to Hydroclimate Extremes in the San Joaquin Valley of California

AbstractTo aid California's water sector to better understand and manage future climate extremes, we present a method for creating a regionally consistent ensemble of plausible daily future climate and streamflow scenarios that represent natural climate variability captured in a network of tree‐ring chronologies, and then embed anthropogenic climate change trends within those scenarios. We use 600 years of paleo‐reconstructed weather regimes to force a stochastic weather generator, which we develop for five subbasins in the San Joaquin Valley of California. To assess the compound effects of climate change, we create temperature series that reflect projected scenarios of warming and precipitation series that have been scaled to reflect thermodynamically driven shifts in the distribution of daily precipitation. We then use these weather scenarios to force hydrologic models for each of the five subbasins. The paleo‐forced streamflow scenarios highlight periods in the region's past that produce flood and drought extremes that surpass those in the modern record and exhibit large non‐stationarity through the reconstruction. Variance decomposition is employed to characterize the contribution of natural variability and climate change to variability in decision‐relevant metrics related to floods and drought. Our results show that a large portion of variability in individual subbasin and spatially compounding extreme events can be attributed to natural variability, but that anthropogenic climate changes become more influential at longer planning horizons. The joint importance of climate change and natural variability in shaping extreme floods and droughts is critical to resilient water systems planning and management in the San Joaquin.