Sizing Allocation Research Articles

Hospital design planning: unscheduled trauma ER visits impacting inpatient space allocation

ABSTRACT Medical healthcare planners need to align outpatient and inpatient design factors that affect hospital space allocation. The Emergency Departments (ED) design must be congruent with the inpatient platform design to limit bottlenecking of patient admissions. However, limited research deciphers the impact of unscheduled ED admissions and the effects on inpatient bed capacity to ensure facility capability. The current study utilizes a case study by comparing the use of space planning criteria of the Department of Defense (DoD) with similar Private healthcare organization departmental metrics to inform space allocation. This study established the significance of unscheduled ED visits metric on inpatient space allocation. Bed capacity was calculated by outlining specific metrics; namely: population at risk, Average Daily Patient Load (ADPL), Average Length of Stay (ALOS), Average Annual Workload, Annual Admissions, and Occupancy Rate that are necessary for the right-sized inpatient hospital designs with a trauma mission. By analyzing existing research, identifying key metrics, highlighting capacity gaps, and showcasing real-world impacts via a case study pilot model. Valuable insights and benefits allow medical planners and administrators to assess and incorporate unscheduled visits as a key factor that influences appropriate hospital facility sizing and resource allocation.

Optimization and Decision-Making Framework for Small Unmanned Aircraft Systems Fleet Design

Many unmanned aircraft applications require or benefit from deploying a fleet to perform a mission. Technology innovations such as rapid prototyping and microelectronics enable the use of unmanned aircraft systems (UAS) at lower costs, so the ability to design a bespoke UAS for a specific mission is an additional advantage. The problem of designing the new UAS and allocating the fleet of these aircraft is a challenging optimization problem. Using constrained multi-objective design optimization with an interactive multicriteria decision-making method, which involves the mission customer, the approach in this paper generates UAS designs and fleet allocations and determines preferential solutions for the customer. The paper presents a case study involving a UAS weather profiling mission for the aircraft sizing and fleet allocation and uses inputs from meteorological subject-matter experts acting as customers to demonstrate the functionality of the approach.

Fleet sizing and charger allocation in electric vehicle sharing systems

In this paper, we propose a closed queueing network model for performance analysis of electric vehicle sharing systems with a certain number of chargers in each neighborhood. Depending on the demand distribution, we devise algorithms to compute the optimal fleet size and number of chargers required to maximize profit while maintaining a certain quality of service. We show that the profit is concave with respect to the fleet size and the number of chargers at each charging point. If more chargers are installed within the city, we show that it can not only reduce the fleet size, but it also improves the availability of vehicles at all the points within a city. We further show through simulation that two slow chargers may outperform one fast charger when the variance of charging time becomes relatively large in comparison to the mean charging time.

Flexible and efficient renewable-power-to-methane concept enabled by liquid CO2 energy storage: Optimization with power allocation and storage sizing

Power-to-methane (PtM) coupled with renewables requires an energy buffer to ensure a steady and flexible operation. Liquid CO2 energy storage (LCES) is an emerging energy storage concept with considerable round-trip efficiency (53.5%) and energy density (47.6 kWh/m3) and can be used as both an energy and material (i.e., CO2) buffer in the PtM process. Integration of LCES with the PtM process realizes co-production of methane and electricity, supports peak shaving of the power grid, and enhances profitability via the sale of electricity at peak hours. This study aims to design, optimize, and comprehensively evaluate the techno-economic performance of the PtM-LCES process using a renewable power mix of solar and wind. A process scheduling model is formulated to understand the impacts of storage sizing and power allocation on the process performance. Then, artificial neural network-based surrogate optimization was performed to establish a cost-optimal design. Investigation at Kramer Junction, California, found that the production cost of methane was 161.6 €/MWh with an efficiency of 76.2% and a renewables penetration of 78.7%. This cost could be reduced by improving LCES's performance and considering electricity price arbitrage in areas with high electricity prices. The electricity price arbitrage and improvement of LCES's performance could lower the cost to 83.8 and ∼60 €/MWh, respectively. Moreover, zero or negative carbon emissions can be achieved if the renewables penetration can be increased to 91.5%. Results indicate that the PtM-LCES process is both energy efficient and economically viable, through which renewable methane can be cost-competitive with fossil natural gas.

Proposal and surrogate-based cost-optimal design of an innovative green ammonia and electricity co-production system via liquid air energy storage

Green ammonia produced via the Power-to-Ammonia (PtA) process has the great potential to decarbonize the energy supply. The current challenge of green ammonia is the unacceptable cost compared to that produced from fossil energy, which arises owing to the low energy efficiency caused by fluctuations in renewable power and high investment costs. This study aims to tackle these issues by integrating liquid air energy storage (LAES) with the PtA process to manage uncertainties from both sides of power supply and demand, thus forming an innovative ammonia and electricity co-production system to supply power flexibly at peak hours and ensure ammonia production at a constant load. The continuous PtA process enables the use of high-temperature electrolysis, and system thermal integration results in the highest production efficiency of 83%. Flexible operation is achieved by adsorbing dispatchable electricity from the grid and outputting peak electricity for power load shifting, therefore, cost reduction can be achieved via electricity arbitrage. A system scheduling model is formulated to understand the impacts of storage sizing and power allocation on the renewables penetration, grid power output ratio, and the cost of ammonia production. An artificial neural network-based surrogate optimization was performed to establish a cost-optimal design. Investigation at the location of Kramer Junction revealed that the cost of ammonia production can be lowered to 360.74 €/tonne based on a system renewables penetration of 77.6% and grid power output ratio of 43.9%. Moreover, the cost could be further reduced by using an external thermal energy coupled LAES in areas with high electricity prices. These findings clearly demonstrate that the proposed co-production system is an economically viable option for short-term electricity and long-term ammonia hybrid energy storage.

Two-Stage Robust Edge Service Placement and Sizing Under Demand Uncertainty

Edge computing has emerged as a key technology to reduce network traffic, improve user experience, and enable numerous Internet of Things applications. In this article, we study an optimal resource procurement problem for a service provider (SP), who can purchase resources from various edge nodes in the edge computing market to serve its users’ requests. How to jointly optimize the service placement, resource sizing, and workload allocation decisions is a challenging problem, which becomes even more complicated when considering demand uncertainty. To this end, we propose a novel two-stage adaptive robust optimization framework to help the SP optimally determine the locations for installing its service (i.e., placement) and the amount of computing resource to purchase from each location (i.e., sizing). The proposed placement and sizing solution can hedge against any possible realization within a predefined demand uncertainty set. Given the first-stage robust solution, the optimal resource and workload allocation decisions are computed in the second stage after the uncertainty is revealed. To solve the two-stage model, this article presents an iterative solution approach by employing the column-and-constraint generation method that decomposes the underlying problem into a master problem and a max–min subproblem associated with the second stage. Extensive numerical results are shown to illustrate the efficacy of the proposed model.

Improving the tactical planning of solid waste collection with prescriptive analytics: a case study

Paper aims This study presents several business analytics tools that allow improving the tactical planning of the collection process for a Colombian solid-waste management company. Originality The extant literature of operations research/analytics applied to these systems focuses on facility location or vehicle routing. Tactical decisions are seldom studied in the operations research/analytics literature devoted to waste management systems. By contrast, the focus of this paper is on tactical decisions: fleet sizing, frequency assignment, route scheduling and internal resource allocation in a new waste transfer station. Research method We follow a multimethodology approach that uses mathematical programming, metaheuristics, and discrete event simulation. The models use historical information of the system, and the solution of a model are used as input data for the other models. Main findings Introducing a new waste transfer station allows an important reduction of the compactors fleet. However, to prevent a collapse in its internal operation an even operation is needed. This is achieved by rescheduling the routes to balance their arrival during the day. Additional benefits can be attained if some soft constraints are relaxed. Implications for theory and practice Practitioners looking for tactical planning tools on waste collection systems have here an example of their application and benefits. Improvements can be achieved by tactical planning without heavily disrupting decisions at the operational level.

Optimal Sharing and Fair Cost Allocation of Community Energy Storage

This paper studies an energy storage (ES) sharing model which is cooperatively invested by multiple buildings for harnessing on-site renewable utilization and grid price arbitrage. To maximize the economic benefits, we jointly consider the ES sizing, operation, and cost allocation via a coalition game formulation. Particularly, we study a fair ex-post cost allocation based on nucleolus which addresses fairness by minimizing the minimal dissatisfaction of all the players. To overcome the exponential computation burden caused by the implicit characteristic function, we employ a constraint generation technique to gradually approach the unique nucleolus by leveraging the sparse problem structure. We demonstrate both the fairness and computational efficiency of the method through case studies, which are not provided by the existing Shapley approach or proportional method. Particularly, only a small fraction of characteristic function (less than 1% for 20 buildings) is required to achieve the cost allocation versus the exponential information required by Shapley approach. Though there exists a minor increase of computation over the proportional method, the proposed method can ensure fairness while the latter fails in some cases. Further, we demonstrate both the building-wise and community-wise economic benefits are enhanced with the ES sharing model over the individual ES (IES) model. Accordingly, the overall value <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> of ES is considerably improved (about 1.83 times). <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The proportion of total electricity bill reduction relative to the ES capital cost.

Portfolio management system in equity market neutral using reinforcement learning

Portfolio management involves position sizing and resource allocation. Traditional and generic portfolio strategies require forecasting of future stock prices as model inputs, which is not a trivial task since those values are difficult to obtain in the real-world applications. To overcome the above limitations and provide a better solution for portfolio management, we developed a Portfolio Management System (PMS) using reinforcement learning with two neural networks (CNN and RNN). A novel reward function involving Sharpe ratios is also proposed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with the Sharpe ratio reward function exhibits outstanding performance, increasing return by 39.0% and decreasing drawdown by 13.7% on average compared to the reward function of trading return. In addition, the proposed PMS_CNN model is more suitable for the construction of a reinforcement learning portfolio, but has 1.98 times more drawdown risk than the PMS_RNN. Among the conducted datasets, the PMS outperforms the benchmark strategies in TW50 and traditional stocks, but is inferior to a benchmark strategy in the financial dataset. The PMS is profitable, effective, and offers lower investment risk among almost all datasets. The novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.

REDUCED-IMPACT LOGGING BY ALLOCATING LOG-DECKS USING MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM IN WESTERN AMAZON

ABSTRACT To reduce the damage caused by logging in the Amazon rainforest, new metaheuristics have been implemented and tested to ensure the sustainability of this economic segment. Therefore, this study aimed to compare alternatives for road sizing and log deck allocation. In a forest management unit, the skidding to log decks was evaluated in two different areas. To determine the skidding/log deck relation, georeferenced points were generated equally spaced every 50 m. In area 1, the Integer Linear Programming (ILP) model and the Multi-Objective Evolutionary Algorithm (MOEA) were compared. In area 2, only the MOEA was considered. In both areas, these models were also compared to the current planning used in the forest management unit. Solutions were then generated to identify the best management alternative. In both areas, the MOEA showed greater efficiency regarding the processing time, as well as the reduction of log decks number and the road sizing. The multi-objective evolutionary approach assists the decision-making process, due to the presentation of alternatives based on Pareto-optimal solutions, making the choice more flexible and well supported.

An optimization model for fleet sizing and empty pallet allocation considering CO2 emissions.

Pallets are the most common equipment for transporting and storing goods. More and more companies are willing to rent pallets. Pallet rental companies need to transport pallets from their pallet rental service stations to customers and take these pallets back when they are unloaded. Hence, managers should scientifically configure vehicles for their pallet rental service stations. The fleet size, which indicates the amount and types of vehicles, can significantly affect the efficiency and costs of empty pallet allocation. Therefore, an optimization model for fleet sizing and empty pallet allocation is proposed using the methods of mixed-integer programming and stochastic programming. The objectives of this model are to maximize the profits of pallet rental companies and minimize carbon dioxide (CO2) emissions from vehicles. A particle swarm optimization algorithm with inertia weight (IPSO) is developed to solve the proposed model because IPSO can avoid becoming trapped in local optima and is able to find a globally optimal solution within a reasonable number of iterations. A numerical example proves the effectiveness of the proposed model and IPSO. The results of numerical tests show that the amount of CO2 emissions from vehicles can affect the decision on fleet sizing and empty pallet allocation. However, if the price, rental fees, or idle costs of the vehicles with low CO2 emissions are too high, managers would not choose them.

Cost allocation for rural electrification using game theory: A case of distributed generation in rural India

This paper deals with an economic aspect of distributed generation systems which rural energy cooperatives may mainly support, since they can effectively coordinate the different stakeholders within an energy access initiative. Members of a rural energy cooperative would share the costs of deploying a distributed generation system, so that each of them can benefit from it. To minimise the overall investment cost, such a cooperative must consider both technical sizing and cost allocation among its members. For the latter, an important issue to take into account is the so-called willingness-to-pay. For obtaining the design of the distributed generation system we propose to use facility location covering (FALCO) models. The solution of the FALCO model provides both the structure of the distributed generation system and the investment cost to be covered by the members of the cooperative. We take advantage of the structure of the primal and dual optimal solutions of the FALCO model to endogenously determine the minimum and maximum willingness-to-pay of the cooperative members and then to apply rationing techniques to obtain the final allocation of costs to each member. Thus, our approach optimises the generator locations and minimises the total system cost, while assigning to each member at least her minimum willingness-to-pay. From our point of view, this method may help the beneficiaries to accept the investment cost allocation. Finally, a case study based on real data from a rural village in Maharashtra, where a group of farmers wanting to buy and share an energy system to pump water for irrigating their fields, is used to illustrate the working of the proposed cost allocation methodology.

UGrid: Reliable Minigrid Design and Planning Toolset for Rural Electrification

A key component of increasing energy access globally is providing rural electrification using off-grid minigrids. Minigrid developers have specific design and planning needs including: (i) equipment sizing and resource allocation, (ii) distribution network layout, and (iii) reliability cost-benefit analysis. Optimization of each aspect is necessary to minimize the cost of electricity for the customer and to maintain a target level of reliability. This work presents an extension of an open-source holistic minigrid design and planning toolset called uGrid, created by the minigrid developer OnePower in Lesotho to perform resource allocation and sizing optimization of energy generation infrastructure based on statistical load estimates. The extension is an improved uGrid with advanced dispatch controls and the newly added uGridNet with distribution network design using Gaussian-mean clustering, network reduction, and N-1 line loss reliability cost-benefit analysis to optimize the network topology considering cost and reliability. A case study is performed using the toolset to design a minigrid for the Ha Makebe village in Lesotho, highlighting pole placement capabilities and changes in network layouts over a range of reliability probabilities. Added costs for reliability range from 18% to 130% for reliability probabilities from 10% to 100%, providing developers with a wide range of options to consider. This combined toolset is designed and packaged to meet the engineering design needs of minigrid developers, with the goal of supporting affordable rural electrification and energy access initiatives worldwide.

Adaptive quantum-inspired evolutionary algorithm for optimizing power losses by dynamic load allocation on distributed generators

In this paper, an Adaptive Quantum-inspired Evolutionary Algorithm (AQiEA) has been applied for minimizing the power losses in the distribution network by suitable placement, sizing and subsequent allocation of load on Distributed Generators (DG) for a varying load with a time horizon of twentyfour hours. Many efforts have been reported in the literature to minimize power losses. However, they have mostly used a fixed load, i.e., nonvarying load, whereas it is well known that load in distribution network varies during the day. An investigation was undertaken to find the reduction in power losses on a timevarying load. It has been found that the average power losses for dynamic load allocation on DGs for every hour have a maximum reduction in power loss as compared with other well-known cases in the literature. Optimal location and size of DG is a difficult nonlinear, non-differentiable combinatorial optimization problem. AQiEA is used to find the appropriate location and capacity of DG for a varying load with a time horizon of twenty-four hours to minimize the power losses. AQiEA doesn?t require additional operators like local search and mutation to improve the convergence rate and avoid the premature convergence. A Quantum Rotation inspired Adaptive Crossover operator is used as a variation operator, which is parameter free. The effectiveness of AQiEA is demonstrated on two test bus systems viz., 33 bus system and 69 bus system, which are used as benchmark problems for validating the proposed methodology as well as for comparative testing amongst existing techniques. Wilcoxon signed rank test is also used to demonstrate the effectiveness of AQiEA. The experimental results show that AQiEA has better performance as compared to some existing ?state of art? techniques.

Optimal Sizing and Spatial Allocation of Storage Units in a High-Resolution Power System Model

The paradigm shift of large power systems to renewable and decentralized generation raises the question of future transmission and flexibility requirements. In this work, the German power system is brought to focus through a power transmission grid model in a high spatial resolution considering the high voltage (110 kV) level. The fundamental questions of location, type, and size of future storage units are addressed through a linear optimal power flow using today’s power grid capacities and a generation portfolio allowing a 66% generation share of renewable energy. The results of the optimization indicate that for reaching a renewable energy generation share of 53% with this set-up, a few central storage units with a relatively low overall additional storage capacity of around 1.6 GW are required. By adding a constraint of achieving a renewable generation share of at least 66%, storage capacities increase to almost eight times the original capacity. A comparison with the German grid development plan, which provided the basis for the power generation data, showed that despite the non-consideration of transmission grid extension, moderate additional storage capacities lead to a feasible power system. However, the achievement of a comparable renewable generation share provokes a significant investment in additional storage capacities.

A Dynamic Supplier Selection and Inventory Management Model for a Serial Supply Chain with a Novel Supplier Price Break Scheme and Flexible Time Periods

A new supplier price break and discount scheme taking into account order frequency and lead time is introduced and incorporated into an integrated inventory planning model for a serial supply chain that minimizes the overall incurred cost including procurement, inventory holding, production, and transportation. A mixed-integer linear programming (MILP) formulation is presented addressing this multi-period, multi-supplier, and multi-stage problem with predetermined time-varying demand for the case of a single product. Then, the length of the time period is considered as a variable. A new MILP formulation is derived when each period of the model is split into multiple sub-periods, and under certain conditions, it is proved that the optimal solution and objective value of the original model form a feasible solution and an upper bound for the derived model. In a numerical example, three scenarios of the derived model are solved where the number of sub-period is set to 2, 3, and 4. The results further show the decrease of the optimal objective value as the length of the time period is shortened. Sufficient evidence demonstrates that the length of the time period has a significant influence on supplier selection, lot sizing allocation, and inventory planning decisions. This poses the necessity of the selection of appropriate length of a time period, considering the trade-off between model complexity and cost savings.

Multi-Objective Optimization-Simulation for Reliability-Based Inter-Basin Water Allocation

A simulation-optimization framework is presented for reliability-based optimal sizing, operation, and water allocation in the Bashar-to-Zohreh inter-basin water transfer project. The problem was formulated as a mixed integer nonlinear program (MINLP), for which two solution approaches were tested, including gradient-based nonlinear programming and simulation-optimization (SO). The SO framework linked the water evaluation and planning system (WEAP) simulation module, benefiting from fast, single-period linear programming, to the multi-objective particle swarm optimization (MOPSO) for multiperiod optimization. The objective functions were minimizing the sizes of the project’s infrastructures and maximizing the reliability of supplying water to agricultural lands. The combination of nonlinear programming and the branch-and-bound algorithm was not able to solve the resulting MINLP. The results of the MOPSO-WEAP framework indicated that the project can supply water for land development in the Dehdasht and Choram agricultural plains, located in the less developed Kohgiluye and Boyer-Ahmad Province of Iran at an acceptable reliability level. For example, for one of the Pareto solutions found corresponding to maximum land development (30,000 ha), an average volume of 237 million cubic meter (MCM) is transferred annually at a 73.2% reliability level with average sizes of water transfer and storage elements. Further, design-operation and hydropower scenarios were also evaluated, and the Pareto solutions were obtained.

Synthesis of tri-generation systems: Technology selection, sizing and redundancy allocation based on operational strategy

Abstract Tri-generation system is a facility which produces heat, power and cooling simultaneously from a single fuel source. In the industry, such system is commonly operated via two strategies; Following Electrical Load (FEL) or Following Thermal Load (FTL). However, these operating strategies may lead to huge amount of energy that is wasted. In this respect, several works have proposed a switching strategy, whereby tri-generation systems would interchange between FEL and FTL modes depending on energy demand. Unfortunately, the design of tri-generation based on this strategy has received limited attention. Besides, tri-generation operations often face challenges in equipment reliability. As tri-generation systems contain a network of interconnected equipment, equipment failures would disrupt the overall performance of a tri-generation system. As such, this work proposes a novel systematic optimisation approach to design a robust tri-generation system which can operate optimally in its operating strategies. In addition, the proposed approach can simultaneously determine type, size and required equipment redundancy (e.g. operating and standby units) of technologies while considering operating strategies in a tri-generation system. A palm biomass-based tri-generation system (BTS) case study is solved to illustrate the proposed approach.

A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters

This paper proposes a multi-objective mathematical formulation and a hybrid approach to solve buffer sizing and machine allocation problems simultaneously in unreliable production and assembly lines. This paper unlike prior researches assumes that time-dependent parameters of production systems are generally distributed (e.g., uniform, normal, gamma, etc.) and not only deterministic or exponential. This paper proposes a multi-objective mixed binary integer non-linear mathematical model to solve the problem of buffer sizing and machine allocation. The proposed mathematical model is capable of purchasing new machines (candidate) and also selling old machines (current available). In other words, this model compares the candidate machines to current available machines in each station based on different aspects and is capable to replace the current machines with candidate machines or to sell some of the current machines without replacement. To solve the mentioned problem, a new formulation for dealing with multi-objectiveness of the problem is proposed. This formulation generates a series of non-dominated solutions, and also, it is capable of generating a non-dominated solution between two adjacent non-dominated solutions determined by decision maker. A hybrid genetic algorithm (HGA) with a new dynamic mutation probability is proposed to solve the model. Since the proposed mathematical model and the proposed solution method are novel, the proposed HGA is compared to simple genetic algorithm and non-dominated sorting genetic algorithm (NSGA-II). The computational results indicate the effectiveness of the proposed HGA.

Hybrid approach for loss recovery mechanism in OBS networks

This letter reports a study of a hybrid burst assembly and a hybrid burst loss recovery scheme (delay-based burst assembly and hybrid loss recovery (DBAHLR)) which selectively employs proactive or reactive loss recovery techniques depending on the classification of traffic into short term and long term, respectively. Traffic prediction and segregation of optical burst switching network flows into the long term and short term are conducted based on predicted link holding times using the hidden Markov model (HMM). The hybrid burst assembly implemented in DBAHLR uses a consecutive average-based burst assembly to handle jitter reduction necessary in real-time applications, with variations in burst sizes due to the non-monotonic nature of the average delay handled by additional burst length thresholding. This dynamic hybrid approach based on HMM prediction provides overall a lower blocking probability and delay and more throughput when compared with forward segment redundancy mechanism or purely HMM prediction-based adaptive burst sizing and wavelength allocation (HMM-TP).