Optimal Decision Scheme Research Articles

A multi-period optimal distribution model of emergency resources for responding to COVID-19 under uncertain conditions

Ideally, optimal emergency resource allocation would have been vital for effective relief work during the COVID-19 outbreak. However, the suddenness of the epidemic and uncertainty of its spread added some difficulties to distributing emergency resources. First, this study introduces triangular fuzzy numbers to describe the uncertainty of supply and demand of emergency resources, and interval numbers to describe the time required for resource transportation under disaster conditions. To minimize the total delivery time and difference in the total satisfaction rate, this study constructs an optimal model for emergency resource distribution under uncertain conditions that considers both efficiency and equity. Subsequently, an improved genetic algorithm (IMGA) is proposed to obtain the optimal decision scheme. Finally, a case study on emergency resource distribution during the COVID-19 pandemic is conducted for model verification. The results demonstrate that the proposed model can improve the efficiency and effect of emergency resource distribution. The model allocates some emergency resources to each demand site during each emergency period, which can help avoid large losses caused by extreme shortages of resources at a certain demand point. The emergency resource allocation scheme considers the transportation time and degree of impact, which is beneficial for enhancing the flexibility of decision-making and practical applicability of distribution operations. A comparative analysis of the algorithms shows that the proposed IMGA is an effective method for managing emergency resource distribution optimization problems because it has higher solving efficiency, better convergence, and stronger stability. These findings can provide decision support for the optimal distribution of large-scale, multiperiod emergency resources during the COVID-19 pandemic.

Deep learning-assisted multi-objective optimization of coke dry quenching system efficiency

Coke dry quenching is a promising technology for energy saving and environmental protection in the industrial sectors. Prediction of system efficiency index (SEI) and dynamic adjustment of operating parameters are essential to ensure high system efficiency. In this study, a deep learning-assisted multi-objective optimization framework was proposed to improve coke quenching efficiency and steam production simultaneously. Firstly, numerous on-site process parameters were collected as the data basis for subsequent model construction, and a system-level heat balance model was established and validated for real-time calculation of coke burning loss rate. Then, an attention-based one-dimensional convolutional neural network was developed as a data-driven surrogate model to establish the mapping relationship between the operating parameters and SEI. Finally, the Non-dominated Sorting Genetic Algorithm II was combined with the Technique for Order of Preference by Similarity to Ideal Solution to rapidly identify the optimal operating parameters under different loads. The optimization results also provided a new insight into the system dynamic characteristics. The results show that the surrogate model can swiftly and precisely predict SEI, with R2 exceeding 0.9. Compared with initial operating conditions, the optimal decision scheme has improved SEI by 82 %, with the optimal coke burning loss rate and main steam flow rate of 0.56 % and 83.6 t/h, respectively. This work will provide valuable decision aids for technicians to reach the desired system efficiency.

Integrated decision-making for repair order acceptance and service resource allocation that distinguishes between in-warranty and out-of-warranty

Manufacturers classify customers into in-warranty and out-of-warranty customers based on their warranty status at the time of product failure. However, previous studies on the decision-making optimization of orders and service resources for repair services ignored the differences between in-warranty and out-of-warranty customers, which will affect the implementation of manufacturers’ repair service businesses. Therefore, this paper proposes an integrated decision-making model for order acceptance and resource allocation for repair services that distinguishes between in-warranty and out-of-warranty and considers the manufacturer’s limited service resources. Considering the conflicts of interest between the manufacturer, in-warranty and out-of-warranty customers, the model aims to maximize the satisfaction of in-warranty and out-of-warranty customers and the manufacturer’s profit. According to the expectations of different customers and the benefits of the manufacturer, the optimal decision scheme can be derived by determining the accepted orders, adjusting the resource allocation scheme and the service sequence of the accepted orders. In addition, to obtain the optimal decision scheme more efficiently, a memetic algorithm based on the nondominated neighbor immune algorithm is proposed, in which two local search operators are designed to improve the search efficiency. Three hundred instances were used to test this model, and extensive experimental results show that the proposed algorithm is more effective than other widely used algorithms. Finally, the benefits that the proposed integrated decision-making model can bring to manufacturers are demonstrated through numerical experiments.

The optimal decision of e-retailer based on return-freight insurance – considering the loss aversion of customers

PurposeThe return behavior of customers has a great impact on the e-retail industry and has resulted in the emergence of return-freight insurance (RI). Additionally, customer loss aversion arising from returns affects e-retailers' decisions and manufacturers' profits. Therefore, the main purpose of the authors' study is to determine how e-retailers and manufacturers choose their RI strategy and pricing according to customers' loss aversion.Design/methodology/approachThe authors propose three scenarios: no RI, customer purchase RI and free e-retail RI (FRI). Meanwhile, the authors also model a Stackelberg game between e-retailers and manufacturers for analysis. Then, according to customer return behavior and loss aversion, the authors study the optimal pricing decision and RI premium allocation scheme for e-retailers and manufacturers under different scenarios.FindingsIt was found that the loss sensitivity reduces customers' willingness to buy RI, which is not conducive to the development of e-retailers and manufacturers. Additionally, with higher loss sensitivity, e-retailers and manufacturers offer FRI to gain higher profits, which supports the implementation of the FRI strategy.Originality/valueThe authors introduce customers' loss aversion into RI to analyze the optimal pricing decisions and profits of e-retailers and manufacturers, enriching the application of loss aversion theory. In addition, this study analyzes the two-way cost-sharing mechanism between manufacturers and e-retailers to provide FRI, which provides a theoretical basis for RI premium sharing.

Optimal Decision of Dynamic Bed Allocation and Patient Admission with Buffer Wards during an Epidemic

To effectively prevent patients from nosocomial cross-infection and secondary infections, buffer wards for screening infectious patients who cannot be detected due to the incubation period are established in public hospitals in addition to isolation wards and general wards. In this paper, we consider two control mechanisms for three types of wards and patients: one is the dynamic bed allocation to balance the resource utilization among isolation, buffer, and general wards; the other is to effectively control the admission of arriving patients according to the evolution process of the epidemic to reduce mortality for COVID-19, emergency, and elective patients. Taking the COVID-19 pandemic as an example, we first develop a mixed-integer programming (MIP) model to study the joint optimization problem for dynamic bed allocation and patient admission control. Then, we propose a biogeography-based optimization for dynamic bed and patient admission (BBO-DBPA) algorithm to obtain the optimal decision scheme. Furthermore, some numerical experiments are presented to discuss the optimal decision scheme and provide some sensitivity analysis. Finally, the performance of the proposed optimal policy is discussed in comparison with the other different benchmark policies. The results show that adopting the dynamic bed allocation and admission control policy could significantly reduce the total operating cost during an epidemic. The findings can give some decision support for hospital managers in avoiding nosocomial cross-infection, improving bed utilization, and overall patient survival during an epidemic.

A Study of Two-level Inventory Allocation for E-commerce Pre-sales Based on Two Return Methods

In recent years, e-commerce shopping festivals based on the presale model have become increasingly popular. Under the pre-sale model, some e-commerce companies have used inventory front-loading methods to give consumers higher time satisfaction for alleviating the pressure caused by order piling. At the same time a new return method has been generated, but existing studies have only considered the traditional return method. In this paper, a dualobjective decision model based on NSGA-II algorithm is developed for maximizing enterprise profit and maximizing consumer time satisfaction under the premise of considering both return methods, and the optimal two-level inventory allocation decision scheme is calculated for e-commerce enterprises. The results show that the existence of two return methods will have a more significant impact on enterprise profit, and therefore is more practical for e-commerce enterprises to make two-level inventory allocation decisions.

A sequential two-stage approach based on variational Bayesian inference for reliability-redundancy allocation

The reliability-related design has been a crucial chain in complex and reliability-critical engineering systems. It serves as a preventive countermeasure to catastrophic failures caused by uncertainties. To keep up with this rapidly developing field, this paper presents a novel metaheuristic, based on the variational Bayesian inference (VBI) to efficiently solve the optimal reliability design. Specifically, the reliability-redundancy allocation problem (RRAP). The proposed metaheuristic starts from a primary population, then leverages VBI to fully excavate the information of feasible individuals from the previous generation, in order to produce the next-generation population. This process is iterated until the solution converges to the optimal decision scheme. In addition, we set up an automatic stratification strategy, so that the new individuals can approach the optimal solution faster. Furthermore, we divide RRAP into a reliability optimization problem (ROP) and a redundancy allocation problem (RAP). This not only reduces the dimension of decision variables, but also speeds up the convergence. ROP and RAP are solved sequentially and iteratively until the preset stopping condition is satisfied. The case studies showcase that the proposed approach can obtain the optimal or near-optimal solution within a reasonable period.

Air combat manoeuvre strategy algorithm based on two-layer game decision-making and the distributed MCTS method with double game trees

ABSTRACT In view of the huge strategy space and high real-time requirement for multi-fighter air combat maneouvre decisions, the target allocation and the manoeuvre decision model are established, respectively and the air combat strategy solving algorithm is proposed based on two-layer game decision-making and the distributed Monte Carlo strategy search method with double game trees. Moreover, the two-layer game decision-making method can precut the huge game tree strategy space, which improves the efficiency of strategy search. The distributed Monte Carlo strategy search method with double game trees can quickly search out the optimal decision scheme of an air combat game based on the opponent’s strategy. The experiment results show that the designed algorithm is effective and improves the efficiency of the decision compared with the Monte Carlo search algorithm of single-layer decision-making.

Bayesian support vector machine for optimal reliability design of modular systems

In a modular system, uncertainties will spread among coupled modules and cause system failure. To cope with this issue, the reliability-based design optimization (RBDO) of modular systems came into being. However, the solution of this design task is a nested triple-loop process, making the computational burden unaffordable for real-world systems. Thus, this paper endeavors to effectively mitigate this computational effort. The individual module feasible approach is first proposed to tackle the coupling effects of modules, whereby, the original optimization problem is converted into a conventional one. Then, the Bayesian-inference-based support vector machine is utilized to build the alternative model for the actual probabilistic constraint function, in the augmented reliability space. The alternative model is constructed using small number of model evaluations, which possesses enough precision everywhere in the augmented confidence region. Finally, the optimal decision scheme is obtained by solving the formulated conventional RBDO using the alternative model. The performance of the proposed method is investigated using several examples.

Air Combat Maneuver Strategy Algorithm Based on Two-Layer Game Decision-Making and Distributed Double Game Trees MCTS under Uncertain Information

In this paper, a model for maneuver decisions in air combat is established based on position situation information, the performance of the fighter, the threat of combat intention, and the multi-fighter collaboration effect. Additionally, a two-layer game decision algorithm based on the double game tree distributed Monte Carlo search strategy is proposed, and the operational rules of interval numbers and possibility degree comparison rules are adopted to solve the designed method. The experiment results show that the model and algorithm are effective in their intended purpose. The two-layer game decision-making and distributed double game tree MCTS can precut the huge game tree strategy space and quickly identify the optimal air combat game decision scheme, which improves the efficiency of strategy searches. By compared experiments, it was found that the proposed algorithm can improve the performance of air combat.

Latency-aware computation offloading and DQN-based resource allocation approaches in SDN-enabled MEC

The proposed mobile edge computing can transfer the computing tasks in mobile applications to the nearby edge devices, effectively reducing the processing pressure of local servers and avoiding delays in backhaul and core networks, thus better solving the problem that cloud computing cannot effectively handle resource allocation. However, the complexity of the wireless environment during the communication process leads to the fact that the computing tasks are easily caused by the increase in the number of traffic and packet loss when they are uploaded to the MEC side through the wireless link, which cannot guarantee a lower total system energy consumption and shorter total delay. To address the problem that mobile devices cannot handle many computationally intensive tasks in a timely manner, this paper proposes a task offloading optimization scheme for SDN-enabled MEC environments. Modeling the computation offloading problem based on Lyapunov optimization, and then analyzes the offloading delay with respect to the offloading gain. In order to guarantee application requirements, minimize energy consumption and latency, and better satisfy user QoS requests, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy designs a DQN-based resource allocation algorithm to deploy a joint optimal offloading decision and resource allocation scheme in a mobile edge computing environment under the limited computational resources and the latency constraints of the computational tasks. Based on the experimental results, it is shown that the proposed task offloading strategy can reduce the overall latency; the proposed resource allocation strategy can reduce the total energy consumption and total latency of the system and improve the successful execution rate of tasks.

Location optimization of emergency medical facilities for public health emergencies in megacities based on genetic algorithm

PurposeThe purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.Design/methodology/approachUsing Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.FindingsComparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.Research limitations/implicationsFirst, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.Practical implicationsThe results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.Social implicationsThe results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.Originality/valueThe research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.

Nonlinear bilevel programming approach for decentralized supply chain using a hybrid state transition algorithm

This paper investigates a decentralized supply chain that is composed of one manufacturer and multiple distributors. The manufacturer produces goods and wholesales them to multiple distributors and then the distributors sell products to various markets. The entire production period of the manufacturer is divided into several intervals. The decision-making problem of the entire decentralized supply chain is presented as a two-echelon coordination game network, in which each decision-maker can influence decision-making of other levels. A Stackelberg game framework is proposed to coordinate the decision-making process. And then two nonlinear bi-level programming (BLP) models are developed to find the optimal equilibrium decision scheme by switching the leader and follower roles between the manufacturer and the distributors. The models consider the manufacturer’s budget constraints in each interval and the market demands are affected by distributors’ selling price and advertising strategies. According to the hierarchy and complexity of bi-level programming problem (BLPP), a nested bi-level method based on hybrid state transition algorithm is proposed to address the BLP models, and mapping approximation strategy is utilized to improve computational efficiency. Finally, the numerical experiments are performed to demonstrate the superiority of the proposed method in terms of accuracy and computational efficiency.

Optimization of a Multi-Energy Complementary Distributed Energy System Based on Comparisons of Two Genetic Optimization Algorithms

The development and utilization of low-carbon energy systems has become a hot topic of energy research in the international community. The construction of a multi-energy complementary distributed energy system (MCDES) is researched in this paper. Based on the multi-objective optimization theory, the planning optimization of an MCDES is studied, and a three-dimensional objective-optimization model is constructed by considering the constraints of the objective function and decision variables. Aiming at the optimization problem of building terminals for the MCDES studied in the paper, two genetic optimization algorithms—Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Sorting Genetic Algorithm III (NSGA-III)—are used for calculation based on an example analysis. The constraint conditions of practical problems were added to the existing algorithms. Combined with the comparison of the solution quality and the optimal compromise solution of the two algorithms, a multi-decision method is proposed to obtain the optimal solution based on the Pareto optimal frontier of the two algorithms. Finally, the optimal decision scheme of the example is determined and the effectiveness and reliability of the optimization model are verified. Under the application of the MCDES optimization model studied in this paper, the iteration speed and hypervolume index of NSGA-III are found to be better than those of NSGA-II. The values of the life cycle cost and life cycle carbon emission objectives after the optimization of NSGA-III are indicated as 2% and 14% lower, respectively, than those of NSGA-II. The primary energy efficiency of NSGA-III is shown to be 20% higher than that of NSGA-II. According to the optimal decision, the energy operation strategies of the example MCDES with each typical day in the four seasons indicate that good integrated energy application and low-carbon operation performance are shown during the four-seasons operation process. The consumption of renewable energy is significant, which effectively reduces the application of high-grade energy. Thus, the theoretical guidance and engineering application reference are provided for MCDES design planning and operation optimization.

Theoretical Concept Study of Cooperative Abnormality Detection and Localization in Fluidic-Medium Molecular Communication

In this paper, we propose a theoretical framework for cooperative abnormality detection and localization systems by exploiting molecular communication setup. The system consists of mobile sensors in a fluidic medium, which are injected into the medium to search the environment for abnormality. Some fusion centers (FC) are placed at specific locations in the medium, which absorb all sensors arrived at their locations, and by observing its state, each FC decides on the abnormality existence and/or its location. To reduce the effects of sensor imperfection, we propose a scheme where the sensors release some molecules (i.e., markers) into the medium after they sense an abnormality. If the goal is abnormality detection, the released molecules are used to cooperatively activate other sensors. If the goal is abnormality localization, the released molecules are used by the FCs to determine the location. In our model, both sensors' imperfection and markers background noise are taken into account. For the detection phase, we consider two sensor types based on their activation strategy by markers. To make the analysis tractable, we assume some ideal assumptions for the sensors' model. We investigate the related binary hypothesis testing problem and obtain the probabilities of false alarm and miss-detection. It is shown that using sensors with the ability of cooperatively activating each other can significantly improve the detection performance in terms of probability of error. For the localization phase, we consider two types of FCs based on their capability in reading sensors' storage levels. We study their performance and obtain the optimal and sub-optimal decision schemes and also the probability of localization error for both perfect and imperfect sensing regimes.

Online machine learning algorithms to optimize performances of complex wireless communication systems.

Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.

Coordinated Development Based Grid-Source-load Collaborative Planning Method of Uncertainty and Multi-agent Game

When planning the power grid, it is necessary to obtain the optimal decision scheme according to the market behavior of different stakeholders. In this paper, the virtual game player "nature" is introduced to realize the deep integration of game theory and robust optimization, and a source network load collaborative planning method considering uncertainty and multi-agent game is proposed. Firstly, the planning decision-making models of different stakeholders of DG investment operators, power grid investment operators and power users are constructed respectively; then, the static game behavior between distributed generation (DG) investment operators and power grid investment operators is analyzed according to the transmission relationship of the three; at the same time, robust optimization is used to deal with DG. In this paper, we introduce the virtual game player "nature" to study the dynamic game behavior between the virtual game player and the power grid investment operator. On this basis, the dynamic static joint game planning model is proposed.

Optimization of Ecological Water Supplement Scheme for Improved Suitable Habitat Area for Rare Migratory Birds in Nature Reserves Using Interval-Parameter Fuzzy Two-Stage Stochastic Programming Model.

The optimization of ecological water supplement scheme in Momoge National Nature Reserve (MNNR), using an interval-parameter two-stage stochastic programming model (IPTSP), still experiences problems with fuzzy uncertainties and the wide scope of the obtained optimization schemes. These two limitations pose a high risk of system failure causing high decision risk for decision-makers and render it difficult to further undertake optimization schemes respectively. Therefore, an interval-parameter fuzzy two-stage stochastic programming (IPFTSP) model derived from an IPTSP model was constructed to address the random variable, the interval uncertainties and the fuzzy uncertainties in the water management system in the present study, to reduce decision risk and narrow down the scope of the optimization schemes. The constructed IPFTSP model was subsequently applied to the optimization of the ecological water supplement scheme of MNNR under different scenarios, to maximize the recovered habitat area and the carrying capacity for rare migratory water birds. As per the results of the IPFTSP model, the recovered habitat areas for rare migratory birds under low, medium and high flood flow scenarios were (14.06, 17.88) × 103, (14.92, 18.96) × 103 and (15.83, 19.43) × 103 ha, respectively, and the target value was (14.60, 18.47) × 103 ha with a fuzzy membership of (0.01, 0.83). Fuzzy membership reflects the possibility level that the model solutions satisfy the target value and the corresponding decision risk. We further observed that the habitat area recovered by the optimization schemes of the IPFTSP model was significantly increased compared to the recommended scheme, and the increases observed were (5.22%, 33.78%), (11.62%, 41.88%) and (18.44%, 45.39%). In addition, the interval widths of the recovered habitat areas in the IPFTSP model were reduced by 17.15%, 17.98% and 23.86%, in comparison to those from the IPTSP model. It was revealed that the IPFTSP model, besides generating the optimal decision schemes under different scenarios for decision-makers to select and providing decision space to adjust the decision schemes, also shortened the decision range, thereby reducing the decision risk and the difficulty of undertaking decision schemes. In addition, the fuzzy membership obtained from the IPFTSP model, reflecting the relationship among the possibility level, the target value, and the decision risk, assists the decision-makers in planning the ecological water supplement scheme with a preference for target value and decision risk.

An Evaluation and Optimization Methodology for Efficient Power Plant Programs

Power demand side management (DSM) regulations stipulate that electric power grid enterprises of cities and provinces must complete their target of power saving through efficient power plant (EPP) programs. This investigation studies power DSM and establishes a supervision and evaluation system for the EPP program. The proposed system can be used to evaluate the performance of an EPP program such as electricity saving and investment benefit. A model of EPP is established. A cuckoo search algorithm is applied for the model optimization and its result gives an optimal decision scheme. Finally, a fuzzy comprehensive evaluation method is used to assess an EPP program. The results show that the established supervision and evaluation system and the model of an EPP program are effective and applicable for power grid enterprises.

Risk analysis of floodwater resources utilization along water diversion project: a case study of the Eastern Route of the South-to-North Water Diversion Project in China

Abstract Based on fuzzy mathematics, a risk assessment model of floodwater resources utilization in a water diversion project was established based on the fuzzy network analytic hierarchy process (F-AHP). First, the weight of each factor was determined through AHP, and then the fuzzy evaluation method (FEM) was used for analysis and comparison. Finally, the optimal decision scheme was determined. The model was applied to the Eastern Route of the South-to-North Water Diversion Project (SNWDP) for floodwater resources utilization risk assessment. The results show that the model can utilize the risk factors of floodwater resources for identification and sorting, and then make a risk evaluation. The risk of floodwater resources utilization in a normal flow year is the lowest and the benefit is remarkable, providing a reasonable control scheme, and reducing unnecessary losses for the risk of floodwater resources utilization.