Optimal Allocation Plan Research Articles

Simulation and Prediction of the Service Quality of China's Private Express Delivery and Its Influencing Factors Based on the System Dynamics Model

With the continuous development of China’s private express delivery, how to improve the service quality and enable private express delivery company to gain a place in the fiercely competitive market has become a major issue faced by the manager. Therefore, we introduce the system dynamics method into the field of service quality evaluation to study the service quality of China’s private express delivery. Through system dynamics modeling, we discover the sensitive factors that affect the service quality of China's private express delivery, namely logistics information technology, staff training, and organization and management capability. According to the results of the sensitivity analysis, policy experiments are conducted to obtain the optimal resource allocation plan. The results show that, if the new investment is regarded as 100%, then the resource allocation plan corresponding to each subsystem is: 37.2% of the new investment should be allocated to the logistics information technology subsystem.

RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment

This paper proposes a hybrid metaheuristic based resource allocation framework named RAFL for load balancing in cloud computing environment. The objective is to proactively minimize the load imbalance across active physical machines and among their considered resource capacities (e.g., CPU and RAM). This prevents overloading/underloading of active physical machines and utilizes their considered resource capacities in a balanced manner. In the proposed framework, a phasor particle swarm optimization and dragonfly algorithm based hybrid optimization algorithm named PPSO-DA is used to generate an optimal resource allocation plan for balancing the load. Simulation experiments are performed using CloudSim simulator to measure the metrics of load imbalance across active physical machines and among their considered resource capacities. Results show that the proposed PPSO-DA algorithm outperforms phasor particle swarm optimization, dragonfly algorithm, comprehensive learning particle swarm optimization, memory based hybrid dragonfly algorithm, sine cosine algorithm, and elephant herding optimization, in finding an optimal resource allocation for balancing the load. The statistical analysis and benchmark testing also validates the relative superiority of PPSO-DA.

Optimally Allocating Ambulances in Delhi using Mutation based Shuffled Frog Leaping Algorithm

This paper presents a reliable and competent evolutionary-based approach for improving the response time of Emergency Medical Service (EMS) by efficiently allocating ambulances at the base stations. As the prime objective of EMS is to save people's lives by providing them with timely assistance, thus increasing the chances of a person's survivability, this paper has undertaken the problem of ambulance allocation. The work has been implemented using the proposed mutation-based Shuffled Frog Leaping Algorithm (mSFLA) to provide an optimal allocation plan. The authors have altered the basic SFLA using the concept of mutation to improve the quality of the solution obtained and avoid being trapped in local optima. Considering a set of assumptions, the new algorithm has been applied for allocating 50 ambulances among 11 base stations in Southern Delhi. The working environment of EMS, which includes stochastic requests, travel time, and dynamic traffic conditions, has been considered to attain accurate results. The work has been implemented in the MATLAB simulation environment to find an optimized allocation plan with a minimum average response time. The authors have reduced the average response time by 12.23% with the proposed algorithm. The paper also compares mSFLA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the stated problem. The algorithms are compared in terms of objective value (average response time), convergence rate, and constancy repeatability to conclude that mSFLA performs better than the other two algorithms.

Bilateral slot exchange and co-allocation for liner alliance carriers of containerized maritime logistics

With the maritime logistics technology evolving toward standardization and modernization, container liner shipping has become the most valued mode of transportation in the shipping industry. Liner carriers share resources in the alliance-based mode to achieve synergy. Specifically, slot exchange is a key and effective alliance-based means to enhance competitiveness and operational effectiveness. To this end, this article looks at how to optimize slot capacity allocation within a container liner alliance under the slot exchange mode in the containerized maritime logistics industry. First, we explained the slot exchange concept and its impact on the shipping business. Second, we identified the key factors that may affect slot allocation decisions. Then we established an optimal multi-objective slot exchange allocation model for liner alliances and solved the problem with the NSGA-II algorithm. Finally, taking the cooperation of two carriers on two trans-Pacific routes as an example, we used the model to establish the optimal slot exchange strategy for each carrier on the shipping alliance's routes to verify the practical application value of this model. The results showed that the model proposed in this article can help develop an optimal slot allocation plan, providing a scientific and effective tool for container liner alliances to formulate slot exchange and allocation strategies.

The impact of extreme weather condition on the voltage regulation in distribution systems with high penetration of roof-top photovoltaic

With the rapid growing penetration of solar power, extreme weather conditions may cause disturbance to power grids. Previous works have studied the impact of extreme weather conditions on active power balance at transmission level, while few studies investigated the potential voltage issue in distribution systems. As most small-scale rooftop photovoltaics are invisible to system operators and cannot participate in voltage regulation in practice, the sudden increase of the unknown amount of reactive power demand caused by extreme weather conditions may lead to a severe voltage fluctuation in the distribution systems with high penetration of rooftop photovoltaics. Hence, this paper investigates this potential voltage issue. We first explicitly modelled the solar power output during extreme weather conditions, and demonstrated the voltage issue in IEEE 33-bus system. Then, we formulated a model for optimal deployment of smart inverters to empower the rooftop photovoltaics at selected nodes to mitigate the voltage issue with minimum cost. With the optimal and efficient allocation plan generated by the proposed model, the distribution systems, with high penetration of solar power, are expected to remain stable and robust to extreme weather conditions.

Optimal Planning Method of Integrated Energy System Considering Carbon Cost from the Perspective of the Whole Life Cycle

China commits its goal of peak carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060. The integrated energy system (IES) is one of the critical approaches to achieving the commitments. While the prevailing evaluation method for calculating the carbon emissions of IES neglected parts of factors influence, the result could not reflect the carbon emissions comprehensively. Considering the insufficiency above, in this paper, the evaluation method of carbon emission based on the whole life cycle of IES is proposed. First, based on the IES energy hub model, a typical park’s carbon emission model has been established. Then, the carbon emission coefficients of energy and equipment in production, transportation and operation are analysed, respectively. Hence, a low-carbon operation optimisation model of the IES is proposed. Later, with the lowest annual carbon emission of the integrated energy system as the optimisation target, the IES’s optimal carbon emission allocation and operation plan are proposed, based on the balance between energy supply and demand in the process of energy and equipment use and operation. As a result, the carbon emission of the IES of the park reduces effectively.

이산사건 모델링 방식 비교를 통한 상호보완과 활용가능성에 관한 연구 - 건강검진센터를 대상으로 하는 스페이스 마크업 모델링 방식을 중심으로 -

Discrete event simulation modeling for spatial analysis consists of a space markup model and a sequence model (flowchart). Space markup models are constructed in the form of network-based NbM and pedestrian-based PbM. Although PbM is the modeling method for spatial analysis, this study intended to identify that applying the conventional NbM developed for production systems to spatial analysis may have a functional utility value to complement PbM. Therefore, this study used NbM and PbM to build the same model of a health examination center and obtained the following conclusions by comparing the simulation analysis results. 1) NbM and PbM did not show significant differences in the time required to examine 50 people and the time required per person, which means that the two modeling methods are compatible with each other. 2) The occurrence of queues, the primary cause of time delays, can be predicted by measuring resource utilization, which is a major clue in establishing optimal resource allocation and queue reduction plans. 3) NbM has advantages in the early stage of projects, such as space programming, and PbM is more advantageous for validation and analyzing the use of common spaces at the end of projects.

Quantity sensitive task allocation based on improved whale optimization algorithm in crowdsensing system

SummaryWith the large‐scale popularity of mobile terminals, crowdsensing technology gradually replaces the existing static sensors with its advantages of high efficiency and low cost, becoming an emerging data collection method. How to assign perception tasks to the best performing users under the premise of ensuring quality and reducing costs to maximize the number of user tasks completed is the focus of the research on quantity sensitive task allocation. Based on this, a solution based on the improved whale optimization algorithm that combines the three operations of nonlinear decreasing convergence factor, optimal local jitter, and dynamic position update is put forward, which is used to solve the proposed task allocation problem. First, modeling the quantity sensitive task allocation problem, and then defining the spatial matching degree and skill matching degree according to the degree of adaptation between users and tasks. Taking into account the user's learning ability during the user's task execution, the skill update mechanism is introduced to update the user's existing skills in a timely manner, so as to improve task allocation effectiveness. Second, comprehensively considering the budget, the user's online time and the perceived task completion quality, and reasonably defining the task allocation problem that maximizes the number of tasks completed. In addition, from the perspective of selecting the best performing user for the task, designing a user selection strategy based on user's priority to reduce the cost of task allocation while ensuring the quality of the perceived task is basically completed. Then, in the process of solving the optimal task allocation plan, the improved algorithm is used to continuously optimize the initial task sequences of each iteration, and the final result can be obtained after a limited number of iterations. Finally, the improved algorithm is compared with other optimization algorithms in the same environment, and the results show that the improved algorithm has higher performance in solving task allocation problem.

Multi-Objective Optimization of the Spatial Structure and Layout of the Protected Area Based on Ecosystem Services: A Case Study of the Yellow River’s Headwaters Region in the Three-River-Source National Park

The optimization of the spatial structure and layout is to determine an optimal and cost-effective land-use allocation plan for protected areas. The key goal is to maximizing the value of ecosystem services. This paper establishes a framework for optimizing the spatial structure and layout of the protected area based on ecosystem services. With the objective of maximizing the value of ecosystem services, it uses the CoMOLA (Constrained Multi-objective Optimization of Land-use Allocation) model for multi-objective optimization under the constraints of area and conversion rules. Taking the Yellow River’s headwaters region in the Three-River-Source (Sanjiangyuan) National Park as the study area, this paper uses the data of the year 2015 as a benchmark, and obtains the optimization results of the study area by 2035. The results show that the total value of ecosystem services of the Yellow River’s headwaters region after optimization will reach RMB [Formula: see text], with a total increase of RMB [Formula: see text] (8.47%). The land covers that contribute most to the value of ecosystem services are rivers, lakes and wetlands (51.55%), and grasslands (40.71%). Among the various types of ecosystem services, the value of provisioning services will increase by RMB [Formula: see text], regulating services by RMB [Formula: see text], supporting services by RMB [Formula: see text], and cultural services by RMB [Formula: see text]. The research results can provide a scientific basis for the spatial optimization of protected areas and the management of national parks.

Parallel collaborative planning for the coupled system of underground heavy-load robot

At present, designing and planning of robots are mainly based on path planning. This mode cannot meet requirements of real-time and precise planning for robots, especially under complex working conditions. Therefore, a parallel collaborative planning strategy is proposed in this paper, which parallel collaborates optimal task allocation planning and optimal local path planning. That is, according to real-time dynamic working environment of robots, the dynamic optimal task allocation planning strategy for coupled system of robot in low coupling state is adopted, to improve real-time working efficiency of underground heavy-load robot. Meanwhile, the parallel elite particle swarm optimization algorithm is adopted to improve accuracy of path tracking and controlling. Finally, the two planning strategies are collaborated parallel to realize intelligent and efficient planning of whole complex coupled system for underground heavy-load robot. The simulation and experiment results show that the parallel collaborative planning algorithm proposed in this paper has perfect controlling effects: Total flow of overall system is saved by 11.03 L, execution time saved by 16.8 s and implementation efficiency has been improved by 10 times. Therefore, the parallel collaborative planning strategy proposed in this paper can not only meet requirements of high efficiency and precision of intelligent robot under complex working conditions, but also greatly improve real-time working effectiveness and robustness of robots, so as to provide a reference for dynamic planning of complex intelligent engineering machinery, and also supply design basis for development of multi-robot collaborative system.

Multi-agent economic analysis in coping with novel corona-virus pneumonia

This paper aims to find a way of allocating masks appropriately during the outbreak of COVID-19. By considering the goals of different perspectives such as mask producers, mask consumers and countries’ government, this paper constructs some mathematical models in order to maximize each group’s objective function. The model takes an assumption that the masks produced are all allocated without friction cost. Based on the model, it is demonstrated that the government plays an important role in mask allocation only after the initial stage of the outbreak of Covid-19. It is also shown that the optimal allocation plan of the mask market is influenced by the government action even if we do not consider direct interference of the government on the market, such as taxes, or price celling.

Stackelberg Model of Anti-terrorism Resource Allocation in Express Transportation Security Checks

The problem of anti-terrorism security checks of express transportation becomes more and more severe under the Internet shopping model. In this paper, a Stackelberg game model of anti-terrorism resource allocation in express transportation security checks is built to discuss, in terms of single and multiple routes, the optimal resource allocation of express company (defender) including physical security measures, in-box sensor technology, non-intrusive security measures at terminal distribution sites and handover course. It shows that, for the single route, express company should trade off the security costs among the first, second and third level security measures, and should allocate the same security costs of physical security measures for every distribution site. For the multiple routes, the transportation route that terrorists insert the hazardous materials has the lowest probability of physical security measures, and therefore is utilized by terrorists. Thus, the optimal allocation plan of security resources should be the same security costs of non-intrusive security measures at the final distribution sites (the third level security measure) among each transportation routes.

A DEA-based incentive approach for allocating common revenues or fixed costs

In practice, the concept of incentives is extensively applied in the allocation process for guiding the behaviours of an organization's units to satisfy the goals of the organization. However, this concept is rarely considered in data envelopment analysis (DEA)-based allocation research. This paper proposes a two-step incentive approach for allocating common revenues or fixed costs. The first step is performance evaluation. Considering the noncooperative game relationship of decision-making units (DMUs), a DEA game cross-efficiency method is selected to measure the efficiency scores of DMUs in this paper. The second step is incentive allocation. Based on the performance evaluation, we propose our incentive method for allocating revenues or fixed costs. We further provide simple equations to calculate the global optimal solution for our nonlinear programme allocation models. Several properties are explored, and we i) obtain the allocation interval rule of DMUs with the incentives, ii) investigate the quantitative relationship between the allocation gap and the optimal allocation plan, and iii) prove that the optimal allocation plan obtained by our allocation model is unique. The results of an empirical application highlight the applicability of our allocation method and solution approach. In this study, we obtain several important practical insights, including that (i) our method has positive effects on performance improvement and (ii) our method can work well even in an information asymmetric decision-making environment.

A Production–Living–Ecological Space Model for Land-Use Optimisation: A case study of the core Tumen River region in China

The study of production–living–ecological space (PLES) is essential for the sustainable use of land resources and regional socioeconomic development, and several studies have adopted PLES-based evaluation indices. However, few studies have investigated the effects of governmental regulation and human activity on the optimal allocation of land for different uses. Crucially, the non-optimal use of land by urban decision-makers leads to multiple problems including wasting potential land resources and trade-offs between economic development and environmental protection. Therefore, in this study, we developed a multi-spatial agent-based optimisation model (MSABOM) coupled with a multi-agent system (MAS) within a machine-learning framework. The MSABOM determines spatially optimised land-use solutions based on the small-scale land-use preferences of stakeholders, and addresses conflicts in the sub-optimal allocation of resources based on the behaviours of model agents and the decision-making environment. The Yanbian Korean Autonomous Prefecture in China was used as a case study to demonstrate the effectiveness of this approach. The results show that (1) the MSABOM can significantly improve the optimisation of PLES, improving the land utilisation rate by 1.22 times; (2) based on an understanding of existing practices, the optimal allocation plan obtained by the agent-based model is more suitable than that obtained by a non-agent-based model; (3) multi-functional land-use patterns can be optimally allocated in space and time, which is extremely useful for coordinating stakeholder participation and addressing conflicts of interest in land-use behaviours; and (4) an urban spatial development coefficient was successfully used to determine the dominant function and functional positioning of PLES, which helps ensure flexible development strategies for spatial planning.

Long-range forecasting in feature-evolving data streams

Accurate long-range forecasting in high-dimensional feature-evolving time series is a pressing issue with multiple applications in optimal resource allocation, monitoring and budget planning. Stochastic nature of feature-evolving time series coupled with their temporal dependency pose a great challenge in their forecasting. This is because the length of input sequence (rows) may vary as data points evolve with their feature values (columns) changing. In high-dimensional feature-evolving heterogeneous time series, it is impractical to train a forecasting model per single time series across millions of metrics, leave alone space required to maintain the forecasting model and evolving time series in memory for timely streaming processing. Thus this paper proposes O̲ne sketchF̲itsA̲llT̲ime series algorithm, which is a stochastic deep neural network framework to address stated problems collectively. Extensive experiments on real-life datasets and rigorous evaluation showcases that OFAT is fast, robust, accurate and superior to the state-of-the-art methods.

Cooperative Task Assignment and Track Planning For Multi-UAV Attack Mobile Targets

This paper proposes a system framework for solving the problem of multi-UAV cooperative task assignment and track planning for ground moving targets. For the combinatorial optimization model, it is solved by a new particle swarm optimization algorithm based on guidance mechanism. In order to plan an effective track for the target more rapidly, a new ant colony optimization algorithm based on adaptive parameter adjustment and bidirectional search is proposed. Furthermore, in the case of target movement, a method of the predicted meeting point is proposed to solve the problem that the moving point cannot be used as the target point of the track planning algorithm. In addition, the track planning problem in the UAV tracking mode is also considered. An online re-planning method is proposed for time-sensitive uncertainties. Finally,the simulation results show that compared with other algorithms, the proposed method can not only effectively plan a reasonable track, but also solve the uncertainty problem, and obtain the optimal task allocation plan, which improves the multi-UAV cooperative combat capability.

Planning constant‐stress accelerated life tests with multiple stresses based on D‐optimal design

AbstractWith the rapid technological advances, products are becoming more reliable. Then, multistress accelerated life testing (MALT) has been adopted in engineering to obtain failure information in a limited time. In order to make the testing procedure more efficient, it is necessary to better design the test plan. However, to date, relevant research on planning of MALT is limited. Multiple stresses will lead to plenty of stress‐level combinations that require too much cost and time to implement. Besides, there may be interactions among multiple stresses, which need more experiments for parameter estimation. To solve these problems, we propose a novel planning method for constant‐stress MALT under lognormal distribution using D‐optimal design, which can reduce required test points efficiently and deal with second‐order effects in models. In ALT, the log‐linear model is often used to describe the life‐stress relationship. Hence, D‐optimal design is adopted in this paper to select test points from the whole test region. Then, optimal unit allocation plans are formulated under V/D‐optimality criterion, respectively, where type I and type II censoring are both discussed. A real case of light‐emitting device (LED) is presented to compare the proposed approach with other two existing methods. The results show that the proposed method performs better than other two existing methods both in prediction accuracy and estimation precision. Moreover, a sensitivity analysis reveals the robustness of the optimal plans determined by the proposed method.

Efficiency and equity in regional coal de-capacity allocation in China: A multiple objective programming model based on Gini coefficient and Data Envelopment Analysis

Both governments and enterprises are giving increasing attention to creating efficient policies for coal de-capacity allocation across Chinese regions. In contrast to existing studies, we consider both efficiency and equity in the allocation of coal capacity reduction for streamlining the implementation of coal de-capacity reform. First, the coal production capacity and overcapacity in the coal production regions was estimated using the boundary production function model. Then, we constructed a multiple objective programming model based on the Gini coefficient and Data Envelopment Analysis (Gi-DEA), and applied it to coal de-capacity allocation. The main results are as follows: (1) The coal production capacity in China exceeded 5.7 billion tons by the end of 2015, and coal overcapacity is prevalent across the country. (2) The optimal allocation plan based on the Gi-DEA model achieve efficiency across the regions, and the Gini coefficient is 0.231, indicating this allocation scheme is efficient and fair. Specifically, the large coal production regions should undertake the majority of coal de-capacity, such as Shanxi, Shandong, and Guizhou, while the small and old coal production regions might undertake a heavier capacity reduction burden, such as Fujian, Guangxi, and Jiangxi. (3) The multiple objective programming model, Gi-DEA, using common weights, achieved overall efficiency of the DMUs, and generated a unique allocation plan.

Mesh grid power quality enhancement with synchronous distributed generation: optimal allocation planning using breeder genetic algorithm

Mesh grid power quality enhancement with synchronous distributed generation: optimal allocation planning using breeder genetic algorithm

Environmentally Sustainable Management of 3D Printing Network: Decision Support for 3D Printing Work Allocation

The purpose of this study is to provide a model for environmentally sustainable management of 3D printing network systems. The proposed model provides not only a flexible structure to describe 3D printing processes but also a computational structure to find the optimal work allocation plan for minimizing environmental impact. A mathematical model is formulated to assist the optimal part-to-printer allocation decision in 3D printing network systems even under uncertainty. Numerical examples show that the proposed model can determine the operation of shared 3D printers in order to have minimum environmental impact. The proposed model can also deal with data uncertainty and provide robust solutions.