Demand Allocation Research Articles

Synchronization-oriented reconfiguration of FPAI under graduation intelligent manufacturing system in the COVID-19 pandemic and beyond

Companies with manufacturing systems that are more responsive and resilient will be able to survive or even gain market shares in the face of the unpredicted variable of an outbreak similar to the COVID-19 pandemic. Motivated by an industrial company restructuring its manufacturing system with the layout of fixed-position assembly islands (FPAI) during the COVID-19 pandemic, this paper introduces the synchronization-oriented reconfiguration of FPAI under Graduation Intelligent Manufacturing System (GiMS). Inspired by the graduation ceremony, a novel manufacturing mode-Graduation Manufacturing System (GMS) with ticket-based reconfigurable structures, is designed for organizing production operations with simplicity and resilience for the layout of FPAI. The IIoT and digital twin-enabled GiMS is developed for transforming real-time visibility in operations to support the reconfiguration of the manufacturing system. A synchronization-oriented reconfiguration mechanism is proposed to achieve the synchronous interaction among changing customer demand, island configuration, and production activities allocation rapidly and cost-effectively. Cloud services integrating the proposed reconfiguration mechanism are developed for managers and onsite operators for supporting the successful reconfiguration implementation with enhanced operational visibility. Through the case study of an industrial company, the effectiveness of the proposed concept and approach is verified.

Solving a Real-World Urban Postal Service System Redesign Problem

Due to recent technological advancements, more diversified customer demand, and increasingly harder competition, traditional postal service systems have experienced significant changes all over the world. In Norway, through a strategic reform called post-in-shop, undertaken in 2013, most postal services are now provided at postal service counters located in retailer stores in order to improve accessibility, operational efficiency, and cost-effectiveness. This has led to a complex decision-making problem for the redesign of urban postal service networks across the country. In this paper, a two-stage method is proposed to solve a real-world urban postal service network redesign problem. First, two location models are employed to determine the optimal locations of postal service counters. In the second stage, a simulation model is built to evaluate the urban postal service system with different location and demand allocation plans under a realistic and stochastic environment. Among other insights, our results show that the proposed two-stage method can be used to effectively improve the accessibility of postal service networks by making optimal location-allocation decisions.

Integrated flight scheduling and fleet assignment with improved supply-demand interactions

Flight scheduling and fleet assignment are important steps of an airline planning process. In light of the reciprocal relationship between air transport supply and demand, a key element of these models is to devise effective methods to both incorporating estimation of total market demand and allocating passengers over the available itineraries in a specific market. In this paper, we present a novel mixed integer nonlinear flight scheduling and fleet assignment optimization model wherein air travel demand generation and allocation are simultaneously and consistently endogenized. Using a nested logit formulation, we jointly model competition among air travel itineraries and appraise the contribution of specific itinerary attributes to demand generation, therefore yielding a more comprehensive and explicit representation of supply-demand interactions. Computational testing based on realistic problem instances reveals that the model can optimize mid-size hub-and-spoke networks within reasonable time. Further analyses illustrate the benefits that can be derived from the application of the proposed approach using real-world data for a major European airline. Results demonstrate that the proposed approach can significantly enhance operating profits by up to 6.9% and better reveal opportunities for demand stimulation against a conventional approach using inelastic trip generation.

Hierarchical Passenger Hub Location Problem in a Megaregion Area Considering Service Availability

The rapid growth of the intercity travel demand has resulted in enormous pressure on the passenger transportation network in a megaregion area. Optimally locating hubs and allocating demands to hubs influence the effectiveness of a passenger transportation network. This study develops a hierarchical passenger hub location model considering the service availability of hierarchical hubs. A mixed integer linear programming formulation was developed to minimize the total cost of hub operation and transportation for multiple travel demands and determine the proportion of passengers that access hubs at each level. This model was implemented for the Wuhan metropolitan area in four different scenarios to illustrate the applicability of the model. Then, a sensitivity analysis was performed to assess the impact of changing key parameters on the model results. The results are compared to those of traditional models, and the findings demonstrate the importance of considering hub choice behavior in demand allocation.

Compliance-aware engineering process plans: the case of space software engineering processes

Safety-critical systems manufacturers have the duty of care, i.e., they should take correct steps while performing acts that could foreseeably harm others. Commonly, industry standards prescribe reasonable steps in their process requirements, which regulatory bodies trust. Manufacturers perform careful documentation of compliance with each requirement to show that they act under acceptable criteria. To facilitate this task, a safety-centered planning-time framework, called ACCEPT, has been proposed. Based on compliance-by-design, ACCEPT capabilities (i.e., processes and standards modeling, and automatic compliance checking) permit to design Compliance-aware Engineering Process Plans (CaEPP), which are able to show the planning-time allocation of standard demands, i.e., if the elements set down by the standard requirements are present at given points in the engineering process plan. In this paper, we perform a case study to understand if the ACCEPT produced models could support the planning of space software engineering processes. Space software is safety and mission-critical, and it is often the result of industrial cooperation. Such cooperation is coordinated through compliance with relevant standards. In the European context, ECSS-E-ST-40C is the de-facto standard for space software production. The planning of processes in compliance with project-specific ECSS-E-ST-40C applicable requirements is mandatory during contractual agreements. Our analysis is based on qualitative criteria targeting the effort dictated by task demands required to create a CaEPP for software development with ACCEPT. Initial observations show that the effort required to model compliance and processes artifacts is significant. However, such an effort pays off in the long term since models are, to some extend, reusable and flexible. The coverage level of the models is also analyzed based on design decisions. In our opinion, such a level is adequate since it responds to the information needs required by the ECSS-E-ST-40C framework.

Short-term traffic flow prediction in bike-sharing networks

For station-based bike-sharing systems, the balance between user demand and bike allocation is critical for the operation. As a basic operational index, the short-term prediction of bike numbers (flow) plays an important role in demand forecasting and rebalancing resources of bike-sharing networks. Many different methods have been proposed for bike forecast in recent years, and the deep learning (DL)-based models have dominated this area because of their competitive performance. However, there still exist challenges in such approaches including: (i) how to appropriately select the training input for the DL model, and (ii) how to effectively utilize both the temporal and spatial features in the data for prediction. In particular, the arbitrary input may limit the model optimization, and the separate consideration of temporal and spatial features could change the original data representation. This paper uses a simple autocorrelation function to select the best input candidates and develops a three-dimensional (3 D) residual neural network to learn spatiotemporal features simultaneously. The proposed DL model is trained and validated using two separate bike-sharing datasets from New York and Suzhou cities. The learned features by two-dimensional (2 D) and 3 D CNN kernels under different input methods are compared. Results show that 3 D CNN outperforms other models and that the proposed input selection method yields better learning results for both datasets. The proposed methods help with a comprehensive DL model workflow and better forecasting accuracy.

A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques

This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle.

Validating Gravity-Based Market Share Models Using Large-Scale Transactional Data.

Customer patronage behavior has been widely studied in market share modeling contexts, which is an essential step toward estimating retail sales and finding new store locations in a competitive setting. Existing studies have conducted surveys to estimate merchants' market share and factors of attractiveness to use in various proposed mathematical models. Recent trends in Big Data analysis allow us to better understand human behavior and decision making, potentially leading to location models with more realistic assumptions. In this article, we propose a novel approach for validating the Huff gravity market share model, using a large-scale transactional dataset that describes customer patronage behavior at a regional level. Although the Huff model has been well studied and widely used in the context of sales estimation, competitive facility location, and demand allocation, this article is the first in validating the Huff model with a real dataset. Our approach helps to easily apply the model in different regions and with different merchant categories. Experimental results show that the Huff model fits well when modeling customer shopping behavior for a number of shopping categories, including grocery stores, clothing stores, gas stations, and restaurants. We also conduct regression analysis to show that certain features such as gender diversity and marital status diversity lead to stronger validation of the Huff model. We believe we provide strong evidence, with the help of real-world data, that gravity-based market share models are viable assumptions for retail sales estimation and competitive facility location models.

A multi-objective optimization for resource allocation of emergent demands in cloud computing

Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

Hierarchical distributed control approach for multiple on-site DERs coordinated operation in microgrid

Micro grid (MG) is a self-controllable small-scale network developed to flexibly manage distributed energy resources (DERs). In this paper, an interlinked three-layer distributed control framework is proposed for the coordinated operation of multiple DERs in MG. In the bottom layer, an inner double-loop voltage and current controller is incorporated with a power droop controller for DER primary voltage and frequency regulation, which explores the fast response capability of DERs to counterbalance load fluctuations in MG. In the middle layer, a distributed secondary frequency and voltage control strategy based on the finite-time discrete consensus theory is implemented to correct the DER frequency and voltage offset in the bottom layer, and the proportional allocation of MG reactive load demands among multiple DERs is also achieved. In the upper layer, a distributed economic dispatch algorithm based on finite-time discrete consensus is implemented to minimize DERs total generation cost by optimizing DER active power reference coupled with the middle and bottom layers. Simulation results demonstrated that the proposed three-layer hierarchical distributed control approach is flexible for the plug-and-play behaviors of DERs and can achieve satisfactory reactive power share among multiple DERs; in addition, the approach effectively regulates DER voltage and frequency and ensures their low-cost operation.

Feasibility study of China's electric power sector transition to zero emissions by 2050

The study explores the potential transition of China's electric power sector to zero emissions by 2050. Using a capacity expansion model (CEPRO) with 31 regions, hourly time resolution, and 39 years of historical reanalysis weather data (MERRA-2), we simulate the expansion and operation of the power sector, considering solar and wind energy as the primary source of generation. The results suggest that zero-emissions power generation can be achieved with mostly wind and solar energy and alternative sets of balancing technologies. We show that a high-renewables power system can operate without significant storage and provide a high level of reliability. Besides storage, the balancing can be achieved by a partially flexible load, expansion of power grid, and backup capacity. The four balancing options and the level of reliability of the system are substitutes for one another, making at least storage or backup generation optional. Most importantly, a high-renewables power system is competitive, with the supply-side levelized system cost of electricity as low as 0.2–0.3 CNY/kWh (about 3–4 US cents per kWh) even without consideration of further reductions in the cost of renewables. • High spatial and temporal resolution modeling of China's electric power sector in 2050 with optimization of generating capacity structure, long-distance grid, energy storage, regional and temporal allocation of demand • 64 scenarios demonstrate feasibility of high-VER generation with high reliability of supply • With current costs of renewables, system-wide cost of supply might be lower than current thermal generation

Taxi trajectory data based fast-charging facility planning for urban electric taxi systems

This study develops a taxi trajectory data based fast-charging facility planning model for an urban taxi system by considering battery degradation and vehicle heterogeneity in driving range. The developed model comprises three functional modules: (i) fast-charging location determination, (ii) fast-charging facility deployment (FCFD) and (iii) FCFD solution tuning. Under the FCFD module, charging demand prediction considering battery degradation and vehicle heterogeneity, charging demand allocation and charger configuration optimization are executed sequentially. The FCFD solution is tuned by an effective backward elimination method to find a more economic and practical planning solution where the minimal number of chargers at each station can be specified. A case study in Singapore is thoroughly conducted, and insightful policy implications are revealed: policy-makers could use the tuning mechanism to significantly save investment and reduce total waiting time for charging; overlooking battery degradation and vehicle heterogeneity will yield a biased electric taxi charging facility planning.

A Decision Support System for Prioritised COVID-19 Two-dosage Vaccination Allocation and Distribution

Global distribution of COVID-19 vaccines is one of the world's most challenging logistics tasks. A timely mass vaccination during a pandemic is a matter of life and death. This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disrupted supply, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.

Price-Directed Cost Sharing and Demand Allocation Among Service Providers with Multiple Demand Sources and Multiple Facilities

Problem definition: We consider capacity sharing through demand allocation among firms with multiple demand sources and multiple service facilities. Firms decide on the allocation of demand from different sources to different facilities to minimize delay costs and service-fulfillment costs possibly subject to service-level requirements. If firms decide to operate collectively as a coalition, they must also decide on a scheme for sharing the total cost. Academic/practical relevance: We study capacity sharing through demand allocation in service systems in the presence of service-fulfillment costs. Our problem is motivated by service collaboration in healthcare involving public–private partnerships. Methodology: We formulate the problem as a cooperative game and identify a cost allocation that is in the core. Results: The cost-allocation scheme we identify is price-directed, and the cost of each firm consists of three components: (1) the delay cost incurred within the firm; (2) a cost paid for the capacity used by the firm at facilities owned by other firms; and (3) a payment received for fulfilling demand of other firms at facilities owned by the firm. Interestingly, we show that the cost-allocation scheme is equivalent to a market equilibrium—that is, it can be implemented in a decentralized fashion. We extend our analysis to settings where the capacity of each facility is endogenously determined and to settings where a service-priority policy is deployed. Our results are robust to a variety of generalizations: partial sharing, partial transfer, facilities modeled as general queueing systems, and convex delay costs. Managerial implications: Our findings provide guidelines for and insights into how to carry out demand allocation and cost sharing among different firms in the presence of service-fulfillment costs to foster service collaboration. In particular, the equilibrium market prices can be viewed as the prices/subsidies for service collaboration in a public–private partnership.

Adaptive Price Estimation in Cognitive Radio Enabled Smart Grid Networks

This paper focuses on the allocation of resources to cognitive users in a two-way cognitive radio facilitated smart grid (SG) network. The resources are essential in controlling the performance of demand response management in the SG, ensuring profit to the power supplier and simultaneously cost-saving to the consumers. However, cognitive users need more power for their data transmission, which compels the utility company to increase its electricity price. Hence, we propose an adaptive resource allocation algorithm based on normalized least mean squares (NLMS) to estimate the electricity price that benefits both the supplier and consumers with optimal allocation of power demands under the constraints of transmission power, system throughput, and the probability of detection. The simulation results validate the performance of our proposed scheme by comparing it with the application of metaheuristic algorithms for maximizing aggregate profit. The impact of the channel parameters on system performance is also studied.

Study on the Impact of Urban Consolidation Center (UCC) Usage in Yogyakarta City's Main Grocery Retailers in Terms of Transportation Cost

Abstract. The increase in urbanization has led to a rise in of the number of vehicles in the city and the frequency of goods delivery in urban environments. The concepts of city logistics have been developed and implemented to cope with those problems in the urban freight transport system. One cooperative freight transport scheme is the urban consolidation scheme. Urban consolidation centers (UCC) have been used by some cities over the last two decades to minimize unnecessary vehicle movement, congestion, and pollution. For a company that sells fast-moving consumer goods (FMCG), business prospects in supermarkets or groceries are currently still quite promising. Yogyakarta City ranks as the 6th most populous city in Indonesia, making it one of the largest cities in the country. Since there are many citizens and students from other towns across Indonesia, many modern retail businesses operate in Yogyakarta. If each of these businesses distribute their goods from their distribution centers (DCs) to their respective retailers, the number of freight transports will be substantial, and the congestion level will increase. This study examines the benefit of a collaboration strategy in which the top four retailers in Yogyakarta use a UCC. The advantage is expressed in total transportation cost. We developed three different scenarios that will be compared. In this research, the gravity location model is used to determine the location of the UCC, and the demand allocation model is used to determine the optimum of demand allocation from the UCC. This study reveals that using a collaborative strategy using a UCC can decrease the total transportation cost of the retailers. Keywords: City logistics, urban consolidation center, location problem, gravity location model, demand allocation model

A Discrete-Time Model for Large-Scale Multi-Modal Transport Networks

The purpose of this paper is to provide a method that can be used to perform resilience analyses on large-scale multi-modal transportation networks. In particular, this work proposes an assignment model through which the allocation of mobility demand on a multimodal transport network is defined at a regional or supra-regional level. The results of the assignment problem are then used as input data of a macroscopic dynamic model specifically developed to represent the dynamics of a multi-modal transport network. Finally, the proposed methodology is applied to represent the behavior of users in the Nguyen-Dupuis test network.

绿色基础设施的生态及社会功能研究进展

随着城市化进程的加快，城市硬化地表不断挤占透水表面，使得绿色基础设施规模不断下降、破碎化程度逐渐加大，严重影响了绿色基础设施服务功能的有效发挥。绿色基础设施的发展经历了早期萌芽、初步形成和快速发展三个阶段，主要在宏观尺度研究区域生态安全，中观尺度促进城市可持续发展，微观尺度解决社区生态环境问题。目前绿色基础设施的研究内容集中在其生态功能和社会功能两个方面，主要关注调蓄降雨径流、消减非点源污染、调节区域微气候、居民健康与福祉和空间布局公平性。而供需结构的合理配置也是决定绿色基础设施服务水平的关键因素。未来的研究应加强与相关学科的交叉融合，完善绿色基础设施功能的测度与评估方法，将绿色基础设施建设与实际规划相结合，以期为区域可持续发展和国家生态文明建设提供支撑与保障。;The world is currently undergoing unprecedented urbanization which has contributed to replacement of the existing natural green spaces with impervious surfaces. The reduction and fragmentation of green infrastructure can result in environmental challenges locally, regionally and beyond, due to biochemical and physical changes to ecological systems. Specifically, the rapid increase in area of impervious surfaces reduces rainwater interception, storage and infiltration, resulting in a concomitant increase in runoff generation in urban catchments. Therefore, nature-based solutions, denoted as green infrastructure, have been widely recommended to reduce stormwater runoff, improve water quality, cool urban air temperature, provide ecosystem sustainability and so on. The development of green infrastructure has experienced three stages:early germination, initial formation, and rapid development. The research mainly focuses on the regionally ecological security in the macro scale, promotes the sustainable development of the city in the meso scale, and solves the community ecological environment problems in the micro scale. At present, the research content of green infrastructure includes two aspects:ecological function and social function, mainly concentrated on the regulation of rainfall runoff, reduction of non-point source pollution, regulation of regional microclimate, residents' health and well-being, and spatial distribution equity. The supply and demand structure of green infrastructure is also the key factor effecting the service quality of green infrastructure. Generally, the service function of green infrastructure will increase with the increase of its scale, but the imbalance of supply and demand allocation of local green infrastructure often leads to the problem of local service surplus or shortage. At present, the development of green infrastructure is difficult to meet the needs of China's rapid urbanization. The quantitative evaluation of green infrastructure function is insufficient, and the research on supply and demand structure is not deep enough. Future research should strengthen the cross integration with related disciplines, improve the measurement and evaluation methods of green infrastructure function, and combine green infrastructure construction with spatial planning, in order to provide support for regional sustainable development and national ecological civilization construction.

Research on compound comprehensive energy evaluation and intelligent control optimization

Under the background of increasing energy demand, integrated energy system is an important way to promote the development of clean energy, support the efficient use of energy and energy saving and emission reduction. This paper firstly establishes a comprehensive energy system evaluation index model composed of three sub-models of economy, environment and energy. On the basis of this model, a comprehensive energy multi-objective optimization mathematical model with the lowest total cost, excellent environmental benefits and high comprehensive energy utilization efficiency is established, and the energy supply and demand allocation optimization method based on improved genetic algorithm is adopted for solution. The simulation proves the effectiveness of the proposed optimization model.

Using Taxi GPS Trajectory Data to Optimize the Spatial Layout of Urban Taxi Stands

The unreasonable layout of taxi stands (TS) in urban areas not only fails to provide bidirectional guidance for drivers and passengers but also wastes spatial resources and aggravates the surrounding traffic. This paper compares the performance of three classical location models in optimizing TS spatial layout, and develops an extended model integrating the p-median and distance factor to support TS site selection in urban planning from multiple perspectives. To this end, taxi demand with spatial–temporal dynamics is extracted from taxi global positioning system (GPS) data to uncover the restrictive distribution characteristics of the setting areas and specific locations of TS with GIS platform. Taxi demand is then subdivided, and potential service points are set up on the road network. With the constraints of the supply and demand environment, we design the TS location models (TSLM) based on the set covering problem (SCP), the maximal covering location problem (MCLP), and the p-median problem (PMP), respectively. Furthermore, the TSLM based on PMP is extended to consider the maximum acceptable distance for passengers. A genetic algorithm-based procedure is introduced for solving the extended TSLM. An experiment conducted in China compares the facility coverage capacity, taxi demand allocation, and passenger access willingness of the optimal layout schemes obtained from four TSLMs. The number of parking spaces at TS is also evaluated. The result demonstrates that extended TSLM outperforms the other three models in the validity of locating TS.