Promoting cost efficiency and uniformity in parcel delivery centre locations and service areas: a GIS-based analysis

This paper provides a systematic framework to determine local delivery centre (LDC) locations and service areas to reduce delivery costs and balance the allocation of parcel delivery loads to promote sustainable LDC development. Based on a case study of a parcel distribution company in Korea, this study proposes two strategies for LDC service area delineation within each terminal service boundary that either minimises the total delivery time or balances the allocation of delivery loads. This research contributes to improving cost efficiency and balancing delivery load allocations in planning LDC locations and service areas that can potentially promote the long-term mutual sustainable development of parcel distribution companies and their local delivery counterparts.     

Data center supply chain configuration design: A two-stage decision approach

Data centers are special-purpose facilities that enable customers to perform cloud based real-time online transactions and rigorous computing operations. Service levels of data center facilities are characterized by response time between query and action, which to a large extent depends on data center location and data travel distance. Another aspect of service level is resource up-time availability, which is determined by data center configuration. Data center location and configuration decisions are, therefore, of great significance to ensure uninterrupted operations in customers of manufacturing and service industries relying on cloud-based computing resources. In this study, following a grid-based location approach, we present two mixed integer linear programming models for capacitated single-source data center location-allocation problems. The first model provides optimal locations, capacities and configurations of data centers, and allocation of demands to open facilities when there is no existing facilities in the region. Our second model considers the decision problem of meeting new demand when the existing demand is met by the already opened facilities. We term these newly arrived demand as replication demand, which results either from emergence of new users of existing customers at distant locations in the future, or as a means of increasing data resilience by creating data replication as a backup. To solve the decision problem for meeting primary and replication demand optimally, we propose a two-stage decision algorithm. The algorithm provides optimal locations, capacities and configurations for new data centers, capacity addition decisions to the existing facilities and subsequent allocation of demands. Both models and solution algorithm are implemented using AMPL programming language and solved with CPLEX solver. The models are found to be scalable and capable to provide high quality solutions in reasonable time.     

Improving spatial accessibility to regional health systems through facility capacity management

In austerity times, the general interest to reduce costs and improve efficiency levels often resulted into local cuts to public expenditure and profound reorganizations of existing service networks, especially in sectors like healthcare. In Italy, a recent reform prescribed the reconfiguration of time-dependent (i.e. emergency) hospital networks with the aim of improving patients’ accessibility conditions. In order to evaluate the impacts determined by this reorganization, we perform a spatial analysis in which we consider the distance from the closest facility as an accessibility measure. Results obtained from the spatial analysis confirm that users effectively benefit from the reorganization process but also that further improvements are possible, especially for the worst served ones. To this end, we also propose solving a mathematical programming model aiming at redistributing the capacities, i.e. the supply of beds, among the hospitals of the network seeking to maximize users’ accessibility. The realized computational experiments show that averagely better and even more equitable accessibility conditions could be obtained by containing the deriving reorganization costs.     

E-Learning系统中的知识链管理模型

针对目前E-learning系统服务对象较单一,知识的分层处理、多层知识的交互协同供给能力较弱,以及E-learning系统的知识管理和处理方式研究较少的问题,对E-learning系统中的知识链及其知识管理系统架构进行了探究,为增强E-learning系统的知识分层管理能力和提高E-learning系统的知识服务能力的研究做铺垫。     

供热企业开展优质服务工作做法与体会

本文对新形势下供热企业开展优质服务工作主要做法进行总结:(1)健全制度,加强领导;(2)强化理念,监督到位;(3)完善预案,科学调配;(4)加强沟通,深化服务,并就提高优质服务水平浅谈几点体会。     

Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand

To ensure a timely response to emergencies, governments are obliged to implement effective ambulance allocation plans. In practice, an emergency medical service (EMS) system works in an uncertain environment, with stochastic demand, response-times, and travel-times. This uncertainty significantly affects ambulance allocation planning. However, few studies in this field adequately consider the effect of spatiotemporal uncertainty in demand, because it is difficult to measure it quantitatively. As a result, few analytic models capture the dynamic nature of an EMS system and, thus, the allocation plans they generate are not efficient in practice. Therefore, this study proposes a simulation-based optimization method for ambulance allocation. A simulation model is constructed to mimic the operational processes of an EMS system, and to evaluate the performance of an ambulance allocation plan in an uncertain environment. Gaussian mixture model clustering is used to derive the uncertain spatial demand. Then, the simulation generates emergency demand based on the obtained spatial distribution. A Gaussian-process-based search algorithm is used together with the simulation model to identify optimal solutions. To validate the proposed method, a case study is conducted using data on emergency patients in the Shanghai Songjiang District. Compared with the current plan adopted in Songjiang, the experimental results demonstrate that the delay time and frequency of the EMS system can be reduced significantly by employing the proposed methods. Furthermore, nearly 41% of the allocation cost can be saved.     

A location-based comparison of health care services in four U.S. states with efficiency and equity

This paper examines the efficiency and equality in geographic accessibility provided by hospitals. We use the criteria efficiency, availability of the service, and equality. Quantitative measures are defined for all criteria, and are measured using a geographical information system. We then compare existing locations with optimal locations satisfying two objectives, one that minimizes hospital–patient distance, and another that captures as many patients as possible within a pre-specified time or distance. The results of our study indicate that the existing locations provide near-optimal geographic access to health care. Some potential for improvement is indicated.     

国家成品油战略储备库选址评估研究

从影响国家成品油战略储备库选址的主要因素出发,建立了比较全面的评价指标体系。在进行选址评价时,针对评价指标体系的层次性,建立了基于多层次模糊评价方法的评估模型。并通过实例分析和计算,验证了其合理性、有效性和可行性。     

Social enterprise factory location and allocation model: Small scale manufacturing for East Africa

The manufacturing location decision for social enterprises that work in the context of sustainable development is rarely based on a quantitative, analytical process. As a result, decisions may be far from profit-maximizing. Location and allocation optimization models have the potential to improve decisions and thus enable such enterprises to scale up their business as well as their impact. We develop and explain a single-period single-factory model, and also a two-echelon location and allocation model, to provide enterprises with information about optimal factory locations, and with future demand allocations and capacity-changing decision information. We apply the models to a company that manufactures and distributes solar cookers in East Africa. Our results illustrate that quantitative location and allocation models can significantly affect social enterprises by improving profitability. The case study shows strong cost-reduction potential of local manufacturing in developing countries due to high transportation costs for small production volumes. We discuss this model-recommended decision by weighing it against associated opportunities and risks. This paper aims to enable and encourage social and sustainability-oriented manufacturing enterprises to apply operations research methods in their strategic factory location decision-making.     

SLA-based optimisation of virtualised resource for multi-tier web applications in cloud data centres

Dynamic virtualised resource allocation is the key to quality of service assurance for multi-tier web application services in cloud data centre. In this paper, we develop a self-management architecture of cloud data centres with virtualisation mechanism for multi-tier web application services. Based on this architecture, we establish a flexible hybrid queueing model to determine the amount of virtual machines for each tier of virtualised application service environments. Besides, we propose a non-linear constrained optimisation problem with restrictions defined in service level agreement. Furthermore, we develop a heuristic mixed optimisation algorithm to maximise the profit of cloud infrastructure providers, and to meet performance requirements from different clients as well. Finally, we compare the effectiveness of our dynamic allocation strategy with two other allocation strategies. The simulation results show that the proposed resource allocation method is efficient in improving the overall performance and reducing the resource energy cost.     

Emergence of innovation networks from R&D cooperation with endogenous absorptive capacity

This paper extends the existing literature on strategic R&D alliances by presenting a model of innovation networks with endogenous absorptive capacity. The networks emerge as a result of dynamic cooperation between firms occupying different locations in the knowledge space. Partner selection is driven by absorptive capacity which is itself influenced by cognitive distance and R&D investment allocation. Under different knowledge regimes, we examine the structure of networks that emerge and how firms perform within such networks. We find networks that exhibit small world properties which are generally robust to changes in the knowledge regime. Certain network strategies such as occupying brokerage positions or maximising accessibility to potential partners pay off, especially in ‘young’ industries with limited involuntary but abundant voluntary spillovers. This particular result is driven by endogenous absorptive capacity.     

Design of financial incentives and payment schemes in healthcare systems: A review

In this paper, we review the existing studies on the design of financial incentives and payment mechanisms for healthcare service providers, which have been published in the literature of operations research & management science. We classify these studies in two ways: first, by the care provider focused on: physicians or hospitals; and next, by categorizing payment/incentive schemes as retrospective or prospective models under each care provider group. The problem setting, including the geographic background and research methodology, are briefly discussed for each paper, as are the findings with regard to accessibility, quality, efficiency, and the facilitation of integration and collaboration, if reported. We also provide a detailed discussion of potential research problems that would offer promising avenues for future study.     

Modeling emergency evacuation with time and resource constraints: A case study from Gujarat

This study develops an off-site emergency response plan for a nuclear power plant in Gujarat, India subject to time constraints with resource limitations and risk of radiation exposure to victims. We formulate an optimization model to capture the effect of delay in evacuation, limited resource availability, and costs associated with resource allocation. A single chain closed queuing network model with class switching is used to model traffic congestion during evacuation. The throughput measures from the queuing network are used as inputs in the optimization model. Further, two resource allocation strategies are suggested and genetic algorithm is used for optimizing resource utilization and evacuation risk. The results indicate that pooling resources among a cluster of affected areas is most suitable for evacuation. Numerical experiments are conducted to analyze the time trade-offs and the effect of service time variability on the expected evacuation time. The proposed model can serve as an important resource planning and allocation tool for emergency evacuation.     

Improving accessibility to rural health services: The maximal covering network improvement problem

Accessibility to health facilities is a critical factor in effective health treatment for people in rural areas of lesser-developed countries. In many areas accessibility is diminished by the lack of all-weather roads, making access subject to weather conditions. Location-allocation models have been used to prescribe optimal configurations of health facilities in order to maximize accessibility, but these models are based on the assumption that the underlying transport network is static and always available. Essentially, past work has ignored the potential impacts of improvements to the transport system in modeling access. In this paper we propose a model that treats the opposite side of the location/transport equation; that is, a model that treats existing facility locations as fixed and improves health service accessibility by upgrading links of the transport network to all-weather roads. This new model, called the Maximal Covering Network Improvement Problem (MC-NIP) is formulated as an integer-linear programming problem. An application of the MC-NIP model to the Suhum District of Ghana is presented, which shows that even a modest level of road improvement can lead to substantial increases in all-season access to health service.     

基于多级库存的电网运维物资安全库存优化配置研究

针对电力公司运维物资多级库存体系与安全库存配置现状,结合安全库存聚集效应,文章提出了周转库安全库存差异聚集策略。在综合考虑库存持有成本与普通、紧急配送成本的基础上,构建了安全库存优化配置模型。通过两类电网运维物资实际数据的分析,验证了差异聚集策略下成本低于全部聚集以及全部分散策略,可以为电力公司物资集约化、差异化、精益化管理以及仓库定额配置提供依据。     

Modeling mobile health service delivery to Syrian migrant farm workers using call record data

A significant number of Syrian refugees under temporary protection in Turkey work in agriculture seasonally in various rural areas during several months a year. These migrant farm workers and their families are deprived of access to the regular health care system and preventive services due to their remote locations. The government supports the delivery of different types of mobile health care services, such as vaccination for children, reproductive health and screening services. While planning the mobile health care service delivery, it is critical to know where the refugees will work during what time frame; hence the demand for the services. By analyzing the call record data of a major mobile network operator in Turkey, we quantify the increase in the volume of calls made by Syrian refugees in various agricultural areas during the harvesting season of local crops. This information helps us to forecast spatial and temporal distribution of demand for mobile health care services at a fine granularity. Taking demand over multiple periods as input into a mathematical programming model, we optimize the routing of mobile clinics that visit locations close to where refugees are concentrated over the given planning horizon. We consider three hierarchical objectives. Given the availability of a number of mobile clinics at community health centers in the districts, the first objective aims to maximize the percentage of refugees that can benefit from each service type within pre-defined close distances. The second objective minimizes the number of clinics needed while covering the maximum percentage of refugees. The third objective minimizes the total travel distance of the clinics, while keeping the maximum coverage level using a minimum number of clinics to achieve this level. We quantify the benefits of centralized planning (by the province directorate) over decentralized planning (by each district separately). We also show the trade-off between the required number of clinics and coverage of potential patients.     

开放合作创新视角下基于云模式的物流资源一体化集成决策研究

基于开放合作创新的思路，文中提出一种全新的物流资源运作管理模式---“云模式”，探讨在开放合作创新的平台上，对多种紧密耦合且相互制约的物流资源进行同步协同和整合的方法与途径。“云模式”的本质是通过松弛物流资源与其所有者之间的归属关系，为社会全体物流资源提供一个开放的平台。物流资源可以通过这个平台，以合作的方式有机的集成在一起，从而实现资源的优化配置、提高物流资源的利用率，降低社会物流的总成本，提高客户对物流服务满意度的目标。文中的研究契合了当前政府相关职能部门和企业在管理决策上的实际需要。     

Provision and price of child care services: For-profits and nonprofits

We study the market for child care services, with a special focus on examining competition between for- and nonprofits. We estimate a two-stage oligopoly model of product differentiation. The first stage estimates a model of endogenous market structure and the second stage corrects for market structure to examine the prices charged and capacity choices for child care centers. We find that the actions of “same-type” providers have a statistically significant impact on a provider’s entry and pricing decisions but we fail to find evidence that the actions of “other types” have a significant impact. Nonprofit child care providers and Head Start centers do not appear to crowd out for-profit providers. Further, we find that for-profits and nonprofits respond differently to market characteristics generating spatial differences in the types of center available in a market. Our data suggest that for-profits are more likely to enter markets with higher percentages of economically disadvantaged students, but they primarily serve those who work, rather than live, in the market. The prevalence of disadvantaged students does not impact the entry decision of nonprofits leaving disadvantaged areas with relatively fewer non-profit options to serve residents. Policies to encourage for-profit daycare would likely lead centers to locate in markets where they can provide service for workers, whereas a policy to encourage nonprofit entry might be more effective in providing low cost care for nearby residents.     

Measuring the spatial accessibility to fire stations using enhanced floating catchment method

Enhancing the spatial accessibility of population to fire services is a key strategy to help improve emergency response, minimise property loss, and reduce injuries and deaths. Given its significance for fire service policy and strategic planning, we draw on small area population forecasts, fire station locations and the road network, and employ the enhanced two-step floating catchment method to compute the levels of spatial accessibility of population to fire services in relation to current and future population growth in Brisbane, Australia. Results show that lower levels of spatial accessibility to fire services exist in the most populated areas compared to those that are least populated. Further, we reveal that some areas are more likely to suffer a reduction in spatial accessibility than others. Spatial accessibility of population to fire services over the period to 2036 is likely to reduce as a consequence of rising demand for service. The identification of locales that experience lower levels of accessibility to fire services will enable fire services agencies to strategically plan infrastructure investment and help enhance the operational efficiency of emergency response.     

The maximal covering location problem with accessibility indicators and mobile units

We study the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units that maximizes the facilities coverage, the accessibility of the zones to the open facilities, and the spatial disaggregation. The main characteristic of our problem is that mobile units can be deployed from open facilities to extend the coverage, accessibility, and opportunities for the inhabitants of the different demand zones. We formulate the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units as a mixed-integer linear programming model. To solve larger instances, we propose a matheuristic (combination of exact and heuristic methods) composed of an Estimation of Distribution Algorithm and a parameterized Maximal Covering Location Problem with Accessibility Indicators and Mobile Units integer model. To test our methodology, we consider the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units model to cover the low-income zones with Severe Acute Respiratory Syndrome Coronavirus 2 patients. Using official databases, we made a set of instances where we considered the poverty index, number of population, locations of hospitals, and Severe Acute Respiratory Syndrome Coronavirus 2 patients. The experimental results show the efficiency of our methodologies. Compared to the case without mobile units, we drastically improve the coverage and accessibility for the inhabitants of the demand zones.