Leveraging multi-tier healthcare facility network simulations for capacity planning in a pandemic

The COVID-19 pandemic has placed severe demands on healthcare facilities across the world, and in several countries, makeshift COVID-19 centres have been operationalised to handle patient overflow. In developing countries such as India, the public healthcare system (PHS) is organised as a hierarchical network with patient flows from lower-tier primary health centres (PHC) to mid-tier community health centres (CHC) and downstream to district hospitals (DH). In this study, we demonstrate how a network-based modelling and simulation approach utilising generic modelling principles can (a) quantify the extent to which the existing facilities in the PHS can effectively cope with the forecasted COVID-19 caseload; and (b) inform decisions on capacity at makeshift COVID-19 Care Centres (CCC) to handle patient overflows. We apply the approach to an empirical study of a local PHS comprising ten PHCs, three CHCs, one DH and one makeshift CCC. Our work demonstrates how the generic modelling approach finds extensive use in the development of simulations of multi-tier facility networks that may contain multiple instances of generic simulation models of facilities at each network tier. Further, our work demonstrates how multi-tier healthcare facility network simulations can be leveraged for capacity planning in health crises.     

Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems

Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance capacity planning of complex product systems (CoPS), intended to value data that is routinely generated and stored, but often disregarded by MROs. State space formulations with multiplicative errors for the simple exponential smoothing (SES), Holt’s linear method (HLM), additive Holt-Winters (AHW), and multiplicative Holt-Winters (MHW) are assessed using real data, comprised of 171 maintenance projects collected from a major Portuguese aircraft MRO. A state space formulation of the MHW, selected using the bias-corrected Akaike information criterion (AICc), is integrated in a Decision Support System (DSS) for capacity planning with probabilistic inference capabilities and used to forecast the workload probability distribution of a future and unprecedent maintenance intervention. The developed tool is validated by comparing forecasted values with workloads of a particular maintenance intervention and with a model simulating current forecasting practices employed by MROs.     

城市灾害应急预案基本要素探讨

目前,城市灾害管理正在逐渐受到各级政府的重视,为了有效地减轻城市灾害的损失,一些大中城市开始制定灾害应急预案.然而,在应该制定什么样的城市应急预案才能真正在救灾抢险中发挥最大作用等方面仍缺少必要的论证和探讨.文章从政府灾害应急救灾的基本程序入手,对城市灾害应急预案的基本要素进行了探讨,提出了城市灾害预测评价和防灾规划是制定城市灾害应急预案的基础,而灾害应急指挥系统、灾害情报体系、救灾抢险体系、应急医疗体系、应急避难体系和交通管理体系六大主要因素构成了城市灾害应急预案的基本要素.     

A logistics model for emergency supply of critical items in the aftermath of a disaster

This paper proposes a logistics model for delivery of prioritized items in disaster relief operations. It considers multi-items, multi-vehicles, multi-periods, soft time windows, and a split delivery strategy scenario, and is formulated as a multi-objective integer programming model. To effectively solve this model we limit the number of available tours. Two heuristic approaches are introduced for this purpose. The first approach is based on a genetic algorithm, while the second approach is developed by decomposing the original problem. We compare these two approaches via a computational study. The multi-objective problem is converted to a single-objective problem by the weighted sum method. A case study is presented to illustrate the potential applicability of our model. Also, presented is a comparison of our model with that proposed in a recent paper by Balcik et al. [6]. The results show that our proposed model outperforms theirs in terms of delivering prioritized items over several time periods.     

关于城市防灾规划的反思

随着城市化进程的加陕,现行防灾规划体系已不能适应发展的要求.本文从城市灾害的形成分析入手,提出防灾规划的基本体系及架构,以期对建立新的城市防灾规划体系有所帮助.     

Robust planning: a new paradigm for demand chain planning

This paper proposes a new paradigm for tactical demand chain planning (DCP), called robust planning, based on risk assessment of the supply and demand chain. The concepts of supply chain management (SCM), and its extension demand chain management (DCM), have been at the center of much recent research. One of the reasons for this is that, over the last years, a significant number of information systems have emerged, which claim to support the concept. The paper argues that these systems mostly adopt a myopic view of planning, based on pure deterministic planning methods. It demonstrates that such an approach fails to coop with the considerable uncertainty of the planning information. The proposed robust planning paradigm is then introduced and its impact explained, using the well-known example of the beer game. It holds the promise of reducing the number of re-planning cycles, through a better characterization of the expected service level performance over a medium planning horizon. Finally, a case study will show the value of robust planning in a European chemical enterprise.     

Redesign strategies of a comprehensive robust relief network for disaster management

We address the problem of designing as well as redesigning a relief network over multiple periods as a strategic decision which plays a critical role in the post-disaster management. Design of the relief network has a significant impact on the effective performance of disaster response operations. For considering both the uncertainty and dynamism of the decision-making environment, a comprehensive scenario-based robust approach embedded in the rolling horizon framework is proposed. The proposed mixed-integer linear programming model is inspired by a real case study of a disaster management in Iran, which aims to minimize the total cost of network management. Furthermore, restorative strategies are considered to increase the efficiency and robustness of the proposed relief network under disaster. To tackle the proposed optimization model, a heuristic solution algorithm is adopted. The results indicate that the proposed robust relief network provides an affordable access to its demand points in a sustainable manner under disaster. In addition, extensive computational results illustrate the efficiency of the proposed model in dealing with the considered disaster management issues.     

城市应急避难基地的规划建设原则——以"长屿硐天"为例

城市应急避难基地是城市为抵御诸如地震、火灾、爆炸和洪水等灾害而修建的安全场所.建设城市应急避难基地是提高城市整体防灾能力的途径之一.应急避难基地的规划建设是一项系统工程,需要科学、合理的规划.以长屿硐天应急避难基地为例,初步探讨了建设应急避难基地的必要性和建设原则.     

Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model.The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way.     

A model for planning locations of temporary distribution facilities for emergency response

We propose a network flow model for dynamic selection of temporary distribution facilities and allocation of resources for emergency response planning. The model analyzes the transfer of excess resources between temporary facilities operating in different time periods in order to reduce deprivation. Numerical analysis shows that the location of temporary facilities is determined by the demand and supply points. This work contributes to the emergency response planning that requires a quick response for the supply of relief materials immediately after a disaster hits a particular area.     

Planning the delivery of relief supplies upon the occurrence of a natural disaster while considering the assembly process of the relief kits

Models for the distribution of relief supplies often assume immediate availability of relief items upon the occurrence of a natural disaster. However, such an assumption does not always apply in realistic settings. In some cases, at least it is necessary to assemble relief items into kits before distributing them among the affected population. This paper presents a rolling horizon methodology that considers dynamic parameters such as demand, capacities and demand priorities for the problem of distributing relief supplies after the occurrence of a natural disaster by including such assembling activities before the delivery.     

Dispatching and routing of emergency vehicles in disaster mitigation using data fusion

The aim of this paper is to develop a robust methodology for the dispatching and routing of emergency vehicles (EVs) in a post-disaster environment with the support of data fusion. In this work, we consider an earthquake scenario with a large number of casualties needing medical attention. Given an influx of information (regarding casualties, road, traffic conditions, etc.), data are fused to provide estimates of the entities under consideration. We use this information to dispatch and route EVs to casualty pickup locations, followed by delivery to appropriate hospitals. Key factors here include patient priorities, clustering criteria, and distance. Similarly, factors affecting the dispatching of EVs from patient locations to hospitals include waiting time at hospital emergency rooms, hospital capacity, and distance. Routes must be generated for EVs by accounting for real-world road networks, existing road damage, congestion, and related issues. We develop a dispatching and routing simulation model, and utilize a case study to evaluate the performance of our proposed methodology.     

A robust optimization approach for decontamination planning of emergency planning zone: Facility location and assignment plan

Establishing a robust facility location and assignment plan to improve the efficiency of the decontamination process is critical to alleviating the physical impact of the radiation leakage that occurs in a nuclear accident. This study develops an approach for optimizing the locations of decontamination facilities and assignments of affected villages. The approach is a robust optimization model that optimizes the worst-case performance. The system dynamic model is integrated into the robust optimization model to simulate the decontamination process and compute the decontamination time. A case study is conducted of the Plume Emergency Planning Zone in China. The results indicate that (1) a decontamination site location plan can be obtained in which each site is located in a different direction, (2) no evacuee will be allowed to travel across the downwind area in an assignment plan, and (3) a larger financial investment does not imply an increased decontamination efficiency. An appropriate budget exists that can balance the decontamination time and cost. The proposed model can assist decision makers in (i) better understanding the effects of decontamination site location and village assignment and (ii) deciding which location and assignment plans should be applied to cope with disruptive nuclear accidents.     

Robust building evacuation planning in a dynamic network flow model under collapsible nodes and arcs

Rapid urbanization has caused various social problems. One typical example is the high population density of a building, particularly in a commercial building or a mega-mall. When an emergency, such as a natural or human-made disaster, occurs in a building with a high population, establishing a proper evacuation plan is required to minimize casualties. Accordingly, the evacuation planning problem, which determines optimal routes for evacuees from disaster-prone areas to safe areas, has been actively studied in various fields. However, research considering the possibility of further collapse of a specific area or intermediate route in the building has been overlooked. We propose a robust evacuation planning problem based on a dynamic network flow model that determines the optimal routes for evacuees from a building that has the potential to collapse. Computational results show that routes passing through areas with the potential to collapse may or may not be optimal for evacuees, depending on the given timeframe. If the timeframe is sufficient, detouring around the collapsible areas could be the optimal plan; however, if the timeframe is insufficient, passing through collapsible areas, with taking the risk, could be the optimal plan.     

A dynamic model for disaster response considering prioritized demand points

This paper addresses the problem of distributing relief supplies after the occurrence of a disaster. We develop a dynamic model to serve demand, while prioritizing the response, according to the level of urgency of demand points. Our model is thought to be applied during a planning horizon and it considers dynamic demand, capacity constraints and priorities. To evaluate the applicability of our model, we use a real case study of a flood occurred in Colombia. We also test the computational solvability of our model and we propose and test different solution methodologies for solving larger instances of our problem.     

The differential impact of product complexity,inventory level,and configuration capacity on unit and order fill rate performance

This paper examines the simultaneous impact of configuration capacity, inventory level, and complexity on service performance as measured by unit and order fill rates in a configure-to-order environment. Demand skew is treated as a control variable. A simulation model based on data from a leading electronics manufacturer is used to test the hypotheses and identify the impact. Results suggest that there are differential direct and interactive effects of examined variables on unit and order fill rates.     

小城镇规划、建设与管理的思考

小城镇建设产业是支柱、生态是基础、文脉是灵魂、功能是核心、信息是生命力 ,在城镇建设过程中应强化规划指导 ,重点培育特色产业 ,加快基础设施建设以提高城镇的承载力和发展水平 ,促进城市健康发展。     

台北市防灾空间规划

经由防救灾据点与道路的现况检讨 ,爰对于台北市中心区之实际防灾空间系统提出检讨与修正。除指定出防灾据点与路径之层级外 ,更初步拟定其规范准则 ,作为未来设施重建、改建与改善时 ,加强防灾机能之参考。     

An evaluation of heuristic methods for determining the best table mix in full-service restaurants

Little research has been done on the optimal mix of supply in service businesses that maximizes revenue. Our research context is the full-service restaurant table mix problem. This problem, which is quite new to the literature, finds the optimal number of different size tables for a restaurant to maximize its value (revenue or contribution) generating potential. Specifically, we examine the effectiveness of eight heuristic techniques for the problem using two experiments. The first experiment uses data from a 240-seat full-service restaurant to evaluate all eight heuristics, while the second experiment investigates the performance of selected heuristics under a broader set of environmental factors. The results of our first experiment showed that the better of the simulated annealing heuristic variants yielded the optimal solution in seven of eight test problems, averaging within 0.1% of optimal. Our second experiment showed that the simplest of the models we investigated yielded solutions within 1% of the simulated annealing solution. Finally, we observed that altering the table mix on a daily basis increased performance by over 1% compared to maintaining the optimal weekly table mix.     

基于市民休闲生活需求的城市规划与管理——以广州市为例

城市的本质是人的居住形态和生活方式,现代社会休闲已成为城市居民的基本生活需求与生存方式,满足市民的休闲生活需求是城市的基本责任,"创造市民休闲生活"应该成为当代城市规划与管理的指导思想.以广州市为例,分析了基于市民休闲生活需求的城市规划方向和管理目标,并提出了具体策略.