Bi-criteria solid transportation problem with substitutable and damageable items in disaster response operations on fuzzy rough environment

Successful planning and scheduling of relief operations play a key role in saving lives and reducing damage in disasters. These disaster operations involve a variety of challenging multi-objective optimization problems, for which soft computing methods are well suited. In this investigation, relief materials and human resources are transported from some distribution centers (DCs) to some delivery points (DPs) through conveyances. The objectives are to minimize both total cost and time for transportation. Some particular models for (a) substitutable items (b) damageable items and (c) safety Factor have been derived. To convert bi-criteria solid transportation problem into a single objective problem, three different techniques (i) Fuzzy interactive satisficing method, (ii) Global Criterion Method and (iii) Convex Combination Method are used. Then the reduced single objective problem is solved by a non-linear optimization technique – Generalized reduced gradient method using LINGO-14.0. The models are illustrated through some numerical examples and optimal results are presented in tabular forms.     

Disaster relief routing: Integrating research and practice

Disaster relief presents many unique logistics challenges, with problems including damaged transportation infrastructure, limited communication, and coordination of multiple agents. Central to disaster relief logistics is the distribution of life-saving commodities to beneficiaries. Operations research models have potential to help relief agencies save lives and money, maintain standards of humanitarianism and fairness and maximize the use of limited resources amid post-disaster chaos. Through interviews with aid organizations, reviews of their publications, and a literature review of operations research models in transportation of relief goods, this paper provides an analysis of the use of such models from the perspective of both practitioners and academics. With the complexity of disaster relief distribution and the relatively small number of journal articles written on it, this is an area with potential for helping relief organizations and for tremendous growth in operations research.     

Analysis and forecast of donations at domestic hunger relief organizations impacted by natural disasters

Food banks are non-profit, charitable organizations that distribute food and products to people in need. Food bank facilities become disaster relief centers after natural disasters. The uncertainty associated with the arrival of donations and demand make the planning and operations of food banks challenging during the disaster relief period. The goal of this research is to analyse and forecast the amount of donations received by food bank facilities in the U.S. when operating as disaster relief centers. This paper analyses the donations received by two food bank facilities affected by Hurricane Harvey in 2017. An extensive numerical study is performed that compares the donation behavior at each facility before and after the hurricane event. Multiple forecasting models are evaluated to determine their accuracy in predicting the observed behavior. The results show that under disaster operations, the best performing techniques for both food banks were smoothing techniques (i.e., CMA and Holt) and econometric models.     

Including deprivation costs in facility location models for humanitarian relief logistics

Humanitarian assistance is meant to save lives and alleviate human suffering during and in the aftermath of man-made and natural disasters. To prevent and strengthen preparedness for the occurrence of such situations, having available relief supplies in the short-term becomes crucial. The lack of access to life-sustaining items implies a loss in people's welfare, treated as an externality called deprivation costs which must be incorporated into decision-making processes. Since typical humanitarian applications are extensions of commercial logistic models, they usually do not account for externalities, leading to high social costs and likely to unfeasible or suboptimal solutions.This paper develops a facility location model for prepositioning supplies in preparation for disasters; the key feature of this formulation being the fact that it explicitly considers deprivation costs in the objective function. The model attempts to minimize the global social costs, as the sum of both private costs (i.e. costs of transportation, inventory costs and fixed costs of facilities) and deprivation costs, determining the amount per type of product to be prepositioned for serving the areas affected by a disaster during the initial response. The model focuses on those assistance interventions that should be carried out immediately, i.e. within the first 24 h of a humanitarian crisis. We applied the model, using real information, to the Colombian Caribbean region, which was affected by floods in 2010 and 2011. Results demonstrate that deprivation costs represent more than 50% of the total social cost.     

Evaluating spatial service and layout efficiency of municipal Wi-Fi facilities for SmartCity planning: A case study of Wuhan city,China

This paper explores an approach to evaluating spatial service and layout efficiency of municipal Wi-Fi facilities for cyber infrastructure planning in the construction of a smart city. Based on the equilibrium principle that Wi-Fi services supply should meet the spatial demand for recreation, urgency, and security, two evaluation models were built. In the first model, the spatial service efficiency of Wi-Fi facilities is measured by a mean ratio of three types of demand spaces covered by Wi-Fi service. In the other model, spatial layout efficiency of each Wi-Fi facility is calculated by a demand load indicator - the sum of the three types of demand space's ratios of served quantity to the average quantity it should carry, and then assessed by relationship with its adjacent facilities. Corresponding statistical methods and analysis processes were also designed using the function modules found in ArcGIS software. The approach was applied to Wuhan city in 2016. After overlaying influence areas of 688 Wi-Fi facilities with demand spaces of 137 access points for recreation, 1200.99 km roads for urgency, 121.54 km² regions for security, the results show that there exists a spatial disequilibrium between supply and demand of Wi-Fi service in Wuhan due to lack of comprehensive planning. Specifically, there are 67 points, 700.76 km roads, and 42.59 km² regions of demand spaces covered by Wi-Fi service, with service efficiencies of 48.91%, 58.35%, and 35.04% respectively and an overall spatial service efficiency of 47.43%. Among all the Wi-Fi facilities, there are 481 carrying saturated demand, 3 straying from the centre of demand space, and 204 redundant. The overall spatial layout efficiency of Wi-Fi facilities is 43.01%. The conclusion is that the dense and massive redundant Wi-Fi facilities should be optimized, especially in the centre areas along the confluence of Yangtze and Han rivers and new Wi-Fi facilities supplemented in other ill-equipped areas. Since many metropolitan cities experience the same problem, this novel approach will find wide application in the future and offer improved evaluation strategies for researchers and policymakers.     

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.     

Relief aid stocking decisions under bilateral agency cooperation

We aim to quantify the benefits of cooperation between humanitarian relief agencies in terms of stocking decisions. We consider two agencies that stock the same type of relief item at different locations prone to individual disaster risks and agree to transship the shortage amount from available stocks in case of a disaster. We incorporate the disaster risk to the Newsvendor model by conditioning the stock quantity decisions on the event that a major disaster occurs within the lifetime of the stocked relief item. We optimize the stock quantity for each agency in response to the other's quantity and compute a Nash Equilibrium solution numerically. We apply this game theoretic approach to the case of earthquake preparedness in Istanbul to optimize the stocking decisions of an agency for shelter units in cooperation with another agency. We investigate the characteristics of the solutions under various parameter settings and identify cases in which cooperation may be beneficial to one or both of the agencies.     

Reliable design of a congested disaster relief network: A two-stage stochastic-robust optimization approach

The preparedness of humanitarian relief networks can be enhanced by pre-positioning resources in strategic locations and using them when disasters strike, a strategy that gives rise to a two-stage planning problem. This paper presents a novel two-stage stochastic-robust optimization approach for integrated planning of pre- and post-disaster positioning and allocation of relief resources, while taking into consideration the uncertainty about demand for relief services and disruptions in the relief facilities and the transportation network. The proposed approach enables planners to effectively use limited historical data and imperfect experts’ opinions to obtain robust solutions while avoiding the over-conservatism of classical robust optimization methods. The objective sought is to minimize the expected total time victims need to receive assistance, including both access time to facilities and waiting/service time in them. Congestion in relief facilities is accounted for by modeling them as queuing systems and penalizing waiting time. A decomposition method based on column-and-constraint generation is implemented to solve the problem, whereas the nonlinear terms corresponding to queuing in the second-stage problem are handled using a direct search procedure. Applicability of the proposed approach is demonstrated through a real case study and the numerical results are analyzed to draw managerial insights.     

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.     

A two-period newsvendor model for prepositioning with a post-disaster replenishment using Bayesian demand update

Humanitarian aid agencies usually resort to inventory prepositioning to mitigate the impact of disasters by sending emergency supplies to the affected area as quickly as possible. However, a lack of replenishment opportunity after a disaster can greatly hamper the effectiveness of the relief operation due to uncertainty in demand. In this paper, a prepositioning problem is formulated as a two-period newsvendor model where the response phase is divided into two periods. The model acknowledges the demand to be uncertain even after the disaster and utilises the Bayesian approach to revise the demand of the second period. Based on the revised demand, an order is placed at the beginning of the second period to be replenished instantaneously. A two-stage solution methodology is proposed to find the prepositioning quantity and post-disaster replenishment quantity, which minimise the total expected costs of relief operations. A numerical example is presented to demonstrate the solution methodology, and sensitivity analysis is performed to examine the effect of model parameters. The results highlight the indifferent characteristics shown by the replenishment quantity with the variation in model parameters.     

Development of an integrated medical supply information system

The integrated medical supply inventory control system introduced in this study is a hybrid system that is shaped by the nature of medical supply, usage and storage capacity limitations of health care facilities. The system links demand, service provided at the clinic, health care service provider's information, inventory storage data and decision support tools into an integrated information system. ABC analysis method, economic order quantity model, two-bin method and safety stock concept are applied as decision support models to tackle inventory management issues at health care facilities. In the decision support module, each medical item and storage location has been scrutinised to determine the best-fit inventory control policy. The pilot case study demonstrates that the integrated medical supply information system holds several advantages for inventory managers, since it entails benefits of deploying enterprise information systems to manage medical supply and better patient services.     

基于中国邮路问题的移动基站切换链路测方法研究

文章基于中国邮路问题,提出了移动基站切换链路测的新方法。根据基站与城市道路之间的关系以及交通规则,提出了移动基站切换链路测方法的有向图模型,使移动基站切换链路测问题成为了中国邮路问题。在移动基站切换链路测方法的有向图模型中,通过把单行道转化为双行道后,使该方法的有向图模型由非欧拉图转化成了欧拉图,经过这样特殊处理后,大大降低了求解移动基站切换链路测问题的复杂性。为了验证该方法的有效性,文章用一个具体的实例来证明了其有效性。     

Exploratory analyses of relief and development operations using social networks

We use social networks to explore how structural factors affect humanitarian organizations’ performance in relief and development operations. Analyses of two recent humanitarian disasters show that having pre-established partnerships among implementers, a central coordinator, high connectivity, and few structural holes facilitates coordination and improves performance. Similarly, cost efficiency analyses of 757 development programs reveal that (i) high performance of donors and beneficiaries is positively related to the amount of implementers they interact with, and (ii) programs connected by common actors are more cost efficient. Finally, short path lengths and frequent connections among actors or programs improve performance in humanitarian operations.     

Hierarchical Models for the Analysis of Likert Scales in Regression and Item Response Analysis

Appropriate modelling of Likert‐type items should account for the scale level and the specific role of the neutral middle category, which is present in most Likert‐type items that are in common use. Powerful hierarchical models that account for both aspects are proposed. To avoid biased estimates, the models separate the neutral category when modelling the effects of explanatory variables on the outcome. The main model that is propagated uses binary response models as building blocks in a hierarchical way. It has the advantage that it can be easily extended to include response style effects and non‐linear smooth effects of explanatory variables. By simple transformation of the data, available software for binary response variables can be used to fit the model. The proposed hierarchical model can be used to investigate the effects of covariates on single Likert‐type items and also for the analysis of a combination of items. For both cases, estimation tools are provided. The usefulness of the approach is illustrated by applying the methodology to a large data set.     

Who Is Eligible? Should Affirmative Action be Group‐ or Class‐Based?

We explore the consequences for eligibility of members of subaltern groups for affirmative action (AA), when AA policies are based on social class criteria rather than on group affiliation (race, ethnicity, or gender), by means of a general model with simplifying assumptions. The model is developed first for the case where everyone eligible for AA becomes a beneficiary, and then for the case where beneficiaries are only those eligibles who are able to meet minimum qualification requirements for the positions at issue—an ability that is (reasonably) assumed to be correlated with socioeconomic status. The model demonstrates that class‐based affirmative action cannot provide as many subaltern‐group beneficiaries as group‐based affirmative action, especially when access to the desired positions hinges on performance qualifications. Data on AA‐targeted subaltern groups in rural India and in the United States are used to illustrate the conclusions of the model.     

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.     

A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty

Disaster response operations revolve around uncertainties. While uncertainties arising due to randomness can be avoided for post-disaster location problem, those arising because of impreciseness may persist long after the disaster's occurrence. Despite the uncertainties and lack of sufficient information about the extent of the damage, disaster response facilities must be established quickly after the occurrence of the disaster. Moreover, the decisions of whether to open, where to locate, and when to open disaster response facilities are based on the amount and quality of information available during the decision-making period. To address these issues, we develop a multi-objective location-allocation model for relief supply and distribution that accounts for the imprecise and time-varying nature of different parameters and time-varying coverage, while also accommodating the subjective attributes necessary to enable establishment and operation of the temporary logistics hubs (TLHs). A credibility-based fuzzy chance-constrained programming model is employed to account for the impreciseness inherent in predicting parameter values during disaster response. The results show where, when, and how many TLHs to open and how to allocate relief supplies. Meanwhile, the sensitivity analysis provides a broader understanding of the impact of limiting the number of TLHs as well as the confidence level and the spread of the symmetric triangular fuzzy numbers on the attainment of the model objectives.     

Aggregation in Input–Output Models: Prices and Quantities

This paper develops a joint theory of aggregation of input–output quantity and price models. The main emphasis is on the problem of aggregation of industries in the models. While aggregation of quantity models is a familiar topic in economic literature, the aggregation of price models is a largely unexplored subject. Here, however, quantity and price models are considered as two parts of a single, composite flow model. This understanding implies that each result in the quantity model has dual counterpart in the price model and vice versa. Through consistent use of this duality principle, significant results are developed for both model types, and a number of well-known results can be stated in a simpler way. Specifically, a number of conditions for perfect aggregation of final demands, primary inputs and industries in price models as well as quantity models are formulated. On this basis a new indicator of aggregation bias is suggested. The indicator can be decomposed into the contributions of each detailed industry, enabling the user to identify atypical industries in each group. Furthermore, the indicator can be computed even with no knowledge of the coefficients of the detailed models.     

山西省农村交通设施现状的因子分析

以市域农村为单位,用因子分析法对山西省农村交通设施现状进行了研究,按照因子分析结果,把山西省市域农村交通设施发展现状分为三类:交通设施较发达地区、欠发达地区和落后地区,为交通设施投资决策提供了一些依据。     

Identification with latent variables

This is an essay on a unified approach to the identifiability problem in static models with and without hidden endogenous variables. As is well known, when some of these variables are unobserved, the prior information requirements for models when all endogenous variables are observed, are still there. In addition, extra prior information that takes the place of the means and covariances of the missing variables will have to be supplied directly or indirectly by the statistical researcher. In the paper we characterize the quality and quantity of the required information for the general linear static model and apply it when the model is i) an econometric demand and supply model with missing observations on the quantity transacted, ii) a factor analysis model with observed characteristics of the test takers and iii) a LISREL Model without fixed exogenous variables. With unknown true parameters, the exact rank conditions are seldom verifiable but we do recommend an implementable check-list that is adequate for almost all parameters.