A Proposal to Improve the Health Care Systems for the Urban Poor in the Squatter Settlements of the Developing Countries

Rapid urbanization and large scale population movements from rural to urban areas have resulted in unprecedented health crises in the developing countries. In addition to communicable diseases, respiratory infections and malnutrition, psycho-social stresses due to marginalization and exclusion from social activities and employment prospects are also prevalent. Considering the rate of urban growth rate and the rapid increase in the percentage of the poor living in urban areas, the debilitating effects of health crises and urban poverty are going to exacerbate if no precautions are taken. In this respect, it is a critical point in time to come up with effective health care strategies for the urban poor. This document provides an insight into the reasons behind the current health problems of the urban poor and the determinants of health in developing countries, and proposes use of operations research to come up with handling strategies for the major subdivisions of the health problem in the developing world

Municipal solid-waste collection and disposal management using geospatial techniques in Maseru City, Lesotho

The use of geospatial techniques plays a crucial role in solid waste management. Collection and transportation of solid waste must be done in an efficient manner to avoid negative environmental impacts. At the time of study, there are no collection and routing system in Maseru City, leading to haphazard collection and disposal of Municipal Solid Waste (MSW). The aims of the study are: (i) To get an understanding and address the challenges faced by relevant stakeholders in solid waste management for Maseru City, (ii) To minimize adverse environmental impacts due to unscientific location of a disposal site and (iii) To minimize transportation costs and time during collection. The objectives of this study are summarized in the following: assess the current solid waste management, model suitable disposal/dump sites, determine MSW collection points and develop an optimal route for MSW collection and disposal in Maseru City. To assess the current solid waste management, 130 households, 73 community waste pickers, 15 Maseru City Council (MCC) management staff and 3 drivers were interviewed, and relevant data collected. Both primary and secondary data collection methods were used. Primary data collection methods included interviews, questionnaires and observations and creating feature classes in a geo database. Secondary data collection was done from relevant government repositories, digitization, and internet web sites. Simple random, area, cluster, and convenience sampling techniques were applied. Geographical Information Systems (GIS) and Remote sensing techniques were used to carry out suitability and network analysis, and location of MSW collection points. The study found out that the dump site (Ts'osane) was used by MCC and was not suitably located, hence more suitable alternative dump sites have been proposed. However, Ts'osane dump site was adopted in the analysis as it is the one used by MCC at the time of study. The researcher also found out that there were no designated MSW collection points and optimal routes, and that solid waste collection was done by both MCC and CBOs. In this regard, 334 collection points have been determined based on population and generated solid waste per Constituency and were randomly located in the study area. However, due to the policy that within 25m from the road no development could take place, only collection points which fell v within 25m from the road were selected and used in the routing analysis. One truck was used in the analysis, although more trucks could be used as it was at the time of study. For future research, there is a need to research on policy so that criteria for locating solid waste disposal and location of collection points is explicitly specified in the law to be able to conduct scientific analyses. A multi modal network analysis that would include all the vehicles used by MCC and the CBOs to develop a comprehensive network analysis that would also include necessary attributes such as road names, type, class, and length is needed

Computer-Aided Manufacturing Planning (CAMP)of Mass Customization for Non-rotational Part Production

This research is aimed at studying the key technologies of Computer-Aided Manufacturing Planning (CAMP) of mass customization for non-rotational part production. The main goal of the CAMP is to rapidly generate manufacturing plans by using of the best-of-practice (BOP) provided by specific companies. A systematic information modeling hierarchy is proposed to facilitate changes in manufacturing plans according to changes in part design. The Object-oriented Systems Analysis (OSA) approach is used to represent information relationships and associativities in the CAMP. A feature-based part information model, a process model, a setup planning model, and manufacturing resource capability models are established. A three-level decision-making mechanism is proposed for the CAMP. At the feature- level, combined features are defined based on part families, and a process model is proposed to describe the information associativities between features and their manufacturing strategies, which include customized cutters and toolpaths. At the part level, graph-based setup planning is carried out by tolerance analysis and manufacturing resource capability analysis. At the machine level, multi-part fixtures are utilized to pursue high productivity. Cycle time is used to evaluate manufacturing plans. Computer software for the CAMP has been developed and integrated with CAD package Unigraphs. The BOP of part families is stored in XML format, which has good extendibility and can be read and edited by standard browsers

Implementing Connections: The Benefits for Greater Philadelphia

This analysis utilizes DVRPC's modeling capabilities to illustrate and quantify the benefits of implementing the policies and goals defined in the Connections Plan, through a Plan scenario, compared to a continuation of our region's business-as-usual Trend scenario. Both scenarios are set in the horizon year of the Plan, 2035, and compared to each other and current conditions (2010)

Logistic system design of an underground freight pipeline system

"July 2014."Dissertation Supervisor: Dr. James Noble.Includes vita.Underground Freight Pipeline (UFP) systems utilize the underground space in metro areas that is otherwise not utilized for freight transportation. Two fundamental logistics issues in the design of a UFP system are network configuration and capsule control. This research develops two capsule control models that minimize total tardiness squared of cargo delivery and associated heuristic algorithms to solve large-scale problems. Two network design models are introduced that minimizes both operational and construction cost of UFP system. The UFP network design Comprehensive Model can only be solved to optimality for small sized problem. To reduce the computational complexity, the UFP network design Two Step Model that is able to generate high quality network design solutions is developed. Then, a case study of a UFP network design in Greater New York area is presented.Includes bibliographical references (pages 159-162)

A mathematical pre-disaster model with uncertainty and multiple criteria for facility location and network fortification

Disasters have catastrophic effects on the affected population, especially in developing and underdeveloped countries. Humanitarian Logistics models can help decision-makers to efficiently and effectively warehouse and distribute emergency goods to the affected population, to reduce casualties and suffering. However, poor planning and structural damage to the transportation infrastructure could hamper these efforts and, eventually, make it impossible to reach all the affected demand centers. In this paper, a pre-disaster Humanitarian Logistics model is presented that jointly optimizes the prepositioning of aid distribution centers and the strengthening of road sections to ensure that as much affected population as possible can efficiently get help. The model is stochastic in nature and considers that the demand in the centers affected by the disaster and the state of the transportation network are random. Uncertainty is represented through scenarios representing possible disasters. The methodology is applied to a real-world case study based on the 2018 storm system that hit the Nampula Province in Mozambique

A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly

The objective of this research is to test the hypothesis that manufacturing complexity can reliably predict product quality in mixed-model automotive assembly. Originally, assembly lines were developed for cost efficient mass-production of standardized products. Today, in order to respond to diversified customer needs, companies have to allow for an individualization of their products, leading to the development of the Flexible Manufacturing Systems (FMS). Assembly line balancing problems (ALBP) consist of assigning the total workload for manufacturing a product to stations of an assembly line as typically applied in the automotive industry. Precedence relationships among tasks are required to conduct partly or fully automated Assembly Line Balancing. Efforts associated with manual precedence graph generation at a major automotive manufacturer have highlighted a potential relationship between manufacturing complexity (driven by product design, assembly process, and human factors) and product quality, a potential link that is usually ignored during Assembly Line Balancing and one that has received very little research focus so far. The methodology used in this research will potentially help develop a new set of constraints for an optimization model that can be used to minimize manufacturing complexity and maximize product quality, while satisfying the precedence constraints. This research aims to validate the hypothesis that the contribution of design variables, process variables, and human-factors can be represented by a complexity metric that can be used to predict their contribution on product quality. The research will also identify how classes of defect prevention methods can be incorporated in the predictive model to prevent defects in applications that exhibit high level of complexity. The manufacturing complexity model is applied to mechanical fastening processes which are accountable for the top 28% of defects found in automotive assembly, according to statistical analysis of historical data collected over the course of one year of vehicle production at a major automotive assembly plant. The predictive model is validated using mechanical fastening processes at an independent automotive assembly plant. This complexity-based predictive model will be the first of its kind that will take into account design, process, and human factors to define complexity and validate it using a real-world automotive manufacturing process. The model will have the potential to be utilized by design and process engineers to evaluate the effect of manufacturing complexity on product quality before implementing the process in a real-world assembly environment

Developing a Tool for the Location Optimization of the Alert Aircraft with Changing Threat Anticipation

The threat to the airspace is posed by the outside world in conventional terms as well as hostilities from within the airspace such as hijacked aircraft. Alert aircraft are located with the sole responsibility of responding to any incident. Different regions of the airspace may have different alert states depending on current intelligence input. Due to non-constant states of threat level, the Turkish Air Force must deploy aircraft to cover the more sensitive regions with a greater number of aircraft with a relatively short response time. This research deals with the problem by developing a tool for the location optimization of the alert aircraft. The tool can adapt to changes in threat anticipation while meeting the objectives of the alert network. Thus, a new location model with backup coverage requirements was formulated, and an interactive tool is developed that is capable of generating the aircraft locations for different user-defined threat anticipation

Using Markov Decision Processes with Heterogeneous Queueing Systems to Examine Military MEDEVAC Dispatching Policies

major focus of the Military Health System is to provide efficient and timely medical evacuation (MEDEVAC) to battlefield casualties. Medical planners are responsible for developing dispatching policies that dictate how aerial military MEDEVAC units are utilized during major combat operations. The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on the request\u27s classification (i.e., zone and precedence level) and the state of the MEDEVAC system. Rejected requests are rerouted to be serviced by other, non-medical military organizations in theater. Performance is measured in terms of casualty survivability rather than a response time threshold since survival probability more accurately represents casualty outcomes. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic baseline policies. Two computational experiments, a two-level, five-factor screening design and a subsequent three-level, three-factor full factorial design, are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system level performance measure. Results indicate that dispatching the closest available MEDEVAC unit is not always optimal and that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Moreover, an open question exists concerning the best exact solution approach for solving Markov decision problems due to recent advances in performance by commercial linear programming (LP) solvers. An analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the LP algorithms executed by CPLEX 12.6 in regards to computational effort. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems