7,696 research outputs found

    Task assignment in home health care : a fuzzy group genetic algorithm approach

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    The assignment of home care tasks to nursing staff is a complex problem for decision makers concerned with optimizing home healthcare operations scheduling and logistics. Motivated by the ever-increasing home-based care needs, the design of high quality task assignments is highly essential for maintaining or improving worker moral, job satisfaction, service efficiency, service quality, and to ensure that business competitiveness remains momentous. To achieve high quality task assignments, the assigned workloads should be balanced or fair among the care givers. Therefore, the desired goal is to balance the workload of care givers while avoiding long distance travels in visiting the patients. However, the desired goal is often subjective as it involves the care givers, the management, and the patients. As such, the goal tends to be imprecise in the real world. This paper develops a fuzzy group genetic algorithm (FGGA) for task assignment in home healthcare services. The FGGA approach uses fuzzy evaluation based on fuzzy set theory. Results from illustrative examples show that the approach is promising

    A permutation flowshop model with time-lags and waiting time preferences of the patients

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    The permutation flowshop is a widely applied scheduling model. In many real-world applications of this model, a minimum and maximum time-lag must be considered between consecutive operations. We can apply this model to healthcare systems in which the minimum time-lag could be the transfer times, while the maximum time-lag could refer to the number of hours patients must wait. We have modeled a MILP and a constraint programming model and solved them using CPLEX to find exact solutions. Solution times for both methods are presented. We proposed two metaheuristic algorithms based on genetic algorithm and solved and compared them with each other. A sensitivity of analysis of how a change in minimum and maximum time-lags can impact waiting time and Cmax of the patients is performed. Results suggest that constraint programming is a more efficient method to find exact solutions and changes in the values of minimum and maximum time-lags can impact waiting times of the patients and Cmax significantly

    Managing the resource allocation for the COVID-19 pandemic in healthcare institutions : a pluralistic perspective

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    vital:16949Purpose: As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective. Design/methodology/approach: The present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions. Findings: After successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated. Research limitations/implications: Using probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition. Practical implications: The study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference. Originality/value: Further, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization. © 2021, Emerald Publishing Limited

    SOLVING FIRE DEPARTMENT STATION LOCATION PROBLEM USING MODIFIED BINARY GENETIC ALGORITHM: A CASE STUDY OF SAMSUN IN TURKEY

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    Fire and traffic accident are the most common problem in our daily lives. Fire department location for the purpose of easy response and recovery is a significant problem today. The best location of a fire department station determines the rate of recovery of many injured people after traffic accident, fire outbreak, and so on. In this paper, we considered the best location of fire stations. In addition, we also considered where the station is setup. Surveying problem is modeled as a mixed integer programming. It is solved by modified binary genetic algorithm, coding with GAMS. Modified binary GA is different from known GA with respect to binary decision variables. Due to this problem, initial value of the objective function was obtained from known GA. Then finally, the best result was achieved from binary GA

    SOLVING FIRE DEPARTMENT STATION LOCATION PROBLEM USING MODIFIED BINARY GENETIC ALGORITHM: A CASE STUDY OF SAMSUN IN TURKEY

    Get PDF
    Fire and traffic accident are the most common problem in our daily lives. Fire department location for the purpose of easy response and recovery is a significant problem today. The best location of a fire department station determines the rate of recovery of many injured people after traffic accident, fire outbreak, and so on. In this paper, we considered the best location of fire stations. In addition, we also considered where the station is setup. Surveying problem is modeled as a mixed integer programming. It is solved by modified binary genetic algorithm, coding with GAMS. Modified binary GA is different from known GA with respect to binary decision variables. Due to this problem, initial value of the objective function was obtained from known GA. Then finally, the best result was achieved from binary GA

    Best matching processes in distributed systems

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    The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies
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