In recent years, there has been a significant increase in health expenditures due to population growth. In this context, hospital administrators have started to look for ways to use existing resources effectively. Ope...
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In recent years, there has been a significant increase in health expenditures due to population growth. In this context, hospital administrators have started to look for ways to use existing resources effectively. Operating rooms are one of the most important units of a hospital. The efficient use of these units is seen as a decrease in cost items and an increase in revenues. At this point, it is aimed to use the operating rooms effectively in this study. The fact that it contains many uncertainties and many stakeholders in its structure complicates the solution process of the operating room scheduling problem. In this study, planning was made that considered the uncertainty in the operation times and the surgeons, nurses, and anesthesiologists in the surgical team. To solve the problem, the logical modeling power of the constraint programming method and the power of the goal programming method to stretch rigid constraints were utilized. In the first stage, the balanced assignment of the surgical team (surgeon-nurse-anesthesiologist) was carried out, while in the second stage, operations were assigned to the operating rooms. The proposed model was evaluated according to the operating rooms' utilization rates and the solution's effectiveness. The results showed that the proposed model successfully created an effective and efficient schedule.
We demonstrate a new approach to conducting a military force structure study under uncertainty. We apply the stochastic preemptive goal program approach, described by Ledwith et al., to balance probabilistic goals for...
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We demonstrate a new approach to conducting a military force structure study under uncertainty. We apply the stochastic preemptive goal program approach, described by Ledwith et al., to balance probabilistic goals for military force effectiveness and the force's cost. We use the Bayesian Enterprise Analytic Model (BEAM), as described in "Probabilistic Analysis of Complex Combat Scenarios," to evaluate effectiveness, expressed in terms of the probability of achieving campaign objectives, in three hypothetical scenarios. We develop cost estimates along with their uncertainty to evaluate the force's research and development, production, and annual operating and support costs. Our summary depicts how the trade-off between various prioritized goals influences the recommended robust force. Our approach enables defense leaders to balance risk in both force effectiveness in various scenarios along with risk in different types of cost categories.
Scheduling and organizing is a crucial part of managing the land in agriculture. To do this, cropping pattern optimization within a set of constraints necessitates optimal land utilization. For crop pattern optim...
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Several factors affect the flexibility and the complexity of the project selection and the contractor selection problems. Project portfolio managers are expected to select the best combination of projects and contract...
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Several factors affect the flexibility and the complexity of the project selection and the contractor selection problems. Project portfolio managers are expected to select the best combination of projects and contractors considering multiple conflicting objectives in a multi-period planning horizon. In this paper, we propose an integrated project portfolio optimization and contractor selection problem. The problem is modeled through a multi-objective Mixed Integer Linear programming (MILP) model. Three solution approaches including goal programming (GP), Fuzzy goal programming (FGP), and fuzzy goal programming considering a fuzzy preference relationship are proposed. All solution approaches have been applied to a real case. The computational results show the out performance of FGP considering fuzzy relations. The time complexity of the proposed models in the sense of the relation of CPU time and the number constraints and variables of the models were discussed.
Food banks play a crucial role in the combat against hunger and food insecurity, being responsible for distributing donated food to the population who are facing restrictions in their access to food. This process pres...
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Food banks play a crucial role in the combat against hunger and food insecurity, being responsible for distributing donated food to the population who are facing restrictions in their access to food. This process presents several challenges in terms of adopting the principles of equity, efficiency, and effectiveness, including when the amount of food available is insufficient to meet all demands. In this context, this study proposes an optimization model using the goal programming technique applied to food distribution in search of improved agility and equity in the process. The model was implemented and analyzed using a fictional scenario and also applied to a realistic scenario that describes the operation of a Brazilian food bank. The results indicate that Chebyshev goal programming is effective in optimizing the distribution process, and promoting equity and efficiency in the assistance provided to social institutions. This approach is an important tool to help managers develop agile process planning strategies.
Optimizing production planning problems is often defined by conflicting objectives, such as minimizing costs, maximizing benefits, and meeting product quantity requirements under uncertainties and within fuzzy environ...
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This study employs fuzzy goal programming (FGP) and goal programming (GP) approach to plan the production of sorghum–Bengal gram–sunflower intercropping in the Northern Dry Zone of Karnataka. In this research, seven...
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In a managerial position, the ultimate objective is to take the right decision for the decision maker (DM) when transportation parameters are uncertain due to the globalization and other uncontrollable influences. In ...
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In a managerial position, the ultimate objective is to take the right decision for the decision maker (DM) when transportation parameters are uncertain due to the globalization and other uncontrollable influences. In this paper, fuzzy membership function tactic based on goal programming to obtain the desired compromise solution of a multi-objective transportation problem (MOTP) in uncertain environment is proposed where the DM can choose a confidence level for different parameters. On the basis of DM's choice on a particular confidence level, a compromise solution is obtain indicating the satisfaction level of the DM if the problem is feasible for this chosen confidence level. Uncertain normal distribution is used to convert the parameters from uncertain to a certain one. Simple linear programming problem (LPP) is designed using fuzzy linear membership function where the upper and lower values of the objectives are the desired goals of the DM. A numerical illustration is furnished to establish the effectiveness of the designed model whereas the single objective transportation problems are solved by TORA and LPPs are solved by using LINGO for operations research. (C) 2020 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
In real-life optimization problems, due to some uncertainty and hesitancy, it might be challenging for the decision-makers to identify the precise values of the parameters. To define this uncertainty, neutrosophic set...
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In real-life optimization problems, due to some uncertainty and hesitancy, it might be challenging for the decision-makers to identify the precise values of the parameters. To define this uncertainty, neutrosophic set theory is an extension of the crisp, fuzzy and the intuitionistic fuzzy set theory in which along with the incomplete information, indeterminate and inconsistent information is also handled. Considering these advantages, a model for the multi-objective fixed-charge transportation problem with the single-valued trapezoidal neutrosophic numbers is formulated here. To derive the Pareto-optimal solution for the presented model, neutrosophic goal programming approach is proposed which is rooted in the principles of maximizing the degree of satisfaction, maximizing the degree of indeterminacy and minimizing the degree of dissatisfaction. For demonstrating the applicability of the proposed approach, a numerical problem within the same framework is solved. Furthermore, the same problem is also solved using the fuzzy programming and intuitionistic fuzzy programming approach and a comparative analysis is conducted. The results demonstrate that the proposed neutrosophic goal programming approach consistently provides better Pareto-optimal solution as compared to the existing approaches. Finally, the implications of this research and outline avenues for the future research are also discussed at the last.
One of the benefits of implementing transportation infrastructure projects is the maximization of network accessibility performance, so that system users' mobility is optimized. However, infrastructure investment ...
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One of the benefits of implementing transportation infrastructure projects is the maximization of network accessibility performance, so that system users' mobility is optimized. However, infrastructure investment decisions rarely consider stakeholders' preferences to network performance measures and the impact of project selection and prioritization on the network accessibility performance. This paper presents a goal programming framework that considers network accessibility performance and the total cost of road infrastructure projects or project bundles to prioritize investments, taking into account stakeholders' preferences to the performance criteria. Results of the case study using the low-volume road (LVR) network showed that the accessibility benefits of LVR projects depend on their relative spatial location in the network, their total project cost, target performance goals, and stakeholders' preferences to performance criteria. The study results also showed that LVR projects could maximize network accessibility even after the occurrence of network disruption events. Therefore, the proposed framework could help the decision-makers develop efficient infrastructure investment plans to mitigate natural and human-made network disruption events.
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