This paper aims to provide a scientific approach that indicates the need to focus on renewable energypotential to meet energy needs in Turkey. Turkey began to take advantage of renewable energy tech-nologies a few yea...
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This paper aims to provide a scientific approach that indicates the need to focus on renewable energypotential to meet energy needs in Turkey. Turkey began to take advantage of renewable energy tech-nologies a few years ago. Accordingly, the issue of determining the best renewable resources for thecountry has been brought to the agenda in recent years. The issue was considered to be a limited multi-objective optimization problem allowing us to achieve a reliable result. However, the parameter of eachresource was considered as a range value, rather than traditionally expressed as an exact value, and amulti-objective decision problem was developed with an interval coefficient. This method allows us toobtain more accurate and reliable results without the need to resort to normalization methods used toeliminate unit differences. According to the results of this study, the most convenient alternatives forTurkey are hydro, wind, and solar power. Thefindings also support decision policies aimed at reachingtargets for the electricity sector in 2023, as put forth by the Ministry of Energy and Natural Resources(MENR). (c) 2021 Elsevier Ltd. All rights reserved
In this paper, we present an improved methodology to compute the omega-primality of a numerical semigroup. The approach is based on exploiting the structure of the problem on a resolution method for optimizing a linea...
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In this paper, we present an improved methodology to compute the omega-primality of a numerical semigroup. The approach is based on exploiting the structure of the problem on a resolution method for optimizing a linear function over the set of efficient solutions of a multiple objectiveinteger linear programming problem. The numerical experiments show the efficiency of the proposed technique compared to the existing methods.
Fragmentation of the forests affects forest ecosystems by changing the composition, shape, and configuration of the resulting patches. Subsequently, the prevailing conditions vary between patches. The exposure to the ...
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Fragmentation of the forests affects forest ecosystems by changing the composition, shape, and configuration of the resulting patches. Subsequently, the prevailing conditions vary between patches. The exposure to the sun decreases from the patch boundary to the patch interior and this forms core and edge areas within each patch. Forest harvesting and, in particular, the clear-cut management system which is still preferred in many European countries has a significant impact on forest fragmentation. There are many indices of measuring fragmentation: non-spatial and spatial. The non-spatial indices measure the composition of patches, while the spatial indices measure both the shape and configuration of the resulting patches. The effect of forest harvesting on fragmentation, biodiversity, and the environment is extensively studied;however, the integration of fragmentation indices in the harvest scheduling model is a new, novel approach. This paper presents a multi-objectiveinteger model of harvest scheduling for clear-cut management system and presents a case study demonstrating its use. Harvest balance and sustainability are ensured by the addition of constraints from the basic principle of the regulated forest model. The results indicate that harvest balance and sustainability can be also achieved in minimizing fragmentation of forest ecosystems. From the analyses presented in this study, it can be concluded that integration of fragmentation into harvest scheduling can provide better spatial structure. It depends on the initial spatial and age structure. It was confirmed that it is possible to find compromise solution while minimizing fragmentation and maximizing harvested area.
Human error is a critical concern in healthcare systems from primary care clinics to operating rooms in hospitals. Prevention or reduction of the chance of occurrence of such errors through increasing human reliabilit...
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Human error is a critical concern in healthcare systems from primary care clinics to operating rooms in hospitals. Prevention or reduction of the chance of occurrence of such errors through increasing human reliabilities is tremendously important and deserves to be focused within personnel scheduling problems in healthcare systems. The present study is to develop a new multi-objectiveinteger mathematical model which includes human errors of nurses to determine optimal shift scheduling of nurses. In addition to medical errors, several constraints in real-world problem including "minimum number of available nurses in each shift", "restrictions on shift rotation for each nurse", and "Minimum and maximum working hours in a week" are also taken into account. Nurses' preference score, allocation costs, penalty cost of violating soft constraints, and human errors are all considered as objectives to be optimized. The multi-objective model, developed in this study, is solved by employing the weighted-sum method. To verify and validate the proposed model, a test problem is also solved. Sensitivity analysis on the model indicates that the solution method can reach acceptable solutions within an acceptable time. The present study is to help decision-makers to achieve optimal scheduling for decreasing costs and improving safety in healthcare systems. Based on this approach, decision makers can totally minimize the number of errors by considering the number of nurses required in each grade as well as proper allocation of them to different work shifts.
Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital fi...
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Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatients, outpatients, and emergency patients. The department employs full-time and part-time interpreters to cover the demand of LEP patients. Two main challenges are facing an interpreting department: 1) there are many interpreting agencies in the market in which part-time interpreters can be chosen from. Selecting a part-time interpreter with the best service quality and lowest hourly rate makes the scheduling process difficult. 2) the arrival of LEP emergency patients must be predicted to make sure that LEP emergency patients are covered and to avoid any service delay. This paper proposes a framework for scheduling full-time and part-time interpreters for medical centers. Firstly, we develop a prediction model to forecast LEP patient demand in the emergency department (ED). Secondly, we develop a multi-objective integer programming (MOIP) model to assign interpreters to inpatient, outpatient, and emergency LEP patients. The goal is to minimize the total interpreting cost, maximize the quality of the interpreting service, and maximize the utilization of full-time interpreters. Various experiments are conducted to show the robustness and practicality of the proposed framework. The schedules generated by our model are compared with the schedules generated by the interpreting department of a partner hospital. The results show that our model produces better schedules with respect to all three objectives.
In order to overcome the problem of the sophisticated tasks and low efficiency of army aircraft test, an optimization test mission scheduling strategy based on ATML is presented. Firstly, test mission scheduling ICOM ...
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ISBN:
(纸本)9781424449804
In order to overcome the problem of the sophisticated tasks and low efficiency of army aircraft test, an optimization test mission scheduling strategy based on ATML is presented. Firstly, test mission scheduling ICOM description and hierarchical framework is proposed, and then standard XML schema about test mission is defined by ATML. Secondly, test mission scheduling model is set up and taboo search evolutionary algorithm to overcoming it is put forward. At last, the good performance of the strategy is proved by experiment.
The recent success of bi-objective Branch-and-Bound (B&B) algorithms heavily relies on the efficient computa-tion of upper and lower bound sets. These bound sets are used as a supplement to the classical dominance...
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The recent success of bi-objective Branch-and-Bound (B&B) algorithms heavily relies on the efficient computa-tion of upper and lower bound sets. These bound sets are used as a supplement to the classical dominance test to improve the computational time by imposing inequalities derived from (partial) dominance in the objective space. This process is called objective branching since it is mostly applied when generating child nodes. In this paper, we extend the concept of objective branching to multi-objectiveinteger optimization problems with three or more objective functions. Several difficulties arise in this case, as there is no longer a lexicographic order among non-dominated outcome vectors when there are three or more objectives. We discuss the general concept of objective branching in any number of dimensions and suggest a merging operation of local upper bounds to avoid the generation of redundant sub-problems. Finally, results from extensive experimental studies on several classes of multi-objective optimization problems is reported.
This paper presents integerprogramming formulations and compares two approaches – weighting and lexicographic – to the multi-objective, long-term production scheduling in make-to-order manufacturing, where both max...
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