The principal objective of this article is to develop an effective approach to solve matrix games with payoffs of single-valued trapezoidal neutrosophic numbers (SVTNNs). In this approach, the concepts and suitable ra...
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The principal objective of this article is to develop an effective approach to solve matrix games with payoffs of single-valued trapezoidal neutrosophic numbers (SVTNNs). In this approach, the concepts and suitable ranking function of SVTNNs are defined. Hereby, the optimal strategies and game values for both players can be determined by solving the parameterized mathematical programming problems, which are obtained from two novel auxiliary SVTNNs programming problems based on the proposed Ambika approach. In this approach, it is verified that any matrix game with SVTNN payoffs always has a SVTNN game value. Moreover, an application example is examined to verify the effectiveness and superiority of the developed algorithm. Finally, a comparison analysis between the proposed and the existing approaches is conducted to expose the advantages of our work.
This paper proposes a new problem by integrating the job shop scheduling, the part feeding, and the automated storage and retrieval problems. These three problems are intertwined and the performance of each of these p...
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This paper proposes a new problem by integrating the job shop scheduling, the part feeding, and the automated storage and retrieval problems. These three problems are intertwined and the performance of each of these problems influences and is influenced by the performance of the other problems. We consider a manufacturing environment composed of a set of machines (production system) connected by a transport system and a storage/retrieval system. Jobs are retrieved from storage and delivered to a load/unload area (LU) by the automated storage retrieval system. Then they are transported to and between the machines where their operations are processed on by the transport system. Once all operations of a job are processed, the job is taken back to the LU and then returned to the storage cell. We propose a mixed-integer linear programming (MILP) model that can be solved to optimality for small-sized instances. We also propose a hybrid simulated annealing (HSA) algorithm to find good quality solutions for larger instances. The HSA incorporates a late acceptance hill-climbing algorithm and a multistart strategy to promote both intensification and exploration while decreasing computational requirements. To compute the optimality gap of the HSA solutions, we derive a very fast lower bounding procedure. Computational experiments are conducted on two sets of instances that we also propose. The computational results show the effectiveness of the MILP on small-sized instances as well as the effectiveness, efficiency, and robustness of the HSA on medium and large-sized instances. Furthermore, the computational experiments clearly shown that importance of optimizing the three problems simultaneous. Finally, the importance and relevance of including the storage/retrieval activities are empirically demonstrated as ignoring them leads to wrong and misleading results.
Metabolic network analysis is an accessible and versatile modeling approach for biology that has taken much inspiration from electric circuit analysis. After introducing its main concepts, we focus on numerical tools,...
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Metabolic network analysis is an accessible and versatile modeling approach for biology that has taken much inspiration from electric circuit analysis. After introducing its main concepts, we focus on numerical tools, such as optimization and sampling, to predict cellular features and behaviors at a large scale. Optimization approaches exploit that metabolic networks are shaped by evolution and are, thus, assumed to embed a fitness condition reflecting the environment that they evolved in. In the past ten years, there is a trend to generalize metabolic network analysis to consortia of interacting species. This raises technical questions on, for example, optimality in consortia but also more general ones on metabolic coevolution, information exchange, and adaptation. This suggests and allows us to explore interesting analogies to technological systems, specifically to smart grids.
Due to industrialization, copper demand has increased over the last decades. Recycling rate of copper is high and its scrap requires less energy than primary production, so sustainable closed-loop supply chain network...
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Due to industrialization, copper demand has increased over the last decades. Recycling rate of copper is high and its scrap requires less energy than primary production, so sustainable closed-loop supply chain network design is considered a primary decision. Besides, the uneven distribution of copper has exaggerated the destructive effects of natural disasters such as earthquakes on mines. To the best of the authors' knowledge, there is no research about copper supply chain network design. In this paper, a copper network is designed and backup suppliers are used as a resilience strategy to reduce the effects of earthquakes on mining operations. Without backup model and with backup model are presented as multi-objective and are compared with each other. In each model, the economic objective is to maximize the supply chain profit;the environmental objective is to minimize water consumption and air pollutants;and the social objective is to maximize social desirability by considering security and unemployment rates. The models are formulated using mixed-integer linear programming and they are solved by epsilon-constraint and weighted sum methods. Results show that, with backup model increases the supply chain responsiveness. Also, the model is able to improve the economic and social performances of the supply chain. But in environmental aspect, it performs worse than without backup model. This is because the backup suppliers are added to the supply chain and their exploitation will create negative environmental effects. In addition, using copper scraps saves costs, energy and the consumption of this non-renewable metal. [GRAPHICS] .
This paper introduces ROmodel, an open source Python package extending the modeling capabilities of the algebraic modeling language Pyomo to robust optimization problems. ROmodel helps practitioners transition from de...
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This paper introduces ROmodel, an open source Python package extending the modeling capabilities of the algebraic modeling language Pyomo to robust optimization problems. ROmodel helps practitioners transition from deterministic to robust optimization through modeling objects which allow formulating robust models in close analogy to their mathematical formulation. ROmodel contains a library of commonly used uncertainty sets which can be generated using their matrix representations, but it also allows users to define custom uncertainty sets using Pyomo constraints. ROmodel supports adjustable variables via linear decision rules. The resulting models can be solved using ROmodels solvers which implement both the robust reformulation and cutting plane approach. ROmodel is a platform to implement and compare custom uncertainty sets and reformulations. We demonstrate ROmodel's capabilities by applying it to six case studies. We implement custom uncertainty sets based on (warped) Gaussian processes to show how ROmodel can integrate data-driven models with optimization.
We consider optimization problems whose objective functions have uncertain coefficients. We assumed that, initially, the uncertain data are given as ranges, and probing of the true values of data is possible. The comp...
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We consider optimization problems whose objective functions have uncertain coefficients. We assumed that, initially, the uncertain data are given as ranges, and probing of the true values of data is possible. The complete probing of all uncertain data will yield the true optimal solution. However, probing all uncertain data is undesirable because each probing may require cost or time depending on the methods of probing. We are interested in finding a solution, which we call F-optimal, that is guaranteed to remain the best solution even after additional F probings of uncertain data. An iterative algorithm to find the F-optimal solution is developed with theoretical probability bounds of the F-optimal solution being true optimal. Special algorithms are also developed for the problems on networks. The extensive computational study shows that the proposed approach could find the true optimal solutions at very high percentages, even with small numbers of probings. (C) 2021 Elsevier B.V. All rights reserved.
This paper proposes an algorithm based on VNS metaheuristcs for k-medoids clustering, which is a NP-hard optimization problem. The VNS algorithm was applied in fifty data bases (instances) with small, medium, and larg...
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This paper proposes an algorithm based on VNS metaheuristcs for k-medoids clustering, which is a NP-hard optimization problem. The VNS algorithm was applied in fifty data bases (instances) with small, medium, and large sizes, considering the number of clusters between 2 and 7. The obtained results from these experiments show the effectiveness of this approach, comparing it with nine other related clustering algorithms and an optimization formulation. Furthermore, we found that our algorithm obtained the optimal solutions for the vast majority of the cases.
The quality of transportation between cities depends on the accessibility of intercity passenger transportation (IPT) facilities, which is closely related to regional spatial development plans. Such plans aim to defin...
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The quality of transportation between cities depends on the accessibility of intercity passenger transportation (IPT) facilities, which is closely related to regional spatial development plans. Such plans aim to define an urban system (i.e., the level of hierarchy to assign to cities of the region under study) with a class of facilities corresponding to each level of hierarchy. We propose optimization models to help in the decision making in urban system planning (USP) problems. Four types of service networks of USP problems are proposed. Multiperiod, multilevel location models with fuzzy IPT demands are presented to minimize the cost of total demand-weighted travel time and IPT service capacity planning. The IPT demands are denoted by triangular fuzzy numbers. The models are converted to linear programming models using the theory of fuzzy numbers. A case study is provided to validate the effectiveness of the proposed model. The comparison results show that the fuzzy method is effective in handling the uncertainty of demand.
We establish a novel numerical quantification method based upon mathematical programming for the ruin-related quantities on collective risk models with fairly general features, such as reflecting, non-homogeneous and ...
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We establish a novel numerical quantification method based upon mathematical programming for the ruin-related quantities on collective risk models with fairly general features, such as reflecting, non-homogeneous and multidimensional dynamics with non-smooth coefficients. Rather than seeking approximations with a great deal of computing effort as in the existing numerical approximation methods, the proposed method aims to instantly provide deterministic upper and lower bounds for such quantities as linear combinations of moments or in the explicit polynomial form, without random number generation or sophisticated numerical algorithms. Given that closed-form solutions are rarely available especially in finite-time problems, the upper and lower bounds have great potential to be useful information that can complement approximate solutions obtained from existing numerical methods. We present numerical results throughout to justify the theoretical developments and convergence results so as to illustrate the effectiveness of the proposed method with low computational complexity. We also demonstrate further improvements of the bounds by scaling the problem domain, employing piecewise polynomial test functions and the exponential tempering of the polynomial bases. (C) 2022 Elsevier B.V. All rights reserved.
At the time of commonplace automation, robotization and the rapid development of IT, high qualifications of employees have become the critical element of every industry system. This follows from their limited availabi...
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At the time of commonplace automation, robotization and the rapid development of IT, high qualifications of employees have become the critical element of every industry system. This follows from their limited availability, frequently high costs of procurement and possible employee absenteeism. Moreover, the concept of Industry 4.0 will transform current industry employees into knowledge employees. This is due to the fact that hard and routine tasks will be executed by robots and computers. This constitutes change in the required employee competences. Unfortunately, the aspect of management and configuration of employee competences is often overlooked in industrial practice. In response to the existing problem, the article puts forward the original model of employee competence configuration which is a basis for responses to numerous questions of managers of production processes, both general ones, e.g., Do we have a sufficient set of competences to execute a production schedule? as well as detailed ones, e.g., Which and how many competences are missing? etc. An important novelty of the presented model is the possibility of its application with both proactive and reactive questions. Due to the discrete and combinatorial nature of the problem under consideration, the use of mathematical programming methods was limited only to small data instances. Therefore, a proprietary dedicated genetic algorithm was proposed to solve this problem, which turned out to be extremely effective. The use of this genetic algorithm has enabled finding a solution depending on the instance data up to 70 times faster than by use of the mathematical programming.
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