This paper concerns the Firefighter Problem (FFP) which is a graph-based problem in which solutions can be represented as permutations. A new crossover operator is proposed that uses a machine learning model to decide...
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ISBN:
(纸本)9783030034931;9783030034924
This paper concerns the Firefighter Problem (FFP) which is a graph-based problem in which solutions can be represented as permutations. A new crossover operator is proposed that uses a machine learning model to decide how to combine two parent solutions of the FFP into an offspring. The operator works on two parent permutations and the machine learning model provides information which parent to select the next permutation element from, when constructing a new solution. Training data is collected during a training run in which transpositions are applied to solutions found by an evolutionary algorithm for a small problem instance. The machine learning model is trained to classify pairs of graph vertices into two classes corresponding to which vertex should be placed earlier in the permutation. In the experiments the machine learning model was trained on a set of FFP instances with 1000 vertices. Subsequently, the proposed operator was used for solving FFP instances with up to 10000 vertices. The experiments, in which the proposed operator was compared against a set of other crossover operators, shown that the proposed operator is able to effectively use knowledge gathered when solving smaller instances for solving larger instances of the same problem.
This paper concerns the Inventory Routing Problem (IRP) which is an optimization problem addressing the optimization of transportation routes and the inventory levels at the same time. The IRP is notable for its diffi...
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ISBN:
(数字)9783319774497
ISBN:
(纸本)9783319774497;9783319774480
This paper concerns the Inventory Routing Problem (IRP) which is an optimization problem addressing the optimization of transportation routes and the inventory levels at the same time. The IRP is notable for its difficulty - even finding feasible initial solutions poses a significant problem. In this paper an evolutionary algorithm is proposed that uses approaches to solution construction and modification utilized by practitioners in the field. The population for the EA is initialized starting from a base solution which in this paper is generated by a heuristic, but can as well be a solution provided by a domain expert. Subsequently, feasibility-preserving moves are used to generate the initial population. In the paper dedicated recombination and mutation operators are proposed which aim at generating new solutions without loosing feasibility. In order to reduce the search space, solutions in the presented EA are encoded as lists of routes with the quantities to be delivered determined by a supplying policy. The presented work is a step towards utilizing domain knowledge in evolutionary computation. The EA presented in this paper employs mechanisms of solution initialization capable of generating a set of feasible initial solutions of the IRP in a reasonable time. Presented operators generate new feasible solutions effectively without requiring a repair mechanism.
This paper concerns a problem of minimizing systemic risk in a system composed of interconnected entities such as companies on the market. Systemic risk arises, when, because of an initial failure of a limited number ...
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ISBN:
(数字)9783030034931
ISBN:
(纸本)9783030034931;9783030034924
This paper concerns a problem of minimizing systemic risk in a system composed of interconnected entities such as companies on the market. Systemic risk arises, when, because of an initial failure of a limited number of elements, a significant part of the system fails. The system is modelled as a graph, with some nodes in the graph initially failing. The spreading of failures can be stopped by protecting nodes in the graph, which in case of companies can be achieved by setting aside reserve funds. The goal of the optimization problem is to reduce the number of nodes that eventually fail due to connections in the system. This paper studies the possibility of utilizing external knowledge for solution construction in this problem. Rules representing reusable information are extracted from solutions of problem instances and are used when solving new instances. Experiments presented in the paper show that using rule-basedknowledge representation for constructing initial population allows the evolutionary algorithm to attain better results during the optimization run.
Despite recent falls in oil and gas prices, the LNG industry remains vibrant with several plants ready for deployment in 2017/2018, and others awaiting final investment decisions. With expected growth in LNG consumpti...
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Despite recent falls in oil and gas prices, the LNG industry remains vibrant with several plants ready for deployment in 2017/2018, and others awaiting final investment decisions. With expected growth in LNG consumption, the higher activity in LNG industry is likely to continue. Considering ongoing activity levels in the LNG market, a retrospective review of natural gas liquefaction technologies and optimization methodologies presented together with implications for their future directions. Variants of mixed refrigerant-based technologies continue to dominate the onshore LNG plant market. For the offshore LNG market, non-flammable-refrigerant-based expander technologies being pursued, due to their safety and space requirements. Currently, deterministic and stochastic approaches dominate the optimization of LNG process plants with the preferred objective of compression energy minimization. However, when a holistic approach considering economical, technical, process safety/reliability is applied, multiple decision criteria are very frequently occurring, giving stochastic approach an upper hand. Increased activity in the use of process knowledge in guiding the stochastic approach for meaningful multi-objective solutions in NG liquefaction is foreseeable. (C) 2017 Elsevier B.V. All rights reserved.
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