Recently, there has been a growing interest in large-scale multi-objective optimization problems within the evolutionary multiobjective optimization (EMO) community. These problems involve hundreds or thousands of dec...
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ISBN:
(纸本)9798400704949
Recently, there has been a growing interest in large-scale multi-objective optimization problems within the evolutionary multiobjective optimization (EMO) community. These problems involve hundreds or thousands of decision variables and multiple conflicting objectives, which pose significant challenges for conventional EMO algorithms (EMOAs). It is generally believed that EMOAs have difficulty in efficiently finding good non-dominated solutions as the number of decision variables increases. To address this issue, in this paper, we propose a novel method that incorporates heuristic initialization and knowledge-based mutation into EMOAs for solving large-scale multi-objective 0-1 knapsack problems. Various large-scale multi-objective 0-1 knapsack problems with an arbitrary number of constraints are generated as test problems to evaluate the effectiveness of the proposed method. Experimental results show that the proposed novel initialization and mutation method significantly improves the performance of the original EMOAs in terms of both the convergence speed in early generations and the quality of the final population.
This study investigates the unrelated parallel machine scheduling problem with release dates to minimise the makespan. The solution to this problem finds wide applications in manufacturing and logistics systems. Due t...
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This study investigates the unrelated parallel machine scheduling problem with release dates to minimise the makespan. The solution to this problem finds wide applications in manufacturing and logistics systems. Due to the strong NP-hardness of the problem, most researchers develop heuristics, and the largest instances they consider are limited to 400 jobs. To tackle this problem, we develop a novel mixed-integer linear program (MILP) with significantly fewer integer variables than the state-of-the-art ones. The proposed MILP does not rely on a binary sequence variable usually used in the existing models. To deal with large-sized instances, a new three-stage matheuristic algorithm (TSMA) is proposed to obtain scheduling decisions. It uses a dispatching rule to sequentially schedule jobs on machines. Then a reassignment procedure is performed to reduce the makespan. Finally, it employs a re-optimisation procedure based on the proposed MILP to perform job moves and exchanges between two selected machines. We conduct numerical experiments on 1440 instances with up to 3000 jobs and 20 machines. Our results first clearly indicate that the proposed model significantly outperforms existing ones. Moreover, the results on large-sized instances show that the proposed TSMA can obtain high-quality near-optimal solutions in a short computation time.
Associated with a strategic optimization of logistics network design to improve supply chain efficiency, we proposed a method termed hybrid tabu search, and applied it to various real-life problems through imposing ad...
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Associated with a strategic optimization of logistics network design to improve supply chain efficiency, we proposed a method termed hybrid tabu search, and applied it to various real-life problems through imposing additionally practical conditions on the basic model according to situations. Since multi-commodity delivery is popular in practice, we can make a more reliable and operational decision by simultaneously taking into accounts dynamic circumstances. In this study, therefore, we have extended the previous method to cope with this aspect that has scarcely considered so far, i.e., dynamic integrated capacitated multi-commodity. Numerical experiments revealed the role of inventory management over planning horizon and the validity of the proposed method through comparison with the results from commercial software.
According to the demand of integrated production and the characteristic of charge plan in the steel industry, firstly, use graph theory to describe charge plan;Secondly, a bi-objective charge plan model is introduced ...
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ISBN:
(纸本)9781424421138
According to the demand of integrated production and the characteristic of charge plan in the steel industry, firstly, use graph theory to describe charge plan;Secondly, a bi-objective charge plan model is introduced in detail considering weight range, flow limitation and other factors;To solve this model, a two-phase heuristic algorithm(THA) is introduced, which deals with the simple graph with vertex clustering method firstly, and then design a novel probability match algorithm to solve the optimization model;Finally, the simulation with large-scale production data shows that the model and algorithm is feasible.
According to the demand of integrated production and the characteristic of charge plan in the steel industry, firstly, use graph theory to describe charge plan;Secondly, a bi-objective charge plan model is introduced ...
详细信息
According to the demand of integrated production and the characteristic of charge plan in the steel industry, firstly, use graph theory to describe charge plan;Secondly, a bi-objective charge plan model is introduced in detail considering weight range, flow limitation and other factors;To solve this model, a two-phase heuristic algorithm (THA) is introduced, which deals with the simple graph with vertex clustering method firstly, and then design a novel probability match algorithm to solve the optimization model;Finally, the simulation with large-scale production data shows that the model and algorithm is feasible.
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