The make-to-order (MTO) manufacturers generally make production plans based on orders, which can help enterprises effectively avoid market risks, reduce market pressure and improve competitiveness. However, due to the...
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ISBN:
(数字)9781728169293
ISBN:
(纸本)9781728169309
The make-to-order (MTO) manufacturers generally make production plans based on orders, which can help enterprises effectively avoid market risks, reduce market pressure and improve competitiveness. However, due to the characteristics of MTO production mode, the order static scheduling problem and rush order dynamic rescheduling problem have become more and more important for these MTO manufacturers. Therefore, in this paper, we take the packaging production line of a typical carbon black production enterprise as the research background to study the carbon black production line static and dynamic multiobjective scheduling problem. Firstly, multiobjective optimization models of both order static scheduling and rush order dynamic rescheduling are established. Then the improved MOEA/D algorithm combined the heuristic algorithm based on heuristic rules and discrete dynamic local search is developed to solve these two models. Based on the actual production data, eight instances of order static scheduling problems of different scales and four instances of rush order dynamic rescheduling problems of different scales are constructed respectively. Experimental results illustrate that the improved MOEA/D is effective and superior in solving these two problems.
n jobs, each job i with a minimum processing time p i and a weight w i , have to be processed on a batching machine with capacity B. Minimization of total weighted completion time for single batching machine is a cla...
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n jobs, each job i with a minimum processing time p i and a weight w i , have to be processed on a batching machine with capacity B. Minimization of total weighted completion time for single batching machine is a classical scheduling problem. Dijkstra's algorithm, the method to find the shortest path in a graph, is used to partition the jobs into batches. And it can get the optimal batches for a certain fixed sequence of the jobs for the batching machine. The batches also can be improved by the cyclic transfer algorithm that is the first time to be used to a batching machine. We give a comparison of the algorithms to the MRLC algorithm in the last part to prove the quality of the cyclic transfer algorithm. And the improvement of the cyclic transfer is over 1 percent.
The increasing installation rate of wind power poses great challenges to the global power system. In order to ensure the reliable operation of the power system, it is necessary to accurately forecast the wind speed an...
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This paper proposes a multiobjective multitasking optimization evolutionary algorithm based on decomposition with dual neighborhood. In our proposed algorithm, each subproblem not only maintains a neighborhood based o...
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In medical image analysis, the demand for interpretable deep neural networks is rapidly growing. However, a major challenge is that most existing interpretative methods are applied after training, leading to a lack of...
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Steel slabs are key in-process products in the steel production process. The slab allocation problem is to allocate slabs to suitable orders by considering complicated technical restrictions and multiple management re...
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Steel slabs are key in-process products in the steel production process. The slab allocation problem is to allocate slabs to suitable orders by considering complicated technical restrictions and multiple management requirements. In practice, thousands of slabs in multiple production lines should be allocated to orders. Due to the complexity and large scale of the problem, it takes planners a long time to make decisions, and the obtained allocation schemes are usually ineffective because of the myopic nature of the rule-based methods. In this article, we present a learning-based solution method for solving a practical slab allocation problem in multiple hot rolling lines. We formulate the problem as an integer programming model, then a data-based method is adopted to evaluate the mismatching cost between slabs and orders. To effectively solve the large-sized problem, a learning-based decomposition strategy is proposed to decompose the original problem into several small-sized subproblems. Then, a branch-and-price algorithm is proposed to optimally solve the subproblems. To further speed up the solution process, a primal heuristics based on column generation is designed to solve the large-sized subproblems. The solution methods have been implemented in a steel company and effectively increased slab utilization and reduced production cost.
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