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首页> 外文期刊>Journal of Computers >A new Approach based on Ant Colony Optimization (ACO) to Determine the Supply Chain (SC) Design for a Product Mix
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A new Approach based on Ant Colony Optimization (ACO) to Determine the Supply Chain (SC) Design for a Product Mix

机译：一种基于蚁群优化（ACO）的新方法，以确定产品组合的供应链（SC）设计

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—Manufacturing supply chain(SC) faces changing business environment and various customer demands. Pareto Ant Colony Optimisation (P-ACO) in order to obtain the non-dominated set of different SC designs was utilized as the guidance for designing manufacturing SC. PACO explores the solution space on the basis of applying the Ant Colony Optimisation algorithm and implementing more than one pheromone matrix, one for every objective. The SC design problem has been addressed by using Pareto Ant Colony Optimisation in which two objectives are minimised simultaneously. There were tested two ways in which the quantity of pheromones in the PM is incremented. In the SPM, the pheromone increment is a function of the two objectives, cost and time, while in MPM the pheromone matrix is divided into two pheromones, one for the cost and another one for the time. It could be concluded that the number of solutions do not depend on if the pheromone is split or is a function of the two variables because both method explore the same solution space. Although both methods explore the same solution space, the POS generated by every one is different. The POS that is generated when the pheromone matrix is split got solutions with lower time and cost than SMP because in the probabilistic decision rule a value of λ = 0.2 is used. It means that the ants preferred solution with a low cost instead of solutions with low time. The strategy of letting the best-so-far ant deposit pheromone over the PM accelerates the algorithm to get the optimal POS although the number of ants in the colony is small. An experimental example is used to test the algorithm and show the benefits of utilising two pheromone matrices and multiple ant colonies in SC optimisation problem.
机译：- 制造供应链（SC）面临不断变化的商业环境和各种客户需求。帕累托蚁群优化（P-ACO）为了获得非主导的不同SC设计，用作设计制造SC的指导。 Paco在应用蚁群优化算法的基础上探索解决方案空间，并为每个目标实现多于一个信息矩阵。通过使用帕累托蚁群优化来解决SC设计问题，其中两个目标同时最小化。测试了两种方式，其中PM中的信息素数量递增。在SPM中，信息素增量是两个目标，成本和时间的函数，而在MPM中，信息素矩阵被分为两个信息素，一个是成本的，而另一个是一个。可以得出结论，解决方案的数量不依赖于信息素是否分裂或是两个变量的函数，因为这两种方法都探讨了相同的解决方案空间。虽然这两种方法都探索了相同的解决方案空间，但每个方法都是不同的POS。当Pherodomone矩阵被分割时产生的POS获得了与SMP的较低时间和成本的解决方案，因为在概率决策规则中，使用λ= 0.2的值。这意味着蚂蚁优选的优选溶液，以低成本而不是低时间的溶液。在PM上放置最佳蚂蚁存款信息素的策略加速了算法来获得最佳POS，尽管殖民地中的蚂蚁数量很小。实验例用于测试算法，并显示在SC优化问题中利用两个信息素矩阵和多蚁群的益处。

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