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Polynomial-time algorithms to solve the single-item capacitated lot sizing problem with a 1-breakpoint all-units quantity discount

机译:多项式算法解决单件电容批量尺寸问题的1断点全单位数量折扣

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This study investigates the single-item capacitated lot sizing problem considering a 1-breakpoint all-units quantity discount. Assuming the fixed ordering cost and the undiscounted unit purchase price to be non-increasing over the periods, the lot sizing problem under study is a special case of that with piecewise concave production cost function investigated by Koca et al. (2014). Koca's DP algorithm solves our problem with a time complexity of O(T-7), where T denotes the number of periods. It, however, fails in the case of large-sized instances in acceptable runtimes due to its higher complexity. Hence, the present study is a response to the need for more efficient algorithms to overcome this shortcoming. First, some properties of the optimal solution are proved. Second, these properties are exploited in an implicit enumeration exact algorithm. Third, certain speed-up techniques are employed to reduce the time complexity of the proposed algorithm from O(T-5) to O(T-4). Finally, a heuristic algorithm is presented for the problem. The proposed algorithms are compared with Koca's DP algorithm and the commercial solver used to solve some test problems. Based on the runtimes observed, the exact algorithms proposed in this study are found to outperform both Koca's DP one and the commercial solver. Moreover, the heuristic algorithm developed herein is found to be faster than both the exact algorithms in finding optimal solutions to most problem instances.
机译:本研究调查了考虑1断点全部单位数量折扣的单项电容批量尺寸问题。假设固定的订购成本和未增加的单位购买价格在此期间不增加,研究中的批量问题是一个特殊的案例,具有科卡等人调查的分段凹版生产成本函数。 (2014)。康卡的DP算法解决了我们的问题,其中o(t-7)的时间复杂度,其中t表示周期的数量。然而,由于其较高的复杂性,它在可接受的运行中的大型实例的情况下失败。因此,本研究是对需要更有效的算法来克服这种缺点的响应。首先,证明了最佳解决方案的一些属性。其次,这些属性以隐式枚举精确算法利用。第三,采用某些加速技术来减少从O(T-5)到O(T-4)的所提出的算法的时间复杂度。最后,提出了一种启发式算法的问题。将所提出的算法与科卡的DP算法和用于解决一些测试问题的商业求解器进行比较。基于观察到的运行时间,发现本研究中提出的确切算法以优于科卡的DP One和商业求解器。此外,发现本文开发的启发式算法比为大多数问题实例查找最佳解决方案的精确算法速度快。

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