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A BB&R algorithm for minimizing total tardiness on a single machine with sequence dependent setup times

机译:一种BB&R算法,可最大程度地减少一台机器上的总拖延时间,其顺序取决于设置时间

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摘要

This paper presents a Branch, Bound, and Remember (BB&R) exact algorithm using the Cyclic Best First Search (CBFS) exploration strategy for solving the 1 |ST_(sd) | ∑ T_i scheduling problem, a single machine scheduling problem with sequence dependent setup times where the objective is to find a schedule with minimum total tardiness. The BB&R algorithm incorporates memory-based dominance rules to reduce the solution search space. The algorithm creates schedules in the reverse direction for problems where fewer than half the jobs are expected to be tardy. In addition, a branch and bound algorithm is used to efficiently compute tighter lower bounds for the problem. This paper also presents a counterexample for a previously reported exact algorithm in Luo and Chu (Appl Math Comput 183(1):575-588, 2006) and Luo et al. (Int J Prod Res 44(17):3367-3378, 2006). Computational experiments demonstrate that the algorithm is two orders of magnitude faster than the fastest exact algorithm that has appeared in the literature. Computational experiments on two sets of benchmark problems demonstrate that the CBFS search exploration strategy can be used as an effective heuristic on problems that are too large to solve to optimality.
机译:本文提出了一种使用循环最佳优先搜索(CBFS)探索策略的分支,绑定和记忆(BB&R)精确算法,用于解决1 | ST_(sd)| ∑ T_i调度问题,这是一种与序列相关的设置的单机调度问题目标是找到总拖延最小的时间表。 BB&R算法结合了基于内存的优势规则,以减少解决方案的搜索空间。该算法会针对预期少于一半的工作迟到的问题按相反的方向创建计划。此外,分支定界算法用于有效地计算问题的更严格的下限。本文还在Luo和Chu(Appl Math Comput 183(1):575-588,2006)和Luo et al。 (Int J Prod Res 44(17):3367-3378,2006)。计算实验表明,该算法比文献中出现的最快的精确算法快两个数量级。对两组基准问题的计算实验表明,CBFS搜索探索策略可以用作对太大而无法求解最优问题的有效启发式方法。

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