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In this paper, we propose an enhanced branch-andbound (B&B) framework for a class of sequencing problems, which aim to find a permutation of all involved elements to minimize a given objective function. We require that the sequencing problems satisfy three conditions: 1) incrementally computable; 2) monotonic; and 3) overlapping subproblems. Our enhanced B&B framework is built on the classical B&B process by introducing two techniques, i.e., dominance rules and caching search states. Following the enhanced B&B framework, we conduct empirical studies on three typical and challenging sequencing problems, i.e., quadratic traveling salesman problem, traveling repairman problem, and talent scheduling problem. The computational results demonstrate the effectiveness of our enhanced B&B framework when compared to classical B&B and some exact approaches, such as dynamic programming and constraint programming. Additional experiments are carried out to analyze different configurations of the algorithm. Index Terms-Branch-and-bound (B&B), caching states, dominance rules, dynamic programming (DP), talent scheduling problem, traveling salesman/repairman problem. I. INTRODUCTION T HE SEARCH techniques for solving combinatorial optimization problems (COPs) have been widely studied in the communities of artificial intelligence, industrial engineering, and operations research during the last century. The simplest and standard framework for solving COPs is to use branch-and-bound (B&B) algorithms [3], [8], [14]. Its basic idea is to systematically and implicitly enumerate all possible candidate solutions, where large subsets of fruitless candidates Manuscript