The reference books should cover the wide range of problem-solving techniques and methods.
For books with a focus on industrial applications, see this other question of this forum
As textbooks, I would recommend to have a look at:
H.P. Williams. Model building in mathematical programming. John Wiley & Sons, 2013.
D. Chen, R.G. Batson, Y. Dang. Applied Integer Programming: Modeling and Solution. John Wiley & Sons, 2009.
MOSEK Modeling Cookbook How to formulate and reformulate conic optimization problems (convex QP, SOCP, SDP, Exponential Cone, Power Cone, and mixed integer). Requires some "mathematical maturity" to understand. This is very helpful for users of CVX, CVXPY, CVXR, YALMIP. Note, this is complementary to H.P. Williams "Model building in mathematical programming", because Williams doesn't cover any conic optimization material.
Graph Theory and Algorithms:
R. K. Ahuja, T. L. Magnanti, and J. B. Orlin. Network flows, 1988.
V. Chvátal. Linear Programming. New York: W.H. Freeman, 1983.
D. Bertsimas and J. N. Tsitsiklis. Introduction to Linear Optimization, Athena Scientific, 1997.
G.B. Dantzig. Linear Programming and Extensions.Reprinted in 1998 by Princeton Press.
G.B. Dantzig and M.N. Thapa Linear Programming 1: Introduction, Springer, 1997 and Linear Programming 2: Theory and Extension, Spinger, 2003. Linear Programming 2, especially, is hard-core. I think these books supersede and render G.B. Dantzig "Linear Programming and Extensions" to be of historical interest only.
D. Bertsimas and R. Weismantel. Optimization over Integers. Belmont, MA: Dynamic Ideas, 2005.
G. Desaulniers, J. Desrosiers, and M. M. Solomon. Column Generation. New York: Springer, 2005.
G. Nemhauser, and L. Wolsey. Integer and Combinatorial Optimization. Wiley, 1988.
L. Wolsey. Integer programming, John Wiley & Sons Canada, 1998
M. Conforti, G. Cornuéjols, G. Zambelli. Integer Programming, GTM 271, Springer, 2014.
S. Boyd and L. Vandenberghe Convex Optimization. Cambridge University Press, 2004 (freely downloadable at provided link). Also serves as good background for non-convex optimization.
A. Ben-Tal, A. Nemirovski Lectures on Modern Convex Optimization, 2013 (most recent version). Very advanced mathematically.
J. Tropp, An Introduction to Matrix Concentration Inequalities, now Foundations and Trends in Optimization, 2014. Goes beyond Ben-Tal and Nemirovski in such areas as operator convexity and matrix (quantum) relative entropy.
L. Vandenberghe and M. Andersen. Chordal Graphs and Semidefinite Optimization, now Foundations and Trends in Optimization, 2015. Advanced material in Semidefinite Optimization (Programming), i.e., SDP.
J. Nocedal, S. Wright. Numerical Optimization. Springer, 2006.
S. Boyd, Seung-Jean Kim, L. Vandenberghe, and A. Hassibi. A tutorial on geometric programming, Optimization and Engineering, 2007. A tutorial journal article covering geometric programming and generalizations and extensions, starting from basics and proceeding to more advanced material.
A. Schrijver. Combinatorial Optimization - Polyhedra and Efficiency. Springer, 2003
A.J. King, and S.W. Wallace. Modeling with Stochastic Programming. Springer, 2012.
J.R. Birch, and F. Louveaux. Introduction to stochastic programming. Springer Science & Business Media, 2011.
A. Shapiro, D.Dentcheva, and A. Ruszczyński. Lectures on Stochastic Programming: Modeling and Theory. SIAM, 2009.
A. Ben-Tal, L. El Ghaoui, and A. Nemirovski. Robust optimization. Princeton University Press, 2009.
P. Kouvelis, and G. Yu. Robust Discrete Optimization and Its Applications. Springer, 1997.
G. Peyré, M. Cuturi, Computational Optimal Transport, now Foundations and Trends in Machine Learning, 2019. Very advanced and theoretical. Shows how to formulate and calculate such things as Wasserstein distance as computational optimal transport problems. This is not an Intro to OR Transportation Problem book.