Operating　Room　Scheduling　(ORS)　is　a　prominent　topic　in　operations　research,　driven　by　its　practical　significance　and　scientific　intricacies,　and　has　garnered　extensive　attention　in　the　literature.　However,　the　critical　role　of　anesthesiologists,　an　integral　component　of　the　ORS　process,　is　frequently　overlooked　in　existing　studies.　This　study　aims　to　address　ORS　from　the　dual　perspective　of　balancing　the　satisfaction　of　both　anesthesiologists　and　patients,　quantified　through　anesthesiologists’　overtime　and　patients’　waiting　time,　respectively.　We　formulate　a　bi-objective　β-robust　optimization　model　that　captures　uncertainty　in　surgical　durations　and　introduces　significant　nonlinear　complexity.　To　solve　this,　we　propose　an　Approximate　Bi-objective　Approach　(ABA)　based　on　the　ϵ-constraint　approach.　Patient　satisfaction　is　precisely　optimized　using　an　established　branch-and-price　algorithm,　while　an　Approximate　Branch-and-Bound　(AB&B)　algorithm　is　introduced　to　improve　anesthesiologist　satisfaction.　This　involves　designing　an　approximate　upper　bounding　scheme　and　implementing　pruning　strategies　based　on　problem-specific　properties　to　reduce　the　search　space.　Additionally,　we　incorporate　a　path　relinking　algorithm　to　generate　high-quality　initial　solutions,　effectively　accelerating　the　solution　process.　We　conduct　a　sensitivity　analysis　to　evaluate　ABA＇s　robustness　and　compare　its　solution　front　with　that　of　the　Non-dominated　Sorting　Genetic　Algorithm　II　(NSGA-II)　algorithm.　Experimental　results　demonstrate　that　the　ABA　outperforms　NSGA-II　in　comprehensive　performance.　©　2025　Elsevier　Ltd