This　paper　addresses　a　real　life　scheduling　problem　characterised　by　multi-resource　operations　whose　completion　simultaneously　requires　more　than　one　(renewable)　resource　of　different　types.　Such　problems　arise　in　various　companies　not　only　in　manufacturing　but　also　in　services.　Solving　such　a　problem　needs　to　address　two　interconnected　subproblems:　a　sequencing　subproblem　and　a　resource-allocation　subproblem　if　more　than　one　resource　is　available　in　some　types.　The　resource-allocation　subproblem　consists　of　allocating　to　each　operation　the　resources　it　requires　while　the　sequencing　subproblem　consists　of　determining　the　order　in　which　each　resource　performs　the　operations　assigned　to　it.　This　paper　focuses　on　the　resource-allocation　subproblem.　It　generalises　the　basic　scheduling　problems　which　consist　of　determining　the　operations＇　starting　or　completion　times　for　a　given　processing　sequence　for　every　resource.　We　consider　a　cost　function　taking　into　account　the　makespan,　the　cost　of　resource　utilisation,　and　the　load　imbalance　among　the　resources　of　the　same　type.　We　first　formulate　the　problem　into　a　mixed-integer　linear　program　(MILP).　To　efficiently　solve　it,　even　in　practical-sized　instances,　an　exact　algorithm　called　BD　(Benders　decomposition　with　enhancing　cuts)　is　developed　where　the　master　problem　only　considers　integer　variables.　We　prove　that　the　slave　problem　can　be　transformed　into　finding　the　longest　paths　in　a　digraph　and　therefore　can　be　solved　with　the　Bellman–Ford　algorithm.　To　enhance　the　efficiency　of　the　method,　equivalent　solutions　are　limited　in　the　master　problem.　The　performance　of　the　approach　is　evaluated　by　comparing　it　against　CPLEX,　a　state-of-the-art　commonplace　MILP　solver,　used　to　directly　solve　the　initial　MILP.　The　computational　results　demonstrate　that　BD　provides　competitive　solutions　in　all　upper　and　lower　bounds.　In　particular,　it　improves,　compared　with　CPLEX,　the　upper　and　lower　bounds　by　5.07%　and　4.63%,　respectively,　in　solving　practical-sized　instances.　The　experiment　also　shows　that　considering　load　balancing　can　make　more　rational　use　of　resources　and　avoid　adverse　effects　caused　by　excessive　workload　of　staff　and　imbalanced　use　of　equipment,　which　is　very　important　in　real-world　production.　©　2024　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.