Modelling　is　an　effective　way　of　designing,　understanding,　and　analysing　bio-refinery　supply　chain　systems.　The　supply　chain　is　a　complex　process　consisting　of　many　systems　interacting　with　each　other.　It　requires　the　modelling　of　the　processes　in　the　presence　of　multiple　autonomous　entities　(i.e.　biomass　producers,　bio-processors　and　transporters),　multiple　performance　measures　and　multiple　objectives,　both　local　and　global,　which　together　constitute　very　complex　interaction　effects.　In　this　paper,　simulation　models　for　recovering　biomass　from　the　field　of　the　biorefinery　are　developed　and　validated　using　some　industry　data　and　the　minimum　biomass　recovery　cost　is　established　based　on　different　strategies　employed　for　recovering　biomass.　Energy　densification　techniques　are　evaluated　for　their　net　present　worth　and　the　technologies　that　offer　greater　returns　for　the　industry　are　recommended.　In　addition,　a　new　scheduling　algorithm　is　also　developed　to　enhance　the　process　flow　of　the　management　of　resources　and　the　flow　of　biomass.　The　primary　objective　is　to　investigate　different　strategies　to　reach　the　lowest　cost　delivery　of　sugarcane　harvest　residue　to　a　sugar　factory　through　optimally　located　bio-refineries.　A　simulation　/optimisation　solution　approach　is　also　developed　to　tackle　the　stochastic　variables　in　the　bioenergy　production　system　based　on　different　statistical　distributions　such　as　Weibull　and　Pearson　distributions.　In　this　approach,　a　genetic　algorithm　is　integrated　with　simulation　to　improve　the　initial　solution　and　search　the　near-optimal　solution.　A　case　study　is　conducted　to　illustrate　the　results　and　to　validate　the　applicability　for　the　real　world　implementation　using　ExtendSIM　Simulation　software　using　some　real　data　from　Australian　Mills.　(C)　2019　by　the　authors;　licensee　Growing　Science,　Canada