In　this　paper,　a　comprehensive　production　planning　problem　under　uncertain　demand　is　investigated.　The　problem　intertwines　two　NP-hard　optimization　problems:　an　assembly　line　balancing　problem　and　a　capacitated　lot-sizing　problem.　The　problem　is　modelled　as　a　two-stage　stochastic　program　assuming　a　risk-averse　decision　maker.　Efficient　solution　procedures　are　proposed　for　tackling　the　problem.　A　case　study　related　to　mask　production　is　presented.　Several　insights　are　provided　stemming　from　the　COVID-19　pandemic.　Finally,　the　results　of　a　series　of　computational　tests　are　reported.　©　2021　Elsevier　B.V.