In　current　interval-valued　linear　regression　models,　meaningless　predictions　may　be　generated　because　the　lower　bounds　of　the　predicted　intervals　may　be　greater　than　their　upper　bounds.　To　avoid　this　problem,　we　propose　a　constrained　interval-valued　linear　regression　model　based　on　random　set　theory.　However,　due　to　the　introduction　of　constraints　in　this　model,　the　expectation　of　the　errors　is　no　longer　zero,　and　estimation　provided　by　traditional　least　square　may　produce　systematic　bias.　To　address　this　issue,　we　introduce　a　two-step　procedure:　in　the　first　step,　a　dummy　variable　is　defined　and　plugged　into　the　regression　model　to　ensure　that　the　expectation　of　errors　is　zero;　least　square　estimation　is　then　used　in　the　second　step.　To　show　the　validity　of　proposed　method,　experiments　on　simulated　and　real　data　are　presented.　©　2017　International　Society　of　Information　Fusion　(ISIF).