Featured　with　zero-carbon,　jam-free,　and　high-capacity,　the　utilization　of　metro　systems　for　collaborative　passenger-and-freight　transport　(i.e.,　the　metro-based　underground　logistics　system,　M-ULS)　has　been　recognized　as　a　favorable　alternative　to　realize　automated　goods　movement　in　future　megacities.　This　paper　proposes　an　equilibrium　chance-constrained　programming　(ECP)　approach　for　the　M-ULS　network　planning　problem　(MULNP)　considering　uncertain　demand　and　costs.　Based　on　the　ongoing　satellite　city　development　project,　the　layout　formation,　facility　components,　and　transportation　workflows　of　a　two-tier　M-ULS　network　are　designed.　The　joint　decisions　(i.e.,　location,　allocation,　capacity,　inventory,　spoke　pipeline　layout,　and　flow　routing)　are　formulated　as　an　ECP　model　with　conjoint　objectives　of　minimal　fixed　cost,　operating　cost,　and　penalty　cost　due　to　low　operating　load,　wherein　the　chance-based　constraints　are　further　reformulated　into　crisp　equivalent　form.　To　yield　high-quality　solutions　for　MULNP　problem,　an　improved　multi-objective　cooperative　co-evolutionary　algorithm　(MoCC)　incorporating　non-dominated　sorting　and　chromosomal　recombination　strategy　is　proposed　and　then　validated　via　two　groups　of　instances.　Results　from　the　Beijing　case　study　suggest　that　the　proposed　method　has　computational　advantages　over　MoPSO　and　NSGA-II　in　acquiring　Pareto　fronts　of　large-scale　MULNP.　The　best　configurations　and　layout　schemes　of　the　M-ULS　networks　in　both　uncertain　and　determin-istic　scenarios　are　analyzed.　Based　on　the　findings,　several　insights　for　real-world　M-ULS　network　decision　-making　are　provided.