An　intelligent　logistics　system　is　an　important　branch　of　intelligent　transportation　systems.　It　is　a　great　challenge　to　develop　efficient　technologies　and　methodologies　to　improve　its　performance　in　meeting　customer　requirements　while　this　is　highly　related　to　people＇s　life　quality.　Its　high　efficiency　can　reduce　food　waste,　improve　food　quality　and　safety,　and　enhance　the　competitiveness　of　food　companies.　In　this　paper,　we　investigate　a　new　integrated　planning　problem　for　intelligent　food　logistics　systems.　Two　objectives　are　considered:　minimizing　total　production,　inventory,　and　transportation　cost　and　maximizing　average　food　quality.　For　the　problem,　a　bi-objective　mixed　integer　linear　programming　model　is　formulated　first.　Then,　a　new　method　that　combines　an　epsilon-constraint-based　two-phase　iterative　heuristic　and　a　fuzzy　logic　method　is　developed　to　solve　it.　Computational　results　on　a　case　study　and　on　185　randomly　generated　instances　with　up　to　100　retailers　and　12　periods　show　the　effectiveness　and　efficiency　of　the　proposed　method.