Optimization of Integrated Reverse Logistics Networks with Different Product Recovery Routes

Ali Niknejad, Dobrila Petrovic

    Research output: Contribution to journalArticlepeer-review

    111 Citations (Scopus)
    118 Downloads (Pure)


    The awareness of importance of product recovery has grown swiftly in the past few decades. This paper focuses on a problem of inventory control and production planning optimisation of a generic type of an integrated Reverse Logistics (RL) network which consists of a traditional forward production route, two alternative recovery routes, including repair and remanufacturing and a disposal route. It is assumed that demand and return quantities are uncertain. A quality level is assigned to each of the returned products. Due to uncertainty in the return quantity, quantity of returned products of a certain quality level is uncertain too. The uncertainties are modelled using fuzzy trapezoidal numbers. Quality thresholds are used to segregate the returned products into repair, remanufacturing or disposal routes. A two phase fuzzy mixed integer optimisation algorithm is developed to provide a solution to the inventory control and production planning problem. In Phase 1, uncertainties in quantity of product returns and quality of returns are considered to calculate the quantities to be sent to different recovery routes. These outputs are inputs into Phase 2 which generates decisions on component procurement, production, repair and disassembly. Finally, numerical experiments and sensitivity analysis are carried out to better understand the effects of quality of returns and RL network parameters on the network performance. These parameters include quantity of returned products, unit repair costs, unit production cost, setup costs and unit disposal cost
    Original languageEnglish
    Pages (from-to)143-154
    JournalEuropean Journal of Operational Research
    Issue number1
    Early online date1 Apr 2014
    Publication statusPublished - 1 Oct 2014


    • Supply chain management
    • Reverse logistics
    • Quality of returned products
    • Uncertainty modelling
    • Inventory control
    • Fuzzy optimisation


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