Various topics related to reverse logistics have been discussed
over the years. Most of them have assumed that facilities are kept
open once they are established, and no returned products or recovery
parts are stocked in intermediate recycling stations. However, firms
may have the right to repeatedly open or close their facilities
according to their economic benefits if they can acquire their
facilities by lease. It also turns out that intermediate recycling
stations like collection centers and disassembly centers usually
stock returned products or parts in their facilities. By
simultaneously relaxing these two assumptions, this study explores a
logistics system with multiple items, each of which consists of some
components among a variety of spare parts. The purpose is to
maximize the total logistics costs by establishing a production
schedule and reverse logistics framework over finite time periods
for a logistics system. The mathematical model established in this
study is a constrained linear integer programming problem. A genetic
based algorithm is developed with the help of linear programming to
find solutions to this problem. Limited computational experiments show that
the proposed approach can produce better feasible solutions than the well-known CPLEX 10.0 software.