Selective multi-depot vehicle routing problem with pricing


ARAS M. N., Aksen D., Tuǧrul Tekin M.

Transportation Research Part C: Emerging Technologies, cilt.19, sa.5, ss.866-884, 2011 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 5
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.trc.2010.08.003
  • Dergi Adı: Transportation Research Part C: Emerging Technologies
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.866-884
  • Anahtar Kelimeler: Collection, Pricing, Reverse logistics, Selective multi-depot vehicle routing, Tabu Search
  • Boğaziçi Üniversitesi Adresli: Evet

Özet

Firms in the durable goods industry occasionally launch trade-in or buyback campaigns to induce replacement purchases by customers. As a result of this, used products (cores) quickly accumulate at the dealers during the campaign periods. We study the reverse logistics problem of such a firm that aims to collect cores from its dealers. Having already established a number of collection centers where inspection of the cores can be performed, the firm's objective is to optimize the routes of a homogeneous fleet of capacitated vehicles each of which will depart from a collection center, visit a number of dealers to pick up cores, and return to the same center. We assume that dealers do not give their cores back free of charge, but they have a reservation price. Therefore, the cores accumulating at a dealer can only be taken back if the acquisition price announced by the firm exceeds the dealer's reservation price. However, the firm is not obliged to visit all dealers; vehicles are dispatched to a dealer only if it is profitable to do so. The problem we focus on becomes an extension of the classical multi-depot vehicle routing problem (MDVRP) in which each visit to a dealer is associated with a gross profit and an acquisition price to be paid to take the cores back. We formulate two mixed-integer linear programming (MILP) models for this problem which we refer to as the selective MDVRP with pricing. Since the problem is NP-hard, we propose a Tabu Search based heuristic method to solve medium and large-sized instances. The performance of the heuristic is quite promising in comparison with solving the MILP models by a state-of-the-art commercial solver. © 2010 Elsevier Ltd.