Loading...

The Multi-Depot Traveling Purchaser Problem with Shared Resources

Hasanpour Jesri, Zahra Sadat | 2022

241 Viewed
  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 55515 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Eshghi, Kourosh; Rafiee, Majid
  7. Abstract:
  8. The Multi-Depot Traveling Purchaser Problem under Shared Resources (MDTPPSR) is a new variant of the Traveling Purchaser Problem (TPP). In this problem, each depot can purchase its products using the shared resources of other depots, and vehicles do not have to return to their starting depots. The routing of this problem is a Multi-Trip, Open Vehicle Routing Problem. A tailored integer programming model is formulated to minimize the total purchasers’ costs. Considering the complexity of the model, we have presented a decomposition-based algorithm that breaks down the problem into two phases. In the first phase, tactical decisions regarding supplier selection and the type of collaboration are made. In the second phase, the sequence of visiting is determined. To amend the decisions made in these phases, two heuristic algorithms based on the removing and insertion of operators are also proposed. The experimental results show that in some in some instances, not only purchasing under shared resources can reduce the total cost by up to 29.11%, but it also decreases the number of dispatched vehicles in most instances. In this dissertation, another proposed approach for reducing purchasing cost has focused on group purchasing with bundles of items. Generally, in product bundle selling, an individual buyer may be reluctant to buy a bundle and prefer to buy a portion of it. By group buying, buyers purchase their products with lower price. We proposed a group purchasing structure for bundles of items in which the buyers’ purchasing power is a function of time and bundles’ expiration date. A platform is also considered that charges sellers after selling their bundles. Three strategies are proposed in terms of three problems. In problem (I), a group of buyers wants to buy those products whose bundle price they can pay for in full and maximize their total savings. In problem (II), which is an extension of problem (I), the products’ importance is also considered. In problem (III), cohorts of buyers try to buy all their products, and the platform could pay the remaining price of the unsuccessful bundles. Here, the remaining products are in the platform’s ownership. Based on the results, while the first strategy accounts for most buyers’ savings, the other two strategies allow more purchases. The total number of purchased products in problem (I) is 18.34% and 25.27% less than in problems (II) and (III) respectively. Based on the results, if the platform has helped in buying bundles, in more than 95% of cases, all products are purchased. Meanwhile, the highest platform income is for problem (III) due to the possibility of selling additional products.

  9. Keywords:
  10. Shared Resources Management ; Parsing Algorithms ; Traveling Purchaser Problem ; Mixed Vehicle Routing Problem ; Group Purchasing ; Dynamic Multi-Depot Vehicle Routing Problems (MDVRP) ; Items Bundles ; Open Vehicle Routing Problem ; Close Vehicle Routing Problem ; Multi-Depots Location

 Digital Object List

 Bookmark

No TOC