Multiple Couriers Planning

It has become very common nowadays, especially after the outbreak of the COVID-19 pandemic, to send or receive items online (food, goods, clothes, documents, . . . ). Hence, it is more and more important for companies to efficiently schedule the dispatching of items through different couriers, in order to minimize the transportation costs.
As the combinatorial decision and optimization expert, the student is assigned to solve the Multiple Couriers Planning (MCP) problem which is defined as follows. We have m couriers that must distribute n ≥ m items at different customer locations. Each courier i has a maximum load size l_i. Each item j has a distribution point d_j and a size s_j (which can represent for instance a weight or a volume). The goal of MCP is to decide for each courier the items to be distributed and plan a tour (i.e. a sequence of location points to visit) to perform the necessary distribution tasks. Each courier tour must start and end at a given origin point o. Moreover, the maximum load li of the courier i should be respected when items are assigned to it. The objective is to minimize the total tour distance.

Usage

Or-Tools

First of all check that inside instGen.py we have MAX_ITEM_NUM = 40

  rm -r tsp_inst
  python instGen.py
  python or_tools_cp.py [1:11 number]

Gurobi

First of all check that inside instGen.py we have MAX_ITEM_NUM = 40

  rm -r tsp_inst
  python instGen.py
  python lp_gurobi.py [1:11 number]

SMT

First of all check that inside instGen.py we have MAX_ITEM_NUM = 15

  rm -r tsp_inst
  python instGen.py
  python SMT_Z3.py [1:11 number]