Solving Delivery Allocation for Logistic Network Using Quantum Annealing

Quantum Annealer plays a significant role in solving the combinatorial optimization problem. The aim of this paper is to find out suitable allocation of delivery agents to different orders in a logistic network while maintaining constraints about allocation and delivery. From the result, we find that reverse annealing yields better ground state energy solution compared to forward annealing and simulated annealing. It is true that with increase in problem size ground state energy decreases. The comparison of ground state energy for simulated annealing, forward annealing and reverse annealing with a number of slots is studied. Reverse annealing yields lesser energy compared to forward annealing for all values of variables. Variation of chain break probability percentage with different Lagrange values is also explored. It is found that for Lagrange value greater or equal to 3, chain break probability percentage is zero and thus a feasible schedule for delivery agents is obtained from the quantum annealing process.