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2124. Joint Replenishment and Heterogeneous Vehicle Routing Problem with Cyclical Schedule
Invited abstract in session MA-49: Lot-sizing with joint replenishment and routing decisions, stream Lot Sizing, Lot Scheduling and Production Planning.
Monday, 8:30-10:00Room: M1 (building: 101)
Authors (first author is the speaker)
| 1. | Ming-Jong Yao
|
| Transportation and Logistics Management, National Yang Ming Chiao Tung University | |
| 2. | Chin Sum Shui
|
| Transportation and Logistics Management, National Yang Ming Chiao Tung University | |
| 3. | Chih-Han Wang
|
| Transportation and Logistics Management, National Yang Ming Chiao Tung University |
Abstract
This study is based on a decision scenario of an industrial gas company that applies the Vendor Managed Inventory (VMI) model and supplies customers with liquid oxygen using a self-operated heterogeneous vehicle fleet. We name it a Joint Replenishment and Heterogeneous Vehicle Routing Problem with Cyclical Schedule. To solve this problem, we formulated it as a non-linear mixed-integer programming model that simultaneously determines the length of the planning cycle (PC), the length of the replenishment cycle, the dates to replenish each customer, and the vehicle routes of each day within PC, such that the average total cost within PC is minimized where the total cost includes inventory holding cost, setup cost, transportation cost, and overtime labor cost. We proposed a genetic algorithm (GA), which is embedded with an innovative encoding and decoding mechanism, local search operators, and a hash function to avoid repetitive fitness evaluation for identical solutions. Our numerical experiments demonstrate that the proposed GA can effectively solve the problem under different lengths of PC and number of customers. Also, when the demand of a customer increases, it can be applied to determine whether the company should expand the capacity of storage installed at the customer. Sensitivity analysis of the vehicle fleet composition showed that deploying a mixed fleet can reduce the average total cost.
Keywords
- Logistics
- Vehicle Routing
- Metaheuristics
Status: accepted
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