Abstract
XYZ is a distributor of various consumer goods products. The company plans its delivery routes daily and in order to obtain route construction in a short amount of time, it simplifies the process by assigning drivers based on geographic regions. This approach results in inefficient use of vehicles leading to imbalance workloads. In this paper, we propose a combined method involving heuristic and optimization to obtain better solutions in acceptable computation time. The heuristic is based on a time-oriented, nearest neighbor (TONN) to form clusters if the number of locations is higher than a certain value. The optimization part uses a mathematical modeling formulation based on vehicle routing problem that considers heterogeneous vehicles, time windows, and fixed costs (HVRPTWF) and is used to solve routing problem in clusters. A case study using data from one month of the company's operations is analyzed, and data from one day of operations are detailed in this paper. The analysis shows that the proposed method results in 24% cost savings on that month, but it can be as high as 54% in a day.
Highlights
Logistics operations are oriented toward the fulfillment of customer needs from the point of origin to the point of consumption
The optimization part uses a mathematical modeling formulation based on vehicle routing problem that considers heterogeneous vehicles, time windows, and fixed costs (HVRPTWF) and is used to solve routing problem in clusters
This paper demonstrates the application of heterogeneous VRP (HVRP) in a consumer-goods distribution company operating in Surabaya, Indonesia
Summary
Logistics operations are oriented toward the fulfillment of customer needs from the point of origin to the point of consumption. Being a popular routing model in logistics studies, a number of variants have been developed, tested, and applied. These variants follow the characteristics of the problem being studied, e.g. VRP with time windows (VRPTW) [1], VRP with pickups and deliveries [2], or more specific variants such as VRPTW with evolutionary algorithm [3], VRP with split deliveries [4], or VRP with multiple objectives [5]. VRP literature grew exponentially with an annual rate of 6.09% between 1956 and 2005, and the period between 1985 and 2006 recorded 918 published VRP articles [9].
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