Approximation Algorithms for the Load Balanced Capacitated Vehicle Routing Problem
Haniyeh Fallah, Farzad Didehvar, Farhad Rahmati

TL;DR
This paper introduces approximation algorithms for the load balanced capacitated vehicle routing problem, addressing the challenge of balancing loads and minimizing costs in NP-hard scenarios with equal or unequal demands.
Contribution
It presents new approximation algorithms with proven ratios for balanced load distribution in the capacitated vehicle routing problem, including special cases and a multi-objective approach.
Findings
Achieves a (1-1/Q)p+3/2 approximation for equal demands.
Provides a 2-approximation algorithm for balanced loads.
Offers a 4-approximation algorithm for unequal demands.
Abstract
We study the load balanced capacitated vehicle routing problem (LBCVRP): the problem is to design a collection of tours for a fixed fleet of vehicles with capacity Q to distribute a supply from a single depot between a number of predefined clients, in a way that the total traveling cost is a minimum, and the vehicle loads are balanced. The unbalanced loads cause the decrease of distribution quality especially in business environments and exibility in the logistics activities. The problem being NP-hard, we propose two approximation algorithms. When the demands are equal, we present a (1-1/Q)p+3/2approximation algorithm that finds balanced loads. Here, p is the approximation ratio for the known metric traveling salesman problem (TSP). This result leads to a 2.5-1/Q approximation ratio for the tree metrics since an optimal solution can be found for the TSP on a tree. We present an improved…
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