Multi-atomic Annealing Heuristic for Static Dial-a-ride Problem

TL;DR

This paper introduces the multi-atomic annealing (MATA) algorithm, a novel meta-heuristic for efficiently solving the static dial-a-ride problem with improved solution quality and faster convergence.

Contribution

The paper presents a new meta-heuristic called MATA with innovative local search operators and request sequencing mechanisms for the dial-a-ride problem.

Findings

MATA finds a feasible solution 29.8-65.1% faster.

MATA achieves solutions 3.9-5.2% better within 60 seconds.

MATA outperforms existing methods in solution quality and speed.

Abstract

Dial-a-ride problem (DARP) deals with the transportation of users between pickup and drop-off locations associated with specified time windows. This paper proposes a novel algorithm called multi-atomic annealing (MATA) to solve static dial-a-ride problem. Two new local search operators (burn and reform), a new construction heuristic and two request sequencing mechanisms (Sorted List and Random List) are developed. Computational experiments conducted on various standard DARP test instances prove that MATA is an expeditious meta-heuristic in contrast to other existing methods. In all experiments, MATA demonstrates the capability to obtain high quality solutions, faster convergence, and quicker attainment of a first feasible solution. It is observed that MATA attains a first feasible solution 29.8 to 65.1% faster, and obtains a final solution that is 3.9 to 5.2% better, when compared to other algorithms within 60 sec.