## 4 Review of Literature
### Ridesharing Problem
#### 4.1.2 Heuristic and Metaheuristic Algorithms
The proposed algorithm is fully distributable among multiple companies with a privacy protection, while ensuring the assignment achieves optimality. With the proposed ridesharing system, enterprises with small market shares can produce high quality of service without monopolising the whole ridesharing service market. Meanwhile, ref. [39] proposed a comprehensive ridesharing model which mainly focuses on Quality of Service (QoS)-orientation and is cost-effective to optimise the user experience and the cost of the ridesharing service. Instead of the original Differential Evolution (DE) that is designed for continuous optimisation, Differential Evolution-based Ridesharing Algorithm (DERA), a set-based DE, is proposed to search the global optimum for the formulated problem. DERA is specifically designed with the incorporation of several new operators such as greedy initialisation, inter-vehicle mutation, route sensitive selection, and individual repairing/re-factoring operators, to improve the performance in searching for the optimal solution. The experimentaL results showed that DERA outperformed other algorithms used in the study. Furthermore, the authors of [40] developed a ride-matching algorithm that splits the drivers' routes and matches riders and drivers again with an extra consideration of a Joint Socialness Score. The score involves data pertaining to the participants' gender, age, employment status and social tendencies and is used to increase the matching of riders which are in the same likelihood range. The route-splitting incorporated in the proposed algorithm can maximise the distance savings and the number of matched riders to be served. The two-fold aim of the proposed algorithm is to serve more riders and to maximise the user benefits in ride-matching in encouraging more people to participate in the ridesharing system. Additionally, the authors of [41] proposed a trip-based graph partitioning approach to decompose the original one-to-one ride-matching problem into smaller multiple subgraphs. A dissimilarity measure based on the shareability of trips is applied to reduce the computational time of sub-graphs when solving them in a parallelised setting. A study addressing commuters' preferred travel mode based on a cumulative prospect value was performed by [42]. With the enlightenment of the decision-making problem in city logistics, the ridesharing market can be enlarged at a low cost. Thus, the goals considered in this bi-objective ridesharing model are maximising the participation rate of ridesharing among commuters and minimising the total cost of vehicles. In a small-scale scenario, a heuristic algorithm is developed to solve it. Whereas, for the large-scale problems, a hybrid algorithm named VNS-NSGAII algorithm, a combination of Variable Neighbourhood Search (VNS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), is employed to produce an optimal Pareto front solution. One-to-Many Ridesharing Problem  
A comparative study in utilising different Multi-Objective Evolutionary Algorithms (MOEAs) to solve the route planning problem in dynamic multi-hop ridesharing was done in [43; 44; 45]. In [46], the authors extended the dynamic ridesharing problem with extra consideration of time windows and solved it by using Genetic Algorithms (GA). Time-dependent route planning is proposed and solved by a genetic local search algorithm [47]. In the following study, [PERSON] and [PERSON] [48] introduced an insertion heuristic into the GA to become a hybrid algorithm to solve the same problem, as proposed in [46]. Insteadof the single-hop tide matching problem, they considered a multi-hop together with time windows in a dynamic ridesharing problem [49]. The potential benefits of proposing meeting points in a ridesharing system are investigated in [50] by performing an extensive simulation study. The existing ridesharing does not consider the preferences of riders; therefore, a group of compatible preferences in a participating optimal sharing cannot be generated. In mitigating the congestion problem, ref.