Wesam Herbawi

The ridematching problem with time windows in dynamic ridesharing: A model and a genetic algorithm

The increasing ubiquity of mobile handheld devices paved the way for the dynamic ridesharing which could save travel cost and reduce the environmental pollution. The ridematching problem with time windows in dynamic ridesharing considers matching drivers and riders with similar routes (with drivers detour flexibility) and time schedules on short notice. This problem is hard to solve. In this work, we model the ridematching problem with time windows in dynamic ridesharing as an optimization problem and propose a genetic algorithm to solve it. We consider minimizing the total travel distance and time of the drivers (vehicles) and the total travel time of the riders and maximizing the number of the matches. In addition, we provide datasets for the ridematching problem, derived from a real world travel survey for northeastern Illinois, to test the proposed algorithm. Experimentation results indicate that the idea of dynamic ridesharing is feasible and the proposed algorithm is able to solve the ridematching problem with time windows in reasonable time.

Evolutionary multiobjective route planning in dynamic multi-hop ridesharing

Ridesharing is considered as one of the promising solutions for dropping the consumption of fuel and reducing the congestion in urban cities, hence reducing the environmental pollution. In this work, we present an evolutionary multiobjective route planning algorithm for solving the route planning problem in the dynamic multi-hop ridesharing. The experiments indicate that the evolutionary approach is able to provide a good quality set of route plans and outperforms the generalized label correcting algorithm in term of runtime.

Comparison of multiobjective evolutionary algorithms for solving the multiobjective route planning in dynamic multi-hop ridesharing

Ridesharing is considered as one of the promising solutions for dropping the consumption of fuel and reducing the congestion in urban cities, hence reducing the environmental pollution. Route planning is a key component for the success of ridesharing systems in which multiple objectives can be optimized. The multiobjective route planning problem in multi-hop ridesharing is categorized as NP-complete. Multiobjective evolutionary algorithms have received a growing interest in solving the multiobjective optimization problems. In this work, we compare the behaviour of different multiobjective evolutionary algorithms for solving the multiobjective route planning in dynamic multi-hop ridesharing. Comparison results indicate that there is no single algorithm, as in literature, that wins all the tournaments regarding all the quality indicators. However, a subset of the algorithms is recommended with better quality and runtime.

Ant Colony vs. Genetic Multiobjective Route Planning in Dynamic Multi-hop Ridesharing

The multiobjective route planning problem in dynamic multi-hop ridesharing is considered to be NP-complete. Evolutionary computation has received a growing interest in solving the hard multiobjective optimization problems. In this study we investigate the behavior of different variants of the ant colony based approach for solving the multiobjective route planning problem and compare the performance of the different variants with the performance of a genetic algorithm recommended for solving the problem. Experimentation results indicate that the ant colony approach encounters poor performance in its native form and competes the genetic approach in some of its variants when combined with local search.

A Multi-objective Genetic Algorithm for Pruning Support Vector Machines

Support vector machines (SVMs) often contain a large number of support vectors which reduce the run-time speeds of decision functions. In addition, this might cause an over fitting effect where the resulting SVM adapts itself to the noise in the training set rather than the true underlying data distribution and will probably fail to correctly classify unseen examples. To obtain more fast and accurate SVMs, many methods have been proposed to prune SVs in trained SVMs. In this paper, we propose a multi-objective genetic algorithm to reduce the complexity of support vector machines as well as to improve generalization accuracy by the reduction of over fitting. Experiments on four benchmark datasets show that the proposed evolutionary approach can effectively reduce the number of support vectors included in the decision functions of SVMs without sacrificing their classification accuracy.

An evolutionary algorithm for the vehicle relocation problem in free floating carsharing

In this paper, we propose a novel algorithmic solution to the vehicle relocation problem in free floating carsharing systems. In this type of systems, a set of vehicles is distributed in a city and made available for customers. After a customer rents a vehicle for a period of time, he/she returns it at any place within the operation area of the vehicle operator. Such type of carsharing systems can quickly become imbalanced in a sense that many vehicles might be left in areas with low customers demand while high-demand areas might contain very few vehicles. Therefore, a relocation process is required to retain the balance by transporting vehicles from low to high-demand areas. The relocation is done by a set of workers sharing a shuttle. We consider maximizing the number of relocated vehicles, that can be done within a given time frame, and minimizing the travel duration of the shuttle. The problem is modeled as a generalization of the pickup and delivery problem which is an NP-hard optimization problem. To solve the problem, we propose an evolutionary algorithm which has been tested on real world problem instances under different settings. Experimentation results showed that the algorithm was able to successfully solve the problem and cope with the different problems settings in reasonable time.

Vehicle relocation problem in free floating carsharing using multiple shuttles

We present an Integer Linear Programming (ILP) formulation and an Evolutionary Algorithm (EA) to solve the vehicle relocation problem in free floating carsharing. In this system, users rent cars and may leave them anywhere on a designated area. After a while, a set of vehicles must be relocated to a discrete set of weighted spots, where other users may rent them again. In order to do it, some shuttles drive a set of operators to vehicles to be relocated and then collect the operators back. The objective is to maximize the weighted sum of served spots on a given time. The ILP model could solve the small instances and gave an upper (UB) and a lower bound (LB) for the others. The UB and LB values were used to evaluate the solution found by EA and showed that EA indeed found good solutions, even optimal ones.

A genetic local search algorithm for multiobjective time-dependent route planning

The multiobjective time-dependent route planning problem is a hard multiobjective combinatorial optimization problem. Metaheuristics showed success in solving many hard optimization problems and recently many efforts have been directed to hybridize elements from different metaheuristics and search methods. The hybridization of genetic algorithms and local search methods proved to be successful in many domains. In this paper we present a genetic local search algorithm for solving the multiobjective time-dependent route planning problem taking the multiobjective route planning in dynamic multihop ridesharing as an example problem. The behavior of the proposed algorithm is compared, on two problem instances using a set of widely used quality indicators, with the behavior of a genetic algorithm proposed for solving the same problem. Experimentation results indicated that the proposed algorithm outperforms the genetic algorithm regarding all quality indicators.