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Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema. [ABSTRACT FROM AUTHOR]

This paper describes an optimization model for an irregular train schedule. The aim is to optimize both the maximum train loading rate and the average deviation of departure intervals under time-varying passenger transport demand for an urban rail transit line in consideration of practical train operation constraints, i.e., headway, running time between stations, dwell time, and capacity. A heuristic simulated-annealing algorithm is designed to solve the optimization model, and a case study of an urban rail transit line is performed to assess its efficacy. The results show that, compared with the current regular train schedule, the total train dwell time under the optimized irregular schedule is reduced from 900 s to 848 s, and the reduction ratio for the maximum train loading rate is from 1.2% to 3.6% for different stations. When the average train departure interval is allowed to vary from 120 to 170 s, the optimized irregular schedule decreases the maximum train loading rate of the collinear and noncollinear sections by 3.21%–4.82% and 2.52%–3.64%, respectively. Sensitivity analysis is performed for a nonnegative weight coefficient, average train departure interval, and proportion of full-length and short-turn routings. The proposed approach can be used to support capacity improvement and schedule optimization for urban rail transit lines. [ABSTRACT FROM AUTHOR]

Bike sharing systems are becoming more and more common around the world. One of the main difficulties is to ensure the availability of bicycles in order to satisfy users. To achieve this objective, managers of these systems set up rebalancing vehicles that displace bicycles to stations that are likely to be in a situation of bike shortage. In order to determine which stations must be supplied on a priority basis and the number of bicycles to be supplied (named in this paper as rebalancing plan), the aim is generally to reduce the lost demand for each station, i.e., the gap between the demand for bicycles and the number of bicycles at a station. On the one hand, this paper proposes an algorithm that evaluates the lost demand in a more realistic way, by describing the behaviour of users faced with a bike-shortage station. It takes into account the possibility that a proportion of users who cannot find bicycles will move to a neighbouring station that is not empty. This proportion depends on the distance between stations and corresponds to the number of users willing to walk a given distance to a neighbouring station. On the other hand, this algorithm provides the value of the objective function to be minimized to a static rebalancing plan algorithm based on a Random Search metaheuristic. The quantities of bicycles to be picked up and dropped off at each station are calculated in a static rebalancing context. The calculation of lost demand based on this algorithm, which simulates user behaviour, was compared with that one obtained by the classical method on a real numerical example obtained from the open data of Parisian Vélibʼ (more than 1200 stations). In addition, the efficiency of the rebalancing algorithm coupled with the user behaviour simulation algorithm was evaluated on this numerical example and allowed to obtain very good results compared to the rebalancing performed by the system operator. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new variant of large-scale vehicle routing problem that arises in the goods distribution of city e-commerce logistics, the multi-depot vehicle routing problem with order split and allocation (MD-CVRP-OSA), which incorporates the issue of split and allocation of online orders into traditional VRP. A mathematical formulation is constructed for the MD-CVRP-OSA, and an efficient metaheuristics algorithm based on a variable neighborhood search (VNS) solution framework is designed to solve it. The proposed method is tested on a large family of instances, including real-world data collected from JD.com, and the effectiveness of the VNS algorithm and also the algorithm components are analyzed. [ABSTRACT FROM AUTHOR]

This paper follows the previous effort of authors and builds the model of transit route network design for low-mobility individuals, proposing an appropriate solution methodology. Firstly, a desired objective, whose priority is to meet transit demands of low-mobility individuals followed by those of general public, is presented to minimize the weighted sum of direct traveler, transfer, and unsatisfied demand costs. Then, a hybrid metaheuristic approach based on ant colony and genetic algorithms is formulated to solve the proposed model in accordance with current conditions (i.e., existing routes that may need to undergo configuration adjustments to different degrees). Finally, the case study of Wenling is presented to highlight the performance and benefits of the proposed model and solution methodology. [ABSTRACT FROM AUTHOR]

The solution to a dynamic context of the Capacitated Vehicle Routing Problem (CVRP) is challenging. Routing and replenishment decisions are necessary by considering the assignment of customers to vehicles when the information is gradually revealed over horizon time. The procedure to solve this type of problems is referred to as route reoptimization, which is the best option for minimizing expected transportation cost without incurring failures of unsatisfied demand on a route. This paper proposes a heuristic algorithm for the reoptimization of CVRP in which the number of customers increases. The algorithm uses proposed performance metrics to reduce route dispersion and minimize length. The initial solution is generated using the savings algorithm and then enhanced using the Record-to-Record travel metaheuristic. By including or reducing new customers in the system, a reoptimization is performed which considers the visited nodes and edges as fixed. The optimization of the algorithm is implemented hierarchically by first minimizing dispersion and then minimizing distance. Next, the local search procedure is executed to improve the solution. A classic optimization is performed on all instances using the original and new customers’ information for later comparison to minimize distance. The efficiency of the proposed algorithm was validated using real-world cases from the literature. The results are promising and show the effectiveness of the proposed method for solving the considered problem by using reoptimization procedures in order to achieve good approximation ratios within short computing times. [ABSTRACT FROM AUTHOR]

Under the constraints of limited spaces and imbalanced traffic volumes (for both in and out directions) of container gates, reversible lane layouts become an economical and practical way to improve the service level of container terminal systems and make the maximum use of the current terminal resources. Together with a consideration of minimized total costs (both construction and operating) of terminal gate system, this paper first developed an optimization model to decide the number and scheduling rules of the reversible lanes at a terminal gate. A metaheuristic algorithm was built to solve the optimal model. Meanwhile, to reflect the randomness and dynamics property of the terminal gate system in practice, parameters that cannot be calculated from conventional analytic methods are obtained through a simulation model. Finally, a hub container terminal in the northeast of China was employed to verify the effectiveness of the proposed method and provide a theoretical foundation for the construction and management of terminal gate systems. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) is the direct application of mobile ad-hoc network (MANET) in which the nodes represent vehicles moving in a city or highway scenario. The deployment of VANET relies on routing protocols to transmit the information between the nodes. Different routing protocols that have been designed for MANET were proposed to be applied in VANET. However, the real-time implementation is still facing challenges to fulfill the quality of service (QoS) of VANET. Therefore, this study mainly focuses on the well-known MANET proactive optimized link state routing (OLSR) protocol. The OLSR in VANET gives a moderate performance; this is due to its necessity of maintaining an updated routing table for all possible routes. The performance of OLSR is highly dependent on its parameter. Thus, finding optimal parameter configurations that best fit VANET features and improve its quality of services is essential before its deployment. The harmony search (HS) is an emerging metaheuristic optimization algorithm with features of simplicity and exploration efficiency. Therefore, this paper aims to propose an improved harmony search optimization (EHSO) algorithm that considers the configuration of the OLSR parameters by coupling two stages, a procedure for optimization carried out by the EHSO algorithm based on embedding two popular selection methods in its memory, namely, roulette wheel selection and tournament selection. The experimental analysis shows that the proposed approach has achieved the QoS requirement, compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

Accurately predicting short-term congestions in ship traffic flow is important for water traffic safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by applying the whale optimization algorithm to an extreme learning machine. The method considers external environmental uncertainty and complexity of ships navigating in traffic-intensive waters. First, the parameters of ship traffic flow are divided into multiple modal components using variational mode decomposition and extreme learning machine. The machine and the whale optimization algorithm constitute a hybrid modelling approach for predicting individual modal components and integrating the results of individual components. Considering a map between ship traffic flow parameters and congestion, fuzzy c-means clustering is used to predict the level of ship traffic congestion. To verify the effectiveness of the proposed method, ship traffic flow data of the Yangtze River estuary were selected for evaluation. Results from the proposed method for predicting ship traffic flow parameters are consistent with measurements. Specifically, the prediction accuracy of the ship traffic congestion reaches 76.04%, which is reasonable and practical for predicting ship traffic congestion. [ABSTRACT FROM AUTHOR]

Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowd sensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants' concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time. [ABSTRACT FROM AUTHOR]

The location selection of distribution centers is one of the important strategies to optimize the logistics system. To solve this problem, under certain environment, this paper presents a new multicriteria decision-making method based on ELECTRE I. The proposed method helps decision-makers to select the best location from a given set of locations for implementing. After having identified decision-makers, the criteria, and the set of locations, the factors influencing the selection are analyzed in order to identify the best location. A sensitivity analysis is then performed to determine the influence of criteria weights on the selection decision. The strength of the proposed method is to incorporate decision-makers' preferences into the decision-making process. In addition, the proposed method considers both quantitative and qualitative criteria. Finally, the selected solution is validated by both tests of concordance and discordance simultaneously. A case study is provided to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Capacitated Vehicle Routing Problem (CVRP) is difficult to solve by the traditional precise methods in the transportation area. The metaheuristic algorithm is often used to solve CVRP and can obtain approximate optimal solutions. Phasmatodea population evolution algorithm (PPE) is a recently proposed metaheuristic algorithm. Given the shortcomings of PPE, such as its low convergence precision, its nature to fall into local optima easily, and it being time-consuming, we propose an advanced Phasmatodea population evolution algorithm (APPE). In APPE, we delete competition, delete conditional acceptance and correspondingevolutionary trend update, and add jump mechanism, history-based searching, and population closing moving. Deleting competition and conditional acceptance and correspondingevolutionary trend update can shorten PPE running time. Adding a jump mechanism makes PPE more likely to jump out of the local optimum. Adding history-based searching and population closing moving improves PPE's convergence accuracy. Then, we test APPE by CEC2013. We compare the proposed APPE with differential evolution (DE), sparrow search algorithm (SSA), Harris Hawk optimization (HHO), and PPE. Experiment results show that APPE has higher convergence accuracy and shorter running time. Finally, APPE also is applied to solve CVRP. From the test results of the instances, APPE is more suitable to solve CVRP. [ABSTRACT FROM AUTHOR]

In the industrial sector, transportation plays an essential role in distribution. This activity impacts climate change and global warming. One of the critical problems in distribution is the green vehicle routing problem (G-VRP). This study focuses on G-VRP for a single distribution center. The objective function is to minimize the distribution costs by considering fuel costs, carbon costs, and vehicle use costs. This research aims to develop the hybrid butterfly optimization algorithm (HBOA) to minimize the distribution costs on G-VRP. It was inspired by the butterfly optimization algorithm (BOA), which was by combining the tabu search (TS) algorithm and local search swap and flip strategies. BOA is a new metaheuristic algorithm that has been successfully applied in various engineering fields. Experiments were carried out to test the parameters of the proposed algorithm and vary the speed of vehicles. The proposed algorithm was also compared with several procedures of prior study. The experimental results proved that the HBOA could minimize the total distribution cost compared to other algorithms. Moreover, the computation time is also included in the analysis. [ABSTRACT FROM AUTHOR]

The last mile travelling problem is the most challenging part when using public transit. This study designs a high-freedom responsive feeder transit (HFRFT) system to serve at the transfer station, given vehicle routes, departure time, and service area based on demand. The proposed feeder transit system employs a travelling mode with multitype vehicles. In order to improve the operation of the HFRFT system, the optimization design methods are suggested for vehicle routes, scheduling, and service area. A mixed integer programming model and its hybrid of a metaheuristic algorithm are proposed to efficiently and integrally solve the vehicle routes and scheduling parameters according to the reservation requirements. A heuristic method is proposed to optimize the service area based on the equilibrium of system supply and demand. Case studies show that the mixed running mode of multiple models can significantly improve the seat utilization, which can also significantly reduce the number of departures and the average travel distance per passenger. The proposed service area optimization method is proved to be feasible to improve the last mile travel. [ABSTRACT FROM AUTHOR]

This study proposes a two-step solution approach for aircraft conflict resolution and fuel consumption due to resolution maneuver occurring in free-route airspace. This model aims to provide a mathematical basis for a decision-support system that is used during the pretactical conflict resolution in air traffic management. Mathematical model of the first step presents alternative entry points on both sides of existing sector entry points to minimize delays by directing aircraft to the most convenient entry points. The second step suggests a vector deflection maneuver to minimize extra fuel consumption caused by conflict resolution. GAMS/CPLEX solver is used to solve the first step of the model but the solution is not produced in a reasonable time. To obtain feasible solutions, genetic algorithm and tabu search algorithms are implemented in the first step. Small size test problems are generated to evaluate the metaheuristic algorithms, and results are compared with GAMS/CPLEX solver solutions. According to this comparison, both metaheuristics algorithms produce near optimal solutions in a reasonably short time. The proposed approach has made significant improvements for airborne delays and extra fuel consumption caused by aircraft conflicts resolution in large-scaled airspaces. [ABSTRACT FROM AUTHOR]

Air contamination becomes an urgent problem to be considered as a result of the rapid growth in traffic all over the world. Traffic emissions differ from vehicle to vehicle depending on the vehicle type, production year, fuel octane number, and periodical maintenance of the vehicle. The majority of drivers do not revise their harmful vehicles emissions regularly. Therefore, effective tracking of high-emitting vehicles can be an important solution for reducing traffic air pollution. This study proposes a location strategy for vehicle remote sensing monitors aided with ID-plate recognizer to capture any violated vehicle emissions. The problem is formulated into a graph theory problem, and then a novel adapted metaheuristic algorithm is used to solve the problem. The methodology, using a benchmark problem, has managed to solve the problem to the optimality. Moreover, its robustness is measured statistically. [ABSTRACT FROM AUTHOR]

Bus driver scheduling aims to find the minimum number of bus drivers to cover a published timetable of a bus company. When scheduling bus drivers, contractual working rules must be enforced, thus complicating the problem. In this research, we develop a column generation algorithm that decomposes this complicated problem into a master problem and a series of pricing subproblems. The master problem selects optimal duties from a set of known feasible duties, and the pricing subproblem augments the feasible duty set to improve the solution obtained in the master problem. The proposed algorithm is empirically applied to the realistic problems of several bus companies. The numerical results show that the proposed column generation algorithm can solve real-world problems and obtain bus driver schedules that are better than those developed and used by the bus companies. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]