Search

Urban public transport is an effective way to solve urban traffic problems and promote sustainable development of urban traffic. A scientific operation scheduling system has an important guiding significance for optimizing the configuration of urban public transport capacity resources, improving the level of operation organization and management, and providing for the sustainability of the transportation system. According to the inhomogeneous distribution of passenger flow along transit lines, this study develops a combinational scheduling model in which the enterprise supplies zonal service based on regular service. The objective function minimizes the sum of passenger travel cost and operation cost, and the simulated annealing algorithm is designed to solve the optimization model. This paper abstracts an ideal example by taking a real-world case of Bus Line 131 in Lanzhou, China. The numerical example is used to verify the validity of the model and algorithm. Results show that the combinational operation scheme can effectively satisfy passengers&rsquo, demand and reduce the total cost by 7.03% in comparison with the regular operation system. The optimal combinational system with the lowest total cost can increase the vehicle load factor and improve the utilization ratio.

Artificial bee colony (ABC) algorithm is one of famous swarm intelligence approaches for continuous optimization. With the help of a solution transformation technique, it can evolve in continuous space but consequences belong to binary space. On the basis of binary ABC, a novel artificial bee colony algorithm (nABC for short) is first proposed for better solving the uncapacitated facility location problem (UFLP). In nABC, a pure crossover operation is proposed to improve information sharing quality and remove random perturbation of original search strategy in employed bees phase. Next, a new frequency of perturbation is presented for enhancing the scale of information sharing between different individuals. Then, a new search strategy without probability mechanism of basic ABC is introduced in the onlooker bees phase. To further balance the enhanced exploitation ability, the original strategy of randomly producing an individual is substituted with an opposition-based learning technique with multiple scout bees and the frequency of perturbation mechanism. To testify the effectiveness and the convergence performance of nABC, it is compared with basic ABC and other famous methods for solving fifteen UFLPs from OR-library. Experimental results demonstrate that the proposed nABC is superior to other state-of-the-art approaches in terms of solution accuracy, convergence speed and robustness.

An urban public transportation super-network model is established in this article. Compared with three existing complicated urban public transportation network models (the C-space network, L-space network, and P-space network), our urban public transportation super-network model first incorporated both station networks and bus line networks explicitly, which connects two non-homogeneous nodes (station and line nodes) through the notion of super networks. The proposed model reflects the interaction and mutual influences between the internal features and internal structures of the urban public transportation super network. And then the function projective synchronization of the dynamic public transportation super-network model is proven, and the high stability of the public transportation super network is confirmed by the Lyapunov stability theorem. Finally, the classical Lorenz chaotic system is applied in a numerical example to illustrate the proposed model and algorithm. The impact of departure frequency, line crowdedness, passenger transport density, and station dwell time on synchronous ability of the urban public transportation super-network model is analyzed. So, this study is a discussion on the urban public transportation super-network balance in order to offer theoretical basis for people to research bus dispatching and bus lines optimization.

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.

With an increased operating speed in Chinese railways in recent years, the number of passengers traveling by trains has been significantly increasing. The research issue is regarding how passengers select their routes when there are no direct trains. Previous studies have dealt with the route selection problem as a multi-objective optimization. The study began by setting up a transportation network which encompasses the departure and terminal stations along with important intermediate stations. Then, six key factors are analyzed and formulated using a multi-objective model, consisting of the train-running time, railway fare, transfer frequencies, distances between transfer stations, transfer interval time, and travel comfort. Furthermore, a two-phase algorithm is employed to solve the model. A rapid searching algorithm for feasible routes based on the train timetable is established, then the weight vector is assigned by introducing the information entropy to obtain satisfied routes. In the end, the two-phase algorithm is tested respectively for railway passengers from Lanzhou to Beijing (with direct trains) and from Lanzhou to Changchun (without direct trains), and the results show that the proposed model and solution algorithm are efficient for obtaining satisfactory routes.

We study coordination mechanisms through penalty schemes to cooperate the behavior of two firms as successive segment carriers to make transport plan separate in freight intermodal market. Based on the different cost structure and service level constraint to two firms, we compare the decision making in two possible decision systems, that is, centralized system and decentralized system. In a centralized system—the first best case as a benchmark is contrasted with decentralized system. In the decentralized system, a Stackelberg game model is formulated between two firms. Some discordant decisions would be made by firmI's overestimate motivation and firmII's undersupply motivation. Our primary objective is to design penalty schemes to coordinate the interactions for two firms. The study shows in a decentralized system, setting suitable penalty schemes can coordinate the two firms' decision. We also study the feasible range of penalty parameters, and some important managerial insights are then deduced. In the end, a numerical example is provided to verify the validity of results, some concluding remarks are presented subsequently.

This paper investigates an oriented spanning tree (OST) based genetic algorithm (GA) for the multi-criteria shortest path problem (MSPP) as well as the multi-criteria constrained shortest path problem (MCSPP). By encoding a path as an OST, in contrast with the existing evolutionary algorithms (EA) for shortest path problem (SPP), the designed GA provides a ''search from a paths set to another paths set'' mutation mechanism such that both of its local search and global search capabilities are greatly improved. Because the possibility to find a feasible path in a paths set is usually larger than that of only one path is feasible, the designed GA has more predominance for solving MCSPPs. Some computational tests are presented and the test results are compared with those obtained by a recent EA of which the encoding approach and the ideas of evolution operators such as mutation and crossover are adopted in most of the existing EAs for shortest path problems. The test results indicate that the new algorithm is available for both of MSPP and MCSPP.

Traffic assignment is a key step in urban traffic planning, and efficient path set significantly affects stochastic user equilibrium (SUE) traffic assignment. Thereby, the main allocation algorithms of the Logit model are analyzed and some disadvantages of them are point out. The conception of efficient path set should consider the path's character and traffic demand. Thus, the conception of path effect degree is defined according to the degree of one path affecting traffic volumes on other paths, and the conception of efficient path set is proposed considering the traffic demand. Moreover, efficient path set searching method is designed by combining the improved Dial algorithm. At last, a numerical example is given and the results verify the efficiency of efficient path set conception

Networks provide a useful way for modeling real world problems and are extensively used in many different types of systems: communications, hydraulic, mechanical, electronic, and logistics. The transportation problem (TP) is known as one of the important network problems. When the TP problem is associated with additional fixed cost for establishing the facilities or fulfilling the demand of customers, then it is called fixed charge transportation problem (fcTP). This problem is one of the NP-hard (IE/OR) problems that are difficult to solve using traditional methods. This paper aims to show the application of spanning tree-based Genetic Algorithm (GA) approach for solving nonlinear fixed charge transportation problem. Our new idea lies on the GA representation that includes the feasibility criteria and repairing procedure for the chromosome. Several numerical experimental results are presented to show the effectiveness of the proposed method.

A matching of a graph is an independent subset of the edges set and a maximum matching is a matching with as many edges in it as possible. The maximum weighted matching problem is to find a maximum matching in a given graph such that the sum of the weights of the edges in it is maximum. In this paper, the concepts of expected maximum fuzzy weighted matching, the α-maximum fuzzy weighted matching and the most maximum fuzzy weighted matching are initialized. According to various decision criteria, the maximum fuzzy weighted matching problem is formulated as expected value model, chance-constrained programming and dependent-chance programming by using the credibility theory, and the crisp equivalents are also given. Furthermore, a hybrid genetic algorithm is designed for solving the proposed fuzzy programming models. Finally, a numerical example is given.

The assignment problem is to find the total costs optimal jobs assignment schedule where n jobs are allocated to n workers, and each worker receives exactly just one job, such that the total cost is optimal. The quadratic assignment problem with penalty takes three types of costs into consideration: direct cost, interactive cost an penalty. In this paper, the fuzzy quadratic assignment problem with penalty is formulated as expected value model, chance-constrained programming and dependent-chance programming according to various decision criteria, and the crisp equivalents are given. Furthermore, hybrid genetic algorithm is designed for solving the proposed fuzzy programming models.

In this paper, we present a new approach which is spanning tree-based genetic algorithm for bicriteria transportation problem. The transportation problem have the special data structure in solution characterized as a spanning tree. In encoding transportation problem, we introduce one of node encoding which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation was designed based on this encoding. And we designed the criterion that chromosome always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy and roulette wheel selection is used. Numerical experiments will be shown the effectiveness and efficiency of the proposed algorithm.

In this paper, we present a hybrid genetic algorithm to solve the bicriteria transportation problem. we absorb the concept on spanning tree and adopt the Prufer number as it is capable of equally and uniquely representing all possible basic solutions. We designed the criterion which chromosomes can be always feasibly converted to a transportation tree. In order to improve the efficiency of evolutionary algorithm, the reduced cost for optimality of a solution was hybridized to genetic algorithm. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.

In order to better solve discrete 0-1 knapsack problems, a novel global-best harmony search algorithm with binary coding, called DGHS, is proposed. First, an initialization based on a greedy mechanism is employed to improve the initial solution quality in DGHS. Next, we present a novel improvisation process based on intuitive cognition of improvising a new harmony, in which the best harmony of harmony memory (HM) is used to guide the searching direction of evolution during the process of memory consideration, or else a harmony is randomly chosen from HM and then a discrete genetic mutation is done with some probability during the phase of pitch adjustment. Third, a two-phase repair operator is employed to repair an infeasible harmony vector and to further improve a feasible solution. Last, a new selection scheme is applied to decide whether or not a new randomly generated harmony is included into the HM. The proposed DGHS is evaluated on twenty knapsack problems with different scales and compared with other three metaheuristics from the literature. The experimental results indicate that DGHS is efficient, effective, and robust for solving difficult 0-1 knapsack problems.

In this paper, we present improved genetic algorithm for solving the fuzzy multiobjective solid transportation problem in which the coefficients of objective function are represented as fuzzy numbers. The ranking fuzzy numbers with integral value are used in the evaluation and selection. The proposed algorithm is incorporated with problem-specific knowledge and conductive to find out the set of nondominated points in the criteria space based on decision maker degree of optimism.

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.

In this paper, we present neural networks for solving multicriteria solid transportation problems. The original problem is transformed into an equivalent continuous problem from the continuous-time dynamic system and its optimal solution can be got. The procedure and efficiency of this approach are shown with numerical simulations.

In this paper, we introduce the neural networks for solving fuzzy shortest path problems. The penalization of the neural networks is realized after transforming into crisp shortest path model. The procedure and efficiency of this approach are shown with numerical simulations.

The proposed Genetic Algorithms (GAs) are incorporated with problem-specific knowledge and the decision maker's degree of optimism, and the criteria space approach for bicriteria linear program conductive to find out the nondominated extreme points in the criteria space.

Design of vertical section of rolling down zone on hump affects rolling condition of car, and efficiency, ability and safety of shunting operating. Taking three factors, say, minimizing rolling time difference between front car and the latter, height of hump and the number of retarders as objectives, considering engineering technology criterion that should be satisfied, a Multi-objective optimization model is established and NSGA-II multi-objective genetic algorithm is adopted to obtain Patero Set in this paper. The model and algorithm are shown by a cast and both of them are reasonable.

Design of vertical section of hump pushing zone affects disconnecting capacity of marshalling yard. There are 2 factors being chosen to be the objective function, one is height difference between reception yard and hump crest, the other is the distance between the position of disconnecting the coupler of cars. Multi-objective optimization model is established. Meanwhile, Random weight multi-objective genetic algorithm is proposed. Pareto set method and reserving elite strategy is introduced as well. The model and algorithm are shown by a concrete example. As a result, both of them are reasonable.

The selection of path set has a great influence on stochastic traffic assignment. By considering path selection character of road users and traffic restriction in real road network, the concept of efficient paths is redefined. Moreover, a vertices-outdegree-based genetic algorithm (GA) is designed which adopts positive integer coding method. Furthermore, the probabilities of crossover and mutation are adjusted by adaptive algorithm and the process of selection is adopted simulated annealing algorithm to maintain the diversity and convergence of population. Thereby, this algorithm does not need to repair chromosome and can cover the shortages of priority - based GA. While finding efficient path sets of multi-OD (Origination-Destination) at the same time, this method can improve the computation efficiency by decoding simultaneously. At last, a case study is given for verifying the efficiency.

In railway passenger transportation systems, it is a practical significance to find riding route. It is very difficult for passengers to choose the best transfer routes when a transportation network is complex. Considering the traits of railway transportation networks, we formulate a 0-1 programming model and a hypergraph model to the optimal path problem with transfer times limited. An improved priority-based encoding genetic algorithm is presented to solve the models. By using the method proposed in the paper, the transfer paths in the railway transportation network are analyzed and calculated. Finally, we give a numerical example. The computer simulations show the effectiveness of the proposed methods.

In this paper, we present a genetic algorithm with a spanning tree representation for solving the bicriteria fixed charge transportation problem. First we consider the fixed charge transportation problem with a single objective function by the spanning tree-based genetic algorithm, and then extend this GA approach to solve the bicriteria problem. Due to the bicriteria program, the fitness function is constructed by dynamic scaling which normalizes the different data from the bicriteria region. The proposed genetic algorithm can find Pareto optimal solutions in the bicriteria space. Computational results will show the performance of the spanning tree-based genetic algorithm.

In this paper, we present an evolutionary program for solving the fuzzy MSTP (multicriteria solid transportation problem) in which the coefficients of objective function are represented as fuzzy numbers. The ranking fuzzy numbers with integral value are used in the evaluation and selection. The proposed algorithm is incorporated with problem-specific knowledge and conductive to find out the set of nondominated points in the criteria space based on decision maker's degree of optimism.

We present an evolutionary computation of the fuzzy mSTP (multicriteria solid transportation problem) in which the coefficients of the objective function are represented as fuzzy numbers. The ranking fuzzy numbers with integral value are used in the evaluation and selection. The proposed algorithm is incorporated with problem-specific knowledge and can find the set of nondominated points in the criteria space based on decision maker degree of optimism.

This paper describes an implementation of genetic algorithm to solve bicriteria solid transportation problem (BSTP), which is the extension of our previous work. The criteria space approach for bicriteria linear program is embedded in the structure of genetic algorithms (GA) for finding the nondominated points in the criteria space. The proposed algorithm is coded with C language. The computation results show that the proposed algorithms is efficient for solving the bicriteria solid transportation problem. >

In this paper, we present a new approach which is spanning tree-based genetic algorithm for solving a multi-objective transportation problem. The transportation problem as a special type of the network optimization problems has the special data structure in solution characterized as a transportation graph. In encoding transportation problem, we introduce one of node encodings based on a spanning tree which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation were designed based on this encoding. Also we designed the criterion that chromosome has always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy with (µ+λ)-selection and roulette wheel selection is used. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.

This study focuses on how to determine the optimum transportation route for hazardous materials under the certain, fuzzy or stochastic environment. On the basis of analysing the transportation route selection problem of hazardous materials TRSP-HM, three objectives are presented and the multi-objective routing programming model MRPM for hazardous materials transportation HMT is put forward under the certain environment, and an improved label algorithm is proposed to solve the MRPM. After defining the maximum-chance optimum route and the α-optimum routes, the multi-objective routing chance-constrained programming model MRCPM and multi-objective routing dependent-chance programming model MRDPM for HMT under the fuzzy or stochastic environment are established respectively. Then, the integration intelligent algorithm is developed to solve the proposed models, which integrates the fuzzy simulation, neural networks, stochastic simulation and genetic algorithm. Finally, the proposed models and algorithms are successfully tested with the help of two real cases.

Urban traffic congestion is one of the most severe problems of everyday life in many cities. In an effort to deal with this problem, traffic control system technologies have concentrated in recent years on dealing with urban congestion. In the paper, a compound mechanism particle swarm optimisation (CMPSO) algorithm has been proposed, and the algorithm has been effectively used in dealing with the optimisation of city latent ringlike road traffic control system. In the algorithm, the tabu search algorithm and the particle swarm algorithm are combined effectively, and the tabu can offset the local optimums and avoid searching one solution repeatedly. Simulations show the CMPSO algorithm cannot only avoid premature convergence effectively, but also has powerful optimisation ability and higher precision. It makes the rate of delay and numbers of stopping reduce obviously.

In order to select the best vehicle scheduling route for hazardous materials, a new method based on uncertain operator is proposed. On the foundation of establishing the indices of hazardous materials' vehicle scheduling routes, some basic theories of Uncertain Linguistic Hybrid Aggregation ULHA operator are used and the multi-attribute vehicle scheduling route decision-making method based on ULHA is set up. Finally, an example shows the method is feasible.

When selecting an optimal route for hazardous materials transportation, many factors are needed to be considered. Through minimising transportation risk and operation distance, multi-objective travelling salesman problem MO-TSP model for hazardous materials transportation route is established. The natural chromosome encoding is used to encode and the roulette and optimal saving strategy are combined for selection, the order crossover is used for crossover operation to improve the traditional genetic algorithm. Then the improved genetic algorithm is used to solve MO-TSP model of hazardous materials transportation route. Finally, the correctness and effectiveness of the model and algorithm are verified with a case. This approach can help decision-makers determine reasonable transportation route for the hazardous materials transportation.

On the basis of analysing the unusual objectives of hazardous material transportation HMT, a new multi-objective optimisation model for hazardous material transportation MOM-HMT is established, which takes into account transportation risk, operation time, the number of sensitive population, risks fairness and multi-batch transportation simultaneously. Then a fuzzy adaptive weighted genetic algorithm FAWGA is set up to solve the MOM-HMT by designing priority-based encoding method, partial matching crossover, fuzzy logic control and adaptive weighted assignment mechanism. Finally, the model and algorithm are applied to a real case. The study results show the new model is feasible and the improved genetic algorithm is more effective than the standard genetic algorithm and the improved ant colony algorithm.

In order to overcome the drawback of basic particle swarm optimisation algorithm, such as being subject to fall into local optimisation and being poor in performance of precision, a chaos multi-population particle swarm optimisation algorithm has been proposed, and the algorithm has been effectively used in dealing with the optimisation of transportation continuous network design problem. In this algorithm, multi-population parallel tactics are introduced to improve the global optimising ability and the chaotic search which behaves well in local searching is introduced to improve the solution. The transportation continuous network design problem is solved based on chaos multi-population particle swarm optimisation algorithm. Two examples are presented to compare the proposed method with some existing algorithms. The simulation results show the new particle swarm optimisation algorithm can effectively alleviate the problem of premature convergence and strengthen the global searching ability. The algorithm presented in the paper can apply to solve the large scale traffic design problems.

The location problem is closely related to people's lives and property, and the harmonious development of society. Based on the discussion of location principle, to meet the demand of emergency rescue point as large, the mathematical model for location problem of emergency service facilities is established. And then the Genetic-Simulated Annealing Algorithm (GSAA) for solving this model is designed, which enriches searching behaviour in optimisation process, and has strong abilities of exploration and exploitation in large real search spaces. In this algorithm, genetic algorithm controls the search direction, and simulated annealing algorithm plays an important role in local optimum convergence. Case study shows that the algorithm is efficient, and it not only gets optimal location results quickly and accurately, but also clearly gets the afflicted points that are covered by the selected facilities. So the model and the algorithm have strong practicability.