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This paper develops a model to analyse hazmat shipments routing in the context of hazmat transportation between the specified origin-destination (OD) pair. A novel aspect of this model is the consideration of risk equity using the standard deviation, an established computation to assess equity. To solve the model, a two-phase method is developed, in which the multi-objective shortest path algorithm is used to obtain the Pareto-optimal paths set, and get the routes using estimation of distribution algorithm after paths choice. We then present a test problem of hazmat shipment with consideration of risk equity and discuss computational results.

Artificial bee colony (ABC) algorithm is a relatively new biologically-inspired optimization algorithm. According to its solution search equation, it can be seen that ABC is good at exploration but poor at exploitation. Inspired by the gravity model, an attractive force model is proposed for choosing a better neighbor of a current individual to improve the exploitation ability of ABC. Then we propose a novel solution search equation, in which the chosen neighbor plays an important role in guiding the searching process in the employed bee phase. Next, a random guiding search is introduced in the onlooker bee phase to balance the foregoing exploitation. Subsequently, multiple solution search equations, a scheme of perturbation frequency, and a multiple scouts search strategy in view of opposition-based learning are also incorporated into the proposed algorithm, called ABCG, to further reach a good compromise between the exploitation and the exploration. Finally, ABCG is tested on a great number of benchmark functions . The experimental results show that ABCG is effective for solving the complex benchmark problems and it can be considered as a competitive approach.

Purpose – Critical links in traffic networks are those who should be better protected because their removal has a significant impact on the whole network. So, the purpose of this paper is to identify the critical links of traffic networks. Design/methodology/approach – This paper proposes the definition of the critical link for an urban traffic network and establishes mathematical model for determining critical link considering the travellers’ heterogeneous risk-taking behavior. Moreover, in order to improve the computational efficiency, the impact area of a link is quantified, a partial network scan algorithm for identifying the critical link based on the impact area is put forward and the efficient paths-based assignment algorithm is adopted. Findings – The proposed algorithm can significantly reduce the search space for determining the most critical links in traffic network. Numerical results also demonstrate that the structure of efficient paths has significant impact on identifying the critical links. Originality/value – This paper identifies the critical links by using a bi-level programming approach and proposes a partial network scan algorithm for identifying critical links accounting for travellers’ heterogeneous risk-taking behavior.

The half close form of heavy haul railway such as Daqin railway is divided into two parts, i.e., the loading region and the unloading region, according to its characteristic. We research the whole process of empty trains distribution. To guarantee loading continuity and stability, a model is proposed to minimise the empty trains multiplying hours of arriving at loading point early or late and subject to the constraints of the required empty trains in loading point, transport capacity in unloading region, the time window of loading point and the type, mode and quantity of empty trains combined in combination station and decomposed in decomposition station. The aim is to improve the efficiency of empty trains returning and reduce the operating costs. The data from Daqin heavy haul railway are performed to testify the model's correctness and effectiveness.

As the transportation safety problem of dangerous goods is becoming more and more important, this paper establishes an optimisation model for the dangerous goods transport based on the improved ant colony algorithm. Firstly, the optimisation model of dangerous goods transport is designed. Secondly, the optimisation algorithm is designed based on the ant colony algorithm and genetic algorithm. Finally, the model and algorithm are verified by a case. The result indicates that the optimisation model and algorithm of dangerous goods transport have an important guiding significance on the safety of dangerous goods transport.

Aiming at hazardous materials transportation (HMT), vehicle routing optimisation models for single vehicle and multiple vehicle are proposed respectively, and the adaptive double populations genetic algorithm are constructed. Firstly, the goal functions of models are minimising the total risk, cost and the running time of hazardous materials vehicle. Then, the load constraint, max-risk constraint and time window constraint are considered. Finally, natural number is used for coding, double populations mechanism and adaptive weighted fitness allocation mechanism are adopted to calculate unit fitness, partial matched-crossover method is adopted for crossover operation, and the inversion mutation operator is adopted for mutation operation. Case study shows the model and algorithm are feasible, the vehicle routing strategy can provide direct reference for hazardous materials transportation decision-making departments and it is an effective way for prevention of hazardous materials transportation accidents.

An improved layered parallel particle swarm optimisation ILPPSO algorithm has been developed based on layered parallel tactics and catastrophe tactics. In the algorithm, the new inertial parameter was used, and two layers independent sub-swarms were cooperative optimised. The catastrophe occurred when inferior sub-swarms sink into a local optimum value, which can make the population evolve forward continuously, prevent an algorithm precocious refrain, insure obtain the global optimum solution. The experiment study shows that the ILPPSO algorithm can improve the searching ability, get the optimum result. Aiming at the expressway interchange jam problem, the traditional ramp fuzzy control algorithm is not ideal. This paper applies the ILPPSO algorithm to optimise the interchange ramp control parameters. The simulation results show that the ICPPSO algorithm was more effective than the standard PSO algorithm and genetic algorithm, and was able to carry out real-time parameters optimisation of interchange ramp control.

Hazardous materials transportation vehicles may become one of the main attack goals under the background of terrorist attacks. This article gives an optimisation method of emergency service facility location for a specific counterterrorism area. Firstly, the multi-objective optimisation model of emergency service facility location is proposed through maximising cover risks, minimising emergency response time, emergency facilities fixed costs and transportation costs. Secondly, the model is solved with an improved genetic algorithm, which use 0-1 encoding method to code chromosome, use roulette with the optimal preservation strategy to conduct choose operation and use adaptive weighted distribution method to assign weight. Finally, a case study with 12 nodes and 21 roads is given to indicate that the optimisation model and algorithm are feasible.

In order to relieve the traffic congestion, especially the traffic burden of the artery in the urban road network, this paper proposes an arterial traffic coordinated control optimisation method by using the cosine modified adaptive genetic algorithm. Firstly, this paper considers the importance of different directions traffic release of intersections coordinated phase, and takes the number of vehicles within the maximised green wave bandwidth as the objective to establish the traffic coordinated control model. Secondly, the cosine modified adaptive genetic algorithm is applied to solve the model. Finally, the traffic coordinated control system including six consecutive intersections is designed in Tianshui Road, Lanzhou, China. The optimisation result shows that the cosine modified adaptive genetic algorithm has a good performance in the optimisation of the traffic coordinated control system.

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.

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.