فصلنامه مهندسی حمل و نقل
سال سیزدهم شماره 3 (پیاپی 52، بهار 1401)

One of the key processes in the banking system, is cash distribution from Treasury Branches and ATMs in the city. Treasury location, fleet, routes, geographical distribution of branches and ATMs, the distribution of money demand, customer satisfaction, are some of the influential factors in optimizing the money transfer process. Furthermore, today’s environmental issues, especially the issue of greenhouse gas (GHG) emissions, have been concentrated. In this paper, a new model of the location-routing vehicles for a bank cash distribution network is developed, in which three objectives of reducing GHG emissions, reducing costs of location-routing, and increasing customer satisfaction are considered simultaneously. Regarding to the difficulty of finding optimal solutions in such integrated models of location-routing problems, a multi-objective genetic algorithm-based on Prometheus methods are proposed. The obtained results are compared with the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective imperialist algorithm (MOICA). The comparison of these results shows the performance of the proposed algorithm for the given problems.

Depth temperature of asphalt layers is one of the important factors in the analysis, design and rehabilitation process of flexible pavements. The predictive models as an alternative to field and laboratory measurements of this factor, are rapid and simple methods to determine the depth temperature of asphalt layers. It should be noted that these models are based on the limited field and laboratory data, therefore, there is a need for developing new models for prediction of the depth temperature of asphalt layers in different traffic and climatic conditions. The main purpose of this study is to develop a model for predicting the depth temperature of asphalt layers based on climatic data. The modeling method used in this study is a stepwise non-linear regression model that predicts the depth temperature of asphalt layers based on the other variables, including the desired depth from the pavement surface, air temperature, average speed and direction of the wind, minimum air humidity and solar radiation. Data was extracted from the Long-Term Pavement Performance (LTPP) database. As a case study, data points collected from pavements in Ohio, USA, has been used for modeling. Furthermore, the developed model is well validated using data from Montana, USA. Performance evaluation and validation of the developed model showed very good correlation between predicted and measured values. Results show the ability of the developed model in predicting the depth temperature of asphalt layers based on existing climatic data with very good prediction accuracy (R2 (LOE) =0.95) and very low bias.

As the complexity of the real-world problems increases, many of them can’t be modeled using current approaches. Therefore, to overcome the current challenges and deficits, new computational models should be constantly presented for solving mentioned problems. Many real-world problems are graph-like in nature, for which irregular cellular automaton (ICA) is a desirable tool. However, ICA is not able to represent the fuzzy concepts. To this end, in this paper, a new computational model called irregular fuzzy cellular automaton is proposed. To this end, the present paper introduces a new computational model, namely fuzzy irregular cellular automata. The proposed model is a combination of fuzzy cellular automata and irregular fuzzy cellular automata, with the aim to combine the advantages of the both models and to alleviate their disadvantages. The proposed model is then used for solving the real-world road safety ranking problem. The computer simulations are conducted to show the effectiveness of the proposed mode for rating different sections of roads. Experimental results showed that the proposed method could estimate the road safety problem with 75% accuracy.

In this study, in order to identify vulnerable links, the performance of the network due to the obstruction of each link is evaluated by considering the simultaneous effects of supply and demand and assuming that drivers are aware of the obstruction status during the trip. Accordingly, by considering a specific routing pattern by users, the performance of the network is evaluated in the case of blockage of that link, and links whose removal does not lead to unacceptable changes in the network versus non-blocking, are considered as reliable links. In the following, it was implemented in the Tehran network based on 0.35 percent as acceptable changes. This value is equal to the minimum value of acceptable performance changes which makes it possible to access all points to evacuation destinations using the remaining reliable links. The basis for access will also be the possibility of creating an access tree. According to the evaluation of network performance due to the blocking of each link in EMME2 software and its comparison with the acceptable limit, 17.3% of the links of Tehran network will be unreliable links as a result of a possible earthquake. The most unreliable links of the network are observed in arterial roads, especially in the CBD. Therefore, although the removal of vulnerable links with arterial type can increase the reliability of the evacuation network by other existing links in terms of continuity and travel time, but this is due to the role and importance of this road type in network integrity and network efficiency, will lead to disruption of the network evacuation capacity. It makes necessary to development of parts of the network in the form of widening or adding links to the network in order to provide the required evacuation capacity. Obviously, if this is not possible or it is costly, especially in areas with dense texture, solutions such as retrofitting buildings as much as possible can be put on the agenda to reduce the demand for evacuation.

The integration of public transport and bicycle modes via using Bike-on-Bus methods could be considered as one of the effective policies to increase the public transport patronage. This article presents the results of a research, undertaken to identify the underlying measures contributing to the drivers’ willingness to choose Bike-on-Bus options and to choose the best favored options. Some of the innovations of this research could be enumerated as the inclusion of a number of new cycling methods, consideration of cycling in the Bike-on-Bus context, and prioritization of underlying factors contributing to the utility rate of using these methods. The Analytic Hierarchy Process method was used to prioritize these measures and to identify the most favored Bike-on-Bus mode options. The required data was collected through the questionnaires completed by two representative sample groups namely, transportation professionals and private car users in Isfahan. The data related to the transportation professionals was used to derive pairwise comparison matrices. The data related to the private car users was then used for the pairwise comparison of options. The results indicated that the most relevant measures to choose the best Bike-on-Bus methods, in descending order of their importance were: total travel time, total trip costs and the feeling of insecurity from theft, respectively. The relative weight of these measures were 0.294, 0.227 and 0.218, respectively. The most favored Bike-on-Bus methods were identified as electric scooters, special cabins for carrying bicycles and folding bikes, respectively. The relative weight of these measures were 0.249, 0.197 and 0.190, respectively.

Container terminals are complex systems in which critical decision-making processes related to maritime logistics operations take place at them. Allocating incoming ships to the berths is the most important problem for port managers. Besides, over the past two decades, the container shipping industry has experienced rapid and continuous growth, which has made the issue of fuel consumption by ships one of the most important concerns of the International Maritime Organization (IMO).  Therefore, this paper intends to consider the issue of optimizing the allocation of berths in a situation where fuel consumption is considered by the port operators and shipping lines. To investigate this, the Iranian Shahid Rajaee port was selected. Based on the physical and operational characteristics of the case study we proposed a multi objective optimization model. This model was solved using the exact Augmented Epsilon Constraint (AEC) method. Given the NP-Hard complexity of the proposed model, the NSGA-II (Non-Dominated Sorting Genetic Algorithm-II) meta-heuristic algorithm has also been used for solving real-world large-size instances. Finally, the results and statistical analyzes illustrate the good performance of the proposed multi-objective mathematical model to reduce the waiting time of ships and their fuel consumption.