Jesús Muñuzuri

Simulation of freight traffic in the Seville inland port

Abstract The Port of Seville is an inland port located in the Guadalquivir River in the south of Spain and it is the unique Spanish inland port. Our research is focused on the simulation of the freight transport process beginning with the movement through the whole estuary of the river and finishing with the vessels arriving to the port dependencies, where the logistic operators’ load and unload processes take place. The simulation presented in the paper is carried out with Arena software and considers all the types of cargo existing in the Seville Port: containers, cereals, cements, scrap, iron and steel and fertilizers. We have simulated the navigation through the Guadalquivir estuary, the lock, the basins and the docks of the port, as well as the logistic activities in the berths. After testing several scenarios, we can state that the facilities of the Port of Seville allow to deal with the incoming logistic flows, except for momentary difficulties in the container traffic. So the improvement measures for the logistic activity must come from other alternative key actions.

Berth allocation planning in Seville inland port by simulation and optimisation

We study the problems associated with allocating berths for containerships in the port of Seville. It is the only inland port in Spain and it is located on the Guadalquivir River. This paper addresses the berth allocation planning problems using simulation and optimisation with Arena software. We propose a mathematical model and develop a heuristic procedure based on genetic algorithm to solve non-linear problems. Allocation planning aims to minimise the total service time for each ship and considers a first-come-first-served allocation strategy. We conduct a large amount of computational experiments which show that the proposed model improves the current berth management strategy.

Modeling Freight Delivery Flows: Missing Link of Urban Transport Analysis

Urban freight transport has barely incited any modeling efforts when compared to passenger cars and public transport, which is mainly due to the lack of available data and the complexity of the delivery route patterns and the involved decision making. We present here a modeling approach consisting of a demand model followed by an entropy maximization procedure to estimate an origin-destination matrix for urban freight transport vehicles, both for business to business and home deliveries, during the morning peak hour. This approach requires relatively few data inputs in comparison with other existing models and represents an initial step toward the inclusion of freight delivery models in overall urban transport planning. The application of the model is illustrated with a case study in the city of Seville, with its efficiency tested by the validation of the results using actual traffic counts.

Estimation of Daily Vehicle Flows for Urban Freight Deliveries

Given its contribution to congestion, pollution, and energy consumption and the complex and changing characteristics of delivery routes, the modeling of urban freight transport is a difficult, highly data-demanding and often unreliable task. Extending other previous works that focused only on the morning peak hour, the authors have developed a trip generation model by using the available data to their maximum extent and adding other parameters that can be found through simple surveys. This trip generation model is then included as part of a four-stage process, with the trip distribution solved through entropy maximization and resulting in the estimation of an origin-destination matrix for freight transport in a city. The application to a case study in the city of Seville and the validation with on-street vehicle counts shows reasonably robust results and provides a simple and effective tool to analyze urban freight deliveries from a macroscopic point of view.

Selecting the location of minihubs for freight delivery in congested downtown areas

Abstract The delivery of freight in urban areas has to face many restrictions and regulations that constrain the efficient flow of goods. One of the most common regulations in medium and large cities is the establishment of access time windows, whereby delivery vehicles can only access the most central and congested areas of the city during a pre-specified period of the day. To avoid the costs imposed on carriers by this regulation while maintaining the social and environmental sustainability benefits, we propose here the establishment of a system of mini-hubs where delivery vehicles may park for the final deliveries to be completed on foot. Given that the optimal location of these mini-hubs is essential for the operation of the system, we formulate a location model and apply a computational process based on genetic algorithms to optimize it. We apply this procedure to a case study in the Spanish city of Seville, showing the effect of mini-hubs on the costs of the overall delivery system.

Estimating the extra costs imposed on delivery vehicles using access time windows in a city

Abstract Analyses performed before introducing access time window policies in the center of European cities often do not evaluate the extra costs imposed on carriers from the additional number of vehicles required and increase in tour length. To facilitate this evaluation, we have developed a vehicle routing algorithm that considers the existence of access time windows and adapts tours to this restriction in the best possible manner. The procedure is based on a genetic algorithm, which we calibrate by analyzing several experiments in a test network. We then apply the algorithm to a real case study in the city of Seville, where local authorities are considering increases in the duration of the time window restriction and the size of the restricted area.

Dynamic approach to solve the daily drayage problem with transit time uncertainty

The intermodal transport chain can become more efficient by means of a good organization of drayage movements. Drayage in intermodal container terminals involves the pick up and delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. This scheduling has traditionally been done once a day and, under these conditions, any unexpected event could cause timetable delays. We propose to use the real-time knowledge about vehicle position to solve this problem, which permanently allows the planner to reassign tasks in case the problem conditions change. This exact knowledge of the position of the vehicles is possible due to the use of a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that these additional data can be used to dynamically improve the solution.

Design and Analysis of a Tool for Planning and Simulating Dynamic Vertical Transport

Nowadays, most of the main companies in the vertical transport industry are researching tools capable of providing support for the design process of elevator systems. Numerous decisions have to be taken to obtain an accurate, comfortable, and high-quality service. Effectively, the optimization algorithm is a key factor in the design process, but so are the number of cars being installed, their technical characteristics, the kinematics of the elevator group, and some other design parameters, which cause the selection task of the elevator system to be a complex one. In this context, the design of decision support tools is becoming a real necessity that most important companies are including as part of their strategic plans. In this article, the authors present a user-friendly planning and simulating tool for dynamic vertical traffic. The tool is conceptualized for giving support in the planning and design stage of the elevator system, in order to collaborate in the selection of the type of elevator (number, type of dynamic, capacity, etc.) and the optimization algorithm.

Dynamic Fuzzy Logic Elevator Group Control System With Relative Waiting Time Consideration

High-rise buildings with a considerable number of elevators represent a major logistical problem concerning saving space and time for economic reasons. For this reason, complex elevator group control systems (EGCSs) are developed in order to manage elevators properly. In this paper, the first entirely dynamic fuzzy logic EGCS to dispatch landing calls so as to minimize waiting time is proposed. The fuzzy logic design described here not only constitutes an innovative solution that outperforms usual dispatchers but also an easy, cheap, feasible, and reliable solution, which is able to be implemented. This is achieved by an intelligent design that joins together the simplicity of fuzzy design with the performance of the most sophisticated controllers by considering the impact of landing call reallocations within the system waiting time scale based on the assessment of the landing call relative/absolute waiting time balancing.

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.