Empty Travel Times Research Articles

Multiobjective Scheduling for Cooperative Operation of Multiple Gantry Cranes in Railway Area of Container Terminal

Railway station plays an important role in improving the operation efficiency of rail-sea intermodal container terminal. The cooperative scheduling of multiple gantry cranes (GCs) can reduce the production and operation cost of railway station. Most studies look into discarding the conflict schemes of GCs, which may obtain excellent scheduling results. These existing conflict-free strategies can not contribute to enhancing the cooperative scheduling performance of multiple GCs. This study integrates container trucks into multiple GCs scheduling environment to eliminate those conflicts, and proposes a mixed-integer programming model considering cooperation between multiple GCs and trucks. The model aims to simultaneously minimize the makespan of the container handling system in station, the total empty travel time of GCs and the total energy consumption of both cranes and trucks. To solve the proposed model, a conflict-free operation strategy for multiple GCs based on hybrid indirect loading and unloading (CFHI) is proposed. CFHI strategy is implemented according to the cooperation between GCs and trucks. An effective multi-objective artificial bee colony algorithm (EMOABC) based on CFHI and fuzzy correlation entropy (FCE) is developed. Within the developed algorithm, an encoding/decoding method based on CFHI is designed to represent and decode the population solutions. The FCE is adopted to evaluate and select the better solutions for next iteration evolution. The effectiveness of the proposed CFHI strategy is verified by comparing it with two popular equipment allocation strategies. Extensive experimental results show that proposed EMOABC is effective to the proposed model. Our findings here have significant implications for the cooperative operation of multiple GCs considering energy consumption in container terminal.

A Metaheuristic Algorithm to Transporter Scheduling for Assembly Blocks in a Shipyard considering Precedence and Cooperating Constraints

Special vehicles named flat transporters are used to deliver heavy ship assembly blocks in shipyards. Because each movement of assembly blocks among workshops needs transporters and the transportations are time-consuming, the scheduling of transporters is important for maintaining the overall production schedule of assembly blocks. This paper considers an optimization transporter scheduling problem for assembly blocks. The objective is to minimize logistics time, which includes empty travel time of transporters and waiting time and delay time of block tasks. Considering time windows of ship blocks, carrying capacity of transporters, and precedence relationships of tasks, a mathematical model is proposed. A hybrid topological graph is used to denote precedence and cooperating relationships of tasks. A metaheuristic algorithm based on the hybrid topological graph and genetic algorithm and Tabu search is proposed. The performance of the algorithm was evaluated by comparing the algorithm to optimal result in small-sized instances and several strategies in large-sized instances. The results showed the efficiency and effectiveness of the proposed algorithm.

A Robust Metaheuristic for the Rail Shuttle Routing Problem with Uncertainty: A Real Case Study in the Le Havre Port

This study addresses the Rail Shuttle Routing Problem (RSRP) in the Le Havre port, where rail shuttles carry out the transfer of containers between maritime terminals and the multimodal terminal. The transfer times of shuttles and the empty travel times of locomotives both depend on several factors and are supposedly uncertain. The aim of this work is to improve the performance of the container transfer system in the Le Havre port, by minimising the total empty travel time of locomotives and protecting against delays. The deterministic problem is first modelled as a Vehicle Routing Problem with Time Windows (VRPTW); then, uncertainties are introduced into this model and Robust Ant Colony Optimisation (RACO) is proposed to solve the problem under uncertainties.

Optimization of Containers Transfer in Le Havre Port: a New Algorithm for the Railway Transportation System

This paper introduces the rail shuttle routing problem with synchronization constraints in which rail shuttles carry out the transfer of containers between maritime terminals and the multimodal terminal of the Le Havre Port. A rail shuttle is composed of two types of vehicles: a locomotive which is an autonomous vehicle able to move between container terminals, and a block of flatcars which is considered as a non-autonomous vehicle that requires to be hitched to a locomotive to move in space. The objective is to minimize empty travel times of autonomous vehicles between container terminals with respecting the synchronization needs between the routing of both types of vehicles to perform the transfer of containers within time windows constraints imposed on task availability. A mathematical formulation of the problem is established and an ant colony algorithm is proposed to solve the problem.

Simulaciona analiza modela za procenu praznog vremenskog ciklusa u vozilima kod neautomatskih sistema za rukovanje materijalima

In designing or redesigning a facility like manufacturing plant or warehouse, an integral part is a proper selection of material handling system. Despite large alternatives, including automated material handling systems with conveyors or automated guided vehicles, most facilities today use for material handling man-driving vehicles (non-automated discrete material handling system). Proper design of such systems requires determination of required number of vehicles. Determination of empty vehicle travel time is based on either time consuming simulation or nonsimulation approach using estimation. The main goal of this paper is to review and analyse some proposed methods for empty travel time estimation of non-automated discrete material handling systems. Results obtained by estimation methods are compared with the simulation results.

Prediction of fuel consumption of mining dump trucks: A neural networks approach

Abstract Fuel consumption of mining dump trucks accounts for about 30% of total energy use in surface mines. Moreover, a fleet of large dump trucks is the main source of greenhouse gas (GHG) generation. Modeling and prediction of fuel consumption per cycle is a valuable tool in assessing both energy costs and the resulting GHG generation. However, only a few studies have been published on fuel prediction in mining operations. In this paper, fuel consumption per cycle of operation was predicted using artificial neural networks (ANN) technique. Explanatory variables were: pay load, loading time, idled while loaded, loaded travel time, empty travel time, and idled while empty. The output variable was the amount of fuel consumed in one cycle. Mean absolute percentage error (MAPE) of 10% demonstrated applicability of ANN in prediction of the fuel consumption. The results demonstrated the considerable effect of mining trucks idle times in fuel consumption. A large portion of the unnecessary energy consumption and GHG generation, in this study, was solely due to avoidable idle times. This necessitates implementation of proper actions/remedies in form of both preventive and corrective actions.

A comprehensive approach to planning the deployment of transportation assets in distributing forest products

Four different modes for transporting wood in the first three stages of the wood supply chain are introduced and compared for real–life–based test cases under various scenarios. They include log–trucks and (foldable) containers. Freight forwarders are employed by sawmills to perform the transports. Their profit is dependent on the used transport mode and the bargained revenue function, which includes different prices for full travel and empty travel times. Tabu search–based algorithms were developed and implemented to solve the emerging vehicle routing and combined vehicle routing and container scheduling problems. The obtained results specify costs and profits of the particular transport technologies and support freight forwarders in their choice of transport mode. It can be concluded that the use of foldable containers could improve the efficiency of transport in wood supply chains considerably.

Fleet sizing of automated guided vehicles: a linear programming approach based on closed queuing networks

We use multi-class closed queuing networks to model operations of automated guided vehicles in a manufacturing or distribution environment. We approximate the dynamics of the system using the first moment balance equations of the embedded stochastic chain representing the network under the steady-state conditions. These moments account for loaded and empty-travel times, as well as times when vehicles are being loaded/unloaded or waiting to be dispatched. We model the steady-state behaviour of the closed queuing network by a linear program whose optimal value is the estimate of the required fleet-size. The result of the analytical model is compared with those of the simulation studies for a set of numerical examples. The comparison shows that the analytical model provides a good estimate for the required number of vehicles.

06/02263 Combustion simulation of turbocharger HSDI Diesel engines during transient operation using neural networks: Galindo, J. et al. Applied Thermal Engineering, 2005, 25, (5–6), 877–898.

Fuel consumption of mining dump trucks accounts for about 30% of total energy use in surface mines. Moreover, a fleet of large dump trucks is the main source of greenhouse gas (GHG) generation. Modeling and prediction of fuel consumption per cycle is a valuable tool in assessing both energy costs and the resulting GHG generation. However, only a few studies have been published on fuel prediction in mining operations. In this paper, fuel consumption per cycle of operation was predicted using artificial neural networks (ANN) technique. Explanatory variables were: pay load, loading time, idled while loaded, loaded travel time, empty travel time, and idled while empty. The output variable was the amount of fuel consumed in one cycle. Mean absolute percentage error (MAPE) of 10% demonstrated applicability of ANN in prediction of the fuel consumption. The results demonstrated the considerable effect of mining trucks idle times in fuel consumption. A large portion of the unnecessary energy consumption and GHG generation, in this study, was solely due to avoidable idle times. This necessitates implementation of proper actions/remedies in form of both preventive and corrective actions.

Vehicle Travel Time Models for AGV Systems under Various Dispatching Rules

The design and evaluation of AGV-based material handling systems are highly complex because of the randomness and the large number of variables involved. Vehicle travel time is a fundamental parameter for solving various flexible manufacturing system (FMS) design problems. This article presents stochastic vehicle travel time models for AGV-based material handling systems with emphasis on the empty travel times of vehicles. Various vehicle dispatching rules examined here include the nearest vehicle selection rule and the longest idle vehicle selection rule. A simulation experiment is used to evaluate and demonstrate the presented models.

Determination of optimal AGV fleet size for an FMS

The required number of AGVs necessary to perform a given level of material handling task in an FMS environment is determined using analytical and simulation modelling. The analytical method involves consideration of load handling time, empty travel time, and waiting and blocking time. Load handling time is computed from given system parameters. Determination of empty vehicle travel is difficult due to the inherent randomness of an FMS. Several research studies for this purpose are discussed and a new model is proposed. It entails formulation of a mixed integer programme with an objective of minimizing empty trips. The constraints are in the form of upper and lower bounds placed on the total number of empty trips starting from or ending at a load transfer station. The phenomena of vehicle waiting and blocking are also discussed. The cumulative impact of these three time estimates are then translated into an initial estimate of AGV fleet size as predicted by individual models. The method is applied to an illustrative example. Finally, simulation methodology is used to validate the initial estimates of fleet size. The results indicate that the different models either under-estimate or over-estimate the actual number of vehicles required in the system. The proposed model, though under-estimates the minimum AGV requirement, yet provides results which are close to the simulation results. Hence, it can be used as an analytical tool prior to the simulation phase of AGVS design.