Optimization of Container Handling Systems in Automated Maritime Terminal
... The method is also evaluated using a simulation study. Dkhil et al. (2013) similarly optimized the QC-AGV-ASC planning problem in automated container terminals by proposing three math-ematical models with the aim of minimizing the loading time and the number of vehicles required. The first model considers a single QC and a single ASC, the second model considers multiple QCs and a single ASC, and the third model considers multiple QCs and multiple ASCs. ...
Considering the uncertainty of the speed of horizontal transportation equipment, a cooperative scheduling model of multiple equipment resources in the automated container terminal was constructed to minimize the completion time, thus improving the loading and unloading efficiencies of automated container terminals. The proposed model integrated the two loading and unloading processes of “double-trolley quay crane + AGV + ARMG” and “single-trolley quay crane + container truck + ARMG” and then designed the simulated annealing particle swarm algorithm to solve the model. By comparing the results of the particle swarm algorithm and genetic algorithm, the algorithm designed in this paper could effectively improve the global and local space search capability of finding the optimal solution. Furthermore, the results showed that the proposed method of collaborative scheduling of multiple equipment resources in automated terminals considering hybrid processes effectively improved the loading and unloading efficiencies of automated container terminals. The findings of this study provide a reference for the improvement of loading and unloading processes as well as coordinated scheduling in automated terminals.
... The growing publications' trend during the recent years demonstrates the vivid research interest on AGVs' routing at freight ports. Dkhil, Yassine, and Chabchoub (2013) developed a bi-objective model to formulate the scheduling problem of activities in a single 'Quayside Cranes-AGVs-Yard Cranes' system under the objectives: (i) to minimise the container handling and transport time; and (ii) to minimise the AGV fleet size. The main idea was to address the task scheduling problem and decrease the operating cost at an automated container terminal. ...
Artificial intelligence and data analytics capabilities have enabled the introduction of automation, such as robotics and Automated Guided Vehicles (AGVs), across different sectors of the production spectrum which successively has profound implications for operational efficiency and productivity. However, the environmental sustainability implications of such innovations have not been yet extensively addressed in the extant literature. This study evaluates the use of AGVs in container terminals by investigating the environmental sustainability gains that arise from the adoption of artificial intelligence and automation for shoreside operations at freight ports. Through a comprehensive literature review, we reveal this research gap across the use of artificial intelligence and decision support systems, as well as optimisation models. A real-world container terminal is used, as a case study in a simulation environment, on Europe’s fastest-growing container port (Piraeus), to quantify the environmental benefits related to routing scenarios via different types of AGVs. Our study contributes to the cross-section of operations management and artificial intelligence literature by articulating design principles to inform effective digital technology interventions at non-automated port terminals, both at operational and management levels.
Every few years, larger containerized vessels are introduced to the market to accommodate the increase in global trade. Although increasing the capacity of vessels results in maximizing the amount of imported and exported goods per voyage, yet it is accompanied with new challenges to terminal planners. One of the primary challenges is minimizing the vessel turnaround time with the least possible cost. In this context, this paper presents the development of a multi-level optimization model using the elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal or near-optimal fleet size combination of the different container handling equipment used in the terminal. The model aims to minimize two conflicting objective functions, namely, vessel turnaround time and total handling cost. Furthermore, the model considers a double-cycling strategy for the container handling process to achieve increased productivity and eventually more reduction in the vessel turnaround time. The model was implemented on a real-life case study to demonstrate its efficiency and the benefit of employing the double-cycling strategy compared with the traditional single-cycling strategy. The results demonstrated the efficiency of employing the double-cycling strategy by providing a reduction of above 20% in both the vessel turnaround time and the total handling cost and an increase of above 25% in the productivity when compared to the traditional single-cycling strategy.
It is a fundamental decision making process in container terminals to allocate container transporting works among ALVs. Nowadays, container terminals tend to be larger in storage space and more efficient in handling. As a result, estimations of ALV travel times could be inaccurate, the scale of ALV work allocation could be quite large, and a fixed handling sequence could be hardly ensured beforehand. Hence, it is presented a real-time dispatching method, consisting of an allocation model for instantaneous ALV dispatching, and a set of events which trigger a new instantaneous dispatching. A modified Hungarian Algorithm is applied to solve the instantaneous dispatching model, and it is verified that the modified Algorithm outperforms the original one, even CPLEX, in solving these allocation problems
This paper describes the development of a minimum flow algorithm to determine the number of automated guided vehicles (AGVs) required at a semi-automated container terminal. At such a terminal the containers are transported by AGVs from the quay cranes to the automated stacking cranes and vice versa. A model and a strongly polynomial time algorithm are developed to solve the case in which containers are available for transport at known time instants.
An alternative to increasing automated guided vehicle (AGV) fleet size to improve vehicle availability for material transport requests is to introduce vehicles with multiple load-carrying capacities. Aside from alleviating communication and congestion problems, the use of multiple-load carriers can potentially reduce the deadhead or unproductive time of vehicles. This paper discusses several key issues related to multiple-load AGV systems and demonstrates the potential benefits of using them. Alternative vehicle dispatching strategies for multiple-load AGVs are discussed, stressing that different considerations are involved in dispatching empty and partially loaded vehicles. The effectiveness of single-load and two-load AGV systems is compared using simulation. The results show that, with multiple-load vehicles, the sensitivity of AGV system performance to guidepath layout is decreased, and the job input rate, which the AGV system can effectively handle without becoming a bottleneck for the overall system, is significantly increased. The relative effectiveness of selected control strategies is also investigated.
Manufacturing systems are composed of FMS (flexible manufacturing system) cells. Each cell takes units like parts as the input and produces other units as the output. The units output by some cells have to be carried to other units. An automated guided vehicle (AGV) is a vehicle which carries the units among the cells by moving around in a space which is composed of guidepaths. The space is logically composed of space objects structured in a tree, where high-level space objects denote broader and more abstract space. In this paper, we would like to model the movement of each AGV as a transaction which holds and releases the space objects. There are different kinds of AGVs with respect to how they can move. For example, one AGV can go backward, and another cannot stop. In this paper, we propose transaction models, i.e. close, semi-open, and open ones with respect to when the AGV releases the objects. We discuss the relation between the types of AGVs and the transaction models.
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.
Analysis techniques and a methodological framework for specifying the
operational characteristics of an automatic guided vehicles system
(AGVS) are presented. It is shown that the design of an AGVS can
determine the minimum number of vehicles required in a system. A large
scale integer program which can be formulated to include such factors as
time phased pickup and dropoff requirements, pickup and dropoff area
floor space capacity, and track congestion is shown to be intractable.
Rather, a simple time dependent model which can be used to find the
minimum number of vehicles is suggested. Other analysis tools for
evaluating time dependent behavior of an AGVS are supplied. A procedure
for dispatching vehicles is developed and a method for measuring the
blocking time caused by congestion is provided.
Hardware failures notwithstanding, the ability of an automated system operating according to promised potential is dependent upon the operational control measures in force. In this paper, some heuristic rules for dispatching Automatic Guided Vehicles (AGVs) in a job shop environment are presented. The rules are useful for assigning priorities to work stations requesting the services of a vehicle for material pickup. The likely effects of these rules on the performance of a job shop are postulated. Simulation results to demonstrate the effects of these rules arc also presented.
One of the important issues in the design of an automated guided vehicles system (AGVS) is the determination of the number of vehicles needed to operate the system in an efficient and economical way. In this paper a multi-criteria optimization model is developed using two goals, cost and throughput performance. By using a trade-off ratio between the goals the number of AGVs needed in the systems is determined. Use of management decision tables to enhance the solution procedure is introduced
In automated container terminals, containers are transported from the marshalling yard to a ship and vice versa by automated vehicles. The automated vehicle type studied in this paper is an automated lifting vehicle (ALV) that is capable of lifting a container from the ground by itself. This study discusses how to dispatch ALVs by utilizing information about pickup and delivery locations and time in future delivery tasks. A mixed-integer programming model is provided for assigning optimal delivery tasks to ALVs. A procedure for converting buffer constraints into time window constraints and a heuristic algorithm for overcoming the excessive computational time required for solving the mathematical model are suggested. Numerical experiments are reported to compare the objective values and computational times by a heuristic algorithm with those by an optimizing method and to analyze the effects of dual cycle operation, number of ALVs, and buffer capacity on the performance of ALVs.
Minimizing time number of tankers to meet a fixed schedule
- G B Dantizing
- D R Fulkerson
Dantizing, G.B., Fulkerson, D.R.: Minimizing time number of tankers to meet a fixed
schedule. Naval Research Logistics Quaterly 1, 217-222 (1945)
- R K Ahuja
- T L Magnati
- J B Orlin
Ahuja, R.K., Magnati, T.L., Orlin, J.B.: Network Flows, Theory, Algorithms, and Appliquations. Prentice Hall, New Jersy (1993)
Dispatching automated guided vehicles in a mega container terminal
- Y Chen
- T.-Y Leong
- J W C Ng
- E K Demir
- B L Nelson
- D Simchi-Levi
Chen, Y., Leong, T.-Y., Ng, J.W.C., Demir, E.K., Nelson, B.L., Simchi-Levi, D.: Dispatching automated guided vehicles in a mega container terminal. The National University of Singapore/Dept. of IE & MS, Northwestern University (1997)
Where to use AGV systems, manual fork lifts, traditional fixed roller conveyor systems respectively
- K J Dahlstrom
- T Maskin
Dahlstrom, K.J., Maskin, T.: Where to use AGV systems, manual fork lifts, traditional
fixed roller conveyor systems respectively. In: Proceedings of the 1st International AGV
Conference, 173-182 (1981)
Comparaison of operating costs between different transportation systems
- T Muller
Muller, T.: Comparaison of operating costs between different transportation systems. In:
Proceedings of the 1st International AGV Conference, pp. 145-155
AN economic model for determining AGV fleet size
- D Sinirech
- J M A Tanchoco
Sinirech, D., Tanchoco, J.M.A.: AN economic model for determining AGV fleet size. International Journal of Production Research 29(9), 1725-1268 (1992)