Automated guided vehicle system for two container yard layouts

Transcription

1 Transportation Research Part C 2 (24) Automated guided vehicle system for two container yard layouts Chin-I. Liu a, *, Hossein Jula b, Katarina Vukadinovic c, Petros Ioannou b a Aplus Flash Technology Inc., 78 Montague Expwy. Suite 4, San Jose, CA 953, USA b Department of Electrical Engineering, University of Southern California, Los Angeles, CA , USA c Department of Transport and Traffic Engineering, University of Belgrade, Belgrade, Federal Republic of Yugoslavia Received 6 August 2; received in revised form 6 March 23 Abstract The explosive growth in the freight volumes has put a lot of pressure on seaport authorities to find better ways of doing daily operations in order to improve the performance and to cope with avalanches of containers processing at container terminals. Advanced technologies, and in particular automated guided vehicle systems (AGVS), have been recently proposed as possible candidates for improving the terminalõs efficiency not only due to their abilities of significantly improving the performance but also to the repetitive nature of operations in container terminals. The deployment of AGVS may not be as effective as expected if the container terminal suffers from a poor layout. In this paper, simulation models are developed and used to demonstrate the impact of automation and terminal layout on terminal performance. In particular, two terminals with different but commonly used yard configurations are considered for automation using AGVS. A multi attribute decision making (MADM) method is used to assess the performance of the two terminals and determine the optimal number of deployed automated guided vehicles (AGVs) in each terminal. The simulation results demonstrate that substantial performance can be gained using AGVS. Furthermore, the yard layout has an effect on the number of AGVs used and on performance. Ó 24 Elsevier Ltd. All rights reserved. Keywords: AGV; Container terminal; Automation; Optimization * Corresponding author. address: chiniliu@yahoo.com (Chin-I. Liu) X/$ - see front matter Ó 24 Elsevier Ltd. All rights reserved. doi:.6/j.trc

2 35 Chin-I. Liu et al. / Transportation Research Part C 2 (24) Introduction The increasing demand of commodity pushes the development of industry and flourishes international trade, which leads to the growth of container volumes transported by ships through seaports. For instance, port of Long Beach, USA has experienced 4% of container growth from 3.5 million Twenty-foot Equivalent Units (TEUs) in 997 to 4 million TEUs in 998 and an increase of 7.6% over 998 to 4.4 million TEUs in 999. A continuing growth is expected in the near future. The fact put enormous pressure on seaport terminals to improve their managements and find better ways of doing daily operations. Old-fashioned solutions such as land expansion are not realistic in many areas due to the scarcity of the land close to the seaports. Therefore, the replacement of the conventional manual operation is inevitable, and the use of automation that can increase the efficiency and reduce the operational cost seems to be the only possible way for the future terminals (Ioannou et al., 2). With the advances in the computing power and vehicle sensor capabilities, the automated guided vehicle systems (AGVS) emerge as candidates for improving the container terminals performance. Currently, AGVS are finding their ways in manufacturing systems for material handling purposes. The use of AGVS as yard vehicles that carry containers is promising. Presently, semi-automated AGVS are successfully applied in ports such as Rotterdam, the Netherlands, and Thamesport, England. Prototypes are under testing at port of Singapore and port of Kaoshiung, Taiwan. A profitable evaluation result of terminal automation using AGVS had also been studied at Norfolk International Terminal (NIT), USA (Larsen and Moses, 996; Towery et al., 996). Interested readers in AGVdesigning and navigational systems in container terminals are refereed to (Durrant-Whyte, 996; Ioannou et al., 2). Due to the cost and complexity involved in port and vessel operations, the simulation models have been used intensively to understand the behavior and test different strategies in the container terminal systems, e.g. see (Bruzzone, 998; Chung et al., 988; Hayuth et al., 994). These simulators differ widely in objectives, complexity, and details. Ballis and Abacoumkin (996) developed a simulation model to evaluate different configurations, such as changes in the yard layout and equipment. Ramani (996) developed an interactive simulation model to analyze container port operations and evaluate the port performance. Yun and Choi (999) developed a simulation model to analyze container handling at the marine container terminal, and obtain estimates for terminal performance indicators. It is worth mentioning that to obtain an efficient terminal, three decision levels shall be considered: strategic, tactical and operational levels. At the strategic level, for instance, the terminal layout and the choice of the material handling system in the yard shall be considered. At the tactical level, problems such as the placement of containers and the paths towards them are the main issues to be addressed. Finally, at the operational level, all detailed daily problems should be solved. The purpose of this paper is to investigate the deployment of fully automated AGVS at the strategic level in container terminals. In particular, two commonly used yard layouts are considered to assess the impact of automation and evaluate the effect of the yard layout on the terminal performance. For each yard layout, three operational scenarios are considered and compared: loading, unloading, and combined loading and unloading operations. Simulation models are developed for evaluating the performance of the two terminals for different scenarios. These models are validated using real-life yard operational data obtained from the NIT. In addition,

3 Chin-I. Liu et al. / Transportation Research Part C 2 (24) to determine the optimal number of AGVs deployed for each scenario in each terminal, a multi attribute decision making (MADM) method is used. The simulation results demonstrate that substantial performance can be gained using AGVS and that the yard layout has an effect on the number of automated guided vehicles used and on performance. The results also indicate that the combined operation shows a great promise in increasing the terminal throughput and achieving high utilization of equipment in the yard. This paper is organized as follows: Section 2 is devoted to the problem and yard layouts descriptions. In Section 3, the developed control logic for conflict avoidance is described. Section 4 presents definitions adopted and used throughout the paper. The performance evaluation and optimization method used in this paper are discussed in Section 5, and the simulation scenarios and results are provided in Section 6. Finally, Section 7 concludes the paper. 2. Problem description and yard layouts Yard layout determines the placement of containers as well as the network of the roads within a terminal. Poor layout delays the loading and unloading operations, introduces congestion, and requires more traveling and re-handling efforts in the yard. The purpose of this paper is to evaluate, through simulation, the impact of deploying AGVS in two commonly used container terminal configurations which, for simplicity, hereafter will be called yard layouts I and II. These layouts are shown in Figs. and 2, respectively. In this paper, our intention is to deploy automated guided vehicles (AGVs) for container movements within the terminals while minimizing the changes on the terminals facilities and existing infrastructures. In the yard layout I, the container stacks are placed horizontally (in parallel with the berth), whereas the stacks are arranged vertically (perpendicular to the berth) in the yard layout II. Both layouts can be found at many container terminals throughout the world. For instance, the yard layout I can be found in the port of Long Beach, USA, and the layout II in the port of Kelung, Taiwan. In the rest of this section, we briefly describe each layout. Fig.. Yard layout I.

4 352 Chin-I. Liu et al. / Transportation Research Part C 2 (24) Fig. 2. Yard layout II. Layout I: In the yard layout I, two four-lane roads (located vertically in the middle of the yard) separate the yard into three major parts, which are called blocks. In each block, horizontally placed two-lane roads divide each block into six container stacks. The two-lane roads between container stacks are called working roads, while the roads surrounding the blocks are the transit roads. The working roads are used solely for the purpose of container pick-up and drop-off activities, while the transit roads are reserved only for AGVs traveling in the yard. The lanes in both working and transit roads are considered to be unidirectional lanes. To prevent the possibility of blocking in the roads, we assume that a working road can only be occupied by a single AGVat a time. That is, once a working lane is occupied by an AVG, no other AGVs are allowed to enter that lane. The AGVs are dispatched in a pre-planned or static way to pick-up and/or drop-off containers. In other words, once a task is assigned to an AGV, the path toward its destination will not be changed. It is assumed that each stack is equipped with one yard crane. Each yard crane loads (unloads) container (containers) to (from) adjacent working lane(s). The AGVs are served by the crane based on the first come first served (FCFS) rule. That is, if both adjacent lanes to a stack are occupied by two AGVs at the same time, the vehicles are served according to FCFS rule. It is also assumed that three ship cranes are serving container ships at the berth, and that AGVs are served based on FCFS rule by ship cranes. We assume that at most six AGVs are allowed to wait in any ship crane queue at each instant of time. Normally, the export containers (i.e., the outbound containers) are placed closer to the berths. This will facilitate and increase the speed of loading operations. In Fig., the portion of the layout labeled by letter ÔAÕ is designated for export containers while Portion ÔBÕ is for import containers. Layout II: The characteristics of the yard layout II are very much similar to those of the yard layout I. The differences between these two layouts are as follows: In yard layout II, there is only one big block, which is separated by vertical two-lane roads into 5 container stacks. There is only one transit road located horizontally close to the berth; all other roads are working roads. The lanes of the transit road are considered to be unidirectional lanes, while the lanes of the working roads are bi-directional.

5 Chin-I. Liu et al. / Transportation Research Part C 2 (24) Similar to the yard layout I, in the yard layout II each stack is equipped with one yard crane; three ship cranes are used at the berth with the same characteristics; and the serving policy for cranes is based on FCFS. As shown in Fig. 2, the yard is also divided into two parts: Part A and Part B for export and import containers, respectively. 3. Control logic The control logic is to guarantee the smooth traffic flow within the yard. It provides the required mobility through avoiding or resolving the possible conflicts and deadlocks. Therefore, the main portion of the developed simulation models consists of the control logic and protocols governing and dictating the motion of AGVs. To do so first, a database describing the yard layout (network) is developed. Then, the intersections, and loading and unloading points (nodes) of the network are defined. Once, the pick-up and drop-off points are assigned to a particular AGV, the path is uniquely determined by using the intermediate nodes. In the following, we describe the possible conflicts in a container terminal and how the designed control logic resolves them.. AGVs traveling in the opposite directions on a same path: Yard layouts I and II are designed in such a way that this type of conflict is avoided. Recall that, in both yard layouts all lanes in transit roads are unidirectional and, thus, the traffic flows in opposite directions are separated. In the yard layout I, the working roads consist of two unidirectional lanes, thus, no AGVs in opposite direction may travel in the same lane. In the yard layout II, even though the working lanes are bi-directional, only one AGVis allowed to enter a working lane at a time. Therefore, no such a conflict may occur in the yard layouts I and II. 2. Different speed of AGVs traveling along the same path: The control logic enforces Low Speed Zones to resolve this type of conflict. That is, wherever a number of AGVs are moving in the same direction with different speeds (e.g., loaded and empty AGVs), the control logic ensures that the speeds of all AGVs in that zone are the same and equal to the lowest possible speed (see also Section 6). 3. AGVs arriving at an intersection from different paths at the same time: The control logic applies a Ômodified first come first passõ (MFCFP) concept, which is similar to the Ôstop signõ rule in urban traffic. Fig. 3 demonstrates a typical intersection within the yard, which is used bellow to describe the MFCFP concept. If two or more AGVs arrive at an intersection at the same time, and there is no possible collision between them, all AGVs will proceed with their maneuvers simultaneously. For instance, let AGVs A and C arrive at an intersection at the same time. AGV A wants to make a right turn. In this case C can proceed with its either right or left turn maneuver since there is no possible collision. When two or more AGVs arrive at a particular intersection from different segments at the same time, the right of the way is given to the vehicle(s) in the transit lane. That is AGVF has priority over G in case G wants to make a left turn into the transit lane.

6 354 Chin-I. Liu et al. / Transportation Research Part C 2 (24) Transit Road E H A B F C G Working Road D Fig. 3. A typical intersection in the yard. Consider that two AGVs arrive at an intersection at the same time from different segments of a transit road. If there is any possible collision between them, the priority of continuing their maneuver will be given to the AGVs based on their intention and in the following order: () to go straight (top priority), (2) to make a right turn, and (3) to make a left turn. For example, if AGVs A and B arrive at a particular intersection and A wants to make a right turn while B wants to make a left turn, the right of the way will be given to A, since turning right has the priority over turning left. When two or more AGVs happened to be at the same segment at the same time, say A and H, and if the leading one, A, stops at an intersection, or in the middle of a segment, the following AGV, H, will stop at least ft (3.48 m) away from the leading one. If these two or more AGVs are traveling in different segments, say A and E or D and A, and the leading vehicle A(D) stops at an intersection (in the middle of a segment), the following vehicle E(A) is barred from entering the next segment, which is occupied by the leading one. This rule is set to ensure that an intersection is always open to the through traffic. If an AGVbegins its maneuver at a particular intersection, say AGVC has already started to make a left turn, other arriving AGVs at that particular intersection, say A and B, should stop at intersection until the end of the maneuver. 4. Definitions In order to assess the performance of operations within a container yard, the following definitions are adopted and used throughout the paper. Definition. Busy period of an AGV. An AGVis called to be in its busy period if it is in one of the following situations: Being served by either a ship crane or a yard crane. Traveling in the yard to load/unload an assigned container. If an AGVis not in its busy period, it is in idle period. For example, the idle period includes the time a particular AGVis waiting in the ship or yard cranesõ queue to be served, and the time during which it stops either at an intersection or in the middle of a segment to prevent any possible collisions.

7 Definition 2. Busy period of a ship (yard) crane. A ship (yard) crane is said to be in its busy period if it is occupied with loading/unloading task. Definition 3. Idle period of equipment i. Equipment i (an AGV, a ship or yard crane) is said to be in its idle period, denoted by idle period(i), when the equipment is not in its busy period. Definition 4. Idle rate of equipment i. The idle rate of equipment i, denoted by IR i is defined as follows, Idle PeriodðiÞ IR i ¼ Busy PeriodðiÞþIdle PeriodðiÞ % where Busy Period(i) denotes the busy period of equipment i (an AGVor a ship/yard crane) which was defined in Definitions and 2, above. Definition 5. Average idle rate (AIR) of equipment i. The average idle rate of N pieces of a class (same type, i.e., AGVs, ship or yard cranes) of equipment i is defined as follows, P N i¼ AIR ¼ IR i N Definition 6. Throughput. The average number of containers being loaded/unloaded per hour per ship crane is referred to as the throughput of a terminal. In other words, the throughput is Throughput ¼ Total number of containers loaded=unloaded to=from the ship Total time elapsed ½in hoursš Number of ship cranes Definition 7. Waiting period of AGV i. AGV i is said to be in its waiting period, denoted by Waiting Period(i), when it is waiting in the queue of a ship or yard crane to be served. Definition 8. Stop period of AGV i. An AGV i is said to be in its stop period, denoted by Stop Period(i), when it is stopped at an intersection or in the middle of a segment to avoid collision. Note that the collision avoidance rules are implemented by the control logic as described in Section 3. Definition 9. Waiting rate of AGV i(wr i ): The waiting rate of AGV i is defined as follows Waiting PeriodðiÞ WR i ¼ Total time elapsed ½in hoursš % Definition. Stop rate of AGV i(sr i ): The stop rate of AGV i is defined as follows SR i ¼ Chin-I. Liu et al. / Transportation Research Part C 2 (24) Stop PeriodðiÞ Total time elapsed ½in hoursš %

8 356 Chin-I. Liu et al. / Transportation Research Part C 2 (24) Definition. Average waiting rate (AWR). The average waiting rate of N number of AGVs is P N i¼ AWR ¼ WR i N Definition 2. Average stop rate (ASR). The average stop rate of N number of AGVs is P N i¼ ASR ¼ SR i N 5. Performance evaluation and optimization Plenty of performance criteria can be used to evaluate the effectiveness of an operation policy within a container yard. The single criterion such as, minimum of total time to complete the jobs, minimal installation cost, minimal mean time in the yard (mean flow time), and minimal mean queue time are just a few criteria that one may consider. Actually, being complex and big as port operation system, single criterion seems not sufficient and may mislead the correct results. More factors need to be taken into account to be able to render more accurate insight about the efficiency and performance of port operations. Multiple attribute decision making (MADM) which deals with problems involve multiple, sometimes conflicting, criteria is more suitable to be applied. MADM is a technique that tries to measure goal accomplishments by establishing reasonable attributes. Based on the chosen attributes, Alternatives are then constructed. Usually, all attributes are not of equal importance to a decision-maker so that every attribute is assigned with different appropriate weight (Hwang and Yoon, 98; Yoon and Hwang, 995). Assume that the set of all attributes is Jm ¼fX ; X 2 ;...; X J g, and the set of all alternatives is W ={A,A 2,...,A I }. Also assume that the outcome of alternative A i with respect to attribute X j is x ij, and w ij is the weight for outcome x ij. Then, V(A i ), the value of alternative A i is defined as: V ða i Þ¼U i ðx i ; x i2 ;...; x ij ; w i ; w i2 ;...; w ij Þ; i ¼ ; 2;...; I The function U i : R J R J! R is the value generator for alternative A i. Function T is the decision-maker that chooses the best alternative A, usually the minimum or maximum, from all alternatives as follows: A ¼ TðVðA Þ; V ða 2 Þ;...; V ða I ÞÞ In this paper, we use one of the most widely used and best known MADM methods, Simple additive weighting method (SAWM). The value of alternative A i in this method is defined as: P J j¼ V ða i Þ¼ w j x ij P J j¼ w ; i ¼ ; 2;...; I j

9 Chin-I. Liu et al. / Transportation Research Part C 2 (24) In general, the weight w ij does not alter by changes in the alternatives, i.e., for different alternatives, i =,2,...,I, the weight w j = = w Ij = w j. Sometimes, the weights in MADM method are normalized so that P j w j ¼. Thus, the SAWM can be formulated as follows: V ða i Þ¼ XJ j¼ w j x ij ;... i ¼ ; 2;...; I and X A ¼fA i j max V ða i Þðor min V ða i ÞÞg;...; i ¼ ; 2;...; I i i j w j ¼ As mentioned above, a number of parameters can be selected in order to optimize the performance of a container terminal. In this paper, we are interested in obtaining the optimum number of AGVs deployed in the container yard so that a specific performance index becomes minimum. We assume that the yard layout (either yard layout I or II), the control logic (described in Section 3), and the number of deployed yard and ship cranes are given. The SAWM method is used to find the optimum number of AGVs while the AIRÕs of the equipment in the yard are chosen as attributes. Therefore, the performance index, A, for yard operations is defined as following: V ða i Þ¼ X3 j¼ w j x ij ; i ¼ ; 2;...; I A ¼fA i j min i V ða i Þg i ¼ ; 2;...; I where w j is the weighted penalty for x ij, and P w j ¼. x i is the average idle rate (AIR) of ship cranes when i AGVs are deployed, x i2 is the AIR of yard cranes when i AGVs are deployed and (out), and x i3 is the AIR of AGVs when i AGVs are deployed. A is the optimum number of AGVs deployed in the yard such that the sum of the weighted AIRÕs of equipment is minimum. 6. Simulation scenarios In this paper, we develop and use simulation models to demonstrate the impact of automation and terminal layout on terminal performance. Three different scenarios are considered in this paper: manual operation, automated terminal using yard layout I, and automated terminal using yard layout II. We start by describing the assumptions we made for characteristics of equipment in the yard. 6.. Assumptions The equipments in the container terminal are assumed to have the following characteristics.

10 358 Chin-I. Liu et al. / Transportation Research Part C 2 (24) AGVs: The speed of an AGVis 5 mph (2.24 m/s) when it is loaded and mph (4.48 m/s) when empty (These speeds are chosen according to the speed of AGVs in the Port of Rotterdam, Netherlands). Yard cranes: Travel speed is 5 mph (2.24 m/s) toward a container. It needs 5 s. for lining up with the stack, and the average time of loading/unloading a container is 5 s. Ship cranes: The speed of a ship crane is assumed to be 75 moves/h. This speed is chosen based on current and expected performance of advanced cranes which are either in use or in conceptual designs. For each automated operation, three different operational scenarios are considered: loading, unloading, and combined loading and unloading operations. In the combined loading and unloading operation, an AGVcarrying a container is unloaded and then loaded with a new container by a ship crane. Therefore, the AGVhas to wait for a complete ship crane loading and unloading cycle (complete move). However, it is assumed that in the single loading or unloading operation, AGVcan leave the ship crane after waiting for 25% of the complete move Base scenario: manual operation The manual operation is considered here to validate the developed simulation models using real-life yard operations. The operation is simulated according to the data provided in [Error! Reference source not found.], [Error! Reference source not found.] for the Norfolk International Terminal (NIT). The following is the summary of the manual operation at the NIT. In the NIT, the ship crane area is equipped with three ship cranes capable of 45 moves per hour, and the container stack storage area has seven rubber tired gantry cranes. Eighteen hostlers are used to move the containers between container stack storage area and the ship cranes. In their analysis (Larsen and Moses, 996; Towery et al., 996), the authors showed that even though the average moving speed of hostlers is about 7 mph (25 ft/s), a hostler moves approximately six containers per hour which gives us the Average Move Time min per move. A move is defined as the movement of a container from an initial pick-up point to a destination point (including the time the hostler waits to get loaded or unloaded). By using the fact that the average distance traveled by a hostler is 5 ft (.284 mile/457.2 m), we conclude that the average actual speed (AAS) in mph of the hostler can be approximated as follows: AAS ¼ Average Distance Average Move Time þ Loading Time þ Unloading Time ¼ :284 :67 þ T l þ T u where T l = Loading Time, and T u = Unloading Time are the times needed for the hostler to get loaded by the yard crane and get unloaded by the ship crane. In this paper, AAS is used as the speed of AGVs in the simulation of manual yard operation Yard layout I: automated operation It is assumed that the dimensions of the yard layout I, shown in Fig., are 7 ft width by 4 ft length (22.5 acres). The storage yard consists of 8 stacks (3 blocks * 6 stacks per block). Each

11 Chin-I. Liu et al. / Transportation Research Part C 2 (24) stack contains 2 cells for storing containers, and can accommodate 36 containers when the containers are stacked 3-high. Therefore, the capacity of the yard is 648 Twenty-foot equivalent units (TEUs). Recall that for the automated operation, three different scenarios are considered. Loading operation: The export containers are placed in the whole yard (Parts A and B in Fig. ) and are to be loaded to the ships. Therefore, 8 container stacks and 8 yard cranes serving each stack are involved in this operation. As shown in Fig., the low speed zone for loading operation is the area marked by Ô*Õ. Unloading operation: The unloaded containers from the ships are stored in the upper nine container stacks (Part B); only nine yard cranes are used. The low speed zone is as the same as that in the loading operation. Combined loading and unloading operation: The export containers, which are stored in Part A, are to be loaded to the ships, while the import containers are to be unloaded from the ships and stored in Part B, simultaneously. That is, an AGVpicks up an export container from Part A, transfers it to the ship cranes, gets unloaded by the a ship crane and loaded with an import container. The import container, then, is moved to Part B. In this operation, 8 yard cranes are used. The low speed zone for the combined loading and unloading operation is marked by Ô+Õ in Fig Yard layout II: automated operation The dimensions of the yard layout II, shown in Fig. 2, are exactly the same as those of the yard layout I. The storage yard consists of 5 container stacks each with 56 cells. Since the container can be stacked 3-high, the capacity of the yard is 72 TEUs. The following describes the loading, unloading, and combined loading and unloading operations in the yard layout II. Loading operation: The export containers are located in the whole yard (Parts A and B in Fig. 2). Since there are only 5 container stacks, totally 5 yard cranes are used. In Fig. 2, the area marked by Ô*Õ is the low speed zone. Unloading operation: The import containers unloaded from the ships are stored in the Part B. Since 5 container stacks are still needed, 5 yard cranes are used. The low speed zone is as the same as that in the loading operation. Combined loading and unloading operation: The export containers are stored in Part A and import containers in Part B. The loading and unloading operations are performed simultaneously. In this operation, 5 yard cranes are deployed, and no low speed zone is needed Analysis of simulation results The software packages Matlab, Simulink and Stateflow are used to perform the simulation of the above mentioned scenarios.

12 36 Chin-I. Liu et al. / Transportation Research Part C 2 (24) Simulation : simulation of manual operations This simulation study is used to validate the developed simulation models using real-life yard operations. Similar to the first scenario used in (Towery et al., 996), 8 AGVs are deployed with the average actual speed (AAS) described earlier. The throughputs achieved in our simulations for this scenario are 27 containers/ship-crane/hour for the yard layout I, and 3 containers/ship-crane/ hour for the yard layout II. These throughputs are very much comparable with the actual statistical throughout obtained at the NIT which is 28 containers/ship crane/hour. Despite the fact that the yard layouts I and II and the characteristics of yard equipment are different from those in the NIT terminal, our simulation result is compatible with the studies in (Larsen and Moses, 996; Towery et al., 996) due to the following factors: The time needed to load a hostler in the manual yard and to unload it by the ship crane is negligible compared to the time needed for the hostler to travel between the two points. Although the three yard layouts are different, since the AGVs are traveling with average speed equal to AAS and the number of AGVs is the same as the hostlers used in [Error! Reference source not found.], the throughput of the three yards should be approximately the same. The maximum speed of ship crane in our simulations is equal to 75 moves per hour, while the crane throughput of NIT is 45 moves per hour. It can be seen that, the speed of the cranes does not affect the throughput much. That is, no matter how fast the ship crane is, if the feeding speed is slow then the output will be low Simulation 2: yard layout I The automated operations for the yard layout I are simulated by using different number of AGVs. Figs. 4 6 demonstrate the results of these simulations for loading, unloading, and combined loading and unloading operations, respectively. The simulation results for each operation are discussed in the following. Loading operation: As the number of AGVs deployed in the yard is increased, the throughput of the terminal reaches its maximum value, 75 moves/hour/ship-crane, as shown in Fig. 4d. However, as the throughput approaches its maximum value, adding more AGVs in the yard could not provide any more improvement. Note that, by adding more AGVs in the yard, AGVs simply spend more time in the ship cranesõ queues waiting to be served. The fact also can be seen in Fig. 4c. The figure demonstrates that the ÔAWR under ship cranesõ is increased drastically as more number of AGVs is deployed in the yard. Similarly, using more number of AGVs in the yard increases the congestion rate. Fig. 4a shows the ASR of AGVs versus the number of deployed AGVs. Note that, when the number of AGVs exceeds 8 in Fig. 4a, AGVs spend more time in the ship cranesõ queues rather than traveling in the yard to do the next loading task. That is the reason why the ASR is slightly decreased. When the number of deployed AGVs increases, more AGVs visit yard and ship cranes. In other words, the idle rates of yard and ship cranes, as shown in Fig. 4b and e, are decreased with number of AGVs used. Note that as the throughput reaches its maximum value, the ship cranes are always busy. However, the fact is not true for the yard cranes, which as shown in Fig. 4e are always underutilized, and consequently the ÔAWR under yard cranesõ is very low. As shown in Fig.

13 Chin-I. Liu et al. / Transportation Research Part C 2 (24) ASR of AGV ( % ) No. of loaded Containers/Hour/Ship Crane (a) (d) AIR of Ship Crane ( % ) AIR of Yard Crane ( % ) (b) (e) Number of AGVs AWR Under Ship Crane ( % ) AWR Under Yard Crane ( % ) (c) (f) Fig. 4. The simulation results of the loading operation for the yard layout I. ASR of AGV ( % ) No. of Unloaded Containers/Hour/Ship Crane (a) (d) AIR of Ship Crane ( % ) AIR of Yard Crane ( % ) (b) (e) Number of AGVs AWR Under Ship Crane ( % ) AWR Under Yard Crane ( % ) (c) (f) Fig. 5. The simulation results of the unloading operation for the yard layout I. 4f, there is some uncertainty involved with AWR. The main reason is in our simulation program the containers (tasks) are randomly assigned to AGVs. Unloading operation: Fig. 5 shows the simulation results of unloading operation. The results indicate that the outcomes of the unloading operation are very much the same as those of loading operation presented above. To avoid repetition, in the following we emphasize the differences between the two operations as well as the important issues in the unloading operation.

14 362 Chin-I. Liu et al. / Transportation Research Part C 2 (24) ASR of AGV ( % ) No. of Loaded/Unloaded Containers/Hour/ Ship Crane (a) (d) AIR of Ship Crane ( % ) AIR of Yard Crane ( % ) (b) (e) Number of AGVs AWR Under Ship Crane ( % ) AWR Under Yard Crane ( % ) (c) (f) Fig. 6. The simulation results of the combined operation for the yard layout I. The comparison between Figs. 5d and 4d indicates that for fewer number of AGVs deployed in the yard, say 5, the throughput of the unloading operation is 6 containers/hour/ship crane which is slightly lower than that of the loading operation, 62 containers/hour/ship crane. The longer distance an AGVhas to travel between Part B and the ship cranes for the unloading operation accounts for the decrease in the throughput. But, actually, the discrepancy of throughput is very small. It is because the waiting time of AGVs in the ship cranesõ queues is also decreased. As we can see from Fig. 5c, the reduction of the waiting time even up the effect of longer distance traveling. As number of AGVs increases within the yard, the AIR of ship and yard cranes shown in Fig. 5b and e, respectively follow almost the same shape as those in Fig. 4b and e. Note that the AIR of yard cranes for the unloading operation is almost half of that of the loading operation since a lower number of yard cranes is used in the unloading operation. Recall that in the unloading operation nine yard cranes are used versus 8 in the loading operation. The lower number of yard cranes used in the unloading operation also accounts for the increase in the waiting rate under yard crane, as shown in Fig. 5f, compared to that of the loading operation. In both loading and unloading operations, heavy traffic can be observed at or around the low speed zone areas. Since in the unloading operation, AGVs spend more time traveling between yard stack area and ship cranes, slightly lower stop rate can be seen in Fig. 5a compared to Fig. 4a. Combined loading and unloading operation: With this operation involves extra task of performing both loading and unloading simultaneously as compared to the single loading or unloading operations. Therefore, more AGVs are needed in the yard to reach the maximum throughput. In our simulation program, the maximum number of deployed AGVs was 24. As shown in Fig. 6d, by using this number of AGVs the throughput has not yet reached itõs maximum value which is 5 containers/hour/ship-crane (for loading and unloading containers). Fig. 6d demon-

15 Chin-I. Liu et al. / Transportation Research Part C 2 (24) strates that for the same number of AGVs, as much as 35% improvement can be seen in the throughput of the combined operation compared to that of the single loading/unloading operations. The combined operation has not doubled the throughput of the terminal as one may have been expected due to the following reasons. () The Low Speed Zones, where AGVs are barred of traveling with their maximum speed, have been expanded in the combined operation. (2) Contrary to the single loading or unloading operation, in which AGVs are traveling between stack area (Part A or B) and ship areas back and forth, in the combined operation AGVs have to undergo an extra task. That is an AGVgets unloaded in Part B, travels empty to Part A, gets loaded in Part A, and then travels to ship crane area. (3) AGVs spend more time in ship crane queues. More specifically, AGVs has to wait for a complete ship crane loading and unloading cycle (complete move). In the single loading or unloading operation the waiting time under ship cranes is 25% of the complete move. In combined operation, AGVs travel more in the stack area (in particular, an extra move between Parts A and B), and undergo extra loading/unloading operations by yard cranes. These extra tasks account for the followings phenomenon: () Reduction in the time AGVs spend in the ship cranesõ queues, see Fig. 6c, and increment in the ship cranes idle rate, see Fig. 6b. (2) Increment in the time they spend in the yard cranesõ queues, see Fig. 6f, and reduction in the yard cranes idle rate, see Fig. 6e. Note that in the combined operation 8 yard cranes are used compared to 9 in the unloading operation. (3) Reduction in the time they travel in the Low Speed Zone, and consequently, reduction in the ASR shown in Fig. 6a Simulation 3: yard layout II Loading operation: The results of the loading operation in the yard layout II is presented in Fig. 7. As illustrated in Fig. 7d, the maximum throughput of 75 moves/hour/ship-crane is almost reached when 8 AGVs are deployed. Compared to 2 AGVs needed in the yard layout I to reach ASR of AGV ( % ) No. of loaded Containers/Hour/Ship Crane (a) (d) AIR of Ship Crane ( % ) AIR of Yard Crane ( % ) (b) (e) Number of AGVs AWR Under Ship Crane ( % ) AWR Under Yard Crane ( % ) (c) (f) Fig. 7. The simulation results of the loading operation for the yard layout II.

16 364 Chin-I. Liu et al. / Transportation Research Part C 2 (24) the same performance, a saving of three AGVs is obtained by using the yard layout II. Therefore, for the same number of AGVs used, the AIR of the ship and yard cranes are significantly lower than those of the yard layout I as shown in Fig. 7b and e, respectively. Another reason to gain lower AIR for yard cranes in the yard layout II is that the number of yard cranes used in this layout is only 5, while it was 8 for the layout I. That is why the yard cranes in the yard layout II are visited more often and the waiting rate under the yard cranes is higher, see also Fig. 7f. Similar to the yard layout I, in the yard layout II the low speed zone is the area with the heaviest traffic of AGVs. As discussed, for the same number of AGVs used in both yard, say 2, higher throughput is observed in the yard layout II which can be translated in more activity in the low speed zones, and higher ASR for AGVs in this layout. Fig. 7a demonstrates the ASR of AGVs for the loading operation. The higher throughput obtained in the yard layout II also implies more activities by ship cranes, which leads to larger waiting rates under ship crane as demonstrated in Fig. 7c. Unloading operation: Fig. 8d shows the throughput of the unloading operation in the yard layout II. Similar to the loading operation, for the unloading operation, a slightly higher throughput can be observed in the yard layout II compared to that of the yard layout I. Therefore, in the yard layout II, the AIR of ship crane, see Fig. 8b, is slightly lower and the AWR of AGVs under the ship cranes, see Fig. 8c, are slightly higher than those in Fig. 5b and c, respectively. Recall that in the unloading operation in the yard layout I only nine yard cranes were used, while this number is 5 yard cranes for the layout II. That is why higher AIR of yard cranes, and lower AWR of ÔAGVs under yard cranesõ can be observed in Fig. 8e and f compared to those of yard layout I presented in Fig. 5e and f, respectively. Combined loading and unloading operation: Similar to combined operation in yard layout I, substantial increase can be seen in the throughput of the combined operation compared to the single loading/unloading operations in the yard layout II. As shown in Fig. 9d, the throughput has not ASR of AGV ( % ) No. of loaded Containers/Hour/Ship Crane (a) (d) AIR of Ship Crane ( % ) AIR of Yard Crane ( % ) (b) (e) Number of AGVs AWR Under Ship Crane ( % ) AWR Under Yard Crane ( % ) (c) (f) Fig. 8. The simulation results of the unloading operation for the yard layout II.

17 Chin-I. Liu et al. / Transportation Research Part C 2 (24) ASR of AGV ( % ) No. of loaded Containers/Hour/Ship Crane (a) (d) AIR of Ship Crane ( % ) AIR of Yard Crane ( % ) (b) (e) Number of AGVs AWR Under Yard Crane ( % ) AWR Under Ship Crane ( % ) (c) (f) Fig. 9. The simulation results of the combined operation for the yard layout II. yet reached its maximum value, (5 containers/hour/ship crane) even though 24 AGVs has been deployed in the yard. The throughput shows up to 35% increase compared to that of the single loading/unloading operations. Comparing the simulation results of the combined operations in the yard layouts I and II, presented in Figs. 6 and 9, a higher throughput is observed for the yard layout II and for the same number of AGVs. Therefore, lower AIR of ship cranes, and higher AWR under ship cranes can be obtained in the yard layout II. Higher throughput together with fewer number of yard cranes deployed in the yard layout II leads to substantial reduction in AIR of yard cranes and increment in AWR under yard cranes, as demonstrated in Fig. 9e and f respectively. The higher throughput can also be translated in more activities in the yard layout II which implies higher ASR for AGVs in this layout compared to that of the yard layout I. Fig. 9a demonstrates the ASR of AGVs for combined operation. Fig. compares the throughput of the loading, unloading, and combined operations obtained from yard layouts I and II, while 8 AGVs are deployed in each yard. The figure demonstrates that the throughput of the yard layout II is slightly higher than that of the yard layout I. Moreover, the combined operation shows a great promise in increasing the terminal throughput and achieving high utilization of equipment in the yard regardless of the yard layouts. To find the optimum number of AGVs in an automated container terminal which results in the minimum sum of the weighted AIRs of ship cranes, yard cranes and AGVs we employ the SAWM technique discussed in Section 5. For simplicity, we limit our discussion to the loading operations in two terminals using yard layouts I and II. The following discussion, however, can be easily extended to the other operations. For convenience, the weighted penalties for attributes (i.e., the AIRs of ship cranes, yard cranes and AGVs) are expressed in the vector form as W T =(w,w 2,w 3 ). Figs. and 2 illustrate the performance index for several weighted penalty vectors in the yard layouts I and II, respectively.

18 366 Chin-I. Liu et al. / Transportation Research Part C 2 (24) No. of Containers/Hour/Ship Cranes loading unloading combined yard layout I yard layout II Fig.. Throughput comparison of two container terminals using layouts I and II (number of AGVs 8, ship cranes speed 75 moves/h)..9.8 Performance Measure w= () w= () w= () w= (/3/3/3) w= (3/5/5/5) w= (5/8/82/8) Number of AGVs Fig.. Performance index for the yard layout I. Performance Measure Number of AGVs Fig. 2. Performance index for the yard layout II. w=() w=() w=() w=(/3/3/3) w=(3/5/5/5) w=(5/8/82/8) In the first three graphs in each figure, the optimum number of AGVs is found for a single criterion decision making. That is, the number of AGVs has been found to minimize the AIR of either

19 Chin-I. Liu et al. / Transportation Research Part C 2 (24) ship cranes, W T = (,,), yard cranes, W T = (,,), or AGVs, W T = (,,). Using single criterion decision making to minimize the yard performance leads to choose (a choice of) the boundary number of AGVs. For instance, using 24 AGVs, the maximum number in our simulation, minimizes the performance index with weighted penalties W T = (,,), W T = (,,), while deploying six AGVs, the minimum number, minimizes the performance index with W T = (,,). In the last three graphs in Figs. and 2, all attributes i.e., the AIR of ship cranes, yard cranes and AGVs, are taken into account. Note that for the multi criteria decision making, the maximum or minimum number of AGVs does not necessarily result in the best performance in the yard. For instance, for the weighted penalty vectors W T = (3/5,/5,/5) or W T = (5/8,/8,2/ 8), the number of AGVs which minimizes the performance index is around 2 for the yard layout I and around 8 for the yard layout II. A more elaborated performance index and/or cost functions can be used to choose the number of the various equipment subject to various constraints, that may include maximum cost, minimum acceptable performance, etc. Our future work in this area will involve the development of such performance indices and cost functions in an effort to optimize the choice of equipment and operations. 7. Conclusion The operation-as-usual in container terminals is unlikely to be able to cope with the complexity of tomorrowõs mobility requirements in a sustainable manner. In this paper, we investigated, through developing simulation models, the impact of deploying automated guided vehicle systems (AGVS) on container terminal systems. In particular, we studied the effect of the yard layout on the performance of automated container terminals through considering two commonly used yard layouts. For each automated yard layout, three operational scenarios were considered and compared: loading, unloading, and combined loading and unloading operations. The developed simulation models were validated using real-life yard operational data obtained from the Norfolk International Terminal, USA. It is shown in this paper that, compared to its manual counterpart, substantial increase in terminalõs throughput can be gained through deploying AGVS. We showed that the yard layout has inevitable effects on the terminal performance and on the number of automated guided vehicles used. The results of our simulations indicated that the combined operation shows a great promise in increasing the terminal throughput and achieving high utilization of equipment in the yard. Acknowledgment The authors would like to thank Dr. Ardavan Asef-Vaziri of the University of Houston, Texas, for numerous discussions we had. References Ballis, A., Abacoumkin, C., 996. A container terminal simulation model with animation capabilities. Journal of Advanced Transportation 3 (),

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