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ScienceDirect Procedia CIRP 40 (2016) 301 – 306
13th Global Conference on Sustainable Manufacturing - Decoupling Growth from Resource Use
Incorporate LEAN and Green Concepts to Enhance the Productivity of Transshipment Terminal Operations Chanjief Chandrakumara, Jeyanthinathasarma Gowrynathan a,*, Asela K. Kulatungaa, Nadarasa Sanjeevanb a
Department of Production Engineering, Faculty of Engineering, University of Peradeniya, Sri Lanka Faculty of Science, University of Peradeniya, Sri Lanka
* Corresponding author. Tel.: +94-77-749-8654. E-mail address:
[email protected]
Abstract Sea freight transportation plays a vital role in the global supply chain. Sri Lanka is located closer to one of the world busiest sea routes: east to west. Since Sri Lanka is located in the Southern tip of the Indian sub-continent, its sea ports could play a pivotal role in transhipment activities of the South Asian region. Presently, Sri Lanka is developing its two of the main sea ports to cater the regional demands. However, still it has not concerned to enhance the productivity of the transhipment operations in detail. This study aims to enhance the transhipment productivity by considering different options using modelling and simulation techniques. The main strategies tested in this study are LEAN and Green concepts towards overall operations. Commercially available simulation package, Flexsim is used for modelling and simulating port operations. The results reveal that incorporating LEAN and green concepts along with multi model transportation strategies between main ports will enhance the overall productivity of the transhipment operations.
© © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the International Scientific Committee of the 13th Global Conference on Sustainable Manufacturing. Peer-review under responsibility of the International Scientific Committee of the 13th Global Conference on Sustainable Manufacturing Keywords: Sea freight transportation,Transshipment, LEAN, Modelling and Simulation
1. Introduction Today, transportation has become one of the basic needs to survive, people require an efficient and effective delivery and handling system to fulfil their logistics requirements. Raw materials extracted and semi finished products are transferred to manufacturers through container terminals (CT) and the finished goods are too distributed via CT. In this regard, CT play an eminent role in freight transportation and as well as the global supply chain. The effectiveness of a CT is measured based on factors like available bays and resources, turnaround time, operations strategies, and complexities involved in the operations. CT Operations can be classified mainly into three categories as quay and yard side and transfer operations. Quay cranes (QC) are operated to unload the inbound containers from and load the outbound containers to the vessels with the assistance of other auxiliaries in the quay side. Inbound and outbound containers have to be transported within and
outwards of the port terminals. In order to cater the transfer requirements prime movers (PM), yard cranes (YC), and automated ground vehicles (AGV) are utilized. Moreover, several decisions have to be taken in related to storage location, planning, resource allocation, and sequence of dispatching at the yards.
Fig. 1. CT in Global Supply Chain.
2212-8271 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the International Scientific Committee of the 13th Global Conference on Sustainable Manufacturing doi:10.1016/j.procir.2016.01.042
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In determining the turnover time of the vessels, these three operations highly contribute, while the yard operations are considered to be the bottleneck which determines the turnaround time. Decisions related to operations have to be made at the operational level to manage the operations and all such decisions impact each other on the way to achieve perceptible performance. For example, decisions on the storage locations of containers directly affect the allocation of yard cranes and the scheduling and dispatching of prime movers, and indirectly affect the efficiency of QCs.
green can be implemented to reduce the turnaround time and non-value added time in an effective manner. However possibilities are identified for improving CT operations, there are few constraints, which have to be taken into consideration. Length of the planning horizon is one of such constraints. For example, yard planning is handled on a time scale of weeks, whereas loading and unloading lists are prepared only a few minutes in advance, or in most cases, in real-time. CT is related to optimization and planning problems which directly have impacts on ship turnaround time. BAP, QCAP, QCSP, PMAP, PMSP and yard planning are few of them. These operations require mathematical, and statistical analyses and studies to determine the optimal solutions in real time. 2. Previous Works
Fig. 2. Sub-problems that impact the ship turn around time
This operation management problem becomes more and more complicated with the involvement of multiple action parties and interactions among various service processes. Since that, it is hard to obtain optimal decisions that favour the overall objectives of the port and its stakeholders. Hierarchical decomposition of the original problem into simpler subproblems is an applicable and affective approach to apply logically. LEAN techniques are developed to maximize customer value while minimizing the resource consumptions, non-value addition operations, idle times etc. Initially, bay allocation is handled by tug boats to complete their transhipment operations while minimizing the waiting times of ships.
An overview of a CT is presented by Iris, and Koster [1] .They have given a classification of the decision problems that arise at container terminals and also presented few quantitative models to solve such problems effectively. A similar study [2] is carried by Ilaria et.al [2010] and further they have focused on streams like: the integrated optimization of interdependent decision problems, the analysis of issues related to traffic congestion in the yard and the tactical planning of operations. Another researcher, Theo [3] provides a bird’s eye view on the economic and logistics market developments which affect the CT and he has identified key market developments in trade and logistics and also analysed how the economic and logistics trends affect the CT authorities.
Another paper presents an in-depth overview of storage yard operations, including the material handling equipment used, and highlights current industry trends and developments and also a classification scheme has been proposed for the scientific papers published in recent times [4]. Similar review work has been carried out for the seaside operations [5]. This paper discusses the current operational paradigms and challenges on seaside operations. Apart from the overview and the economic trends, terminal operations problems like: BAP, QCAP, PMAP, resource optimization are evaluated as individual, and integrated models. A study [6] investigates and integrated After allocating bays, required number of QCs have to be decision problem that deals with the simultaneous optimization allocated while reducing the idle time of QCs during the of berth allocation, tugboat and quay crane assignment. Also, dispatching and loading operations. Therefore, enough PMs an integrated simulation model of port resource allocation have to be utilized for the transfer operations while reducing problem is developed by Ilati et.al. [2014] and they propose an the empty travel time, and waiting time correspondingly. These effective evolutionary path re-linking algorithm to handle BAP waiting times, and empty travel times are considered as nonin their paper. Meanwhile, Daniela, and Elena [7] discuss value added time, which is considered to be a loss. Such nonQCAP in one of their researches. Ilati et.al. [2014] and they value added operations are visualized in Figure 3 in blue propose an effective evolutionary path re-linking algorithm to colour. Not only time related issues, but also there are handle BAP in their paper. Meanwhile, Daniela, and Elena [7] unidentified operations in different phases, where LEAN and discuss QCAP in one of their researches. They propose an integrated simulation Quay Side Stack Side approach to handle QCAP. QCSP is a global problem in most of the ports. In PM SC QC 2008, Legato et.al. [8] came up with an loading unloading dispatching integer programming model, where they Ship have implemented two distinct PM travelling PM travelling SC movement QC travelling simulation algorithms of simulated annealing and adaptive balance QC loading SC PM extrapolative and exploitative search in loading unloading order to minimize the turnaround time of Fig. 3. Operations flow of a CT vessels. A 0/1 mixed integer
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programming model is developed by Daniela, and Elena [7] to determine the optimal assignment of QCs. PMs play a vital role in transportation between stacks and QCs. Transportation vehicles Optimization and allocation problems are discussed in many studies. Among such studies, the problem of vehicle dispatching at container terminals in a dynamic environment is presented by Nguyen, and Kim. This study mainly consider the problem of assigning delivery orders under uncertain conditions [9]. An overview of transport operations and the material handling equipment, current industry trends and developments have been presented by Carlo et.al.[10] The paper also discusses and challenges current operational paradigms of transport operations. Wong, and Kozan have investigated the container storage location, and the sequencing and scheduling of machine operations in the port. An integrated approach has been proposed to reduce ship service time, and also a numerical investigation has been presented. Authors have used metaheuristic algorithms to solve the problem [11]. Also, many of the researchers have emphasized the requirement of having an integrated scheduling plan, and an integrated resources plan for an efficient CT. Henry, and Ying have proposed a mixedinteger programming model [12], which considers different constraints related to the integrated operations between different types of handling equipment. Meanwhile, they have developed a heuristic algorithm called multi-layer generic algorithm to overcome the computation difficulty for solving their model. Port terminals and material handling operations are evaluated in another study [13]. Authors have presented a straddle carrier scheduling procedure that can be used by a terminal scheduler to control the movement of straddle carriers. Yang et.al. [2013] investigated green supply chain management (GCSM) and firm performance dimensions based on global container shipping service [14]. Authors use a cluster analysis methodology by assigning the firms into 4 major groups, and examining the differences in firm performance and GCSM dimensions. Aunporn [15] has highlighted the importance of greening a CT through a case study. In his case study, he has showcased the positive transformations and outcomes by applying Port, Safety, Health and Environmental System (PSHEMS), a concept which emphasizes the advantages of greening a port. From the above cited references it is very obvious that researchers have studied and analysed different CT operations, and related problems. Among such researches most of the authors have analyses the port operations sequentially for unloading and unloading cycles. Also it is rare to find researches on reshuffling and related consequences. Therefore it is decided to evaluate the simultaneous port operations with container shuffling by developing simulation models using Flexsim. Also, it is decided to design an integrated scheduling plan, and an integrated resources plan for a CT, based on the LEAN and green concepts in this study. 3. Methodology A structured methodology was used in order to handle several container terminals operations optimization problems. Initially, BAP is studied. In BAP, we had considered the condition of first-come first served at the port terminals. After allocating berths for ships, it was our concern to allocate QCs
for the specific ships. Different combinations of QCs and different number of containers in ships were tested in order to improve the efficiency of the operations. Quay crane scheduling problem, work force planning, and stowage planning were not evaluated in our study. Then the prime mover allocation problem is analysed by efficiently allocating PMs to the ships. In each phase mathematical modelling was developed individually. Thereafter individual and integrated simulation models were built using simulation software. The proposed methodology is given in Figure 4, and the handled problems in this project are highlighted. Initially, mathematical models for each problem were developed. These models visualized the container terminal operations like resource allocation, calculation of turnaround time, number of entities in the waiting line, etc. Thereafter the same operations were developed as simulation models using simulation software and the simulation results for different scenarios were compared for further analyses. Simulation software based approach was used for this study. Flexsim was chosen to simulate different CT operations. Initially, BAP was modelled using Flexsim considering the first come first served scenario for the ship arrivals. In the simulation model tug boat assignments, ship arrivals, ship waiting lines, ship transshipment, and ship departures were considered. Thereafter the QCAP was modelled as an isolated problem. Scheduled arrivals, constant inter arrival times, and random arrivals were considered for the QCAP model separately. The PMAP followed the QCAP and it was considered as an isolated model similar to the BAP model. But considering the requirement of analysing the complexities in CT operations, an integrated model of the QCAP and the PMAP was considered for further analyses.
Fig. 4. Problems of CT operations
4. Analyses and Results Due to the complexities, both QCA and PMA were considered as an integrated model for the analysis and the number of containers to be dispatched and loaded from/ to a ship assumed as 12 and the SC allocated for a stack was 1. 15 different scenarios (SN) were generated by varying the parameters like: number of QCs, number of PMs, number of stacks etc. Initially, container unloading and loading operations were considered as sequential operations. In here, containers were dispatched from
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ships to the designated stacks, and thereafter loaded to ships. This model was tested and the simulation results were obtained. Thereafter, simultaneous model [Figure 5] for loading and unloading process was developed in Flexsim environment. In this model, QC dispatch the container using PM to the stack, and the empty PMs carried the containers from the closest stack to the QC for loading. Modelling results of the sequential (SE) and simultaneous (SI) models were then compared. Table 1: Simulation Results for Simultaneous Model for Container Dispatching and Loading. No. Q C
Time/ minutes ETT ETT – SE -SI
P M
WT – SE
WT-SI
TA – SE
TA – SI
1
5
248
54
42
2
10
343
55
42
4.26
1307
1777
4.43
1307
15
258
53
1777
14
4.61
1311
4
20
125
1777
76
14
4.63
1327
5
5
1772
194
85
29
3.62
1118
1555
10
216
17
28
5.66
905
974
15
64
17
97
5.81
894
973
8
20
64
17
96
6.11
894
972
9
3
69
17
26
4.53
875
929
10
4
93
17
26
3.65
875
894
11
5
128
29
26
4.27
935
953
6
156
36
26
4.45
935
953
13
10
88
41
13
4.65
917
953
14
15
93
46
14
4.57
917
922
15
20
95
48.
15
3.58
917
834
SN
3
6 7
12
2
3
4
Table 1 represents the comparisons of the simulation results of sequential and simultaneous models. Since, turnaround time (TA), waiting time (WT), and empty travel time (ETT) were given the most priorities, these factors were taken into the consideration to propose an integrated scheduling plan. Figure 6 shows the graphical variation of the turnaround time of the above models and it can be revealed that for the scenarios 1-5, there is a large time interval between simultaneous model and separate model. This large variation is due to the waiting time
of QCs for the available PMs. But, in scenarios 6-15 we can observe that the variation between the turnaround times is small when comparing to scenarios 1-5. In cases 6-15, turnaround time of ships mainly depend on the QC operations times, not in the PM waiting or travelling times. From Figure 7 it can be inferred that the PM waiting times for simultaneous model is comparatively less. Similarly, when considering the PM empty travel times [Figure 8] simultaneous model shows low values compared to separate model. Though PM waiting times and empty travel times are less in simultaneous model, the turnaround time of ships of separate model is comparatively less/ equal to that of simultaneous model. But it is fair to an extent to decide that separate model is better than simultaneous model when considering the entire turnaround times. But, there are other factors like costs of planning, availability of resources, etc. which are not evaluated in this study. 5. Conclusions This research was carried out in order to study the port terminals, operations, problems, and solutions in detail. Even though several studies reveal numerous solutions and strategies to improve the services and standards of the CT, still there are spaces for improvements. Among such improvements, implementing LEAN and green concepts in CT is one of the most promising solutions. In this study, it is clearly revealed that there are operations, which have tedious waiting times, empty travelling times, and accumulating queues, which are obviously non-value adding. Implementing LEAN concepts truncate/ eliminate the waiting times, empty travel times, and length of waiting lines. Truncating the waiting times, and empty travel times reduce the resource consumptions, environmental impacts which are caused due to the emissions and waste assimilations. Meanwhile, reducing or eliminating the waiting line save space, which directly reduce the land acquisitions and improve the pollutants sequestration. Therefore it can be concluded that implementing LEAN will direct CT towards green while producing numerous environmental, social, and economical benefits to the nation, and as well as to the entire world.
Fig. 5. Flexsim simulation model for simultaneous operations.
305
SHIP TURNAROUND TIME/ MINUTES
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TURNAROUND TIME - SIMULTANEOUS AND SEPERATE MODELS
2000
1500
1000
500 Turnaround Time-Seperate
Turnaround Time-Simultaneous
0 2Q5P
2Q10P
2Q15P
2Q20P
3Q5P
3Q10P
3Q15P
3Q20P
4Q3P
4Q4P
4Q5P
4Q6P
4Q10P
4Q15P
4Q20P
SCENARIOS Fig. 6. Turnaround time for separate and simultaneous models.
PM WAITING TIME - SIMULTANEOUS AND SEPERATE MODELS
PM WAITING TIME/ MINUTES
PM Waiting Time-Seq
PM Waiting Time-Sim
400 350 300 250 200 150 100 50 0
Fig. 7. PM waiting times for separate and simultaneous models.
PM EMPTY TRAVEL TIME/ MINUTES
PM EMPTY TRAVEL TIME - SIMULTANEOUS AND SEPERATE MODELS PM Empty Travel Time-Separate
PM Empty Travel Time-Simultaneous
120 100 80 60 40 20 0 2Q5P
2Q10P
2Q15P
2Q20P
3Q5P
3Q10P
3Q15P
3Q20P
4Q3P
4Q4P
4Q5P
SCENARIOS
Fig. 8. PM empty travel time for separate and simultaneous models.
4Q6P
4Q10P
4Q15P
4Q20P
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5. Limitations and Recommendations Few limitations were encountered during this study. Among them limitations related to the student version of simulation software was very dominant. Inability to access real-time data from Sri Lankan ports was another important issue and it led the researchers to use data from previous works. Since, ports and their related operations are more complicated further studies can be done in order to find optimal solutions. Greening CT is one of the hot topics and this area can be further analysed. Developed models can be tested and validated for local ports in Sri Lanka. References 1.
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