simulation and optimization for batch order ... - ACM Digital Library

Proceedings of the 2010 Winter Simulation Conference B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds.

SIMULATION AND OPTIMIZATION FOR BATCH ORDER PICKING PROBLEM: THE APPLICATION OF ANT COLONY ALGORITHM

Bo Xing Wen-Jing Gao Kimberly Battle Tshilidzi Marwala

Fulufhelo V. Nelwamondo

Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg, 2006, SOUTH AFRICA

Modeling and Digital Science Unit Council for Scientific and Industrial Research Pretoria, 0001, SOUTH AFRICA

ABSTRACT This research deals with the batch order picking (BOP) problem in a man-on-board type of automated storage and retrieval system (AS/RS). When retrieval requests consist of multiple items and the items are kept in different locations, the storage/retrieval (S/R) machine must travel to various storage places to complete each batch of orders. In this article an ant colony optimization (ACO), in particular MAX– MIN ant system (MMAS) algorithm is employed for the resolution of BOP problems with multiple stock locations. Meanwhile a multi-agent simulation software called NetLogo is also used to demonstrate the importance of parameter selection in MMAS. The aim of this research is to minimize the total travel distance and travel time of S/R machine. 1

INTRODUCTION

As more companies look to cut costs and improve productivity within their warehouses and distribution centres, picking has come under increased scrutiny. Order picking - the process of retrieving products from storage (or buffer areas) in response to a specific request - is a very capital-intensive operation in warehouses with automated systems (De Koster, Le-Duc, and Roodbergen 2007). For these reasons, warehousing professionals consider order picking as the highest-priority area for productivity improvements. A well-known example of order picking system is represented by automatic storage/retrieval system (AS/RS). As an alternative to traditional warehouse, AS/RS has been widely accepted as part of advanced manufacturing system. Normally there are several kinds of activities involved in an AS/RS order picking process. Among these we will focus on picking multiple items from their different storage locations, which is referred to as batch order picking (BOP) problem in the literature (Henn et al. 2009). An advantage of BOP is that the length of a tour for a batch of orders is shorter than the sum of the individual                                  most economical way of picking customer orders, minimizing costs by reducing distance traveled. The aim of this paper is to explore the application of ant colony optimization (ACO) meta-heuristic approach to BOP problem, in particular, BOP in the context of man-on-board type of AS/RS. For this reason, we first briefly review the background knowledge of AS/RS and ACO in Section 2 and 3 respectively; then, the proposed optimization problem is explained in Section 4; it is followed by a numerical example in Section 5; next Section 6 elaborates the selected ACO algorithm; experiment set-up and results analysis are given in Section 7; finally, the conclusions are drawn in Section 8.

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BACKGROUND KNOWLDEGE OF AS/RS

2.1

What is AS/RS?

Automated Storage and Retrieval Systems (AS/RSs) are warehousing systems that are used for the storage and retrieval of products in both distribution and production environments (Roodbergen and Vis 2009). Typically, as shown in Figure. 1, an AS/RS is composed of the following five parts:

Figure 1. A typical layout of AS/RS. x

Storage Structure: The storage structure is the rack frame work, made of fabricated steel, which supports the loads contained in the AS/RS. x Storage and Retrieval (S/R) Machines: The S/R machines are used to accomplish storage transactions, delivering loads from the input station into storage, and retrieving loads from storage and delivering them to the output station. x Storage Modules: The storage modules are the unit load containers of the stored material. These modules are generally made to a standard base size that can be handled by the carriage shuttle of the S/R machine. x Pick-up and drop-off (P/D) stations: The P/D station is where loads are transferred in and out of the AS/RS. They are generally located at the end of the aisles for access by the external handling system that brings loads to the AS/RS and takes loads away. x Control Modules: The control modules are where computer controls and programmable logic controllers are used to determine the required location and guide the S/R machine to its destination. Since AS/RS was first introduced in the 1950s, AS/RS have been adopted not only as alternatives to traditional warehouses but also as a part of advanced manufacturing systems. This is because AS/RS have many benefits including improved inventory management and control, increased storage capacity to meet long-range plans, and quick response to locate/store/retrieve items (Groover 2001).

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AS/RS under this Research

Based on above-mentioned factors, we choose the following settings for this study of AS/RS: man-onboard type of less-than unit load AS/RS, single control command with multi stops within one trip, and first come first served (FCFS) request selection rule. A man-on-board type of AS/RS represents an alternative approach to the problem of retrieving individual items or small product cartons from storage rack. In this system, a human operator rides on the carriage of the S/R machine which permits individual items to be picked directly at their storage locations. This offers an opportunity to increase system throughput. In order to illustrate the ideas we proposed in this paper, the following assumptions have also been made: x The AS/RS has only a single-aisle and one-sided rack served by a single S/R machine. This will not lose generality, compared with the results of analyzing multi-aisles/two-sided racks AS/RS, since each aisle is operated independently with one S/R machine. x The size of man-on-board AS/RS and the maximum capacity of its S/R machine are known. x There is only one P/D station that exists in the proposed AS/RS, which is located at the lower left-hand corner of the rack. Berg and Gademann (1999) pointed out that this layout will save the stacker more travel time than remote P/D station layout. x The pallet transfer time and the crane acceleration or deceleration effects are ignored without affecting the relative performance of the path control policies. x The horizontal and vertical speeds of the S/R machine are constant during the test. x The S/R machine can achieve simultaneous horizontal and vertical travel. 3

BACKGROUND KNOWLDEGE OF ACO

Ant colony optimization (ACO) lies within the broader field of research called swarm intelligence, which seeks to apply biologically inspired techniques to the solution of difficult problems. The behavior of social insects, fish, and birds have been used to devise solutions to many such problems. As the name implies, ACO is inspired by the behavior of ants. A fundamental concept underlying the behavior of social insects such as ants is that of self-organisation      formed higher-level pattern of structure or function that is emergent through the interaction of lower-   (Flake 1998). Emergent refers to a property of a collection of simple lower-level subunits, that comes about through the interactions of the subunits. For example, the organisation of an ant colony is said to       -level behaviors of the ants, and not from any single ant. The potential benefits of imitating the structures and behaviors of some animal societies in designing solutions to man-made problems include: robustness (arising from the ability of the society as a whole to survive when individuals may fail); flexibility (from the ability to adapt to changing environments); and decentralisation (removing the need to program for overall control) (Galea 2007). Ant algorithms (Caro 2004) are heuristics inspired by various behaviors of real ants that rely on stigmergy, such as cemetery organisation and brood sorting. ACO is a particular instantiation of ant algorithms motivated by the foraging strategies of ants, which have been observed capable of finding the shortest path between their nest and a food source (Dorigo, Birattari, and Stützle 2006). This is attributed to the chemical substance, a pheromone, that ants lay on the paths they follow to and from their nest, and the amount of which is used to guide their decision making when confronted with more than one path. When a new food source is first located there is no pheromone to guide ants and so each will have made a random decision when presented with different paths, depositing pheromone as they travel. Several paths by different ants may therefore have been taken to reach the same food source. Since pheromone evaporates, the shortest path found by ants will accumulate the most pheromone, as ants can travel over this path more quickly and deposit more pheromone. Ants tend to select paths with more rather than less pheromone on them when presented with a choice, and therefore they eventually converge on the shortest path.

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PROBLEM DESCRIPTION

In practice, a series of small order picking requests is often found in a cellular manufacturing system. For this reason, the order consolidation policy which rearranges the customer requests into batches is always applied. This means in a single BOP, several requests which are generally made of multiple items to be picked within the system (less than unit load), are directly taken as one order. In the context of BOP, the total S/R machine travel distance in serving any set of order picking requests is calculated as the sum of the distance travelled starting from the origin (i.e., P/D station), visiting each item location once before finally returning to the origin after serving the last order. As a result, there is a need to optimize the total travel distance and the total travel time as well. With this in mind, we propose the BOP travel path optimization problem as follows: given a set of orders, each consisting of a number of ordered items, based on the capacity limitation of the picking device (i.e., S/R machine in our case), what is the shortest travel path to pick up all these items from their relative locations during each order picking cycle? 5

NUMERICAL EXAMPLE

In this example, we first suppose there are 15 rows and 50 columns of equally sized storage locations in a rack (see figure below). Each location has identical height and width (1 meter × 1 meter) and stores only one type of product. Then we are randomly given 40 retrieve requests at any time period. The attribute list of different items is shown in Table 1 in which the sequence of order is decided based on FCFS rule. Before picking a set of orders, effectively grouping orders into batches can accelerate product movement within the storage zone. Consequently according to the capacity limitation of S/R machine (maximum 10 items at a time); these 40 orders are divided into four batches: A, B, C, and D. Table 1: Sequence of Order based on First Come First Served (FCFS) Batch #

A

B

Sequence of Order 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 19 19 20

Item Code I13 I08 I18 I11 I09 I36 I05 I25 I01 I16 I34 I20 I23 I15 I21 I40 I19 I07 I31 I26

Item Location (4, 6) (12, 2) (7, 10) (2, 1) (1, 8) (5, 8) (4, 10) (2, 11) (0, 12) (24, 2) (35, 13) (36, 2) (42, 9) (43, 4) (23, 10) (22, 8) (14, 0) (17, 12) (27, 11) (46, 0)

Batch #

C

D

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Sequence of Order 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Item Code I27 I14 I38 I06 I29 I03 I17 I02 I12 I32 I39 I33 I28 I37 I35 I10 I24 I22 I30 I04

Item Location (15, 10) (47, 10) (49, 2) (48, 12) (33, 5) (10, 4) (13, 8) (10, 9) (20, 7) (30, 2) (18, 2) (31, 7) (16, 5) (11, 12) (34, 0) (32, 12) (39, 1) (38, 10) (45, 7) (37, 6)

Xing, Gao, Battle, Marwala and Nelwamondo

Figure 2: Front View of Storage Rack and the random Distribution of Items Finally, the horizontal and vertical speed of S/R machine are also given as follows: vh = 1 meter/second, and vv = 0.5 meter/second. 6

MAX-MIN ANT SYSTEM FOR BATCH ORDER RETRIEVAL

Among various variants of ACO algorithms, MAX–MIN ant system (MMAS) is one of the most successful extensions (Stützle and Hoos 2000). It significantly improves the performance of original ant system . It is not only very simple to understand and implement, but at the same time gives very good solutions. According to the problem description in the Section 4, we find out that the BOP shares the same nature as the TSP. For instance, salesman in the TSP can be represented by S/R machine in the BOP which is set to start from the P/D station and return to the same location after each order picking cycle. Similarly, the nodes in the TSP can also be represented by the picking item locations in the BOP. The artificial ants use the MMAS algorithm to randomly select an item location as the next destination and each location can only be visited once during a tour construction. In the BOP, a permutation array can also be used as in !"$    solution. The elements in the array are the item location indices visited by the ant. Each ant has a short-term memory mechanism that stores the array elements and so avoids repeat item location visits. This process is iterated until all the ants construct a feasible solution. 6.1

Construction Solution:

Initially, m ants are put on randomly chosen item locations. At each construction step, ant k applies a probabilistic action choice rule, called random proportional rule, to decide which location to visit next. In particular, the probability with which ant k, currently at location i, chooses to go to location j is as follows:

pijk where Kij

­W ijD uKijE ° ® ° ¯

¦

W ilD uKilE

If cij  N( s p )

cil N ( s p )

(1)

0

Otherwise

1/ dij is a heuristic value that available a priori; N( s p ) is the set of feasible components, that is,

edges (i, l) where l is a location that the ant k has not visited yet (i.e., the probability of choosing a location outside N( s p ) is zero). By this probability rule, the probability of choosing a particular arc (i, j) in-

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Xing, Gao, Battle, Marwala and Nelwamondo creases the value of the associated pheromone trail W ij and of the heuristic information value Kij ; and the parameters D and E control the relative importance of the pheromone versus the heuristic information, which is given by Kij (i.e., if D =0, the closest cities are more likely to be selected; while if E =0, only pheromone amplification is at work.). 6.2

Pheromone Trail Updating Rule:

In MMAS only one single ant is used to update the pheromone trail after every iteration cycle. Consequently, the modified pheromone trail updating rule is given as follows:

W ij m UW ij  'W ijbest where 'W ijbest case 'W ijbest

(2)

1/ Lbest , and Lbest denotes the solution cost of either the iteration-best (ib), in which

1/ Lib or the global-best (gb), in which case 'W ijbest

1/ Lgb .

The point of using only the best ant for updating is to make the search much more aggressive. By adopting this modification, MMAS has some extra features to balance exploration versus exploitation. One of these is the choice between the use of Lib and Lgb. When Using Lgb results in strong exploitation, Lib will be used as an alternative option. Meanwhile, we have to wait for a certain number of updates until we can use Lgb again. 6.3

Pheromone Trail Limits:

To avoid a search stagnation situation in the early stage, Stützle & Hoos (2000) defined an upper and lower limit ( W min and W max ) on the pheromone values. This is also where the name of MMAS comes from. In particular, the imposed pheromone trail limits have the effect of limiting the probability pij of selecting a location j when an ant is in location i to the interval [ pmin , pmax ], with 0 < pmin % pij % pmax %'

W max (t ) 1/(1  U ) u Lbest

(3)

W min (t ) W max u (1  U decay ) / avg u pdecay

(4)

where p decay n1 pbest , n is the total number of locations, and avg is the average number of options that the ant has to choose at any decision point. 6.4

Pheromone Trail Initialization and Re-initialization:

At the start of the MMAS algorithm, the initial intensities of all pheromone trails are set to a maximum pheromone value (i.e., W max ). This leads to the interpretation of ȡ as the learning rate of the algorithm. High value of ȡ always causes the pheromone values of undesirable arcs to fall more quickly which in turn will force MMAS to converge more rapidly (McCallum 2005). As a further means of increasing the exploration of paths that have only a small probability of being chosen, in MMAS pheromone trails are occasionally re-initialized. Pheromone trail re-initialization is typically triggered when the algorithm approaches the stagnation behavior (as measured by some statistics on the pheromone trails) or if for a given number of algorithm iterations no improved tour is found (Dorigo and Stützle 2004). 1666

Xing, Gao, Battle, Marwala and Nelwamondo 7 7.1

EXPERIMENT SET-UP AND RESULTS ANALYSIS Parameter Settings

In the context of agent-based simulation, the settings of the model parameter is a crucial step for the implementation process. Indeed, agent-based algorithms are generally characterized by various parameters, which together determine the global performance of the system. That means even small changes made to a single parameter can sometimes lead to a radical modification of the whole algorithm. This is also the truth for the ant-agent-based simulation and optimization algorithm, MMAS. In order to demonstrate the importance of parameter settings, we use NetLogo as a platform and its Ants model as an example. NetLogo is a programmable modelling environment for simulating natural and social phenomena. It was authored by Uri Wilensky (1999) and has been in continuous development ever since at the Center for Connected Learning and Computer-Based Modelling. NetLogo is particularly well suited for modelling complex systems developing over time. Modellers can give instructions to hundreds or thousands of "agents" all operating independently. This makes it possible to explore the connection between the microlevel behavior of individuals and the macro-level patterns that emerge from the interaction of many individuals (Wilensky 2009). Ants model (Wilensky 1997), which can be found in Netlogo 4.1/Models Library/Biology, is a simple demonstrator of how a colony of ants forages for food. In the model, initially we will have a nest in the center of the simulation world, and three food sources around the nest. Though each ant follows a set of simple rules, the colony as a whole acts in a sophisticated way. The principle underlying this is that when each ant finds a piece of food, it carries the food back to the nest, dropping a chemical called pheromone as it moves. When other ants "sniff" the pheromone, they will follow it towards the food. As more ants carry food to the nest, they reinforce the pheromone trail . The ant colony generally exploits the food source in order, starting with the food closest to the nest, and finishing with the food most distant from the nest. Once the colony finishes collecting the closest food, the pheromone trail to that food naturally disappears due to the evaporation, freeing up ants to help collect the other food sources. The more distant food sources require a larger "critical number" of ants to form a stable pheromone trail. Thus the evaporation rate plays a key role in the performance of the whole model. As shown in the following figure, the evaporation is either too low or too high will cause the ant colony fail to converge to the closet food source. Only when the evaporation rate falls within the reasonable range, the ant colony can successfully find two closet food sources.

Figure 3: The Impact of Pheromone Evaporation Rate Based on the lessons learned from above example, and also after referring to the suggestion found in the literature such as Dorigo and Stützle (2004), we employ the following parameter settings (see Table 2) in

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Xing, Gao, Battle, Marwala and Nelwamondo this research. The algorithm is programmed in MATLAB 7.0 and executed on a Core 2 Duo 2.2GHz computer with 2GB RAM and Windows XP Operating System. Table 2: Parameter Settings of MMAS Number of ants (m) 11

7.2

Pheromone trail impact parameter (D ) 1

Heuristic information impact parameter (E ) 3.5

Pheromone evaporation rate (ȡ) 0.5

Maximum iteration time (Imax) m×100

Results Analysis

7.2.1 Batch A As shown in Figure 4, the initial retrieval +  > $@QYZ13Y I08Y I18Y I11Y I09Y I36Y I05Y I25Y I01Y I16Y P/D. The picking distance travelled by S/R machine is 135.94 meter. After executing MMAS algorithm, we getter a better retrieval sequence solution for Batch A which is $@QYZ11Y I08Y I16Y I18Y I36Y I05Y I25Y I01Y I09Y I13Y P/D. In this case the picking distance travelled by S/R machine has been reduced to 67.55 meter. Totally 68.39 meter of travel distance has been saved for Batch A.

Figure 4: Optimized Retrieval Path for Batch A 7.2.2 Batch B In terms of Batch B, the initial retrieval +    $@QYZ34Y I20Y I23Y I15Y I21Y I40Y I19Y I07Y I31Y I26Y P/D (see Figure 5). The picking distance travelled by S/R machine is 184.59 meter. After executing MMAS algorithm, we getter a better retrieval sequence solution for Batch A which $@QYZ19Y I20Y I26Y I15Y I23Y I34Y I31Y I21Y I40Y I07Y P/D. In this case the picking distance travelled by S/R machine has been reduced to 106.27 meter. Totally 78.32 meter of travel distance has been saved for Batch B.

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Figure 5: Optimized Retrieval Path for Batch B 7.2.3 Batch C As shown in Figure 6, the initial retrieval +  > \$@QYZ27Y I14Y I38Y I06Y I29Y I03Y I17Y I02Y I12Y I32Y P/D. The picking distance travelled by S/R machine is 167.71 meter. After executing MM"            +       >  \   $@QYZ 03Y I32Y I38Y I14Y I06Y I29Y I12Y I27Y I17Y I02Y P/D. In this case the picking distance travelled by S/R machine has been reduced to 115.33 meter. Totally 52.38 meter of travel distance has been saved for Batch C.

Figure 6: Optimized Retrieval Path for Batch C 7.2.4 Batch D For the last batch, the initial retrieval +  $@QYZ27Y I14Y I38Y I06Y I29Y I03Y I17Y I02Y I12Y I32Y P/D. The picking distance travelled by S/R machine is 169.14 meter. After executing MMAS algorithm, we getter a better retrieval sequence solution for Batch D   $@QYZ28Y I39Y I35Y I24Y I30Y I22Y I04Y I33Y I10Y I37Y P/D (see Figure 7). In this case the picking distance travelled by S/R machine has been reduced to 110.27 meter. Totally 58.87 meter of travel distance has been saved for Batch D.

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Figure 7: Optimized Retrieval Path for Batch D 7.3

Performance Measure

In the literature, the total travel time is often referred to as the expected amount of time for the S/R machine to perform a list of storage or retrieval operations (i.e., BOP for our case). Commonly, the measure of the total travel time in a BOP cycle is a significant indicator to the performance of the generic AS/RS. For this reason, according to the given speed of S/R machine, we calculate the total travel time T by using the following equations (Shivanand, Benal, and Koti 2006):

th

Dh / vh

(5)

tv

Dv / vv

(6)

T

max (th , tv )

(7)

where Dh and Dv are the relative horizontal and vertical distance between any two locations. Table 3: Comparison of Total Travel Time of S/R Machine Batch Initial total travel time # (second) A 128 B 223 C 187 D 190

Total travel time achieved by MMAS (second) 77 120 130 133

From the table we can see that the total travel time has been largely saved after implementing MMAS algorithm. 8

CONCLUSIONS

AS/RS is an important materials handling facility that offers flexibility to keep pace with the rapidly changing demands of manufacturing. Considering the context of cellular where BOP is always the case, it is necessary to find techniques that will improve the performance of AS/RS. With this motivation, we devote ourselves to the use of MMAS algorithm, in which artificial ants build solutions stochastically, us-

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Xing, Gao, Battle, Marwala and Nelwamondo ing heuristic information and an artificial pheromone trail. Experimental results show that such algorithm is efficient and work well on the proposed BOP problem. REFERENCES Berg, J. P. van den and A. J. R. M. Gademann. 1999. Optimal routing in an automated storage/retrieval system with dedicated storage. IIE Transactions 31:407-415. Caro, G. D. 2004. Ant Colony Optimization and its application to adaptive routing in telecommunication networks. Ph.D. Thesis, Universite Libre de Bruxelles, Brussels, Belgium. De Koster, R., T. Le-Duc, and K.J. Roodbergen. 2007. Design and control of warehouse order picking: a literature review. European Journal of Operational Research 182(2):481-501. Dorigo, M., M. Birattari, and T. Stützle. 2006. Ant colony optimization: artificial ants as a computational intelligence technique. Institut de Recherches Interdisciplinaires et de D´eveloppements en Intelligence Artificielle (IRIDIA), Technical Report No. TR/IRIDIA/2006-023. Bruxelles, Belgium: Universite Libre de Bruxelles. Dorigo, M. and T. Stützle. 2004. Ant colony optimization. Cambridge, Massachusetts: The MIT Press. Flake, G. W. 1998. The computational beauty of nature: computer explorations of fractals, chaos, complex systems and adaptation. Cambridge, Massachusetts: The MIT Press. Galea, M. 2007. Fuzzy rules from ANT-inspired computation. Ph.D. Thesis, School of Informatics, University of Edinburgh, Edinburgh, UK. Groover, M. P. 2001. Automation, production systems, and computer-integrated manufacturing. PrenticeHall. Henn, S., S. Koch, K. Doerner, C. Strauss, and G. Wäscher. 2009. Metaheuristics for the order batching problem in manual order picking systems. Faculty of Economics and Management Magdeburg, FEMM Working Paper No. 20. Magdeburg, Germany: Otto-von-Guericke-University Magdeburg. McCallum, T. E. R. 2005. Understanding how knowledge is exploited in ant algorithms. Ph.D. Thesis, School of Informatics, University of Edinburgh, Edinburgh, UK. Roodbergen, K. J., and I. F. A. Vis. 2009. A survey of literature on automated storage and retrieval systems. European Journal of Operational Research 194(2):343-362. Shivanand, H. K., M. M. Benal, and V. Koti. 2006. Flexible manufacturing system. New Age International (P) Ltd. Stützle, T. and H. H. Hoos. 2000. MAX-MIN ant system. Future Generation Computer Systems 16(8):889-914. Wilensky, U. 1997. NetLogo ants model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Available via [accessed April 14, 2010]. Wilensky, U. 1999. NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Available via [accessed April 14, 2010]. Wilensky, U. 2009. NetLogo 4.1 user manual. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. AUTHOR BIOGRAPHIES BO XING received his Master of Science degree in Mechanical Engineering from the University of KwaZulu-Natal, South Africa in 2008. His research interests include the advanced manufacturing system design & analysis, computational intelligence, mechatronics, and robotics. His email address is .

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Xing, Gao, Battle, Marwala and Nelwamondo WEN-JING GAO received the Diploma degree in Economics from the University of Kassel, Germany in 2008. Her research interests include computational intelligence, operations research and advanced manufacturing design and analysis. Her email address is . KIMBERLY BATTLE received her PhD degree from University of Pennsylvania, USA in 1995. Her research interests are mainly in large scale optimization & modeling, operations research, and world class manufacturing. Her email address is . TSHILIDZI MARWALA is a Professor and the Dean in the Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa. He received his PhD degree from Cambridge University, UK in 2000. His research interests include the applications of computation intelligence techniques to various engineering areas. His email address is . FULUFHELO V. NELWAMONDO received his PhD degree from the University of the Witwatersrand, South Africa in 2008. Prof. Nelwamondo is also a visiting Professor at the Faculty of Engineering and the Built Environment, Universit of Johannesburg, South Africa. He is an active researcher in engineering, with more focus on computational intelligence. His email address is .

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