A Sequential Order Picking and Loading System for Outbound Logistics Operations

Transcription

1 A Sequential Order Picking and Loading System for Outbound Logistics Oerations K.L. Choy, G.T.S. Ho, H.Y. Lam, Canhong Lin, T.W. Ng Deartment of Industrial and Systems Engineering, the Hong Kong Polytechnic University, Hong Kong Abstract--With short roduct life-cycles, frequent delivery schedules of roducts are demanded by customers. Efficient outbound logistics oerations are key goals of comanies wishing to maintain customer satisfaction and achieve cometitive advantage. However, in current warehouse oerations, order icking and loading are erformed searately and roducts are icked manually from warehouses according to customer orders and stored temorarily in staging areas in order to fully utilize the caacity of carriers. In ractice, the searation of order icking and the loading rocess usually results in roblems of excess storage buffers, long oeration time, and quality deterioration of sensitive roducts left in the staging area. In this aer, in order to tackle these roblems, a sequential order icking and loading system (SOPLS) integrating order icking and loading rocesses is roosed. The Genetic Algorithm (GA) technique is adoted in the system to identify the otimum icking sequence of tours with minimal travel distance. In addition, arameter settings for GA generation are tested in the simulation to find the otimal solution. The validation of the feasibility of the roosed system is illustrated through a red wine storage scenario. By using the SOPLS in the case comany, it is found that oeration time for outbound logistics and roduct deterioration are greatly reduced. I. INTRODUCTION With increasingly cometitive markets and comressed roduct life-cycles, short roduct develoment cycles are oular trends nowadays [11]. In such circumstance, customers need frequent delivery schedules of roducts. In order to meet customers requirement and resond quickly to markets, efficient warehouse oerations are essential. Generally, order-icking is identified as one of the most labour- and caital intensive activities in warehouses. Products are icked from storage locations according to customers requirement and stored in the staging areas to maximize the utilization rate of transort carriers. However, roblems arise due to the searation of order icking and the loading rocess during outbound oerations. One of these roblems is that extra storage buffers are needed for temorary storage of icked roducts, resulting in an increase of rental costs. On to of that, a relatively longer time is required to locate the icked roducts in the staging area and load them onto a container, thus increasing the time sent on this already long oeration. In addition, deterioration of roducts is another ossible roblem resulting from the use of the staging area. The environment of the staging area is not customized to roducts storage requirements. For fragile and sensitive roducts like wine which should be handled under secial conditions (i.e. a secific range of temerature and humidity), being left on the staging area could be a threat to the quality of the roduct [18]. Thus, it is crucial to develo a system to integrate icking and loading rocesses in order to otimize warehouse oeration efficiency and eliminate roblems that arise from the searation of order icking and loading rocesses. In this aer, a sequential order icking and loading system (SOPLS) is roosed with the adotion of the Genetic Algorithm technique to formulate the icking and loading lan with the shortest travel distance by otimizing the icking sequence of each tour. This research aer is divided into six sections. Section 2 contains a literature study that includes a review of current warehouse oeration environments, genetic algorithm based icking and loading aroaches. This is followed by the architecture of SOPLS in section 3. Section 4 is a case study conducted to validate the feasibility of the roosed system. Results and advantages of adoting the SOPLS are resented in Section 5. Section 6 is the conclusion of this aer. II. LITERATURE REVIEW A. Current warehouse oeration environment The warehouse is an imortant connection between the ustream suliers and downstream customers functioning as a lace for good storage [23][26]. The basic warehouse oerations include receiving, storage, order icking and ackaging, and shiing [3]. Among the four rocesses, order icking is regarded as the most labour demanding and costly activity that deserve greatest attention [27]. Many researchers have roosed different methods related to otimization of the order icking rocess in order to minimize the oeration costs [2][12][14][21]. However, little attention has been aid to integrating the order icking and loading rocesses. B. Order-icking In general, order icking is considered as the rocess of retrieving finished roducts from storage areas to fulfill customers requirements [6-7]. Different order-icking methods can be found in the industry such as sequential zone icking, batch-icking, wave icking and basic (single)-order icking [29]. According to Baker [1], selection of an aroriate order-icking method is a comlex rocess as it is closely related to warehouse layout and oeration. In common ractice, several decisions have to be considered in order to have efficient order-icking [25]. Due to the comlicated nature of the order icking rocess, many researches focused on the imrovement of the order- icking rocess by otimizing the travel time and distance [5][8]. In articular, De Koster et al. [4] roosed a mixed-integer linear rogram to find out the otimal number of zones in a synchronized zoning system such that the total order-icking time and assembly time is minimized. Gademann and Van de 507

2 Velde [9] considered the order batching roblem with minimization of the total travel time. From the ast literature, it can be seen that major consideration in the order-icking roblem is on minimization of the icker s travelling distance and time. C. Loading Loading is the rocess that follows order icking. After order icking, icked items will be checked, acked and eventually loaded into containers for transortation. Generally, the acking and loading rocess attemt to ack items in a container or transort carrier together as densely as ossible in order to reduce the waste of sace. Therefore, items are stored in the staging area to save sace. In general, there are four kinds of container loading roblems; they are stri acking, knasack loading, bin-acking and multi-container loading [22]. Martello [19] also develoed an algorithm incororating both an exact and an aroximate algorithm for three-dimensional bin acking roblems. Zhang et al. [30] roosed a heuristic block-loading algorithm for three-dimensional container loading roblems on the basis of multi-layer searching. Junqueira et al. [15] resented a mixed integer linear rogramming model to solve the container loading roblem taking into consideration the stability of the cargo and the load bearing constraints. To summarize, most researchers focus solely on either icking or loading rocess. Articles in the literature that consider the relationshi between icking and loading are limited. D. Genetic algorithm based icking and loading aroaches A Genetic algorithm (GA) is a heuristic aroach to solve comlex otimization roblems based on the concet of natural evolution [24]. With the evolutionary otimization technique, the GA starts from the initial solutions and stos searching for the global otimal solution when termination criteria are fulfilled [20]. The GA has been widely adoted in the otimization of warehouse oerations. Hsu et al. [13] roosed a GA-based model to handle order batching roblems in warehouses by minimizing travel distance. Zhang et al. [32] develoed GA-based heuristics for handling item arrangement in a multile-level warehouse in order to minimize the total transortation cost. Zhang et al. [31] roosed a GA-based heuristic for minimizing the transortation cost of an adjacent aer-reel layout roblem. Tsai et al. [28] roosed a batch icking model using a multile-ga aroach for finding the most aroriate batch icking lans. Karabulut and Inceoglu [16] suggested a hybrid GA for formulating a three-dimensional bin acking strategy with a high utilization rate. Lam et al. [17] develoed a decision suort model with the use of GA to hel formulate warehouse order oerations by classifying new customer orders and grouing similar orders into case grous. Furthermore, the GA has also been alied in solving loading roblems. Gehring and Bortfeldt [10] have roosed to adot a hybrid GA in container loading roblems. From studies in the literature it has been found that the GA aroach is a widely used technique for otimization in warehouse oerations. To summarize, revious research efforts have mainly focused on minimization of icker s travelling distance/time on order icking and utilization of sace in transort carriers in loading roblems. Limited attention has been aid to combining order icking and loading oerations to solve scheduling roblems. To increase the efficiency of warehouse oerations, the GA has been found to be a romising tool for formulating the otimal routing. This aer aims to make use of genetic algorithms to integrate order icking and loading rocesses in order to otimize the erformance of outbound logistics oerations. III. SYSTEM ARCHITECTURE OF SOPLS To imrove the warehouse oerations, a sequential order icking and loading system (SOPLS) is roosed which combines order icking and the loading rocess and finds the shortest travel distance for each icking tour. Figure 1 shows the system architecture of SOPLS, which consists of four modules. They are (i) Data Collection Module (DCM), (ii) Mathematical Module (MM), (iii) Sequence Determination Module (SDM) and (iv) Decision Module (DM). Figure 1 - Architecture of the SOPLS 508

3 (i) Data collection module (DCM) Data rovided from customers orders such as order quantity, delivery date and handling requirements are evaluated and transformed by the warehouse management system (WMS). On the other hand, inventory information is sorted into different categories by WMS based on its roerties and stored in a database system. In this module, roduct data such as storage location, inventory level, size, weight, icking, acking and loading roerties together with data related to the loading oeration, including the weight, caacity, size, tye and number of available transort carriers such as delivery truck, are collected and stored in a centralized database. (ii) Mathematical module (MM) In this module, mathematical formulation of the roosed model is resented. Table 1 shows that notation of the roosed GA-based model. The objective function resented in (1) is to minimize the total travelling distance of order ickers from the starting oint (I/O) to final storage location. Constraint (2) and (3) are the volume and weight limit of each vehicle. Constraint (4) is to check if vehicle v travels on arc (i, j). Constraints (5) and (6) are about each customer s entry and exit. Constraint (7) and (8) define the deot s entry and exit. Constraint (9) ensures the route continuity. TABLE 1 - NOTATION OF A GA-BASED MODEL Notation Definition Indices and Sets : index for vehicle i,j index for storage locations P set of all vehicles N set of all storage locations Data and arameters Volume of items at location i Weight of items at location i Volume limit of vehicle Weight limit of vehicle Distance between location i and j Decision variables =1 if the vehicle covers arc (i,j); 0 otherwise =1 if the location j follows location i in the same visiting sequence; 0 otherwise Objective Function min d c x (1) i in jn P in jn Subject To v x V wix in jn x y P W P (2) P (3) i, j N (4) in i j jn j i jn in jn ji y 1 j N (5) y 1 i N (6) y0 j P (7) yi0 P (8) x x i N, P (9) jn ji ji In the above model, c reresents the distance between two storage locations for each order icking oeration. In a warehouse, roducts stored at different locations within an area. The storage locations are reresented by x-coordinate and y-coordinate. The distance is therefore reresented as follow: 2 a a b 2 c b2 where a : x-coordinate of the storage location b : y-coordinate of the storage location (iii) Sequence determination module (SDM) In this module, GA is used to determine the otimal order icking and loading sequence based on the defined objective function and constraints in the mathematical module. Evolver 5.5 develoed by Palisade Cororation is used to erform the GA calculations and a number of simulation exeriments are conducted by alying different GA arameter settings. In SOPLS, real integer numbers and binary numbers are used to encode the tour sequence and tour division resectively. The tour sequence denotes the sequence of visiting a location while tour division searates different divisions of visiting. In the tour division region, 1 means that the tour will end after visiting the corresonding location. The searation oint of visiting deends on the constraints of the objective function. Each ick tour ends before the total volume or weight of icked items is greater than the volume or weight caacities of a transort carrier. Figure 2 shows an examle of chromosome encoding. Figure 2 - Illustration of chromosome encoding 509

4 For examle, icker first visits location 43, followed by location 2 and finishes at location 68. The total volume or weight of icked items from location 43, 2 and 68 is smaller than the volume or weight caacity of a transort carrier. However, if the icker continues the ick tour and visits location 90, the total volume or weight of icked items including location 90 will exceed the volume or weight caacity. As a result, ick tour 1 should terminate at location 68. All icked items are then loaded into a delivery truck. The same oeration lan alies to other icking tours. Tour 1: I/O oint location 43 location 2 location 68 I/O oint Tour 2: I/O oint location 90 location 22 I/O oint Tour 3: I/O oint location 15 location 95 location 35 I/O oint (iv) Decision module (DM) The outut of SDM is the suggested icking and loading sequence and recommended arameter settings for the GA calculation. The algorithm stos the otimization rocess in Decision Module (DM). Traditionally, ick lists are the internal orders for order ickers and the design of ick lists is based on external customer orders. With the hel of SOPLS, warehouse ersonnel can more easily reare oerative lanning for outbound logistics oerations because SOPLS rovides a comrehensive way to draw u ick lists. The identification number, ick storage location, ick quantity of each SKU, required material handling equiment and comletion time of each icked item are all listed on the ick list. Order ickers erform order icking oerations according to the ick list. IV. CASE STUDY This section covers (i) comany background, (ii) roblems in ABC Comany and (iii) imlementation of SOPLS in ABC Comany. A. Comany background ABC Comany is a third arty logistics (3PL) service rovider with its headquarters in Being and which rovides warehouse and distribution oerations in Shanghai and Guangzhou. The comany has half a million square-feet of storage sace, over 70 trucks, lorries and container trucks. The major function of the comany is to rovide third arty warehousing and logistics solutions for different commercial goods, mainly wine roducts, fashion goods, electronic comonents and cosmetics. In resonse to the growing consumtion and exanding Asian markets for wine, ABC Comany began its wine logistics business several years ago. The comany offers a one-sto solution for wine logistics services with its own temerature-controlled warehouses and trucking fleet. The cellars have 24-hour guards on duty and CCTV surveillance. Furthermore, an inventory controlled management system is used to hel track the real-time delivery status and location of individual wine lots. B. Problems in ABC Comany wine cellars In order to maintain wine quality, ABC Comany offers comrehensive facilities during wine warehousing and transortation. The room temerature of cellars is ket at 14 to 18 and the humidity around 60% - 80%. In addition, low-intensity lighting is rovided in warehouses to revent harmful ultraviolet light from threatening the quality of the red wines. Other than warehouse facilities, a temerature and humidity controlled trucking fleet is also used to hel revent quality deterioration of red wines during transortation rocesses. However, wine deterioration may still occur after the icking rocess. In ractice, the icked wines are laced temorarily in the staging area before being loaded onto delivery carriers. As the staging area is only for temorary storage of icked roducts, the environment there is only at room temerature and has natural light. Therefore, red wines in the staging area are exosed to threats of unsecified temerature range, dryness, vibration or extended eriods of natural light. These threatening external environment factors may change the comosition of the wine and result in deterioration of wine quality. Besides, the level of security and safety in the staging area is another concern. The staging area is an oen area without CCTV or other surveillance aliances. Product security is not guaranteed and incidents of loss of wine may occur. In addition to low security and safety level, extra storage buffering is another issue that needs attention. The staging area occuies extra sace in the warehouse that reduces area for storage there. Besides, during the loading rocess, it may take a relatively long time for oerators to find secific wines from amongst large amounts of icked wines before loading them into a truck. This makes the oeration time longer, lowering the working efficiency. Therefore, there is a need to imlement the SOPLS in ABC Comany in order to enhance the erformance of warehouse oerations. C. Imlementation of SOPLS in ABC Comany In this section, the imlementation of SOPLS in the warehouse system of ABC Comany, to streamline the outbound logistics oerations, is described. Assumtions are made for simler illustration of SOPLS in the case, which are (i) all transort carriers for delivering red wines are delivery trucks which have identical volume and weight caacities; (ii) wines are of standard sizes and ackaging. The imlementation of SOPLS in ABC Comany consists of two stes: (i) Data collection Data to be collected in ABC Comany includes storage locations of the wines, volume of a bottle of wine, weight of a bottle of wine, the number of trucks available, volume caacity and weight caacity of a truck. Data of the current situation of comany is summarized in Table

5 TABLE 2 - TRUCK AND STORAGE INFORMATION Truck Information Number of Trucks Available 4 Volume Caacity of a Truck (cm 3 ) 3,000,000 Weight Caacity of a Truck (kg) 3,500 Storage Information Volume of a bottle of wine 750ml/1.25L/1.5L/2L/2.5L Number of wine bottles in a box Standard 8 bottle box/ 12 bottle box / 16 bottle box Number of boxes in a storage 1/2/3 location The volume and weight of a bottle of red wine range from 750ml to 2.5L and from 1kg to 3kg resectively. Wines are stored in rectangular boxes in different locations of the warehouse. In each storage location, from 1 box to 3 boxes of wines are stored. Each box may contain 8 bottles of wine, 12 bottles of wine or 16 bottles of wine. There are five standard volumes available for the bottles of wine. In this ilot study, the total delivery volume is 9,603,000 cm 3 and total delivery weight is 13,700kg. There are 4 trucks available for these orders so the total volume caacity and weight caacity of the trucks are 12,000,000 cm 3 and 14,000kg resectively. The warehouse in this scenario is a square warehouse. Ordered wines are stored at 250 different locations of the warehouse. Coordinates are used to reresent the storage locations of red wines and the unit is the meter. Each location has its own x-coordinate and y-coordinate and both x-coordinates and y-coordinates range from 0 to 100. An I/O oint is located at the lower left corner of the warehouse with coordination (0, 0). (ii) Genetic algorithm setting After collecting the data, a GA is used to determine the otimal order icking and loading sequence. In this research, Evolver 5.5 is used and two sets of generations are tested. In the exeriments, the oulation sizes, crossover rate and mutation rate are constant. Table 3 shows the arameter settings in GA. TABLE 3 - GA PARAMETER SETTINGS Parameter Settings Poulation size 80 Number of Generations 1000/10000 Crossover rate 0.8 Mutation rate 0.05 V. RESULTS AND DISCUSSION In this section, case study results generated from SOPLS and the advantages of the SOPLS model after conducting the ilot study in ABC Comany for three months are resented. A. Results generated from SOPLS The GA has been run 10 times with two settings of arameters. GA solutions of SOPLS in two arameter settings are shown in Table 2 including the average distance, average running time and average best generation. Figure 3 and 4 show the fitness values obtained by the setting of oulation with number of generation 1000 and resectively. According to the results in Table 4, the number of generation increases with shorter average travel distance but longer average system running time. TABLE 4 - GA SOLUTIONS Poulation size Generations Average distance (m) Average running time Average best generation s s Figure 3 - Fitness value obtained by the setting of oulation 80 and number of generations 1000 Figure 4 - Fitness value obtained by the setting of oulation 80 and number of generations

6 TABLE 5 - TIME REDUCTION AFTER SOPLS Before (Searation of Picking and Loading rocesses) After (with SOPLS) Percentage of Imrovement Time to load the wine in staging area 8 min 0 min Locate and ick u wine from staging area and lace on allet 10 min 0 min Total time in staging area 18 min 0 min Total Oeration time (Picking and Loading) 37 min 19 min 48.65% TABLE 6 - REDUCTION IN WINE DETERIORATION AND DAMAGE Before (Searation of Picking and Loading rocesses) After (with SOPLS) Percentage of Imrovement Quantity of deteriorated wine er month % Quantity of wine damaged/ lost er month, in % bottles Total % B. Advantages of SOPLS Model With the hel of SOPLS, it is found that (i) the oeration time and (ii) amount of wine deteriorated, damaged and lost, are reduced. (i) Imrovement in oeration time Comared with the revious method in which order icking and the loading rocess are searated, the total time saved by eliminating the ste of loading the wine into the staging area as well as locating and icking u the wine from the staging area and lacing it onto the allet is reduced by 18 minutes. The total oeration time of order icking and loading is reduced by 49%. This is because staging areas are not necessary for temorary storage of goods with the execution of SOPLS. Table 5 summarizes the time reduction in oerations after the launch of SOPLS. (ii) Reduction in wine deterioration and damage Table 6 shows the reduction in wine deterioration, damage and loss er month. Before SOPLS, wines had to store temorarily in the staging area where the temerature, humidity, light are not controlled according to the requirements of the wine. Since wine is sensitive to surrounding conditions, uncontrolled conditions could cause the wine to deteriorate and dereciate in value. With SOPLS, icked goods are loaded to the transortation carriers directly without waiting in the staging area. This hels revent the quality of the wine from deteriorating in fluctuating environment of the staging area. Thus, the quantity of deteriorated wine er month is reduced by 92% after the launch of SOLPS. In addition, the roblem of low security and safety level in the staging area can also be resolved. Thus, the amount of wine damaged and lost is reduced by 80% after the launch of SOPLS. VI. CONCLUSION Order icking and loading are outbound logistics oerations but ractically considered as indeendent rocesses. The searation of them results in temorary storage of goods in a staging area. However, staging areas are oen areas with no strict environment and security control, causing roblems of extra storage buffering, long oeration time, and quality deterioration of roducts. This aer resents a sequential order icking and loading lanning system (SOPLS) to suort the warehouse management ersonnel when making decisions on routing of order ickers and lanning of order icking and loading rocesses. SOPLS integrates the order icking oeration with the loading oeration by adoting a GA to determine the otimal icking sequence with minimization of travel distance. The validation of the roosed system was illustrated in a red wine storage scenario. After a ilot study in a case comany, the oeration time and quantity of wine deterioration, damage and loss are reduced. Thus, it is roved that this system is secifically beneficial to warehouse management ersonnel in lanning order icking and loading rocesses. In order to further validate the SOPLS model, future research work will be considered to conduct more cases in handling different roducts for enhancing outbound logistics oerations. ACKNOWLEDGMENTS The authors would like to thank the Research Office of the Hong Kong Polytechnic University for suorting the current roject (Project Code: G-UC66). REFERENCES [1] Baker, P. and M. Canessa (2009); "Warehouse design: A structured aroach", Euroean Journal of Oerational Research, Vol. 193 (2), [2] Bortfeldt, A. and D. Mack (2007); "A heuristic for the three-dimensional stri acking roblem", Euroean Journal of Oerational Research, Vol. 183, [3] Coyle, J.J., E.J. Bardi and C.J. Langley (2003); The management of business logistics, a suly chain ersective, West Publishing Co., Canada. [4] De Koster, R. M. B., T. Le-Duc and Y. Yugang (2008); "Otimal storage rack design for a 3-dimensional comact AS/RS", International journal of roduction research, Vol. 46 (6), [5] De Koster, R., K. J. Roodbergen and R. van Voorden (1999); "Reduction of walking time in the distribution center of De Benkorf", In New trends in distribution logistics, Sringer Berlin Heidelberg, [6] De Koster, R., T. Le-Duc and K. J. Roodbergen (2007); "Design and control of warehouse order icking: A literature review", Euroean Journal of Oerational Research, Vol. 182,

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