topic: transport and logistics industry in digital age · “science and traffic development”...

Topic: Transport and Logistics Industry in Digital Age

Proceedings of the International Scientific Conference “Science and Traffic Development” (ZIRP 2018)

International Scientific ConferenceSCIENCE AND TRAFFIC DEVELOPMENT

10th - 11th May 2018Opatija, Croatia

ORGANIZERS

University of ZagrebFaculty of Transport and Traffic Sciences

Univesity of PardubiceJan Perner Transport Faculty

ISSN 2623-5781

International Scientific Conference SCIENCE AND TRAFFIC DEVELOPMENT

TRANSPORT AND LOGISTICS INDUSTRY IN DIGITAL AGE

10th – 11th May, Opatija, Croatia

Editors Ivona Bajor, PhD

Andrija Vidović, PhD

Chairman of Programme Committee Tomislav Josip Mlinarić, PhD, HR

Programme Committee Mario Anžek, PhD, HR Dario Babić, PhD, HR Davor Brčić, PhD, HR Diana Božić, PhD, HR

Tomislav Fratrović, PhD, HR Yuejie Han, PhD, CHN

Hans-Dietrich Haasis, PhD, GER Eduard Ivanjko, PhD, HR

Niko Jelušić, PhD, HR Biljana Juričić, PhD, HR

Artur Kierzkowski, PhD, PL Natalija Kavran, PhD, HR Tomislav Kljak, PhD, HR

Tomaž Kramberger, PhD, SI Fernando Liesa, PhD, ES Luka Novačko, PhD, HR

Tomasz Nowakowski, PhD, PL Dragan Peraković, PhD, HR Marjana Petrović, PhD, HR Marcin Seredynski, PhD, LU

Marko Ševrović, PhD, HR Ružica Škurla Babić, PhD, HR

Mateusz Zajac, PhD, PL Goran Zovak, PhD, HR

Chairman of Organising Committee Mario Šafran, PhD, HR

Organising Committee Darko Babić, PhD, HR Ivona Bajor, PhD, HR

Hrvoje Baričević, PhD, HR Ernest Bazijanac, PhD, HR

Barbara Bujačić, HR Martina Furdić, HR

Hrvoje Gold, PhD, HR Herbert Kopfer, PhD, GER

Zoran Ković, HR Vladimir Medved, PhD, HR

Melita Milenković, HR Jasmina Pašagić Škrinjar, PhD, HR

Margareta Rihtarić, HR Kristijan Rogić, PhD, HR

Bojan Rosi, PhD, SI Tomislav Rožić, PhD, HR

Dario Soldo, HR Ratko Stanković, PhD, HR

Zoran Starčević, HR Antonio Zrilić, HR

Managing Editors Tomislav Rožić, PhD

Ante Kulušić

Production Editor & Computer Text Design Ante Kulušić

URL http://www.fpz.unizg.hr/zirp/

Publisher Faculty of Transport and Traffic Sciences

University of Zagreb

International Scientific Conference

TRANSPORT AND LOGISTICS INDUSTRY IN DIGITAL AGE 10th – 11th May, Opatija, Croatia

I-IV

Authors Papers

Page

1. Antulov-Fantulin, B. Rogošić, T. Juričić, B. Andraši, P.

AIR TRAFFIC CONTROLLER ASSESSMENT OF THE FREE ROUTE AIRSPACE IMPLEMENTATION WITHIN ZAGREB AREA CONTROL CENTRE

1-10

2. Babojelić, K. Novačko, L. Hozjan, D.

APPLICATION OF SIMULATION TOOLS FOR ANALYSIS ANDOPTIMAL CHOICE OF INTERSECTION CONTROL TYPE

11-22

3. Brčić, D. Jurak, J. Emanović, M. Kalauz, Z.

PERFORMANCE OF PUBLIC TRANSPORT BUSES REGARDING TO ELEVATION CHARACTERISTICS

23-32

4. Cvetek, D. Tišljarić, L. Mandžuka, B. Jurčević, M.

APPLICATION OF NLP ALGORITHMS IN ITS MARKET ANALYSIS

33-42

5. Czaja, P. Restel, F. J.

USING OF DATABASES ON UNWANTED EVENTS TO INCREASE RELIABILITY AND SAFETY OF RAILWAY TRANSPORT PROCESSES

43-52

6. Čaušević, S. Muharemović, E. Memić, B. Begović, M.

INTEGRATION OF LOGISTICS INFORMATION SYSTEMS WITH ELECTRONIC SALES CHANNELS

53-62

7. Čaušević, S. Čolaković, A. Hasković, A.

THE MODEL OF TRANSPORT MONITORING APPLICATION BASED ON INTERNET OF THINGS

63-72

8. Čaušević, S. Deljanin, A. Berković, M. Hasković, A.

DIGITAL TRANSFORMATION AND INNOVATION IN LOGISTIC - SITUATION AND PRIORITIES OF DEVELOPMENT IN THE AREA OF BOSNIA AND HERZEGOVINA

73-84

9. Drljača, M. Grgurević, D.

SUPPLY CHAIN IN THE CONTEXT OF CIRCULAR ECONOMY

85-92



II-IV

Authors Papers

Page

10. Fiolić, M. Babić, Dario Babić, Darko Ščukanec, A.

DETERMINING THE “PEEK POINT” OF ROAD MARKINGS RETROREFLECTION

93-99

11. Furda, M. Rogić, K. Šafran, M. Bajor, I.

LOGISTICS SERVICE PROVIDER'S WAREHOUSE OPERATIONS EFFICIENCY ANALYSIS – CASE STUDY

101-111

12. Govorčinović, D. Slavulj, M. Ćosić, M.

CYCLELOGISTICS MANAGEMENT IN ZAGREB URBAN AREA

113-122

13. Hozjan, D. Bernat, V. Babojelić, K.

ROADSIDE SERVICE FACILITIESIN THE REPUBLIC OF CROATIA

123-134

14. Javni, Z. Pašagić Škrinjar, J. Furdić, M.

COMPARATIVE ANALYSIS OF EXTERNAL COSTS OF DIFFERENT MODES OF TRANSPORT

135-140

15. Jerinić, D. Juričić, B. Huljev, G.

NEW AERODROME OPERATING PROCEDURESFOR OPERATIONAL AIR TRAFFIC IN THE VICINITY OF ZADAR AIRPORT

141-152

16. Kavran, N. Berbić, I. Fabijanec, V. Kulej, M. Rogić, M.

BIOMASS TERMINALS DEVELOPMENT 153-161

17. Lakhmetkina, N. Maksimova, M.

EURASIAN INTEGRATION «ONE BELT, ONE ROAD». THE DIGITAL AGENDA

163-172

18. Lukanić, T. Šipuš, D. Abramović, B.

UTILISATION OF A CLOCK-FACE TIMETABLE IN RAILWAY TRANSPORT

173-180



III-IV

Authors Papers

Page

19. Novak, D. Pavlinović, M. Trojko, F.

AN OUTLINE OF SECTORAL QUALIFICATIONS FRAMEWORK IN AVIATION INDUSTRY

181-187

20. Novak, D. Andraši, P. Rubil, M.

FLIGHT TEST VALIDATION OF PBN CONCEPT OF OPERATION IN TERMINAL AREA IN SIMULATED ENVIRONMENT

189-194

21. Pashkevich, A. Burghardt, T. E. Kubek, D.

ANALYSIS OF DRIVERS’ EYE MOVEMENTSAHEAD OF INTERSECTIONS: OBSERVATIONS OF HORIZONTAL ROAD MARKINGS

195-202

22. Pavlin, S. Modić, A. Bračić, M.

AIRPORT SECURITY AND PASSENGER AND BAGGAGE HANDLING TECHNOLOGIES IMPROVEMENTS

203-209

23. Petrić, I. Petrović, M. Rožić, T.

PROPOSAL OF INTEGRATED PASSENGER TRANSPORT FOR BJELOVAR AREA

211-220

24. Pilko, H. Majcan, D. Šarić, Ž. Zovak, G.

TURBO ROUNDABOUTS: CASE STUDY OF CROATIA 221-229

25. Radišić, T. Vidović, A. Ivošević, J. Wang, B.

CHALLENGES AND SOLUTIONS FOR URBAN UAV OPERATIONS

231-241

26. Rigo, B. Stanković, R. Božić, D.

APPLYING SIMULATION METHODS IN OPTIMIZING LOGISTIC PROCESSES

243-253

27. Schöbel, A. Schöbel, C.

DATA CONVERTER FROM OPENTRACK INTO KRONECKER

255-262



IV-IV

Authors Papers

Page

28. Schönberger, J. THE VEHICLE ROUTING PROBLEM WITH PLATOON FORMATION

263-274

29. Sumpor, D. Tokić, S. Stanić, V. Petrović, N.

ANALYSIS OF THE POSSIBLE CYCLE TIME SAVINGS ON TRAM LINE NO. 12 IN ZAGREB

275-282

30. Świeboda, J. PROSPECTS FOR THE DEVELOPMENT OF INTERMODAL TRANSPORT IN POLAND

283-291

31. Šimunović, Lj. Šojat, D. Radulović, B. Plivelić, Z.

AN ESTIMATION OF OPERATING SPEED INCREASE BY TRAM SIGNAL PRIORITY

293-299

32. Šoštarić, M. Jakovljević, M. Buhin, D. Lale, O.

NON-MOTORIZED TRANSPORT INTEGRATION – CASE STUDY ZAGREB CITY CENTRE

301-309

33. Šporčić, M. Pašagić Škrinjar, J. Furdić, M.

ANALYSIS OF METHODS FOR DETERMINING TRANSPORT ROUTES

311-317

34. Štefančić, T. Bede-Jakovinac, I. Bajor, I. Ivaković, M.

ANALYSIS OF WAREHOUSE LAYOUT INFLUENCE ON PICKING PROCESS

319-329

35. Topić, J. Maletić, F. Škugor, B. Deur, J.

SOFTWARE TOOL FOR PLANNING OF CITY BUS TRANSPORT ELECTRIFICATION

331-344

36. Wan, Y. Haasis, H.

DIGITALIZATION OF MARITIME BUSINESS AND LOGISTICS

345-350

T. Štefančić, I. Bede-Jakovinac, I. Bajor, M. Ivaković: Analysis of Warehouse Layout Influence on Picking …

319

TOMISLAV ŠTEFANČIĆ, bacc.ing.traff.1

E-mail: [email protected]

IVANA BEDE-JAKOVINAC, bacc.ing.traff.1

E-mail:[email protected]

IVONA BAJOR, PhD1

E-mail: [email protected] MORANA IVAKOVIĆ, mag.ing.traff.2

E-mail: [email protected] 1 University of Zagreb

Faculty of Transport and Traffic Sciences Vukelićeva 4, 10000 Zagreb, Croatia

2 Ministry of the Sea, Transport and Infrastructure Prisavlje ul. 14, 10000, Zagreb, Croatia

ANALYSIS OF WAREHOUSE LAYOUT INFLUENCE

ON PICKING PROCESS

ABSTRACT

Engineering principle used in calculating efficiency of the picking process is guided by the phrase: “A process which you can measure, you can optimise”. The result of performance analysis is a key indicator of efficiency or inefficiency and it points to where cost reducing improvements can be made. The core subject of research is the warehouse layout and its influence on the picking process. Focus of paper is also directed to advantages, disadvantages and picking process efficiency calculations under multiple layouts.

KEY WORDS

warehouse; warehouse layout; warehouse performance; picking efficiency; literature overview;

1. INTRODUCTION

Warehouse as an important component in the supply chain is often part of research, especially considering process analysis. Despite technology, labour and space costs associated with operations, warehouses are still unavoidable due to reasons that among other include fluctuating demand and demand costs in bulk transportation and value added services [1].

First thing which is critical to know before any warehouse planning is type of goods which will be stored. Storage layout is chosen depending on the type of goods and storage equipment.

The warehouse layout decisions such as height, width, lane depth, aisle width, cross aisle location and location of I/O point are important as they affect both capital investment (construction od facility) as well as operational costs (space costs, handling costs, etc.)[1].

The dimensions of time, space, and costs are three cornerstones by which an efficient and effective warehouse is measured. A warehouse that is able to accomplish its mission while saving time, space, and costs in the process is on its way to world-class status. Knowing how to use these measures in warehouse design becomes a definite advantage to a warehouse manager [2].

Warehousing represents the management of space and time. The warehousing costs refer to the money allocated for the goods to be moved into or outside the warehouse. These expenses cover equipment and energy costs like ordering, storing and loading the goods, as well as more human costs like labour, shipment or delivery. Warehousing itself refers to taking care, transportation,


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loading, unloading, packing and processing of goods between the production and consumption for commodity and other various functions. According to authors Bartholdi and Hackman [3], the warehouse process of order picking takes 70% of time and 55% of costs which makes it a significant process in a warehouse.

The aim of this article is to review the recent research conducted by reference authors in the field of performance evaluation depending on the storage layout. Considering that the picking as a warehouse process takes the biggest amount of time and represents the biggest cost, this paper will show its optimisation due to the storage layout.

2. WAREHOUSE LAYOUT

Warehouse layout is one important factor affecting the order picking proces. Layout design has an effect of more than 60% on the total travel distance, which proves the relationship between warehouse layout and order picking travel distance. Therefore, warehouse layout has to be taken into account while designing the order picking system.

Usually, the unit-load warehouse layout is based on a rectangular shape, in which stock keeping unists (SKUs) arrive on pallets and leave on pallets. Because pallets are mostly standardized and handled one at a time, models of space and labour are simple linear models. It takes about n times the space to store n pallets; and it takes about n times the labour to pick n pallets. Furthermore, in such warehouses, the number of trips to pick full pallets is equal the number of pallets sold. When picking in volume, cartons are stored on pallets and so replenishing is a unit-load process, but picking is not, it creates extra complexities in models of space and labour [4].

Larger warehouses impose greater travel distances for the items inside, and greater travel distances lead to higher labour costs. Unit-load warehouses can, for slow-moving items, be rarely visited, but many have several workers actively storing and picking pallets. The labour cost associated with unit-load warehousing can be significant, especially for large retailers and other importers. The key of reducing labour (picking) cost is organising warehouse layout and storage places [5].

2.1 Classic Single Depth Pallet Rack Warehouse

In traditional warehouse design, storage racks are arranged to form parallel picking aisles, with a cross aisle along the bottom. Figure 1 shows the typical warehouse layout characterized a parallel storage aisle with inbound, outbound (I/O) - point located in the middle. If there are additional cross aisles in a traditional design to facilitate travel between picking aisles, they are arranged at right angles to the picking aisles. Cross aisles are appropriate for order picking operations, in which more than one location is visited per trip, but are inappropriate for the single-command operations considered in this article [5].

In Figure 1. recieving and shipping are on the same side of the warehouse, product flows in and out the same side of the warehouse which is sometimes referred to as a U-flow configuration. The best storage locations, which are at the middle of the common receiving and shipping dock are convenient because a location that is close to receiving would also be close to shipping.


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Figure 1 – Classic single depth pallet racking, U-flow

Source: [6]

Some basic characteristics of U-flow configuration are:

▪ Receiving and shipping located on the same side of warehouse ▪ Makes the more convenient locations still more convenient, less convenient locations even

worse ▪ Appropriate when product movement has strong ABC analysis ▪ Provides dock flexibility for both shipping and receiving, e.g. if one experiences a surge of

activity it can make use of additional doors from other function [6].

2.2 The Flying-V Warehouse Layout

The flying-V (Figure 2.) layout has a curved cross aisle instead of a cross aisle that is perpendicular to the aisles. This enables travel from the starting point (depot) to the storage locations that can be 10% shorter than those of the standard layout. Disadvantage assumes that pickers must make a rather sharp turn when entering the lower part of the pick aisle from the crossing aisle [7]. To solve this problem and simultaneously reduce travel distances, authors suggest fishbone storage layout to be used.

Figure 2 – Flying-V layout

Source: [7]

The model that relaxes the straight middle aisle assumption comes up with a V-shaped middle aisle with an increasing intersection angle with the pick aisles as one moves away from the I/O point. The Flying-V design offers about 10% improvement over the traditional design, because the main advantage behind the Flying-V design is the crossing aisle that cuts diagonally across the picking aisles which enables the workers to move faster and get to most of the locations more quickly [8]. Workers still need to travel in a strict rectilinear fashion only for locations near the bottom of the warehouse.


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2.3 The Fishbone Warehouse Layout

Fishbone warehouse layout assumes that the picking aisles must be parallel to each other while picks along the bottom of the Flying-V design still require rectilinear travel. The fishbone aisle design (Figure 3.) overcomes this disadvantage by rotating picking aisles in this region by 90 degreees.

Figure 3 – Fishbone warehouse layout

Source: [7]

Using the fishbone layout it is possible to reach any of the storage locations by almost straight travel from the depot. This strongly decreases the required travel distances. It must be noted that the fishbone and flying-V layouts appear to be most effective for unit load operations, when the number of stops per route is once or twice [7].

The fishbone layout also incorporates the V-shaped cross-aisles, below the V are horizontal, while the aisles above the V are vertical. The expected distance to a pick in the fishbone design are approximately 20% shorter than of those in a traditional warehouse. Similar to traditional layouts with cross-aisles, these alternative layouts also require a facility 3-5% larger than those of traditional layout, which was designed to minimize the footprint of a warehouse. According to authors Đukić and Opetuk [9], a comparison of fishbone warehouse that has been optimized for dual-command with traditional warehouses which have been optimized in the same manner has shown that an optimal fishbone design reduces the dual-command travel by 10-15%.

A drawback of the fishbone design is limited access to the storage space due to the single central P&D point.

3. KEY PERFORMANCE INDEX - PICKING

The order picking process in a warehouse involves selecting and gathering specified amount of right SKU’s in accordance with the order and it is composed of lifting, moving, picking, putting, packing and other related activities [11]. The process involves one individual collecting order, or more warehouse workers, also order picking can be manual or automated. In manual order picking, the picker gathers units from their locations and then transports them to a packaging area. In the case of automated picking, that is, automated storage and retrieval systems, system retrieves one or more unit loads and places them to a picking station. After that, the picker takes products on orders, and the remaining items on the unit loads are transferred to storage again.

Order picking typically accounts for about 55% of warehouse operating costs, and order picking itself may be further divided into activities described in the following table [10]:


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Table 1 – Order picking activities

Activity Order picking time [%]

Traveling 55

Searching 15

Extracting 10

Paperwork and other activities 20

Source: Made by the authors according to 10

The warehouse worker’s travelling makes up the greatest part of the expense in order-picking, which in itself is the most expensive part of warehouse operating expenses. Much of the design of the order-picking process is directed to reducing this unproductive time. In manual order-picking each worker is given a pick sheet, which lists the SKUs to be picked, in what amounts and where they are to be found. The SKU’s are listed in the order in which they are normally encountered as the picker moves through the warehouse [12].

[13] (1)

Where are:

∑∆t (Pick) - time between the instants when operator starts to pick an order and when the picking finishes (hour)

OrdLi Pick - number of order lines picked (nb/month)

This formula represents the picking time, lead time to pick an order line. Picking time can be reduced by choosing an optimal storage layout and by planning the SKU’s through ABC analysis or placing I/O on the different sides of the warehouse.

[13] (2)

Where are:


WH Pick - sum of employee labour hours working in picking activity (hour/month)

Pickp represents picking productivity, it is productivity indicator. Productivity can be defined as the

level of asset utilization, or how well resources are combined and used to accomplish a specific or

desirable result. Picking productivity is the total number of products picked per labour hours in a

picking activity. Every warehouse has typically labour-intensive activities. This indicator can be looked

as labour efficiency, but labour efficiency includes working hours.

[13] (3)

Where are:

Cor OrdLi Pick - number of order lines picked with the right product and the right quantity (nb/month)



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Picking accuracy is the number of orders picked correctly per orders picked. It measures quality which can be divided in two groups: some metrics are related to internal quality operations, where others focus on the customer level. Picking accuracy is one of the few metrics which measure the internal warehouse quality of operations. Picking accuracy can vary depending on the picker, while human error is a key factor here.

Picking productivity and picking time can be improved by reducing the time or travel between SKU location for each order. SKUs with a lot of movement per storage location must be stored in the best locations. If we identify those SKUs that generate the most frequent visits per storage location, it can be stored in best locations.

[3] (4)

For example, SKU’s that are stored in average quantity 5 and which moved 20 units last year would have generated about 20/5 = 4 visits per storage location, which is more than SKU’s that move 100 units but was stored in quantity 50.

So to minimize labour costs the following actions are required:

▪ Rank all the available pallet positions of the warehouse from least cost to greatest cost ▪ Rank all SKUs from most to least turns ▪ Move down the list, assigning the pallets of the next fastest-turning SKU’s to the next best

locations available [3].

3.1 Routing Methods in Traditional Layout Warehouse

The picking starts at the depot, and the picker has to visit all pick locations and finally has to return to the depot. The objective of routing policy is to sequence the items on the pick list to ensure a route with efficient travel through the warehouse. The layout of this type of warehouse consists of parallel aisles, and two cross aisles, one in the front, and the other in the back.

In addition, optimal routes may seem illogical to order pickers. So in this paper the most common practice for routing orders will be described.

The most common practice for routing the order picker is an S-shape seen in Figure 4 (traversal).

Figure 4 – S-shape routing Source: 4

Under this policy, the order picker begins by entering the pick aisle closest to the I/O point. Any isle containing at least one item is traversed over the entire length. No backtracking is permitted in any isle. Aisles with no picks are skipped. After picking the last item, the order picker takes the shortest route back to the I/O point [4]. S-shape starts with the left-most pick aisle and traverses in


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order each nonempty pick aisle completely except possibly the right-most one, resulting in a serpentine path.

Second most common strategy is the largest gap strategy shown in Figure 5. It looks similar to the mid-point strategy except the order picker enters an aisle as far as the largest gap within an aisle.

Figure 5 – The largest gap routing Source: 4

The gap represents the separation between the two adjacent picks, between the first pick and the front cross-aisle, or between the last pick and the rear cross-aisle. If the largest gap is between two adjacent picks, the order picker performs a return route from both ends of the cross-aisle. Otherwise, a return route from either the front or rear cross-aisle is used. So the largest gap within an aisle is the portion of the aisle that the order picker does not traverse [4]. According to Moayad [14] the heuristic calculation for the largest gap separation should calculate each pick aisle, and the maximum of the largest gap between any two items, the distance between the front corner and the item nearest to it, and the distance between the back corner and the item nearest to it. The picker starts traversing the left-most pick aisle completely, enters from the back cross aisle to the pick aisles, picking the items until the largest gap, and returning to the back cross aisle. After traversing the right most nonempty pick aisle completely, the same procedure is applied from the front cross aisle for the pick aisles where the distances are maximum.

Using the combined (composite) heuristic, aisle with picks are either entirely traversed or entered and left at the same end. This methods were originally developed for single-block warehouse.

Figure 6 – Composite routing Source: 4

The combined routing is an extension of aisle-by-aisle in that it also allows for entering and exiting a pick aisle more than once, but the sub aisle can still be entered only once.


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4. CASE STUDY

Case study in this paper will be a review and retrospect. The researcher [14] took sample of 600 items and 6 feasible layout alternatives for implementation of the methodology and for performing the desired numerical analysis in such a way to combine effects of the three storage assignment policies and the warehouse shape on the efficiency of order picking in terms of the travel distance and performance.

The author presented six different feasible warehouse layout structure alternatives represented by D= [(M1=30, B1=31), (M2=15, B2=60), (M3=10, B3=90), (M4=8, B4=112), (M5=6, B5=148), (Mf=5, Bf=178)]. The alternatives are selected to be investigated with the three storage assignment policies. All six layout structures share the same dimensional parameters of Wc=11m, Wm=5.8m and Wb=0.8m and number of random runs R. The layouts in Figure 7 have different dimensions between the six warehouse layout alternatives.

Figure 7 – Layout representation of the six alternatives in D*

The software for this simulation was Python language, in order to facilitate efficient implementation and to obtain full sets of results for a wide range of scenarios and situations. Entered parameters were M, B, Wc, Wm, Wb and size of the pick list N. The total expected travel distances versus the pick list size under each of the full/turnover policies, including the north-north and north-south assignment directions, nearest location and random storage policies for the layouts are shown on the Figure 8.


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Figure 8 – Values of the three storage policies for the six layouts Source: [14]

By comparing the total expected travel distance values from all six layouts using the three different policies, as the Figure 8 shows, the effects of the three storage assignment policies can be determined. The full-turnover policy, in which its two distinct assignment directions, north-north, and north-south, have similar outcomes results in the lowest total expected travel distances. This is due to the declining trend of pm(m) values obtained by the full-turnover policy.

The picker is more likely to enter the first few aisles which are closest to the depot and have the highest pm(m) values, rather than entering all M aisles including other aisles with lower pm(m) values, to retrieve the N picks.

The nearest-location policy results in moderate total expected travel distances compared to the full-turnover and the random policies. This is caused by the cyclic behavior of pm(m) values among the aisles, which means that the order picker is more likely to enter aisles with a peak pm(m) value than the other M aisles, including aisles with valley pm(m) values, to retrieve the N picks. The number of aisles in this case is higher than those in full-turnover policy, which is why the full-turnover policy generally results in shorter total expected travel distances compared to the nearest-product policy.

The random storage policy results in the largest total expected travel distances among the three storage policies. Order picker will more likely enter more of the M aisles to retrieve the N picks, when compared to previous storage policies. The results also clearly indicate that the total expected travel distance values obtained by a policy generally approach the values of the order policies as the size of the pick list increases [14].

Looking at the Figure 8 and comparing the first and fourth layout performance, a big difference can be observed. In the first layout the Total Expected Travel Distance is about 1400m and on the


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forth layout the Total Expected Travel Distance is about 900 m., which is 500 meters less. If we convert that data to percents, the third layout is 57% more efficient than the first. Earlier in this paper, it was mentioned that the picking cost includes activities such as travelling, searching and extracting. The travelling cost participates with 57% in full picking process cost. The best way to reduce travelling cost is to reduce travel distance and that travel distance could be reduced by optimizing the storage layout.

5. CONCLUSION

Warehouse represents the management of space and time. When optimizing the space segment, time segments could also be reduced. Before any warehouse planning, it is critical to know which type of goods will be stored.

The focus of this article was to show how storage layout affects the process of picking. It is shown that the warehouse process of order picking takes 70% of time and 55% of costs which makes it a significant process in a warehouse.

In order to reduce time segment (travel distance) which is more than 60% effect of the warehouse layout, three types of storage layouts were analysed.

Classic single depth pallet rack warehouse layout is more appropriate for order picking operations in which more than one location is visited per trip. The U-flow configuration makes it more convenient for the locations that are convenient and less convenient locations even worse.

The Flying-V design offers about 10% improvement over the traditional design, because cross aisle cuts diagonally across the picking aisles and offers an advantage in the way that the workers can get to most locations more quickly. Workers need to travel in a strict rectilinear fashion only for the locations near the bottom of the warehouse.

The fishbone layout shortens the expected distance of picking for approximately 20% compared to a traditional warehouse, because it overcomes the disadvantage of travelling in a rectilinear position by rotating bottom picking region aisles by 90 degrees.

The last part of the article shows a case study with six different layouts (in width and length). As it is mentioned earlier the order picking time includes 55% of travelling. The case study showed that the optimal layout can reduce travel distance by 57%.

According to before mentioned facts, it is visible that one of the ways to reduce time of travelling and picking time is to choose an optimal warehouse layout, thus making the picking process more efficient.

REFERENCES

[1] Rakesh V., Gajendra K.: Layout Optimization of a Three Dimensional Order Picking Warehouse, Shailesh J Mehta School of Management, Indian Institute of Technology Bombay, 2015.

[2] Napolitano, M.: The time, space & cost guide to better warehouse design, 2 nd ed., Boonton, Distribution Group, 2003.

[3] Bartholdi J. J, Hackman ST. Warehouse and distribution science, The Supply Chain and Logistics Institute. Atlanta: School of Industrial and Systems Engineering; 2011.

[4] Tetemeke Tsige M.: Improving Order-Picking Efficiency via Storage Assigments Strategies, University of Twente, Enschede, The Netherlands, 2013.

[5] Kevin R., Russell D.: Optimal Unit-Load Warehouse Designs for Single-Command Operations, University of Arkansas, Fayetteville, Arkansas, 2011.


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[6] John J. Bartholdi, Steven T. Hackman: The Supply Chain and Logistics Institute School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, 2011.

[7] Kees Roodbergen J.: An explanation of some rack layout concepts for warehouses, White paper, University of Groningen, 2011.

[8] Russel D. Meller, Kevin R. Gue: The application of New Aisle Designs for Unit-Load Warehouses, Department of Industrial Engineering, Uneversity of Arkansas, Auburn University, 2009

[9] Đukić G., Opetuk T.: Warehouse Layouts, Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb

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topic: transport and logistics industry in digital age · “science and traffic development”...

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