Studying and building automated storage and retrieval algorithm in cold warehouse

In this thesis, an improvement is considered through minimize the expected travel distance of two optimal criteria are determining storage location algorithm and path planning in narrow

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY

-

TRUONG NGOC CUONG

STUDYING AND BUILDING AUTOMATED STORAGE AND RETRIEVAL ALGORITHM IN COLD WAREHOUSE

NGHIÊN CỨU XÂY DỰNG GIẢI THUẬT LƯU TRỮ VÀ TRUY HỒI HÀNG HÓA TỰ ĐỘNG TRONG KHO LẠNH

Major: Mechatronic Engineering

ID Code: 60520114

MASTER THESIS

HO CHI MINH CITY, December 2018

CÔNG TRÌNH ĐƯỢC HOÀN THÀNH TẠI TRƯỜNG ĐẠI HỌC BÁCH KHOA –ĐHQG -HCM Cán bộ hướng dẫn khoa học 1: TS Phùng Trí Công

Cán bộ hướng dẫn khoa học 2: PGS Nguyễn Duy Anh

Cán bộ chấm nhận xét 1:TS Nguyễn Huy Hùng

Cán bộ chấm nhận xét 2:PGS.TS Nguyễn Thanh Phương

Luận văn thạc sĩ được bảo vệ tại Trường Đại học Bách Khoa, ĐHQG Tp HCM ngày 20 tháng 12 năm 2018

Thành phần Hội đồng đánh giá luận văn thạc sĩ gồm:

(Ghi rõ họ, tên, học hàm, học vị của Hội đồng chấm bảo vệ luận văn thạc sĩ)

1 PGS.TS Nguyễn Quốc Chí

2 TS Đoàn Thế Thảo

3 TS Nguyễn Huy Hùng

4 PGS.TS Nguyễn Thanh Phương

5 TS Lê Thanh Hải

Xác nhận của Chủ tịch Hội đồng đánh giá LV và Trưởng Khoa quản lý chuyên ngành sau khi luận văn đã được sửa chữa (nếu có)

TRƯỜNG ĐẠI HỌC BÁCH KHOA VIỆT NAM Độc lập - Tự do - Hạnh phúc

NHIỆM VỤ LUẬN VĂN THẠC SĨ

Họ tên học viên: Trương Ngọc Cường MSHV: 1770205

Ngày, tháng, năm sinh: 01/02/1994 Nơi sinh: Bà Rịa Vũng Tàu Chuyên ngành: Kỹ Thuật Cơ Điện Tử Mã số: 60520114

I TÊN ĐỀ TÀI: Nghiên cứu xây dựng giải thuật lưu trữ và truy hồi hàng hóa tự động

trong kho lạnh

II NHIỆM VỤ VÀ NỘI DUNG:

Khảo sát thực trạng kho lạnh tại Việt Nam,

Nghiên cứu giải thuật lưu trữ hàng hóa trong kho dựa trên phương thức tối ưu hóa

vị trí và hoạch định đường đi ngắn nhất

Xây dựng mô hình kho với 480 ô chứa pallet cùng phần mềm quản lý kho tối ưu

III NGÀY GIAO NHIỆM VỤ : 15/01/2018

IV NGÀY HOÀN THÀNH NHIỆM VỤ: 02/12/2018

V CÁN BỘ HƯỚNG DẪN : TS Phùng Trí Công - PGS.TS Nguyễn Duy Anh

PGS TS Nguyễn Duy Anh

TRƯỞNG KHOA KHOA CƠ KHÍ

(Họ tên và chữ ký)

I would like to send my deepest gratitude to Dr Phung Tri Cong and Assoc Prof Nguyen Duy Anh for his devotion and guidance which are a great motivation for me to overcome difficulties of the thesis

I would especially like to acknowledge the support of teachers at Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology who has guided

me over the past six years, the knowledge teachers have taught is really precious and is the foundation for me to complete the project

My family and friend continues to amaze me with their constant love and support Without your help during my studies and through my life I would not be all that what I am right now

Thank you!

An effectiveness of a storage and retrieval system in cold warehouse is assessed based on operating costs, which can be improved by re-designing warehouse layout or upgraded to automated system But cost intensive and high time consumption are gating

to implement those proposals In this thesis, an improvement is considered through minimize the expected travel distance of two optimal criteria are determining storage location algorithm and path planning in narrow aisle racking system A warehouse layout is designed for 480 storage locations on 16 pallet racking, separated by 4 storage aisles and 1 pick aisle Storage and retrieval of goods is carried out by 2 forklift trucks The system is able to deal with variations in environment conditions such as deadlocks

or traffic jams by applying the windows time concept combine with A-star algorithm Simulated results show that proposal algorithm help to reduce up to 29% travel distance compared with traditional policies

Tính hiệu quả của hệ thống lưu trữ và truy hồi hàng hóa trong kho lạnh được đánh giá dựa trên chi phí vận hành và có thể được cải thiện bằng cách bố trí lại không gian kho hoặc nâng cấp lên hệ thống tự động Nhưng chi phí cao và tốn thời gian dài là những trở ngại để thực hiện các đề xuất trên Trong luận văn này, việc giảm chi phí kho được thực hiện bằng cách rút ngắn quãng đường di chuyển hàng hóa dựa trên hai yếu tố là tối ưu hóa giải thuật tìm kiếm vị trí lưu trữ trên các kệ chứa và hoạch định đường đi ngắn nhất cho từng nhiệm vụ của xe nâng trong kho lạnh có lối đi hẹp (single aisle) Mô hình kho lạnh được thiết kế với dung tích 480 ô chứa bố trí trên 16 kệ Các dãy kệ được ngăn cách bởi 1 lối đi chính và 4 lối đi phụ Việc lưu trữ và truy hồi hàng hóa được thực hiện bởi 2 xe forklift Hệ thống có khả năng xử lý được một số biến thể của môi trường như ách tắc giao thông hoặc bị tê liệt bằng giải thuật giám sát theo thời gian kết hợp với giải thuật tìm đường đi bằng thuật toán A-star Kết quả mô phỏng cho thấy giải thuật đề xuất giúp giảm đến 29% tổng quãng đường di chuyển hàng hóa so với các giải thuật lưu trữ truyền thống

I pledge that the thesis Studying and building automated storage and retrieval

algorithm in cold warehouse is my own research It is entirely of my own work and

has not been submitted to any other college or higher institution, or for any other academic award in this College The data and materials in the dissertation are truthful and all references, inheritance are cited and fully referenced

Truong Ngoc Cuong

ACKNOWLEDGEMENT i

ABSTRACT ii

DECLARATION iii

TABLE OF CONTENTS v

LIST OF FIGURES 6

LIST OF ACRONYMS 8

Motivation 1

Literature Review 1

1.2.1 Warehousing operations 2

1.2.2 Most common mode of pallet racking systems in Vietnam 4

1.2.3 Storage Policy 7

1.2.4 Planning path 12

Research problem 14

Objectives 15

Thesis structure 15

Chapter 2: METHODOLOGY 12

2.1 Assumption and Layout design 12

2.1.1 Assumption [1-2] 12

2.1.2 Warehouse layout and Routing 12

2.2 Forklift Truck – System configuration and kinematic modeling 13

2.3 Auto – Localization Algorithm 16

2.3.1 Basic of ABC, COL policy and continuous cluster method 16

2.3.2 Storage and retrieval strategy base on A-star Algorithm 22

2.4 Dynamic Routing by time windows method 23

Chapter 3: SIMULATED SOFTWARE DEVELOPMENT 28

3.2 Main program 29

3.3 Software Interfacing 35

Chapter 4: SIMULATED RESULT AND DISCUSSION 37

4.1 Travel distance improvement under localization policy 37

4.2 The efficiency of dynamic routing algorithm by travel distance comparison ……… 40

Chapter 5: CONCLUSION & FUTURE WORK 42

Chapter 6: REFERENCES 44

LIST OF FIGURES

Figure 1.1: Overall Warehouse management activity……… 2

Figure 1.2: Warehouse Operating ……… 3

Figure 1.3 Selective Pallet Racking in cold warehouse……… ….4

Figure 1.4 Adjustable pallet racking for narrow aisles……… ………5

Figure 1.5 Mobile pallet racking system ……… 6

Figure 1.6 Storage policy……….…….7

Figure 1.7 A typical Zone positioning for three class in a square in rack…….………9

Figure 1.8 Static routing problem……… …….14

Figure 2.1: Warehouse layout ……… 13

Figure 2.2: An industrial forklift truck ……….14

Figure 2.3: Kinematics model of a forklift ……… 14

Figure 2.4: ABC storage policy ………16

Figure 2.5: Continuous cluster concept ……… 17

Figure 2.6: Storage location under Continuous cluster policy ……….…18

Figure 2.7: The mapping model in simulation ……….21

Figure 2.8: Travel Distance Index for Rack S-III ………22

Figure 2.9: Deadlock and Traffic Jams ………27

Figure 2.10: Time windows with deadlock between 2 paths……… 28

Figure 2.11: Conflict-free routes ……….28

Figure 3.1: Structure of software ……….29

Figure 3.2: Five fields of data structure ……… 29

Figure 3.3: Structure of ID code ……… 31

Figure 3.4: Flowchart genera Check code algorithm……… 31

Figure 3.5: Main program flowchart ………32

Figure 3.6: Storage Algorithm Flowchart ………33 Figure 3.7: Retrieval Algorithm Flowchart ……… 34

Figure 3.8: Optimal Storage location determining by A-Star Algorithm ……….35

Figure 3.9: Simulated warehouse layout ……… 36

Figure 3.10: Color convention of goods ……… 37

Figure 3.11: Warehouse Space……… 37

Figure 4.1: Warehouse Space under Random Policy ……… 39

Figure 4.2: Warehouse Space under continuous cluster policy ……… 39

Figure 4.3: Travel distance under localization policy ……… 40

Figure 4.4: Travel distance improvement under ……… 40

Figure 4.5: Travel distance of static and dynamic routing ……… 42

LIST OF ACRONYMS

• AGV: Automatic Guided Vehicle

• AS/RS: Automatic Storage and Retrieval System

• COI: Cube Per Order Index

• COL: Closest Open Location

• DRP: Distribution Requirements Planning

• FIFO: First In First Out

• FIFS: First In First Served

• LIFO: Last In First Out

• MDVRP: Multi Depot Vehicle Routing Problem

• GR day: Goods receipt day

• ODV: Order Distance Vector

• RAN: Random Storage Assignment

• SKU: Stock Keeping unit

• VRPTW: Vehicle Routing Problem with Time Windows

• WCS: Warehouse control system

• WMR: Warehouse Management System

• WHM: Warehouse Management

Chapter 1: INTRODUCTION Motivation

According to report of The World Bank 2018, on average logistics costs make up some 13 percent of GDP in developed countries In the most efficient countries, such as the United States, those costs are around 8 percent, whereas in the least efficient countries they can be as high as 25 percent Logistics costs are strongly (inversely) correlated with the Logistics Performance Indicator, including Economics, Technology, and Policies From a technical perspective, applying technology in logistical operations, especially in warehouse management System (WMS), is an important factor to reduce operating costs throughout the supply chain WMS can help control the movement and storage of materials within a warehouse and process associated transactions, such as receiving, picking and shipping Having a strong WMS will help reduce costs, increase inventory accuracy and storage capacity and improve customer satisfaction In WMS, optimizing warehouse space and travel distance are important tasks which is not only help to save operation cost and travel time, but also helps to manage goods more easily There are currently a few research have fully exploring the simultaneous optimization of both determining storage location and path planning while this combination not only optimal warehouse space, but by planning the path and finding the optimal storage location for each storage cycle, it significantly shortens the travel path This topic will take a new approach in current warehouse layout, where the optimization

of space and distance will be two goals to implement

Literature Review

The map in Fig.1.1 is a summarized factors that can be impacted to cold warehouse operation Following from the introduction, the objective is to optimize the storage determination and routing policy during storage and retrieval process, layout design is not consider in this research since it is related to limited of physical warehouse Relevant articles about storage policy are discussed in section 1.2.1 Routing policy research history are described in Sections 1.2.2 In section 1.2.3, the research problem and in detail solution will be found out

1.2.1 Warehousing operations

Warehousing operations can be divided into several functions (See Fig 1.2) mainly: Receiving, which consists of unloading of products from transportation vehicles to receiving docks, inspection of products for decencies or missing products, and updating warehouse inventory records to reflect changes

Put-away can be described as a process of sorting pallet in pallet racking as well

as storing goods information Some commonly storage policies used include random, dedicated, class-based and closest open location (COL) Which includes moving products from receiving dock to assigned storage locations, shipping dock or other areas

in the warehouse, and moving products between these areas [1, 2, 3, 6]

Order picking is a process of retrieving articles from their storage locations in response to a specific customer request This is a fairly complex and costly process in warehouse management especially in warehouses with automated systems [1, 2, 3] These authors estimate that order picking can account for up to 65% of the operating costs of a warehouse and thus its optimization is crucial to reduce costs Base on put-away or expiration day, some order picking policies is used such as first in first out (FIFO), last in first out (LIFO), first expire first out (FEFO) [2]

Fig 1.1 Overall warehouse management activity

Shipping, which includes loading products onto transportation vehicles, inspection

of products to be shipped, and updating warehouse inventory records It can additionally include sorting and packaging of products

RECEIVING

 Schedule Carrier

 Unload vehicle

 Inspect for damage

WAREHOUSE PROCESS

Put-away

 Identify Product

 Identify Product Location

Shipping Preparation

1.2.2 Most common mode of pallet racking systems in Vietnam

Choosing right pallet racking system can make a huge cost saving in warehouse management A well-designed system can improve productivity and increase warehouse storage space by 40% or more, and help your operation adapt to changing inventory needs Subsection 1.1.1, 1.1.2, 1.1.3 and 1.1.4 are shown several type of pallet racking use in Vietnamese warehouse

 Selective Pallet Racking

Characteristics: This is the most popular racking system, which consists of beams

linked to the vertical frame In standard designs, it reaches a height of 8 - 10m and can lift shelves up to 12m and in automatic operation systems, it can be as high as 30m

Application: The system is suitable for large storage needs with all types of cargo

handling in stock Contrary to the model of stacking goods, forklift truck can be accessed directly to each pallet and easily change cell height and intervals between beams to optimize shelf space

 Adjustable pallet racking for narrow aisles

Characteristics: This is an adjustable racking system which shelves are arranged close together to avoid wasting the aisle space, minimizing the space of cold storage - where warehouse space is proportional initial investment costs and operating costs

Fig 1.3 Selective Pallet Racking in cold warehouse

The system has minimized the passageway and maximizing the height of the shelf (altitude above 10m)

Advantages:

- Can reach each pallet

- Configured to be the maximum storage density, the highest efficiency

- The rate of pallet filling is higher than other systems

- Save storage space, because of expanding storage space according to height

Defect:

- Difficulties in controlling vehicles when they need to import / export goods in high-density boxes because of limited visibility

 Mobile pallet racking system

Characteristic: The purpose of the system is to maximize the free space in the warehouse The system was design includes storage shelves placed on mobile racks This type of warehouse system is used when the storage space is very expensive, the need to save space

Advantages:

- The system saves up to 90% of aisle space

- Can access any pallet easily

Fig 1.4 Adjustable pallet racking for narrow aisles

Operation: Shelves can be operated locally or via remote controllers, and crane

vehicles are required to retrieve goods All shelves are equipped with a sensor system to ensure the shelves stop when encountering obstacles

 Drive-In/Drive-Thru Rack

Characteristic: This system consists of racks designed so that forklifts can go

deep into each shelf, pallet support rails are arranged higher than the height of the vehicle that can be easily moved In the process of warehousing or ex-warehousing, all pallets must be moved in order (from inside to outside, from top to bottom or vice versa)

- Make good use of space because it does not take space for aisle

- Allowing forklifts to move flexibly

- Can increase storage capacity up to 75% compared to regular shelves

Fig 1.5 Mobile pallet racking system

1.2.3 Storage Policy

Storage policy is related to assign products to storage locations when new products arrive in the warehouse Determining storage location - localization is understood as the process of selecting the optimal storage location among all positions in warehouse, so that the travel time is minimization, thus saving the total operating costs of the warehouse In addition, each storage location should be closely managed based on information such as type of goods, stored time, coordinates If storage policy is effective, there is shorter path will be found in determining storage location process for each cycle

of pallet transferring in warehouse [1, 2, 7, 35]

In storage process, localization will be built based on the following strategy: Random, dedicated, class-based and closest open location (COL) [1, 2, 3]

Fig 1.6 Storage policy

In a random storage policy, each SKU (Stock-keeping unit - an inventoried item)

is randomly assigned to an empty location in a warehouse [2] Random storage is a completely shared storage policy where all incoming storage pallets can be stored in any aisle and any open location in the rack Also, no grouping of storage location with the same SKU is usually made This makes random storage the easiest storing strategy to apply, since it makes no distinction between storage totes based on SKU features When

a pallet is retrieved from for picking, it can be returned to a different rack location or even a different aisle [34, 35]

In dedicated (or fix slot) storage policy, each SKU is placed at a fixed position in

stock This policy helps to manage and control system easier because each area in warehouse only contains one or a few type of items The storage locations are dedicated,

meaning that each SKU is assigned a number of locations in the rack, where only that SKU can be stored This is referred to as dedicated storage [34] When dedicated storage

is used, the COI (cube-per-order index) rule is a well-known ranking method, which takes the space requirements of the SKUs into account The COI is defined as the ratio

of the number of storage locations assigned (or calculated) to an item With this measure, the SKUs with the smallest COI are positioned closest to the I/O point [34, 35] Dedicated storage can be problematic, since the locations need to be allocated according

to the maximum space requirement of each SKU The selected locations need to be reserved even when an SKU is out of stock These requirements induce the needed storage space [1, 21] For example, random storage needs about 70% of the space requirement of dedicated storage

This result is based on the assumptions that the changes in inventory levels of different SKUs are independent, and most of the time the space requirement of an SKU

is less than its capacity of inventory [21] In some applications with double-deep racks,

it is possible to decrease the amount of rearrangements with dedicated storage by allowing only the same SKU to be stored in both front and back positions [20] In the installed system this strategy could not directly be applied because of the compartmented location Also it is expected that there will be several thousand SKUs in one aisle which makes it very difficult maintain the integrity of a dedicated storage location allocation An alternative approach to dedicated storage

based storage policy is a combination of the above two methods

Class-based storage partitions all products into two or more classes and reserves a block of storage locations within the rack for each class The class partition is based on some criterion, for example COI, duration of stay or turnover rate It can also be based on the affinity of items, meaning that items which have a higher chance of getting picked in the same order get stored close to each other

This can be problematic though, since the items for the same order are not necessarily retrieved sequentially [22] If the turnover rate is used, the items with the highest turnover rate are allocated to the class whose storage zone is closest to the I/O point Inside the zone item locations are chosen according to an open location selection

rule [4] Thus, class-based storage is a combination of dedicates and random storage policies The goal of using product classes is to achieve the potential effectiveness of full-turnover based storage while maintaining a part of the flexibility of random storage [34, 35] A higher number of classes can potentially impacted to large of travel time savings, but also increases the needed storage space For a single-deep rack it has been studied, that most of the gain from full-turnover storage can be obtained by using a small amount of classes For example 96% of the potential improvement can be got with 6 classes, and over 99% with 12 classes [34, 35] Using many classes can be hard to manage In practice, the number of classes is usually 2 – 3 After deciding the number

of classes, there is the problem of deciding, how many and which SKUs belong to which classes Zoning, i.e the division of the storage rack into different zones

Zone sizing is commonly chosen based on an ABC-analysis In case of two classes, the A class is reserved 20% and the B class 80% of rack area With three classes, the sizes for A, B and C classes are 20%, 40% and 40%, respectively [33, 34, 35] The classes are usually L-shaped or rectangular In Fig 1.4 typical example of zone division

is presented for three classes

With double-deep racks, zoning is not as simple If the zones were allocated according to Fig 1.4, each zone would require its own rearrangement area The fill level

of each zone would need to be controlled so that there would always be a chance to move a tote in an allowed location Even so, the zone positioning of this figure might not work well double deep rack This is due to the previously mentioned issue that the back positions have a higher retrieval costs than front positions Instead of storing fast

Fig 1.7 A typical Zone positioning for three class in

a square in rack

moving items in front of other fast movers, it could be beneficial to have a class of slow moving items stored in back positions Because the order of SKUs arriving to the storage cannot normally be controlled, this type of zoning would be difficult to implement and maintain The implementation of a class-based storage policy requires a lot of parameters to make all the decisions listed above The values for these parameters should be carefully chosen based on real production data Otherwise, applying the class-based storing strategy might not bring any advantages compared to random storing [17]

Closest open location is a popular method in storage, that allow SKUs to be sorted

in to a slot where is closest to the Input / Output point [3] This means that an incoming pallet is stored to the closest unoccupied location from the I/O point, with respect to travel time With double deep racks, the COL rule also needs to decide, whether to allow storing pallets in front of other ones, if there are locations with two open positions available This decision also includes combining two storing tasks in the same double-deep location Storing pallets to front positions forces the crane to perform rearranging movements, which increases retrieval times The COL rule is used in the installed system with some additional priority rules One of these rules is for keeping both rack sides approximately equally filled Another modification is made for very high fill levels (> 85%) to maintain some open locations also in the front of the rack for forced re-arrangements

In retrieval process, Order picking is a process of retrieving articles from their storage locations in response to a specific customer request This is a fairly complex and costly process in warehouse management especially in warehouses with automated systems These authors estimate that order picking can account for up to 65% of the operating costs of a warehouse and thus its optimization is crucial to reduce costs [4] Base on put-away or expiration day, some order picking policies is used such as first in first out (FIFO), last in first out (LIFO), first expire first out (FEFO) [2] put-away or expiration day were used to find the storage location, some common strategy are first in first out (FIFO), last in first out (LIFO), first expire first out (FEFO) [1, 2]

1.2.4 Planning path

Path planning is defined as a process for selecting the most optimal path from all the solutions The optimal path is determined based on two factors: the distance from I/O point to selected storage location is the shortest and there is no deadlock or traffic jams while vehicles move in the system Strategy of route planning could be classified into two categories: Static and dynamic routing [4, 5]

For the static routing, there is only 1 storage location and 1 fixed path is choose in advance for each task of the forklift, the selection will not change during the task execution This strategy is commonly use in the case there is only one vehicle hand on all task in the warehouse For system with two or more vehicles, collisions could be occur when forklifts move simultaneously such as deadlock and traffic jams, affect drastically the system performance and static algorithm cannot adapt to change [6, 7] Once the collision occurs, the selected location and path must be changed to avoid accidentally system operating The process to re-localization and re-routing to avoid accident is carried out throughout once the collision happen is called dynamic routing

In Static routing, some researchers have been looking for methods to optimize order-picking routes A developed algorithm be finds an optimal route for minimizing the travel distance in a rectangular warehouse with crossovers only at the end of the aisles [33] Since heuristics are easier to understand and they form routes that are fairly consistent in nature, there is still need for them to be studied An evaluation of different order picking routing policies are discussed in [34] According to his study, composite and largest gap are the best heuristics The policy is found out the routing methods for warehouses with more than two cross aisles A branch-and-bound algorithm is used that generates shortest order picking routes Performance comparisons between heuristics and the branch-and-bound algorithm are given for various warehouse layouts and order sizes [33, 34, 35]

For dynamic routing, new approach was formed to optimize the travel time and distance The key feature of this algorithm is that it avoids collisions, deadlocks and live locks already at the time of route computation (conflict-free routing), whereas standard approaches deal with these problems only at the execution time of the routes In addition,

the algorithm considers physical properties of the vehicle and certain safety aspects implied by the particular application The comparison with static approach shows that the conflict-free approach (dynamic) is superior to the static one Additionally, the presented algorithm is able to provide fast answers of both approaches shows that the conflict-free approach is superior to the static one Additionally, the presented algorithm

is able to provide fast answers [18]

After that, a vehicle routing problem with time windows (VRPTW) is a generalization of the vehicle routing problem where the service of a customer can begin within the time windows defined by the earliest and the latest times when the customer will permit the start of service In this paper, they present the development of a new optimization algorithm for its solution The LP relaxation of the set partitioning formulation of the VRPTW is solved by column generation The results indicate that this algorithm proved to be very successful on a variety of practical sized benchmark VRPTW test problems The algorithm was capable of optimally solving problems of a size six times larger than any reported to date by other published research [19]

High light for project in public [20, 21] is the proposed GA employs an indirect encoding and an adaptive inter-depot mutation exchange strategy for the fixed destination multi depot vehicle routing problem (MDVRP) with capacity and route-length restrictions A comparison of the GA’s approach with other non-GA approaches show that although Gas are competitive for the MDVRP and the existing GA upon which it improves the solution quality for a number of instance

Another study about dynamic routing come from paper [24] they released the router system which is able to solve traffic jams and collisions (see Fig 1.5), generate conflict-free and optimized paths before sending the final paths to the robotic forklifts base on combine time windows and dijkstra algorithm It also guarantees optimized routes before sending the final paths to the robotic forklifts

Fig 1.8 Static routing problem: in (a) deadlocks and in (b) traffic jam

A study in public [25] show that a dynamic routing method for supervisory control

of multiple automated guided vehicles (AGVs) that are traveling within a layout of a given warehouse In dynamic routing a calculated path particularly depends on the number of currently active AGVs’ missions and their priorities

Another author show performance of Gene Bank technique with a proposed novel Ordered Distance Vector (ODV) based EV Technique in terms of convergence rate (%), quality solution and convergence diversity [26]

A new concept of an automated storage and retrieval system with pallet-shuttle high-density storage system is described in a study [29] With the algorithm illuminated

in this paper, the system can route and schedule vehicles efficiently, and the utilization rate of vehicles can achieve more than 98% and congestion rate of system less than 2%, with an appropriate number of vehicle

Research problem

As previous section mentioned, a number of independent studies have been conducted to find alternative ways to improve warehouse operating costs and achieve certain results However, most of the work are relatively discrete manner so the efficiency has not been fully converged Combination among more than one optimal algorithms in storage and retrieval process at the same time can make significant cost and time saving

In this thesis, an optimal algorithm is performed at two stages of searching for storage location and path planning for simultaneously two forklifts Two definitions of

storage strategy based on A * and time windows is redefined as the foundation for an integrated algorithm that optimizes the search and archiving process

Objectives

In this paper, the presented algorithm to determinate available storage location in storage process to decrease travel distance and providing storage method to optimized warehouse space Under those strategy, storage space will be divided into several class, each class has a travel distance index, which is the distance between input/output (I/O)

to storage location on rack Goods are entered into the warehouse and be placed in the class which has smallest travel distance index Especially if the zone is divided into smaller zones to form a cluster, the efficiency of algorithm is higher

This article contribute by constructing an algorithm of dynamic routing strategy In that, storage location is determine by continuous cluster method and A-star algorithm, collision is solved through the time windows concept

Thesis structure

This thesis has four main chapters In chapter 1, the overall warehouse management will be shown up, with focus on Storage and retrieval process which is the most costly in operating Chapter 2 begins the theoretical part by presenting central concepts of manage strategy and providing assumption of warehouse space and operating rule as well as propose algorithm base on continuous cluster method and time windows to deal with traffic or deadlock issue Chapter 3, on the other hand, the combination of all algorithm was propose in previous chapter to main program with interactive interface through V-REP environment and a MATLAB control centre In Chapter 4, concludes the theoretical part by presenting some of the common practices used to organize warehousing activities, the result will be analysis to show the effective The conclusion and next work will be illustrate in Chapter 5 References are summarized

in latest chapter

Chapter 2: METHODOLOGY 2.1 Assumption and Layout design

A design of the popular cold warehouse is proposed The system specifications, storage conditions and import and export scenarios are given to check the efficiency of the algorithm

2.1.1 Assumption [1-2]

The system is built based on some special characteristics of cold store for preservation

of aquatic products but we can adjust it to suit different types of storage

 The capacity of system are 480 storage locations, each location contain 1 SKU (stock keeping unit – an inventoried item) System containing 6 types of frozen shrimp which are named A1, A2, A3, A4, A5 and A6

 Goods are organized into the pallet Each pallet is a SKU This is the smallest item in system Pallet is placed on single pallet racking and order picker can reach all items

in the rack regardless of rack’s height

 Pick out time is undefined for all SKUs in system

For frozen shrimp products, the requirement in storage process is if goods were come first, it will be sorted in pallet racking first (FIFS) and travel distance for each moving cycle is the shortest to prevent damage under wrong temperature In retrieval process, pallet is removed base on import day, the oldest good in system is the earliest move out This requirement is necessary to ensure the goods are not in warehouse too long

2.1.2 Warehouse layout and Routing

Caron, Marchet, and Perego found that the layout design greatly affected to order picking distance [7] According to their study, layouts affect over 60% of the total distance traveled in storage Therefore, designing layout is an important foundation task before building the management algorithm

From assumptions were presented in section 2.1.1, warehouse system with 1 single pick aisle and 2 storage aisles is recommended (see Fig 2.1) Warehouse space is divided into 16 pallet racking (16 lines) The line consider in this paper include 5 tiers (A, B, C, D and E) and 6 bays (are distinguished by the digits from 1 to 8), totally 40 storage locations is located at each line These lines are named Roman numerals I to XVI The design help to be easily reach all items in the pallet racking and access to depot by using 2 separate Input and Output points

2.2 Forklift Truck – System configuration and kinematic modeling

The forklift is proposed in this thesis is a standard industrial forklift with a turn mechanism provided by CLARK Company (see Fig 2.2) The car was designed with 4 wheels including 2 caster wheels in front, one dummy caster wheel at the right rear and one wheel at the left rear, which is used for driving and steering the forklift The forklift car has three basic motions (3 DOF), a straight-forward motion, rotation around the vehicle's axis (center of 4 wheels), and vertical translation along the elevation The forklift can be operated in the narrow aisle racking warehouse That mean the turning radius is less than the aisle dimension [6]

spin-O-X-Y and o-x-y are the frame and the body coordinates of the forklift, respectively (see Fig 2.3) Let v and θ be the velocity of the forklift in the x-direction and the rotational angle of the x-axis with respect to the X-axis Let δ be the steering

Fig 2.1 Warehouse layout

angle of the driving wheel Under the assumption that the wheels do not slip, let OICR

be the instantaneous center of rotation of the forklift Finally, let l and a be the length of the forklift and the offset of the center of the driving wheel from the centerline

sin cos

x

a l

2.3 Auto – Localization Algorithm

2.3.1 Basic of ABC, COL policy and continuous cluster method

We focus on the ABC storage (class-based storage) and COL policy since they are basic policies for developing continuous cluster method Per mention at section 1.2.1, ABC storage policy is a combination of dedicated and randomized policy (see Fig 2.4) Randomized policy is used for sorting SKU (stock keeping unit) within a class, and dedicated policy is used among different classes Under COL policy, SKU is sorted into storage location where is closest to the I/O point [2, 3, 4]

Under ABC storage policy, warehouse space is divided into three classes (see Fig 2.4) Each class contain goods with the same parameters (same good type or same travel distance) In storage and retrieval process, goods will be sorted on each class with priorities in turn are A, B and C In each class, goods are arranged randomly into a certain location

All storage locations of class A are considered to have the same distance and they have same priority in storage process, although the actual distance of each storage

Fig 2.4 ABC storage policy

location to I/O point is different from the others So if goods is stored in the same class, the travel distance is not really optimal This argument is correct when the number of pallets is smaller than the number of available storage location in class A

To increase the efficiency of the algorithm, the ABC storage and COL policy are combined together That mean each class is divided into several smaller classes, each of which is called a cluster (see Fig 2.5) The radius of cluster is the travel distance from storage location on racking to I/O point In storage and retrieval process, SKUs are priority arranged into the cluster which has smallest radius By this method, the storage location is found always has travel distance less than the ABC class method If the number of clusters in each class is greater, the algorithm is more efficient To see the effectiveness of the algorithm, a warehouse system is simulated and analyzed in the following section 3 Continuous cluster method is a combination of ABC storage (class-based storage) and Closest Open Location policy – COL [2, 3, 4]

In detail, each class of ABC policy is divided into several smaller classes, each of which is called a cluster In Fig 2.5, Fig 2.6, each cluster is represented by a different color, the cells with the same color have same valued ij The radius of cluster is the travel distance from storage location on racking to I/O point In storage and retrieval process, SKUs are priority arranged into the cluster which has smallest radius By this method, the storage location is found always has travel distance less than the ABC class method

If the number of clusters in each class is greater, the algorithm is more efficient