Companies have been trying continuously to reduce their logistics costs in the current competitive markets. Warehouses are important components of the logistics systems and they must be managed effectively and efficiently to reduce the production cost as well as maintain customer satisfaction. Order-picking is the core of warehouse operations and an order-picking system (OPS) is essential to meet customer needs and orders. Failure to perform the OPS process properly results in high costs and customer dissatisfaction.
1. Introduction
Manufacturing companies confront different challenges to fulfill customer orders and distinguish between them. Many manufacturing companies consider reducing and eliminating costs and improving productivity in their logistic system important improvement activities. On the other hand, the warehouse is one of the most critical in-house logistics components, playing a crucial role, among other activities, in the supply chain of any manufacturing company. To meet customer needs, the OPS, as part of the warehouse preparation process, has an important place in studies. Sixty percent of all productive activities and 55% of warehouse operating costs include order-picking processes
[1]. In each supply system, there are parts-warehousing-related activities, such as receiving parts, warehousing, ordering, and shipping along product lines
[2]. For reducing warehouse operating costs, in assembly lines, it is highly significant for the company to supply necessary materials within the factory to promote its competitive power. In a factory, materials should be provided with high efficiency and flexibility. The complexity of the material and parts transportation system will lead to an increase in production simultaneously. Researchers, in recent years, have been increasingly motivated to develop OPS enhancement policies
[3]. Internal logistics aspects also need a great deal of attention due to the constraints and resources available to reduce costs and increase the flexibility of production schedule changes to meet customer demands by expanding the ability of line feeding systems and offshore components. In this regard, using new methods of feeding materials and parts such as picking seem necessary
[4]. Picking refers to some factors, including moving, picking, deploying, and packing parts according to the amount of the order required to meet the production schedule
[5]. Receiving materials and parts from the warehouse, preparing them, and moving them along product lines are the most strenuous activities in an internal logistic system. According to studies by Zulj et al.
[5], order picking activities account for 65% of total operating costs in the internal logistics process. The order pickings are based on the workforce and the flexibility of the workforce against changes due to existing constraints
[6]. Other concepts used in the picking process are as follows: travel distance, travel time, layout, channel configuration, dimension and size of the picking, order batching, ordering of picking parts, minimizing travel distance, maximizing occupational health, picking list (required orders), stress, and psychological conditions
[7]. The process of OPS is performed in different ways, as discussed below:
In the part assignment method for picking parts, the picker is carried, by a parts transporter or courier container, or he picks up the pieces himself
[8]. In this method, the picking station is defined separately for each individual. The type of transfer operation is also proportional to the kind of concerned picking process. The part assignment method is also ergonomically beneficial to the operator
[4]. On the other hand, the method of assigning a piece refers to time, when each part has its picker in a specific place
[8].
Figure 1 shows the process map of the picking operation. As seen, picking begins with receiving parts from the warehouse. There are two different ways of warehousing: batching and picking operations. If the received items do not meet the quality standards and documentation required for the factory, they will be returned to the manufacturer’s dock. Picking operations are performed either manually or automatically, using semi-robotic or robotic equipment. Each moving part to the picking area is also selected based on batching order and picking operations. New market developments encompassing e-commerce, globalization, increasing customer expectations, and new regulations have intensified the competition between warehouses. It has forced warehouses to manage large numbers of small orders in limited opportunities
[9]. A successful e-commerce ordering process contains multiple different suppliers and programs that need to be quickly implemented.
Figure 1.
Process map of the picking operation [10].
In this regard, completing orders is one of the most important fundamentals to succeed in the on-line business. Moreover, to overcome shortcomings in the ordering process, several aspects should be carefully considered. The easier the process for the end customers is, the more satisfied they would be with you. Furthermore, to decrease the warehouse costs and applying new methods in the internal procurement system, some recent trends need to be more efficiently performed in the procurement industry.
The concepts and definitions of OPS are described in Table 1.
Table 1.
The concepts and definitions.
| Order-Picking |
Order picking refers to the operation of retrieving items from storage locations to fulfill customer orders [11]. |
| Makespan |
Makespan is the time when all batches are picked [12]. |
| Tardiness |
Tardiness defines as the positive value between the completion time of a customer order with its due date [13]. |
| Routing |
Routing policies define the sequence of storage locations that should be visited in each pick round to retrieve all items on a pick list [2]. |
| Assignment |
Orders should be retrieved by order pickers within tight time windows. The job assignment planning problem determines the sequence according to which orders or batches of orders should be retrieved, as well as the assignment of these (batches of) orders to a limited number of order pickers [14]. |
Order
Batching |
Order batching policies define rules on which customer orders to combine in a single pick round. These policies can be either static (i.e., all orders are known at the beginning of the planning period) or dynamic (i.e., customer orders become available over time) [2]. Order batching is the grouping of customer orders into picking orders (batches) [14]. |
2. Order-Picking System
2.1. Order-Picking Planning and Their Combination
2.1.1. Warehouse Layout
A warehouse comprises various areas and domains, including transportation operations, parts receiving area, parts warehousing area, and order-picking area
[15][28]. Aisle is also referred to as the path between the rows of a cellar. Moreover, in the recent literature related to customized picking, three types of assignments have been introduced to determine the optimum anchors.
Generally, in the design of a warehouse, all operational processes and the type of system that uses the design must be considered. Product characteristics and demand can also affect the selected system. On the other hand, in a design, compatibility between areas and requirements should be considered and the design should be so as to improve the performance of the system
[16][29].
Figure 25 graphically depicts a typical order-picking area.
Figure 2. Order-picking Area in Stock [16].
According to
Table 2, most of the studies in the warehouse were on parallel aisles and other methods were less favored by the researchers.
Table 2. Areas of study of warehouse layout types in picking.
| Auditors |
Layout Method |
Number |
2.2.4. Space Optimization
Warehousing management policy is a crucial issue in logistic management. It must be managed effectively and efficiently to reduce the production cost as well as the customer satisfaction. Synchronized zoning system is a warehousing management policy that aims to increase the warehouse utilization and customer satisfaction by reducing the customer waiting time
[56][18].
Table 78 shows the studies performed to optimize the space available for OPS.
Table 7. Overview of the reviewed articles on Space optimization.
| Auditors |
Assignment |
Routing |
Batching |
Sequencing |
Tardiness |
Others |
| Berglund & Batta |
2.2.5. Makespan
To minimize the order-picking makespan, it is necessary to simultaneously solve order batching, assignment and pickers routing.
Table 89 shows six research works aimed at makespan minimization.
Table 8. Overview of the reviewed articles on Minimization of Makespan.
| Auditors |
Assignment |
Routing |
Batching |
Sequencing |
Tardiness |
Others |
| Ardjmand et al. [12], Azadnia et al. [[13], Chen et al. 28[17], Cheng et al. [18], Cortes et al. ], Chabot et al. [50][19], De Vries et al. [, Clark & Meller [38]20, Glock et al. [48]], Henn & Wäscher , Henn et al. [45], Lerher [72][21], Henn , Mowrey & Parikh [41][14], Hong & Kim. , Neumann & Medbo [69[22], Isler. [23], Schrotenboer et al. ], Öztürkoğlu et al. [30][24], Henn [25], Men’endez et al. [26], Theys et al. [27] |
Parallel aisle |
14 |
| Scholz et al. [32] |
Roodbergen et al. ✓ |
✓ |
✓ |
| Li et al. [38], Van Gils et al. | ✓ |
| [67] |
|
✓ |
✓ |
|
[16] |
, Yu et al. [73 |
], Lee & Murray [74] |
|
✓ |
|
|
|
|
Berglund & Batta [28], Öztürkoğlu & Hoser [29], Öztürkoğlu et al. |
Azadnia et al. [13][30] |
cross aisle |
3 |
| |
✓ |
✓ |
|
| Chen et al. [17] |
|
✓ |
✓ |
✓ |
|
|
| De Koster et al. [1], Ene & Öztürk [59], Kübler et al. [39], Öztürkoğlu & Hoser [29], Van Gils et al. [ |
Li et al. [ |
| Ardjmand et al. [36] |
✓ |
| 31], Scholz et al. |
Henn [14][32] |
, Meńendez et al. [Parallel aisle with a middle aisle |
[26]2 |
|
| Matusiak et al. [57 | |
]✓ |
✓ |
| ✓ |
✓ |
✓ |
|
|
|
79] |
|
Hong [47] |
|
|
| Leung et al. [68], Neumann & Medbo. [69 | ✓ |
] |
|
| ✓ |
|
|
|
|
Schrotenboer et al. [24] |
| Hong [47 | |
] | ✓ |
|
|
|
✓ |
|
✓ |
|
| Henn & Wäscher [21], Koch & Wäscher [70] |
|
|
✓ |
|
|
|
| Bevan [61], Calzavara et al. [71], De Vries et al. [20], Tappia et al. [66] |
|
|
|
|
], Ene & Öztürk [59], Öztürkoğlu & Hoser [29] |
|
✓ |
✓ |
|
|
|
| Schrotenboer et al. [24] |
|
✓ |
|
|
✓ |
|
| Battini et al. [60], Bevan [61], Chien et al. [ | |
|
|
✓ |
2.2.3. Productivity
-
Small size orders are performed with a great variety of parts.
-
Some orders will affect the efficiency of the order-picking process due to either the seasonality of the order, the intention of moving the order forward, or various delivery locations based on point-of-sale information. Table 67 shows an overview of the studied articles regarding the maximum productivity of the order-picking process.
| Hsieh & Huang |
| [ |
| 33], Scholz et al. [ |
stated that the process of picking and selecting pieces for picking is complete when the picker returns to the original location after selecting the last batch. In these papers, the authors used the mathematical model to solve small samples. In contrast, for larger samples, they used a variable neighborhood descent (VND) algorithm followed by numerical experiments, showing that the algorithm offers excellent solutions for path optimization.
Figure 36 introduce items, such as storage location assignment, zone assignment of the warehouse, picking area, ordering, routing, labor assignment, labor force level, and labor assignment in an interconnected structure.
Figure 3. Order-picking process aspects (Van Gils et al. [2]).
2.1.2. Study of the Components of the Order-Picking Process
Investigating the assignment and sequencing of the batch to multi-pickers concurrently with the optimal routing problem, checking the overall time of the custom multi-picker picking process, or applying a categorized warehousing method are generally done to maximize profit. In
Figure 47 and
Table 3, components of the order-picking process can be observed. As shown, it tries to optimize order assignment (sorting and routing of the shippers resulting in minimizing process time). In addition, the workload balanced between the shippers is maintained.
Figure 4. Frequency of study areas examined in this study.
Table 3. The Picking Process Components Study Section.
| Performance Index |
Articles |
| Cost |
27 |
| Equipment |
9 |
| Storage space |
37 |
34] |
parallel aisle with cross aisle |
2 |
| Matusiak et al. |
| Assignment |
8 |
[35] |
Parallel aisle with 3 cross aisles |
| Routing | 1 |
| 54 |
Ardjmand et al. [36] |
| Batchingparallel aisles and two front and rear cross |
1 |
| 43 |
Valle et al. [8], Valle & Beasley [37] |
parallel aisles and two or more cross-aisles |
2 |
| Sequencing |
14 |
Clark & Meller [38] |
Fishbone |
1 |
| Tardiness |
6 |
Kübler et al. [39], Chen et al. [40] |
picker-to-parts |
2 |
| Ergonomy |
11 |
Mowrey & Parikh [41] |
Mixed-width aisle |
1 |
| Pan et al. [42] |
pick-and-pass |
1 |
| Kulak [43], Roodbergen et al. [16] |
multiple cross aisles |
2 |
| Cano et al. [44] |
single-block and multiple-block |
1 |
| Henn et al. [45] |
U-shaped central aisle |
1 |
| |
Total |
34 |
Van Gils et al.
[2] and Scholz et al.
[32][20]
Chackelson et al.
[46][62] used both simulation solutions to simultaneously examine picking processes to reduce operating costs and a hybrid algorithm to generate batches and assignment and sequencing customer needs. Several studies studied scheduling problems together in a multi-block warehouse, while combining batching and routing into a single scheme. After assigning the order to the packer, any order picker can begin the journey to the warehouse to select and pick. The orders should be prioritized according to the production plan and delivered to the pickings. In the meantime, the lost time is one of the most important factors to consider to prevent tardiness and increase productivity. For example, when a picker stops to prepare an order batch, it is one of the cases of tardiness in the order-picking process. The studies reviewed in Van Gils et al.
[2] addressed the reduction or elimination of tardiness through redesigning the picking process, batch organization, routing, prioritization, and picking activities.
Moreover, to find the most economical routes, warehouse layout, piecewise assignment, and parallel scheduling of equipment pickings, travel routes have been studied in all logistic processes and are mainly the most influential factors in terms of costing and timing.
2.2. Performance Measure
2.2.1. Tardiness
Table 45 is an overview of tardiness minimization in the order-picking process considered in selecting the literature. Accordingly, assignment, routing, batching, and sequencing could affect tardiness minimization of the assumed goal.
Table 45 also shows six research works aimed at tardiness minimization.
Table 4. Overview of the reviewed articles in minimizing Tardiness.
| Auditors |
Assignment |
Routing |
Batching |
Sequencing |
| |
As can be seen, few studies have examined the issue of tardiness, and only Schulz et al.
[32][20] simultaneously examined the four issues of assignment, routing, batching and sequencing to reduce tardiness. In a parallel warehouse, Scholz et al.
[32][20] studied a routing problem, which is a special model of the traveling salesman problem (tsp), and using mathematical modeling, they showed that the problem under study is superior to the tsp formulation. Previous research has focused on only one of the problems with ops, but the study by Scholz et al.
[32][20] is only about a specific issue on a small scale and in the form of manual picking. On the other hand, five of the six studies were related to 2015 onwards, suggesting that the issue of tardiness has been considered in recent years, although few studies have been conducted on the impact on OPS.
2.2.2. Cost
The process of receiving materials and parts from the warehouse and preparing is the most costly activity in the intra-factory logistics system [48][78]. Table 56 shows the focus of the studied articles to minimizing the cost.
Table 5. Overview of the reviewed articles in minimizing cost.
| Auditors |
Assignment |
Routing |
Batching |
Sequencing |
Tardiness |
Others |
| Akilbasha & Atarajan [49], Chabot et al. [50], Chen et al. [40], Kaur & Kumar |
As different industry managers look for ideal, simple, intuitive, and reliable solutions, cost reduction solutions are suggested in the order-picking process.
Table 3 indicates an insufficient balance between decision-making variables about planning problems as well as a tendency towards more studies about routing and storage space at a higher speed than other aspects.
Furthermore, based on
Table 910, mathematical programming methods have been mainly used for routing and batching problems to explore the aspects of the picking process simultaneously. This suggests that the majority of researchers have tried to minimize distance in order to reduce cost and time, mainly because routing is metric and calculable. Moreover, at the operational level, there is still a need to integrate more planning problems and calculate the real-world features.
Table 9. Studies using mathematical programming methods.
| Articles |
Solution Methods |
Assignment |
Routing |
Batching |
Sequencing |
Tardiness |
| ✓ |
], Chen et al. [17], Cheng et al. [18 | ✓ |
]✓ |
|
, Li et al. [31 |
| Chen et al. [40] |
ACO |
✓ |
✓ |
✓ |
✓ |
|
67] |
|
✓ |
✓ |
|
|
|
| Ardjmand et al. [12] |
✓ |
✓ |
✓ |
| Koch & Wäscher [70 | |
] | |
|
| , Leung et al. [68], Schrotenboer et al. [24], Feng & Hu [80], Cano et al. [81], Ene & Öztürk [59], Azadnia et al. [13], Chen et al. [17], Kuo et al. [56] |
GA |
✓ |
✓ |
✓ |
✓ |
✓ |
Chackelson et al. [46], Chun et al. [75] |
✓ |
✓ |
Zulj et al. ✓ |
[ |
|
5], Henn [ |
| Kuo et al. [ | |
| 56]25], Muter & Öncan , Cheng et al. [18], Chun et al. [75], Öztürkoğlu et al. [30 | |
✓ |
|
|
|
| ] |
PSO |
✓ |
✓ |
✓ |
|
|
Cano et al. [44] |
|
✓ |
✓ |
Lee & Murray [74 | ✓ |
] | |
|
| |
✓ |
|
|
|
|
New market developments such as e-commerce, globalization, rising customer expectations, and new market regulations have forced warehouses to handle more orders in less time. In this matter, awareness of the impact of individual planning on picking planning for performance management and thus increasing customer service are required in overall performance.
| Cortes et al. |
| [ |
| 19], Kulak [43], Zulj et al. [5], Henn & Wäscher [21] |
TS |
|
✓ |
✓ |
|
|
| Schrotenboer et al. [24] |
|
✓ |
|
|
| Ardjmand et al. [12], Ardjmand et al. [36 | ✓ |
] |
PSA-ACO |
| ✓ |
✓ |
✓ |
✓ |
|
Kuo et al. [56] |
✓ |
|
|
| Matusiak et al. | |
|
|
| [35] |
A∗-algorithm |
|
✓ |
✓ |
|
|
Pan et al. [42] |
|
|
✓ |
|
|
| Matusiak et al. [35] |
SA |
|
✓ |
✓ |
|
| |
Battini et al. [4], Battini et al. |
| Ene & Öztürk [59][60] | , Calzavara et al. | , Li et al. [31] | [71], De Vries et al. [20], Glock & Grosse | , Manzini et al. [64 | [76], Gu et al. [15], Grosse et al. | ] | [63], Manzini et al. [64], Marchet et al. [9], Schwerdfeger & Boysenl [65], Tappia et al. [66], Thomas & Meller [77], Van Gils et al. [2], Vanheusden et al. [78] |
|
|
|
integer programming |
|
✓ | |
|
✓ |
| ✓ |
| |
| |
| Scholz et al. [34] |
exact method |
|
✓ |
|
✓ |
|
| Ardjmand et al. [36] |
CG |
✓ |
✓ |
✓ |
✓ |
|
| Glock & Grosse [76], Isler [23], Kübler et al. [39], Theys et al. [27], Valle & Beasley [37], Zhang et al. [58], Henn [25], Vanheusden et al. [78] |
Heuristic |
✓ |
✓ |
✓ |
✓ |
|
| Zulj et al. [5], Chabot et al. [50], Henn [14], Matusiak et al. [57], Scholz et al. [32], Men´endez et al. [26], Muter & Öncan [79] |
VNS |
✓ |
✓ |
✓ |
✓ |
✓ |
| Tang et al. [82] |
Lagrang |
|
|
✓ |
|
|
| Valle et al. [8], Lee & Murray [74] |
B&B |
|
✓ |
✓ |
|
|
| Henn & Wäscher [21] |
Hill climbing |
|
|
✓ |
|
|
Andriansyah et al. [83], Berglund & Batta [28], Chackelson et al. [46], Chien et al. [62], Elbert et al. [84], Giannikas et al. [85], Ho & Lin [86], Hong & Kim [22],
Hsieh & Huang [33], Lu et al. [87], Pan et al. [42], Roodbergen et al. [16], Tappia et al. [66], |
Simulation |
✓ |
✓ |
✓ |
|
|
| Gupta & Kumar [88], Kaur & Kumar [51] |
Fuzzy |
|
✓ |
|
|
|
Acimovic & Graves [53], De Koster et al. [1], Glock et al. [48], Grosse et al. [7],
Grosse et al. [6], Grosse et al. [63],
Henn et al. [45], Mowrey & Parikh [41]
Schleyer & Gue [89], Thomas & Meller [77], Van Gils et al. [67], |
Analytical models |
|
✓ |
✓ |
|
|
| Battini et al. [4], Battini et al. [60], Calzavara et al. [71], Cano et al. [44], Çeven & Gue [55], Hong [47], Lerher [72], Öztürkoğlu & Hoser [29], Pan & Wu [90], Scholz & Wäscher [91], Schwerdfeger & Boysenl [65] |
Other |
|
✓ |
✓ |
✓ |
✓ |
| , Mowrey & Parikh |
| [41], Öztürkoğlu et al. [30], Park & Kim [52] |
|
✓ |
|
|
|
|
| Acimovic & Graves [53] |
|
|
✓ |
|
|
|
| Cergibozan & Tasan [54], Çeven & Gue [55] |
|
|
✓ |
✓ |
|
|
| Kuo et al. [56] |
✓ |
|
|
|
|
|
| Chackelson et al. [46], Matusiak et al. [57] |
✓ |
✓ |
✓ |
|
|
|
| Zhang et al. [58] |
✓ |
|
✓ |
✓ |
|
|
| Azadnia et al. [13] |
|
✓ |
✓ |
|
✓ |
|
| Cheng et al. [18 | 62], Grosse et al. [6], Grosse et al. [63], Manzini et al. [64], Schwerdfeger & Boysenl [65], Tappia et al. [66] |
|
|
As can be seen in
Table 67, most studies have focused on routing and batching. However, Matusiak et al.
[57][67] simultaneously studied categories of assignment, routing, and batching to maximize the productivity in OPS. Chen et al.
[17][22] also simultaneously evaluated the effect of routing, batching, and sequencing on efficiency. Matusiak et al.
[57][67] tried to reduce the total processing time by focusing on the skill level of the operator and the individual difference between the pickers. They changed the batching methods and saved a total of 6% in time. They further used multi-level modeling and used the Adaptive Large Neighborhood Search algorithm to solve the model. In the warehouse, Chen et al.
[17][22] used a routing problem with a limited domain, which is not applicable to large-scale problems. They employed an integer model focusing on sequencing, batching, and routing, and used genetic algorithms to solve the problem.
Table 6. Overview of the reviewed articles in maximization of Productivity.
| Auditors |
Assignment |
Routing |
Batching |
Sequencing |
Despite the importance of human resources in the warehouse environment, a small number of articles have evaluated the labor planning problems, such as determining the daily labor force level, assigning the labor in order-picking areas, and assigning jobs to pickers
[6][7][63][6,7,65].
It should be further noted that as the warehouses offer multiple services to customers, the availability and performance of human resources ensure the quality of service to customers and the performance of order-picking. For instance, determining the level of the workforce combined with the assignment of order pickers and the planning problem makes focusing on the number of pickers a challenging opportunity for future research. Previous studies have mainly focused on reducing order-picking time. Working in an atmosphere with increasing pressure and limited time raises the likelihood of selection errors. In recent years, the number of articles analyzing multi-objective picking planning problems has risen dramatically. Nonetheless, currently, research works are mostly on the storage location, order batching, and routing. At a tactical level of picking problem, little attention has been paid to research. Previous studies have not only combined planning problems but have also simplified the order collection systems. The results developed in this paper indicate the need for more research on the congestion of warehouse aisles and the combination of picking operations problems in the future. In terms of cost reduction, the combination of tardiness with other planning problems has not yet been investigated. In addition, to improve efficiency, few studies have been performed in the field of assignment, sequencing, and tardiness, and the effect of different combinations of these problems on productivity improvement has not been studied. Moreover, the effect of combining planning problems has been only studied by Ardjmand et al.
[12][36][12,46] and has received less attention. On the other hand, the effect of labor fatigue as well as workload balance on the combination of tardiness and other planning problems can be examined.
Encyclopedia
