Order-Picking System

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Davood Jaafari	+ 2532 word(s)	2532	2021-11-17 08:30:49	\|
2	The format is correct	Lindsay Dong	+ 861 word(s)	3393	2021-12-13 03:24:32	\|

This entry is adapted from the peer-reviewed paper 10.3390/app112210641

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.

order-picking system routing warehouse

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:

To allocate the piece to the picker
Picker assignment for parts

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]. 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.

Assignment of storage and batching
Allocating storage and routing location
Wrapping and routing

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]. Figure 2 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
Ardjmand et al. ^[12], Azadnia et al. ^[13], Chen et al. ^[17], Cheng et al. ^[18], Cortes et al. ^[19], De Vries et al. ^[20], Henn & Wäscher ^[21], Henn ^[14], Hong & Kim. ^[22], Isler. ^[23], Schrotenboer et al. ^[24], Henn ^[25], Men’endez et al. ^[26], Theys et al. ^[27]	Parallel aisle	14
Berglund & Batta ^[28], Öztürkoğlu & Hoser ^[29], Öztürkoğlu et al. ^[30]	cross aisle	3
Li et al. ^[31], Scholz et al. ^[32]	Parallel aisle with a middle aisle	2
Hsieh & Huang ^[33], Scholz et al. ^[34]	parallel aisle with cross aisle	2
Matusiak et al. ^[35]	Parallel aisle with 3 cross aisles	1
Ardjmand et al. ^[36]	parallel aisles and two front and rear cross	1
Valle et al. ^[8], Valle & Beasley ^[37]	parallel aisles and two or more cross-aisles	2
Clark & Meller ^[38]	Fishbone	1
Kübler et al. ^[39], Chen et al. ^[40]	picker-to-parts	2
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] 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 3 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 4 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
Assignment	8
Routing	54
Batching	43
Sequencing	14
Tardiness	6
Ergonomy	11

Chackelson et al. ^[46] 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 4 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 4 also shows six research works aimed at tardiness minimization.

Table 4. Overview of the reviewed articles in minimizing Tardiness.

Auditors	Assignment	Routing	Batching	Sequencing
Scholz et al. ^[32]	✓	✓	✓	✓
Azadnia et al. ^[13]		✓	✓
Henn ^[14], Meńendez et al. ^[26]			✓	✓
Hong ^[47]			✓
Schrotenboer et al. ^[24]		✓

As can be seen, few studies have examined the issue of tardiness, and only Schulz et al. ^[32] simultaneously examined the four issues of assignment, routing, batching and sequencing to reduce tardiness. In a parallel warehouse, Scholz et al. ^[32] 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] 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]. Table 5 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 ^[51], 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], Ene & Öztürk ^[59], Öztürkoğlu & Hoser ^[29]		✓	✓
Schrotenboer et al. ^[24]		✓			✓
Battini et al. ^[60], Bevan ^[61], Chien et al. ^[62], Grosse et al. ^[6], Grosse et al. ^[63], Manzini et al. ^[64], Schwerdfeger & Boysenl ^[65], Tappia et al. ^[66]						✓

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 6 shows an overview of the studied articles regarding the maximum productivity of the order-picking process.

As can be seen in Table 6, most studies have focused on routing and batching. However, Matusiak et al. ^[57] simultaneously studied categories of assignment, routing, and batching to maximize the productivity in OPS. Chen et al. ^[17] also simultaneously evaluated the effect of routing, batching, and sequencing on efficiency. Matusiak et al. ^[57] 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] 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	Tardiness	Others
Li et al. ^[38], Van Gils et al. ^[67]		✓	✓
Chen et al. ^[17]		✓	✓	✓
Matusiak et al. ^[57]	✓	✓	✓
Leung et al. ^[68], Neumann & Medbo. ^[69]		✓
Hong ^[47]			✓		✓
Henn & Wäscher ^[21], Koch & Wäscher ^[70]			✓
Bevan ^[61], Calzavara et al. ^[71], De Vries et al. ^[20], Tappia et al. ^[66]						✓

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]. Table 7 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 ^[28], Chabot et al. ^[50], Clark & Meller ^[38], Glock et al. ^[48], Henn et al. ^[45], Lerher ^[72], Mowrey & Parikh ^[41], Neumann & Medbo ^[69], Öztürkoğlu et al. ^[30] Roodbergen et al. ^[16], Yu et al. ^[73], Lee & Murray ^[74]		✓
De Koster et al. ^[1], Ene & Öztürk ^[59], Kübler et al. ^[39], Öztürkoğlu & Hoser ^[29], Van Gils et al. ^[67]		✓	✓
Chackelson et al. ^[46], Chun et al. ^[75]	✓	✓	✓
Cano et al. ^[44]		✓	✓	✓
Schrotenboer et al. ^[24]		✓			✓
Kuo et al. ^[56]	✓
Pan et al. ^[42]			✓
Battini et al. ^[4], Battini et al. ^[60], Calzavara et al. ^[71], De Vries et al. ^[20], Glock & Grosse ^[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]						✓

2.2.5. Makespan

To minimize the order-picking makespan, it is necessary to simultaneously solve order batching, assignment and pickers routing. Table 8 shows six research works aimed at makespan minimization.

Table 8. Overview of the reviewed articles on Minimization of Makespan.

Auditors	Assignment	Routing	Batching	Sequencing
Ardjmand et al. ^[36]	✓	✓	✓	✓
Ardjmand et al. ^[12]	✓	✓	✓
Zulj et al. ^[5], Henn ^[25], Muter & Öncan ^[79]			✓
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.

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 9, 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. ^[40], Chen et al. ^[17], Cheng et al. ^[18], Li et al. ^[31]	ACO	✓	✓	✓	✓
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	✓	✓	✓	✓	✓
Kuo et al. ^[56], Cheng et al. ^[18], Chun et al. ^[75], Öztürkoğlu et al. ^[30]	PSO	✓	✓	✓
Cortes et al. ^[19], Kulak ^[43], Zulj et al. ^[5], Henn & Wäscher ^[21]	TS		✓	✓
Ardjmand et al. ^[12], Ardjmand et al. ^[36]	PSA-ACO	✓	✓	✓	✓
Matusiak et al. ^[35]	A∗-algorithm		✓	✓
Matusiak et al. ^[35]	SA		✓	✓
Ene & Öztürk ^[59], Li et al. ^[31], Manzini et al. ^[64]	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		✓	✓	✓	✓

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].

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] 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.

References

De Koster, R.; Le Duc, T.; Zaerpour, N. Determining the number of zones in a pick-and-sort order-picking system. Int. J. Prod. Res. 2012, 50, 757–771.
Van Gils, T.; Ramaekers, K.; Caris, A.; de Koster, R.B. Designing efficient order-picking systems by combining planning problems: State-of-the-art classi- fication and review. Eur. J. Oper. Res. 2018, 267, 1–15.
Franzke, T.; Grosse, E.H.; Glock, C.H.; Elbert, R. An investigation of the effects of storage assignment and picker routing on the occurrence of picker blocking in manual picker-to-parts warehouses. Int. J. Logist. Manag. 2017, 28, 841–863.
Battini, D.; Calzavara, M.; Persona, A.; Sgarbossa, F. Additional effort estimation due to ergonomic conditions in order-picking systems. Int. J. Prod. Res. 2017, 55, 2764–2774.
Žulj, I.; Kramer, S.; Schneider, M. A hybrid of adaptive large neighborhood search and tabu search for the order-batching problem. Eur. J. Oper. Res. 2018, 264, 653–664.
Grosse, E.H.; Glock, C.; Jaber, M.Y.; Neumann, P. Incorporating human factors in order picking planning models: Framework and research opportunities. Int. J. Prod. Res. 2014, 53, 695–717.
Grosse, E.H.; Glock, C.H.; Neumann, W.P. Human factors in order picking: A content analysis of the literature. Int. J. Prod. Res. 2016, 55, 1260–1276.
Valle, C.A.; Beasley, J.E.; da Cunha, A.S. Optimally solving the joint order batching and picker routing problem. Eur. J. Oper. Res. 2017, 262, 817–834.
Marchet, G.; Melacini, M.; Perotti, S. Investigating order-picking system adoption: A case-study-based approach. Int. J. Logist.-Res. Appl. 2015, 18, 82–98.
Habazin, J.; Glasnović, A.; Bajor, I. Order Picking Process in Warehouse: Case Study of Dairy Industry in Croatia. Promet-Traffic Transp. 2017, 29, 57–65.
Tompkins, J.A.; White, J.A.; Bozer, Y.A.; Tanchoco, J.M.A. Facilities Planning, 3rd ed.; John Wiley & Sons: New York, NY, USA, 2010.
Ardjmand, E.; Shakeri, H.; Singh, M.; Bajgiran, O.S. Minimizing order picking makespan with multiple pickers in a wave picking warehouse. Int. J. Prod. Econ. 2018, 206, 169–183.
Azadnia, A.H.; Taheri, S.; Ghadimi, P.; Mat Saman, M.Z.; Wong, K.Y. Order batching in warehouses by minimizing total tardiness: A hybrid approach of weighted association rule mining and genetic algorithms. Sci. World J. 2013, 2013, 246578.
Henn, S. Order batching and sequencing for the minimization of the total tardiness in picker-to-part warehouses. Flex. Serv. Manuf. J. 2015, 27, 86–114.
Gu, J.; Goetschalckx, M.; McGinnis, L.F. Research on warehouse design and performance evaluation: A comprehensive review. Eur. J. Oper. Res. 2010, 203, 539–549.
Roodbergen, K.J.; Vis, I.; Don Taylor, G., Jr. Simultaneous determination of warehouse layout and control policies. Int. J. Prod. Res. 2015, 53, 3306–3326.
Chen, T.L.; Cheng, C.Y.; Chen, Y.Y.; Chan, L.K. An efficient hybrid algorithm for integrated order batching, sequencing and routing problem. Int. J. Prod. Econ. 2015, 160, 158–167.
Cheng, C.Y.; Chen, Y.Y.; Chen, T.L.; Yoo, J.W.J. Using a hybrid approach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem. Int. J. Prod. Econ. 2015, 170, 805–814.
Cortés, P.; Gómez-Montoya, R.A.; Muñuzuri, J.; Correa-Espinal, A. A tabu search approach to solving the picking routing problem for large-and medium-size distribution centres considering the availability of inventory and K heterogeneous material handling equipment. Appl. Soft Comput. 2017, 53, 61–73.
De Vries, J.; De Koster, R.; Stam, D. Aligning order picking methods, incentive systems, and regulatory focus to increase performance. Prod. Oper. Manag. 2016, 25, 1363–1376.
Henn, S.; Wäscher, G. Tabu search heuristics for the order batching problem in manual order-picking systems. Eur. J. Oper. Res. 2012, 222, 484–494.
Hong, S.; Kim, Y. A route-selecting order batching model with the S-shape routes in a parallel-aisle order-picking system. Eur. J. Oper. Res. 2017, 257, 185–196.
Isler, C.; Righetto, G.; Morabito, R. Optimizing the order-picking of a scholar and office supplies warehouse. Int. J. Adv. Manuf. Technol. 2016, 87, 2327–2336.
Schrotenboer, A.H.; Wruck, S.; Roodbergen, K.J.; Veenstra, M.; Dijkstra, A.S. Order picker routing with product returns and interaction tardiness. Int. J. Prod. Res. 2017, 55, 6394–6406.
Henn, S. Algorithms for on-line order batching in an order-picking warehouse. Comput. Oper. Res. 2012, 39, 2549–2563.
Menéndez, B.; Bustillo, M.; Pardo, E.G.; Duarte, A. General variable neighborhood search for the order batching and sequencing problem. Eur. J. Oper. Res. 2017, 263, 82–93.
Theys, C.; Bräysy, O.; Dullaert, W.; Raa, B. Using a TSP heuristic for routing order pickers in warehouses. Eur. J. Oper. Res. 2010, 200, 755–763.
Berglund, P.; Batta, R. Optimal placement of warehouse cross-aisles in a picker-to-part warehouse with class-based storage. IIE Trans. 2011, 44, 107–120.
Öztürkoğlu, Ö.; Hoser, D. A discrete cross aisle design model for order-picking warehouses. Eur. J. Oper. Res. 2019, 275, 411–430.
Öztürkoğlu, Ö.; Gue, K.R.; Meller, R.D. A constructive aisle design model for unitload warehouses with multiple picking and deposit points. Eur. J. Oper. Res. 2014, 236, 382–394.
Li, J.; Huang, R.; Dai, J.B. Joint optimization of order batching and picker routing in the online retailer’s warehouse in China. Int. J. Prod. Res. 2017, 55, 447–461.
Scholz, A.; Schubert, D.; Wäscher, G. Order-picking with multiple pickers and due dates simultaneous solution of order batching, batch assignment and sequencing, and picker routing problems. Eur. J. Oper. Res. 2017, 263, 461–478.
Hsieh, L.F.; Huang, Y.C. New batch construction heuristics to optimise the performance of order-picking systems. Int. J. Prod. Econ. 2011, 131, 618–630.
Scholz, A.; Henn, S.; Stuhlmann, M.; Wascher, G. A new mathematical programming formulation for the single-picker routing problem. Eur. J. Oper. Res. 2016, 253, 68–84.
Matusiak, M.; de Koster, R.; Kroon, L.; Saarinen, J. A fast simulated annealing method for batching precedence-constrained customer orders in a warehouse. Eur. J. Oper. Res. 2014, 236, 968–977.
Ardjmand, E.; Ghalehkhondabi, I.; Young, W.A., II; Sadeghi, A.; Weckman, G.R.; Shakeri, H. A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations. Expert Syst. Appl. 2020, 159, 113566.
Valle, C.A.; Beasley, J.E. Order batching using an approximation for the distance travelled by pickers. Eur. J. Oper. Res. 2020, 284, 460–484.
Clark, K.A.; Meller, R.D. Incorporating vertical travel into non-traditional cross aisles for unit-load warehouse designs. IIE Trans. 2013, 45, 1322–1331.
Kübler, P.; Glock, C.H.; Bauernhansl, T. A new iterative method for solving the joint dynamic storage location assignment, order batching and picker routing problem in manual picker-to-parts warehouses. Comput. Ind. Eng. 2020, 147, 106645.
Chen, F.; Wang, H.; Xie, Y.; Qi, C. An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse. J. Intell. Manuf. 2016, 27, 389–408.
Mowrey, C.H.; Parikh, P.J. Mixed-width aisle configurations for order-picking in distribution centers. Eur. J. Oper. Res. 2014, 232, 87–97.
Pan, J.C.H.; Shih, P.H.; Wu, M.H. Order batching in a pick-and-pass warehousing system with group genetic algorithm. Omega 2015, 57, 238–248.
Kulak, O.; Sahin, Y.; Taner, M.E. Joint order batching and picker routing in single and multiple-cross-aisle warehouses using cluster-based tabu search al-gorithms. Flex. Serv. Manuf. J. 2012, 24, 52–80.
Cano, J.A.; Correa-Espinal, A.; Montoya, R.A.G. Mathematical programming modeling for joint order batching, sequencing and picker routing problems in manual order-picking systems. J. King Saud Univ.-Eng. Sci. 2020, 32, 219–228.
Henn, S.; Koch, S.; Gerking, H.; Wäscher, G. A U-shaped layout for manual order-picking systems. Logist. Res. 2016, 6, 245–261.
Chackelson, C.; Errasti, A.; Ciprés, D.; Lahoz, F. Evaluating order-picking performance trade-offs by configuring main operating strategies in a retail dis- tributor: A design of experiments approach. Int. J. Prod. Res. 2013, 51, 6097–6109.
Hong, S.; Johnson, A.L.; Peters, B.A. Order batching in a bucket brigade order-picking system considering picker blocking. Flex. Serv. Manuf. J. 2016, 28, 425–441.
Glock, C.H.; Grosse, E.H.; Elbert, R.M.; Franzke, T. Maverick picking: The impact of modifications in work schedules on manual order-picking processes. Int. J. Prod. Res. 2017, 55, 6344–6360.
Akilbasha, P.; Atarajan, P.G. An innovative exact method for solving fully interval integer transportation problems. Inform. Med. Unlocked 2018, 11, 95–99.
Chabot, T.; Lahyani, R.; Coelho, L.C.; Renaud, J. Order-picking problems underweight, fragility and category constraints. Int. J. Prod. Res. 2017, 55, 6361–6379.
Kaur, A.; Kumar, A. A new method for solving fuzzy transportation problems using ranking function. Appl. Math. Model. 2011, 35, 5652–5661.
Park, J.; Kim, B.I. The school bus routing problem. Eur. J. Oper. Res. 2010, 202, 311–319.
Acimovic, J.; Graves, S.C. Making better fulfillment decisions on the fly in an online retail environment. Manuf. Serv. Oper. Manag. 2014, 17, 34–51.
Cergibozan, Ç.; Tasan, A.S. Order batching operations: An overview of classification, solution techniques, and future research. J. Intell. Manuf. 2019, 30, 335–349.
Çeven, E.; Gue, K.R. Optimal wave release times for order fulfillment systems with deadlines. Transp. Sci. 2015, 51, 52–66.
Kuo, R.; Kuo, P.; Chen, Y.R.; Zulvia, F.E. Application of metaheuristics-based clustering algorithm to item assignment in a synchronized zone order-picking system. Appl. Soft Comput. 2016, 46, 143–150.
Matusiak, M.; De Koster, R.; Saarinen, J. Utilizing individual picker skills to improve order batching in a warehouse. Eur. J. Oper. Res. 2017, 263, 888–899.
Zhang, J.; Wang, X.; Chan, F.T.S.; Ruan, J. On-line order batching and sequencing problem with multiple pickers: A hybrid rule-based algorithm. Appl. Math. Model. 2017, 45, 271–284.
Ene, S.; Öztürk, N. Storage location assignment and order-picking opti- mization in the automotive industry. Int. J. Adv. Manuf. Technol. 2012, 60, 787–797.
Battini, D.; Glock, C.H.; Grosse, E.H.; Persona, A.; Sgarbossa, F. Human energy expenditure in order-picking storage assignment: A bi-objective method. Comput. Ind. Eng. 2016, 94, 147–157.
Bevan, S. Economic impact of musculoskeletal disorders (MSDs) on work in Europe. Best Pract. Res. Clin. Rheumatol. 2015, 29, 356–373.
Chien, C.M.; Gong, Y.; De Koster, R.; VanNunen, J. A flexible evaluative framework for order-picking systems. Prod. Oper. Manag. 2010, 19, 70–82.
Grosse, E.H.; Glock, C.H.; Jaber, M.Y. The effect of worker learning and forgetting on storage reassignment decisions in order-picking systems. Comput. Ind. Eng. 2013, 66, 653–662.
Manzini, R.; Accorsi, R.; Gamberi, M.; Penazzi, S. Modeling class-based storage assignment over life cycle picking patterns. Int. J. Prod. Econ. 2015, 170, 790–800.
Schwerdfeger, S.; Boysen, N. Order-picking along a crane-supplied pick face: The SKU switching problem. Eur. J. Oper. Res. 2017, 260, 534–545.
Tappia, E.; Roy, D.; De Koster, R.; Melacini, M. Modeling, analysis, and design insights for shuttle-based compact storage systems. Transp. Sci. 2017, 51, 269–295.
Van Gils, T.; Ramaekers, K.; Braekers, K.; Depaire, B.; Caris, A. Increasing order-picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. Int. J. Prod. Econ. 2018, 197, 243–261.
Leung, K.; Choy, K.; Siu, P.K.; Ho, G.; Lam, H.; Lee, C.K. A B2C e-commerce intelligent system for re-engineering the e-order fulfilment process. Expert Syst. Appl. 2018, 91, 386–401.
Neumann, W.P.; Medbo, L. Ergonomic and technical aspects in the redesign of material supply systems: Big cases vs. narrow cases. Int. J. Ind. Ergon. 2010, 40, 541–548.
Koch, S.; Wäscher, G. A grouping genetic algorithm for the order batching problem in distribution warehouses. J. Bus. Econ 2016, 86, 131–153.
Calzavara, M.; Glock, C.H.; Grosse, E.H.; Persona, A.; Sgarbossa, F. Analysis of economic and ergonomic performance measures of different rack layouts in an order-picking warehouse. Comput. Ind. Eng. 2017, 111, 527–536.
Lerher, T. Travel time model for double-deep shuttle-based storage and re-trieval systems. Int. J. Prod. Res. 2015, 54, 2519–2540.
Yu, Y.; De Koster, R.; Guo, X. Class-based storage with a finite number of items: Using more classes is not always better. Prod. Oper. Manag. 2015, 24, 1235–1247.
Lee, H.-Y.; Murray, C.C. Robotics in order picking: Evaluating warehouse layouts for pick, place, and transport vehicle routing systems. Int. J. Prod. Res. 2018, 57, 5821–5841.
Chun, C.-C.; Kang, J.-R.; Hou, C.-C.; Cheng, C.Y. Joint order batching and picker Manhattan routing problem. Comput. Ind. Eng. 2016, 95, 164–174.
Glock, C.H.; Grosse, E.H. Storage policies and order-picking strategies in u shaped order-picking systems with a movable base. Int. J. Prod. Res. 2012, 50, 4344–4357.
Thomas, L.M.; Meller, R.D. Analytical models for warehouse configuration. IIE Trans. 2014, 46, 928–947.
Vanheusden, S.; Van Gils, T.; Braekers, K.; Ramaekers, K.; Caris, A. Analysing the effectiveness of workload balancing measures in order-picking operations. Int. J. Prod. Res. 2021, 1–25.
Muter, I.; Öncan, T. Order batching and picker scheduling in warehouse order picking. IISE Trans. 2021.
Feng, X.; Hu, X. A Heuristic Solution Approach to Order Batching and Sequencing for Manual Picking and Packing Lines considering Fatiguing Effect. Sci. Program. 2021, 2021, 8863391.
Cano, J.A.; Cortés Achedad, P.; Campo, E.A.; Correa-Espinal, A. Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm. Int. J. Electr. Comput. Eng. IJECE 2021, 11, 2516–2524.
Tang, L.; Wang, G.; Liu, J.; Liu, J. A combination of Lagrangian relaxation and column generation for order batching in steelmaking and continuous-casting production. Nav. Res. Logistics. (NRL) 2011, 584, 370–388.
Andriansyah, R.; De Koning, W.W.H.; Jordan, R.M.E.; Etman, L.F.P.; Rooda, J.E. A process algebra based simulation model of a miniload–workstation order picking system. Comput. Ind. 2011, 62, 292–300.
Elbert, R.M.; Franzke, T.; Glock, C.H.; Grosse, E.H. The effects of human behavior on the efficiency of routing policies in order picking: The case of route deviations. Comput. Ind. Engineering. Ind. Eng. 2017, 111, 537–551.
Giannikas, V.; Lu, W.; Robertson, B.; McFarlane, D. An interventionist strategy for warehouse order picking: Evidence from two case studies. Int. J. Prod. Econ. 2017, 189, 63–76.
Ho, Y.C.; Lin, J.W. Improving order-picking performance by converting a sequential zone-picking line into a zone-picking network. Comput. Ind. Eng. 2017, 113, 241–255.
Lu, W.; McFarlane, D.; Giannikas, V.; Zhang, Q. An algorithm for dynamic order-picking in warehouse operations. Eur. J. Oper. Res. 2016, 248, 107–122.
Gupta, A.; Kumar, A. A new method for solving linear multi-objective transportation problems with fuzzy parameters. Appl. Math. Model. 2012, 36, 1421–1430.
Schleyer, M.; Gue, K. Throughput time distribution analysis for a one-block warehouse. Transp. Res. Part E: Logist. Transp. Rev. 2012, 48, 652–666.
Pan, J.C.H.; Wu, M.H. Throughput analysis for order-picking system with multiple pickers and aisle congestion considerations. Comput. Oper. Res. 2012, 39, 1661–1672.
Scholz, A.; Wäscher, G. Order batching and picker routing in manual orderpicking systems: The benefits of integrated routing. Cent. Eur. J. Oper. Res. 2017, 25, 491–520.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Engineering, Industrial

Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Davood Jaafari

View Times: 743

Update Date: 13 Dec 2021

Table of Contents

Video Upload Options

Confirm