A literature review on distribution center project and planning

Marina Cardoso Guimarães


Santa Catarina Federal University - UFSC, Florianópolis, Santa Catarina, Brazil.

Guilherme Luz Tortorella


Santa Catarina Federal University - UFSC, Florianópolis, Santa Catarina, Brazil.


Highlights: Given the difficulties of practical implementation of a Distribution Center (DC) project, it is essential to provide an elaborate DC project study. Therefore, this study seeks to answer the research questions related to the main decisions for designing and planning the DCs and whatever the characteristics, advantages and barriers of such decisions.

Aim: Identify the main DC project and planning methods, consolidating their characteristics, difficulties, and highlighting gaps and research opportunities.

Design / Methodology / Approach: The methodology of this work is a systematic review of the literature divided into three stages, which are: (i) definition of the bibliographic portfolio and research axes; (ii) bibliometric analysis; and (iii) analysis and discussion of theoretical lenses, presenting gaps and research opportunities. The retrieved articles and the conclusions of this research are restricted to the search period until July 2018.

Results: This study describes how the DC implantation project concept was applied, which methods are proposed and which difficulties were found in the implantation and project, according to the literature. As a result, despite the consensus on the practical importance of DC project, most research assesses project stages in isolation. The stages of location definition and the order picking process are the most mentioned. And finally, the context and distribution network are little considered in the analysis of DC project decisions.

Research Limitations: The study derives from the research of scientific articles from specific databases and is analyzed from the perspective of the theoretical lens of distribution networks. Therefore, the literature analysis through other perspectives may present other results and research gaps.

Practical Implications: In practical terms, the research assesses how DC planning and implementation takes place in the context of distribution networks, thus helping managers to make decisions during the project of new DC installations, ensuring better results in their operation.

Originality / value: Few investigations extensively analyze the distribution network and all phases of a DC project planning targeting a specific business segment, making practical application less feasible, a gap filled by this study.

Keywords: Distribution centers; Project and planning of facilities; Literature review.


Companies are increasingly looking to accelerate material flow by reducing the time between order receipt and delivery and, consequently, inventory costs. In this context, there is a tendency to increase inventory centralization, facilitating direct and continuous delivery at each point of the supply chain, highlighting the relevance of Distribution Centers (DC) for business performance (Rheem, 1997; Nozick et al., 2001; Rodrigues; Pizzolato, 2003). The implementation of a DC in the supply chain is the result from the need for more efficient, flexible and dynamic distribution that can respond quickly to customer demand (Rodrigues; Pizzolato, 2003; Hiremath et al., 2013; Santos, 2015). Thus, a significant part of the effectiveness of logistics activities depends on the way DCs operate in supply chains (Baker, 2004; De Santis et al., 2018).

Several decision support models for DC operations have been proposed in the literature, but there is still considerable difficulty in applying these models to guide their operations effectively (Gu et al., 2007; Baker; Canessa, 2009). In other words, a solid theoretical basis for DC project still seems to be missing (Rouwenhorst et al., 2000; Goetschalckx et al., 2002; Dotoli et al., 2015; Vieira et al., 2017; De Santis et al., 2018; Holzapfel et al., 2018). Vieira et al. (2017) point out that a company's distribution strategy ensures the success of its internal operations, and the way its internal activities are organized is influenced by the characteristics of its distribution strategy. Therefore, such methods should consider the relevant characteristics of the context in which the DC is inserted and its distribution network, in order to improve the results of these projects. Such a gap in the literature has raised the following research questions:

  1. What are the main decisions for designing and planning DCs?

  2. What are the characteristics, advantages and barriers of such decisions?

To answer these questions, this article aims to identify the main stages and decisions of DC projects, and analyze these decisions from the perspective of the contextual characteristics that the company is inserted, indicating advantages and barriers. Thus, the literature will be evaluated through the theoretical lens of distribution networks. The method used for this research is a literature review, as it intends to reinforce the proposed object of study, besides justifying the research differential, from the identification of gaps and perspectives (Paré et al., 2015).

The contribution of this study is made in three ways. First, from an academic point of view, the research aims to bring together content about DC project planning, raise the state of the art on the topic, and present gaps and research opportunities. Secondly, in practical terms, research envisages assessing how DC planning and implementation takes place in the context of distribution networks. Finally, this research helps to make decisions during the project of new DC installations, ensuring better results in their operation.


Demand for DCs has recently increased due in part to the wider variety of products and services being offered and the shift to smaller customer orders (Higginson; Bookbinder, 2005; Zhuge et al., 2016). Centralizing the flow of materials and information through DC facilitates higher levels of supply chain efficiency (Litomin et al., 2016), increasing the relevance of proper DC project and planning (Hou et al., 2010; Hua et al., 2016).

The processes contemplated by a DC are: receipt, storage, order picking, shipping (Rouwenhorst et al., 2000; Gu et al., 2007; Vieira et al., 2017), cross-docking (Choy, 2012; Faber et al., 2013), product packaging (Faber et al., 2013; Vieira et al., 2017), and returns (Faber et al., 2013), among others. The combination of such processes reduces order preparation, execution and delivery time, reducing total shipping cost and improving service level (Litomin et al., 2016).

As detailed by Tompkins et al. (2013) in Figure 1, facility planning can be subdivided into location and project. Localization addresses the macroscopic issues of the facility, such as accessibility of logistics and proximity modes with suppliers and customers. The installation project deals with the microelements, which include the systems of installations, physical arrangement and material handling. Although these project phases provide very general guidelines when considering DC project, other specific decisions must be taken into account (Ballou, 2006), which gives rise to the need to properly identify such methods.

Figure 1. Plant Planning Project Phases


Source: Made from Tompkins et al. (2013)


To achieve the research objective, which is to identify, from a systematic literature review, the main DC project and planning methods, a bibliographic research is used. The bibliographic research is made from the survey of theoretical references already analyzed and published, such as books and scientific articles. The bibliographic research is made from the survey of theoretical references already analyzed and published, such as books and scientific articles (Fonseca, 2002). The work method is proposed in two steps: a) bibliometric analysis; and b) analysis and discussion of theoretical lenses. To achieve the steps, the bibliographic portfolio is defined.

To define the bibliographic portfolio (BP) used in the research, the research axes were first defined: (i) implementation project and (ii) distribution center. Then, the keywords were combined to search for publications in titles, abstracts and/or keywords. The scientific articles were identified by keywords in the Scopus, Web of Science and Science Direct databases, as suggested by Ntabe. et al. (2015) and Chen et al. (2017) on the theme of logistics and supply chain. Also, in searches on the Capes journal portal on the subject related to keywords, among the articles presented and peer reviewed, 90% were in these three databases. In order to validate the keywords used in the initial search, a compliance check was performed. For this, three articles with high citation (over 400) from the portfolio initially identified in the databases were selected, and their keywords compared to those used in the research axes (Ensslin et al., 2010). Thus, it was possible to identify that the articles also have the term “project and planning of facilities”. Therefore, such term was incorporated into the search axes and a new search in the same databases was performed, concluding the crude BP. The search period in the databases occurred during July 2018.

For the filtering process, both publications and the following criteria were analyzed (Ensslin, 2010): (i) removal of duplicate articles; (ii) weighting of the Journal Article only; (iii) article titles aligned with the research theme; (iv) abstracts aligned with the research theme; and (v) review of the full text of articles aligned with the research theme. The software used for the registration and selection of articles was EndNote X7®. As shown in Table 1, the Science Direct database obtained the largest number of publications. By eliminating duplicate and non-journal articles, the base has been reduced to 5,570. Finally, the abstracts of the papers were read individually to check the contribution to the theme and, of the 1,623 articles, 203 presented the abstract aligned to the theme, and in the full reading of the article, only 67 presented a real contribution to this research, in order to form the bibliographic portfolio (see Table 2).

Table 1. Bibliographic Portfolio Survey


Source: The authors

Table 2. Article filtering process


Source: The authors


Bibliometric analysis was divided into two stages: (i) analysis of basic variables and (ii) analysis of advanced variables.

Analysis of basic variables

In the consolidation of the basic variables, the first step analyzes the BP regarding the most relevant journals and authors, in addition to the year of publication and research methods of the articles. For the advanced bibliometric analysis, the articles were counted for the following variables: (i) analysis of DC project proposals; and (ii) difficulties in implementing the DC project.

As for journals, Table 3 shows the distribution of publications by journal contained in the BP. In this regard, it is highlighted the European Journal of Operational Research, IIE Transactions, International Journal of Production Research, which feature more than eight publications each. Based on the 67 articles that make up the BP, 166 authors were identified, of which 12 present more than two published articles (see Table 4). It should be noted that the author René B. M. de Koster presents the largest number of publications (five articles) in the BP.

Table 3. Number of publications per journal


Source: The authors

Table 4. Number of publications by author


Source: The authors

For the year of publication of the articles of BP, as shown in Figure 2, it is clear that the theme of DC project proposal is not recent, since the first publications date from the 1980s. However, it should be noted that from 2005 there was a significant increase in publications on the topic, reaching its peak in 2017, with eight papers published. This fact demonstrates the growing relevance of adequate DC project, given the complexity that supply chains have been acquiring in recent years in a globalized market (Higginson; Bookbinder, 2005; Zhuge et al., 2016).

Among these studies, according to Figure 3, half used the literature review research method to propose the DC project and 33% used the case study analysis method. Therefore, there is a shortage in DC project proposals that structure a planning and project methodology based on theory and practice. Among the works of BP related to DC project, the literature review is the most used (Rouwenhorst et al., 2000; Gu et al., 2007; 2010).

Figure 2. Time evolution of BP publications


Source: The authors

Figure 3. Literature research methods for DC project proposals


Source: The authors

Advanced Variable Analysis

For analysis of the first advanced variable proposed by DC project, Rouwenhorst et al. (2000) propose that a DC project process goes through several steps which are: concept, data acquisition, functional specification, technical specification, equipment selection, layout, planning, and control policy selection. Alternatively, these steps may be situated at a strategic, tactical or operational level. Thus, DC project can be viewed as a set of decisions at the various project levels, and for each level, decisions are put into perspective using three aspects of a DC: (i) processes; (ii) resources; and (iii) organization.

At the strategic level, project decisions have a long-term impact, especially decisions that concern high investments. Thus, the main aspect of DC project involved at the strategic level is process (aspect i), which is evidenced from the decisions regarding the project of the process flow and the selection of storage system types. At the tactical level, several medium-term decisions must be made, usually related to the “resource” (e.g. storage system size and number of employees) and “organization” aspects (e.g. layout determination). Finally, at the operational level, the main decisions concern personnel and equipment (resource) assignment and control issues (Rouwenhorst et al., 2000).

The research by Gu et al. (2007; 2010) identifies operating problems related to the four major DC functions (receiving, storing, order picking, and shipping). In addition, they include performance appraisal and support tools for DC project, involving five main steps (see Table 5). The stages and decisions of the DC project of Gu et al. (2007) were adapted by grouping a few steps that are listed, including a DC location step, which is often quoted in BP. The first two steps, determining the overall structure of the DC and its sizing, complement the steps already cited by Rouwenhorst et al. (2000). In the layout stage you have the detailed conFiguretion of the DC, such as aisle and picking areas, as well as pallet stacking patterns in the storage areas. The equipment selection step comprises decisions focused on the appropriate automation level for the DC, such as types of equipment for storage, transport and order picking. Finally, the operation strategy selection determines how the DC will be operated with regard to order storage, collection, organization and picking. Such decisions impact the operational efficiency of the information and material flows on the DC, which increases the need for their alignment with the decisions of the previous steps.

Bodner et al. (2002) classify the DC project literature into two broad categories. The first category addresses the general problem of DC project, while the second category addresses the specifics of DC project, such as the project of a storage system or an order picking system. Vieira et al. (2017) divide the DC project into distribution strategies, internal activities and characteristics of distribution operations, and propose a decision model for DC operations based on strategic, tactical and operational aspects, such as Rouwenhorst et al. (2000). The study by Hsieh and Tsai (2006) points out that previous work on the order picking operation of a DC is usually limited to the order picking policy and picking script, and few focus on a combined solution about designing the number of layout aisles, order picking policy, storage allocation planning, average picking density within a corridor, and so on. Finally, Baker and Canessa (2009) interviewed companies to verify the steps that follow when designing a DC.

Rouwenhorst et al. (2000) and Bodner et al. (2002) indicate that there are a large number of studies on the analysis of DC project planning steps, such as layout and material handling; however, the literature presents scarcity of a basis for the overall DC project. Based on this context, from the BP reading, the analysis of the DC project planning steps is made, based on the steps of Gu et al. (2007), as detailed in Table 5. This stage has the largest number of articles published on the definition of the order picking process.

Order picking is one of the most laborious and time consuming processes on a DC (Franzke et al., 2017). It can be defined as the process of retrieving products from storage locations to fulfill customer orders (De Koster et al., 2007), and is a critical supply chain process that directly influences customer satisfaction. For the authors in the DC project stage, which is the definition of the DC operations, order picking is one of the processes to be defined and should include batch, sequencing and picking decisions.

The second most cited step, considering installation planning, is the location of a DC. The possible reason is that the location of a DC influences the choice of the supplier, and these sourcing decisions influence the total cost of distribution. In addition, supplier-related costs have become more significant in recent years with increasing market volatility (Nozick; Turnquist, 2001; Huang et al. 2012). Therefore, in addition to the overall DC project work being scarce, also considering the DC project steps, many phases also have few publications, such as equipment selection definitions and storage operation strategy, emphasizing the need for studies in all stages of project planning.

Table 5. DC project steps and decisions and citation frequency


Source: Prepared from Gu et al. (2007)

The second advanced variable analyzed contemplated the difficulties in implementing the DC project. Bodner et al. (2002) point out that research carried out in DC project is rarely applied in practice, as industry professionals often follow their experience and knowledge in project design. One of the justifications for such a gap between theory and practice is the lack of a procedure that integrates conceptual project structures with models for specific problems. Similarly, Gu et al (2007) point out that DC project research has focused heavily on order storage and collection, mainly because these are the two functions of a DC that have the greatest impact on overall operational performance. In addition, they argue that research results are insufficiently shared with industry professionals. Furthermore, Gu et al. (2010) suggest that the elaboration of more case studies supported by computational tools may help to bring academic questions closer to practical application.

Vieira et al. (2017) highlight as limitation the fact that each activity of the DC is independently analyzed and projected, leading to low-level and interrelated solutions. DC project is a highly complex task where trade-offs have to be analyzed at each stage. In this sense, the lack of a widely accepted system for analyzing the feasibility of DC projects characterizes an additional difficulty (Rouwenhorst et al., 2000). For Baker and Canessa (2009), although there seems to be consensus on the overall structure of the approach, there is less consensus on the exact nature of the tools/methods to be used at each stage. The development of a comprehensive methodology for DC project, therefore, seems to be a latent opportunity for future research.

Theoretical Lens - Distribution Networks

The term “theoretical lens” is relatively new and is used by researchers in the processes of knowledge expansion. In validating an emerging theory, existing Theoretical Lenses can be used to explain how the theory is related to the literature (Birks et al., 2013). The term theoretical lens does not require the lens itself to be a theory; however, it is necessary that the procedure of using a lens contributes in several ways to it (Holweg; Pil, 2008). Authors may introduce the term as a way of explaining or justifying the specific categories in which they classified data as part of the analysis process. Similarly, the theoretical lens a researcher chooses may explain why he or she has selected levels of analysis from a larger population to guide data collection (Pan; Tan, 2011; Niederman; March, 2019). Thus, the evidence found in BP was analyzed under the emphasis given to Distribution Networks (DN).

One of the key drivers of a supply chain's productivity and overall profitability is its DN, which can be used to achieve a variety of supply chain goals, from low cost to high service level (Javid; Azad, 2010). In distribution, DCs are often of high importance as they contribute to improving such distribution flows, from the factories where the goods are produced, to the points of consumption, usually distributors (Ambrosino; Scutella, 2005; Choy et al., 2012). The purpose of a DN analysis is to determine the best distribution system to minimize installation, storage, transportation and inventory costs, and to provide a high degree of customer service. DN types are classified according to the number of levels in the network and the type of routes between them (Zhao et al., 2011).

Each establishment of a network is a node and each level can have one or more nodes. Nodes are grouped into up to five levels, such as factories (1st level), which ship products to one or more DCs (2nd level), which can transfer to regional depots and/or transit points, which are out of stock (3rd and 4th levels, respectively), and send to end customers (5th level). Figure 4 illustrates a DN and the five levels. In the connection between network nodes, in turn, the types of routes can be supplied directly from factories to DCs, from DC routes to regional depots or transit points, from DCs to small customers, and DC mixed routes to regional depots, transit points and customers (Ambrosino; Scutella, 2005). To facilitate the analysis of the literature, Figure 5 presents a codification of the existing types of networks and distribution routes.

Figure 4. Types of Distribution Networks


Source: The authors

Figure 5. Network classification and distribution routes


Source: The authors

Finally, Table 6 analyzes the network classification and distribution route of the BP studies and the DC project steps identified in the studies. In the analysis, the network classification and distribution route is related to the BP references that cite each type of classification. Likewise, these references relate to the DC project steps and decisions addressed in their studies. It is possible to identify that of the six stages of DC project, some stages (E2, E5 and E6) are mentioned in several studies; however, stages E1, E3 and E4 have low relation with the network type and distribution route, when analyzing by citation frequency. Moreover, studies address one or two decisions at each stage of the DC project, which points out that works addressing DN typically detail specific aspects of DC project.

Table 6. Network classification and distribution route relationship and BP DC project stage


Source: The authors


The objective of this work was to identify, from a systematic literature review, the main methods of project and planning of DCs, consolidating their characteristics, advantages and barriers. To this end, a quantitative (bibliometric) and qualitative (theoretical lenses) analysis of the content available in the literature was performed, which enabled the identification of gaps and opportunities for future research on the subject. Although a large amount of work has been identified on the specific stages of DC project, the literature presents a shortage of a robust and common theoretical basis for the overall DC project.

Despite the results obtained, it is worth highlighting some limitations of this study. Firstly, it is important to highlight that the literature review does not present the tools used in the stages and decisions of the DC project, as each study and segment may present a way to implement these stages, which would greatly expand the scope of the research. The theoretical lens analysis is related to distribution networks and business segment of the companies; however, future studies could analyze the contents of the same bibliographic portfolio under the lens of other approaches, such as transport modes and the relationship between the DC project stages.

Lastly, in the stage of collecting the scientific articles to consolidate the bibliographic portfolio, the works were identified on the research available in the cited databases. However, on these bases, they can bring additional contributions to the results.

Based on the above, for future research opportunities, the paper identifies several gaps related to the implementation of a DC project planning. Thus, two main directions for future research are highlighted: (i) broad methodology for the DC project stages; and (ii) analysis of the DC project implementation in the distribution network.

Comprehensive methodology for DC project stages

EDue to the scarcity of DC project proposals that structure a theoretical and practical project and planning methodology, research developed in DC project is rarely applied in practice. As argued by Gu et al. (2007), survey results are poorly shared with industry professionals. In addition, Baker and Canessa (2009) point out that there is little consensus on the tools and methods to be used at each step. Thus, the development of a comprehensive methodology for DC project seems to be a latent opportunity for future research, identifying the tools and methods to be used for each step.

Added to this is the need for evidence of its practical application, which can be obtained from the development of case studies or research/action on the subject. The proposition of such methods could also contemplate the peculiarities of each type of industry, since few works from BP explicitly describe the analyzed context. In this sense, the inclusion of the effect of characteristics such as volume and added value of the products can lead to a better definition of the method used for the DC project.

Analysis of the DC project implementation in the distribution network

The success of a company's distribution strategy plays a critical role in supporting internal DC operations, and the way the internal activities of different DCs are organized is influenced by the characteristics of distribution operations (Vieira et al., 2017). Despite the above, few studies address the distribution network with relevance in the DC project stages (only 13 studies from BP). Existing works deal with the stages of internal (DC project) or external (DR) operations in isolation, quite possibly due to the complexity of simultaneous analysis of both operations.

As a recommendation, future research should detail the project stages of the DC taking into account the specific characteristics of DR. Such studies would quantify the relationship between DC project stages and decisions and the definition of the distribution network and route, providing practical arguments for the importance of these definitions in DC project decisions. In practical terms, continuing research on this topic could indicate how these relationships impact the organization as a whole.


(Note: *The demarcated references are those included in the bibliographic portfolio of the research).

*Accorsi, R.; Manzini, R.; Maranesi, F. (2014), “A decision-support system for the design and management of warehousing systems”, Computers in Industry, Vol. 65, No. 1, pp. 175-186.

*Ambrosino, D.; Scutella, M. G. (2005), “Distribution network design: New problems and related models”, European journal of operational research, Vol. 165, No. 3, pp. 610-624.

*Andriansyah, R.; Etman, L. F. P.; Adan, I. J.; Rooda, J. E. (2014), “Design and analysis of an automated order-picking workstation”, Journal of Simulation, Vol. 8, No. 2, pp. 151-163.

*Bahrami, B.; Aghezzaf, E. H.; Limere, V. (2017), “Using simulation to analyze picker blocking in manual order picking systems”, Procedia Manufacturing, Vol. 11, pp. 1798-1808.

*Baker, P. (2004), “Aligning distribution center operations to supply chain strategy”, The International Journal of Logistics Management, Vol. 15, No. 1, pp. 111-123.

*Baker, P. (2008), “The design and operation of distribution centers within agile supply chains”, International Journal of Production Economics, Vol. 111, No. 1, pp. 27-41.

*Baker, P.; Canessa, M. (2009), “Warehouse design: A structured approach”, European Journal of Operational Research, Vol. 193, No. 2, pp. 425-436.

*Baker, P.; Halim, Z. (2007), “An exploration of warehouse automation implementations: cost, service and flexibility issues”, Supply Chain Management: An International Journal, Vol. 12, No. 2, pp. 129-138.

Ballou, R. H. (2006), Logística empresarial: gerenciamento da cadeia de suprimentos. 5 ed. Bookman, Porto Alegre.

*Bassan, Y.; Roll, Y.; Rosenblatt, M. J. (1980), “Internal layout design of a warehouse”, AIIE Transactions, Vol. 12, No. 4, pp. 317-322.

Birks, D. F.; Fernandez, W.; Levina, N.; Nasirin, S. (2013), “Grounded theory method in information systems research: its nature, diversity and opportunities”, European Journal of Information Systems, Vol. 22, No. 1, pp. 1-8.

*Bodner, D. A.; Govindaraj, T.; Karathur, K. N.; Zerangue, N. F.; Mcginnis, L. F. (2002), “A process model and support tools for warehouse design”, In Proceedings of the 2002 NSF design, service and manufacturing grantees and research conference. pp. 1-8.

*Bozer, Y. A.; White, J. A. (1996), “A generalized design and performance analysis model for end-of-aisle order-picking system”, IIE transactions, Vol. 28, No. 4, pp. 271-280.

*Brunaud, B.; Bassett, M. H.; Agarwal, A.; Wassick, J. M.; Grossmann, I. E. (2018), “Efficient formulations for dynamic warehouse location under discrete transportation costs”, Computers & Chemical Engineering, Vol. 111, pp. 311-323.

*Brynzér, H.; Johansson, M. I.; Medbo, L. (1994), “A methodology for evaluation of order picking systems as a base for system design and managerial decisions”, International journal of operations & Production Management, Vol. 14, No. 3, pp. 126-139.

*Caron, F.; Marchet, G.; Perego, A. (2000), “Optimal layout in low-level picker-to-part systems”, International Journal of Production Research, Vol. 38, No. 1, pp. 101-117.

*Cakmak, E.; Gunay, N. S.; Aybakan, G.; Tanyas, M. (2012), “Determining the size and design of flow type and u-type warehouses”, Procedia-Social and Behavioral Sciences, Vol. 58, pp. 1425-1433.

*Çelk, M.; Süral, H. (2014), “Order picking under random and turnover-based storage policies in fishbone aisle warehouses”, IIE transactions, Vol. 46, No. 3, pp. 283-300.

*Chen, C. T. (2001), “A fuzzy approach to select the location of the distribution center”, Fuzzy sets and systems, Vol. 118, No. 1, pp. 65-73.

Chen, L.; Zhao, X.; Tang, O.; Price, L.; Zhang, S.; Zhu, W. (2017), “Supply chain collaboration for sustainability: A literature review and future research agenda”, International Journal of Production Economics, Vol. 194, pp. 73-87.

*Choy, K. L.; Chow, H. K. H.; Poon, T. C.; Ho, G. T. S. (2012), “Cross-dock job assignment problem in space-constrained industrial logistics distribution hubs with a single docking zone”, International Journal of Production Research, Vol. 50, No. 9, pp. 2439-2450.

*Claeys, D.; Adan, I.; Boxma, O. (2016), “Stochastic bounds for order flow times in parts-to-picker warehouses with remotely located order-picking workstations”, European Journal of Operational Research, Vol. 254, No. 3, pp. 895-906.

*Cruz-Domínguez, O.; Santos-Mayorga, R. (2016), “Artificial intelligence applied to assigned merchandise location in retail sales systems”, South African Journal of Industrial Engineering, Vol. 27, No. 1, pp. 112-124.

*Dallari, F.; Marchet, G.; Melacini, M. (2009), “Design of order picking system”, The international journal of advanced manufacturing technology, Vol. 42, No. 1-2, pp. 1-12.

*Daniels, R. L.; Rummel, J. L.; Schantz, R. (1998), “A model for warehouse order _picking_”, European Journal of Operational Research, Vol. 105, No. 1, pp. 1-17.

De Koster, R.; Le-Duc, T.; Roodbergen, K. J. (2007), “Design and control of warehouse order picking: A literature review”, European journal of operational research, Vol. 182, No. 2, pp. 481-501.

*De Koster, R. B.; Le-Duc, T.; Zaerpour, N. (2012), “Determining the number of zones in a pick-and-sort order picking system”, International Journal of Production Research, Vol. 50, No. 3, pp. 757-771.

De Santis, R.; Montanari, R.; Vignali, G.; Bottani, E. (2018), “An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses”, European Journal of Operational Research. Vol. 267, pp. 120–137.

Dotoli, M.; Epicoco, N.; Falagario, M.; Costantino, N.; Turchiano, B. (2015), “An integrated approach for warehouse analysis and optimization: a case study”, Comput. Ind. Vol. 70, p. 56–69.

Ensslin, L.; Ensslin, S. R.; Lacerda, R. D. O.; Tasca, J. E. (2010), “ProKnow-C, Knowledge Development Process–Constructivist”, processo técnico com patente de registro pendente junto ao INPI. Brasil:[sn].

*Faber, N.; De Koster, M. B. M.; Smidts, A. (2013), “Organizing warehouse management”, International Journal of Operations & Production Management, Vol. 33, No. 9, pp. 1230-1256.

Fonseca, J. J. S. Metodologia da pesquisa científica. Fortaleza: UEC, 2002. Apostila.

*Franzke, T.; Grosse, E. H.; Glock, C. H.; Elbert, R. (2017), “An investigation of the effects of storage assignment and picker routing on the occurrence of picker blocking in manual picker-to-parts warehouses”, The International Journal of Logistics Management, Vol. 28, No. 3, pp. 841-863.

*Gademann, N.; Velde, S. (2005), “Order batching to minimize total travel time in a parallel-aisle warehouse”, IIE transactions, Vol. 37, No. 1, pp. 63-75.

*Gill, A. (2009), “Determining loading dock requirements in production-distribution facilities under uncertainty”, Computers & Industrial Engineering, Vol. 57, No. 1, pp. 161-168.

Goetschalckx, M.; Vidal, C. J.; Dogan, K. (2002), “Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms”, European Journal of Operational Research, Vol. 143, No. 1, pp. 1-18.

*Gong, Y.; De Koster, R. B.; Frenk, J. B. G.; Gabor, A. F. (2013), “Increasing the Revenue of Self‐Storage Warehouses by Facility Design”, Production and Operations Management, Vol. 22, No. 3, pp. 555-570.

*Gu, J.; Goetschalckx, M.; Mcginnis, L. F. (2007), “Research on warehouse operation: A comprehensive review”, European Journal of Operational Research, Vol. 177, No. 1, pp. 1-21.

*Gu, J.; Goetschalckx, M.; Mcginnis, L. F. (2010), “Research on warehouse design and performance evaluation: A comprehensive review”, European Journal of Operational Research, Vol. 203, No. 3, pp. 539-549.

*Gue, K. R.; Meller, R. D.; Skufca, J. D. (2006), “The effects of pick density on order picking areas with narrow aisles”, IIE transactions, Vol. 38, No. 10, pp. 859-868.

*Guo, X.; Yu, Y.; De Koster, R. B. (2016), “Impact of required storage space on storage policy performance in a unit-load warehouse”, International Journal of Production Research, Vol. 54, No. 8, pp. 2405-2418.

Hadlich, A. R. (2011), Procedimento Metodológico para Estudo de Macrolocalização de Centros de Distribuição com Uso do Modelo de P-Medianas Adaptado, Dissertação de mestrado em Engenharia Civil, Universidade Federal de Santa Catarina, Florianópolis, SC.

*Hall, R. W. (1993), “Distance approximations for routing manual pickers in a warehouse”, IIE transactions, Vol. 25, No. 4, pp. 76-87.

Higginson, J. K.; Bookbinder, J. H. (2005), “Distribution centres in supply chain operations”, In Logistics Systems: Design and Optimization. Springer, Boston, MA, pp. 67-91.

Hiremath, N. C.; Sahu, S.; Tiwari, M. K. (2013), “Multi objective outbound logistics network design for a manufacturing supply chain”, Journal of Intelligent Manufacturing, Vol. 24, No. 6, pp. 1071-1084.

*Ho, Y. C.; Liu, C. F. (2005), “A design methodology for converting a regular warehouse into a zone-picking warehouse”, Journal of the Chinese Institute of Industrial Engineers, Vol. 22, No. 4, pp. 332-345.

Holweg, M.; Pil, F. K. (2008), “Theoretical perspectives on the coordination of supply chains”, Journal of operations management, Vol. 26, No. 3, pp. 389-406.

Holzapfel, A.; Kuhn, H.; Sternbeck, M. G. (2018), “Product allocation to different types of distribution center in retail logistics networks”, European Journal of Operational Research, Vol. 264, No. 3, pp. 948-966.

Hou, J. L.; Wu, Y. J.; Yang, Y. J. (2010), “A model for storage arrangement and re-allocation for storage management operations”, International Journal of Computer Integrated Manufacturing, Vol. 23, No. 4, pp. 369-390.

*Hsieh, L. F.; Tsai, L. (2006), “The optimum design of a warehouse system on order picking efficiency”, The International Journal of Advanced Manufacturing Technology, Vol. 28, No. 5-6, pp. 626-637.

*Hua, X.; Hu, X.; Yuan, W. (2016), “Research optimization on logistics distribution center location based on adaptive particle swarm algorithm”, Optik, Vol. 127, No. 20, pp. 8443-8450.

*Huang, R.; Menezes, M. B.; Kim, S. (2012), “The impact of cost uncertainty on the location of a distribution center”, European Journal of Operational Research, Vol. 218, No. 2, pp. 401-407.

*Huertas, J. I.; Díaz Ramírez, J.; Trigos Salazar, F. (2007), “Layout evaluation of large capacity warehouses’, Facilities, Vol. 25, No. 7/8, pp. 259-270.

*Javid, A. A.; Azad, N. (2010), “Incorporating location, routing and inventory decisions in supply chain network design”, Transportation Research Part E: Logistics and Transportation Review, Vol. 46, No. 5, pp. 582-597.

*Jarvis, J. M.; Mcdowell, E. D. (1991), “Optimal product layout in an order picking warehouse”, IIE transactions, Vol. 23, No. 1, pp. 93-102.

*Kuo, R. J.; Kuo, P. H.; Chen, Y. R.; Zulvia, F. E. (2016), “Application of metaheuristics-based clustering algorithm to item assignment in a synchronized zone order picking system”, Applied Soft Computing, Vol. 23, No. 1, pp. 93-102.

*Lam, C. H.; Choy, K. L.; Ho, G. T.; Lee, C. K. M. (2014), “An order-picking operations system for managing the batching activities in a warehouse”, International Journal of Systems Science, Vol. 45, No. 6, pp. 1283-1295.

*Lee, C. K. M.; Lv, Y.; Ng, K. K. H.; Ho, W.; Choy, K. L. (2018), “Design and application of Internet of things-based warehouse management system for smart logistics”, International Journal of Production Research, Vol. 56, No. 8, pp. 2753-2768.

*Lin, C. H.; Lu, I. Y. (1999), “The procedure of determining the order picking strategies in distribution center”, International Journal of Production Economics, Vol. 60, pp. 301-307.

*Litomin, I.; Tolmachov, I.; Galkin, A. (2016), “Use of the Distribution Center in the Ukrainian Distribution System”, Transportation Research Procedia, Vol. 16, pp. 313-322.

Niederman, F.; March, S. (2019), “The “Theoretical Lens” Concept: We All Know What it Means, but do We All Know the Same Thing?”, Communications of the Association for Information Systems, Vol. 44, No. 1, pp. 1.

Ntabe, E. N.; Lebel, L.; Munson, A. D.; Santa-Eulalia, L. A. (2015), “A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues”, International Journal of Production Economics, Vol. 169, pp. 310-332.

*Nozick, L. K.; Turnquist, M. A. (2001), “Inventory, transportation, service quality and the location of distribution centers”, European Journal of Operational Research, Vol. 129, No. 2, pp. 362-371.

*Pan, J. C. H.; Shih, P. H.; Wu, M. H.; Lin, J. H. (2015), “A storage assignment heuristic method based on genetic algorithm for a pick-and-pass warehousing system” Computers & Industrial Engineering, Vol. 81, pp. 1-13.

Pan, S. L.; Tan, B. (2011), “Demystifying case research: A structured–pragmatic–situational (SPS) approach to conducting case studies”, Information and Organization, Vol. 21, No. 3, pp. 161-176.

Paré, G.; Trudel, M.C.; Jaana, M.; Kitsiou, S. (2015), “Synthesizing information systems knowledge: A typology of literature reviews”, Information & Management, Vol. 52, No. 2, p. 183-199.

*Parikh, P. J.; Meller, R. D. (2008), “Selecting between batch and zone order picking strategies in a distribution center”, Transportation Research Part E: Logistics and Transportation Review, Vol. 44, No. 5, pp. 696-719.

*Parikh, P. J.; Meller, R. D. (2009), “Estimating picker blocking in wide-aisle order picking systems”, IIE Transactions, Vol. 41, No. 3, pp. 232-246.

*Petersen, C. G. (1999), “The impact of routing and storage policies on warehouse efficiency”, International Journal of Operations & Production Management, Vol. 19, No. 10, pp. 1053-1064.

*Purba, H. H.; Aisyah, S. (2018), “Productivity improvement picking order by appropriate method, value stream mapping analysis, and storage design: a case study in automotive part center”, Management and Production Engineering Review, Vol. 9.

Rheem, H. (1997), “Logistics, A trend continues”, Harvard Business Review, Vol. 75, No. 1, pp. 8-9.

Rodrigues, G. G.; Pizzolato, N. D. (2003), “Centros de Distribuição: armazenagem estratégica”, artigo apresentado no XXIII Encontro Nac. de Eng. de Produção, Ouro Preto, MG, Brasil, 21-24 de out. 2003.

*Roodbergen, K. J.; Sharp, G. P.; Vis, I. F. (2008), “Designing the layout structure of manual order picking areas in warehouses”, IIE Transactions, Vol. 40, No. 11, pp. 1032-1045.

*Rouwenhorst, B.; Reuter, B.; Stockrahm, V.; Van Houtum, G. J.; Mantel, R. J;. Zijm, W. H. (2000), “Warehouse design and control: Framework and literature review”, European Journal of Operational Research, Vol. 122, No. 3, pp. 515-533.

Santos, A. (2015), “Centros de distribuição como vantagem competitiva”, Revista de Ciências Gerenciais, Vol. 10, No. 12, pp. 34-40.

*Salehi, H.; Tavakkoli-Moghaddam, R.; Nasiri, G. R. (2015), “A multi-objective location-allocation problem with lateral transshipment between distribution centers”, International Journal of Logistics Systems and Management, Vol. 22, No. 4, pp. 464-482.

*Segura, E.; Carmona-Benitez, R. B.; Lozano, A. (2014), “Dynamic location of distribution centres, a real case study”, Transportation Research Procedia, Vol. 3, pp. 547-554.

*Shen, Z. J. M.; Qi, L. (2007), “Incorporating inventory and routing costs in strategic location models”, European journal of operational research, Vol. 179, No. 2, pp. 372-389.

*Škerlič, S.; Muha, R.; Sokolovskij, E. (2017), “Application of modern warehouse technology in the Slovenian automotive industry”, Transport, Vol. 32, No. 4, pp. 415-425.

*Sooksaksun, N.; Kachitvichyanukul, V.; Gong, D. C. (2012), “A class-based storage warehouse design using a particle swarm optimisation algorithm”, International Journal of Operational Research, Vol. 13, No. 2, pp. 219-237.

*Teo, C. P.; Shu, J. (2004), “Warehouse-retailer network design problem”, Operations Research, Vol. 52, No. 3, pp. 396-408.

Tompkins, J.; White, J.; Bozer, Y. A.; Tanchoco, J. (2013), Planejamento de instalações. Editora LTC.

*Vieira, J. G. V.; Toso, M. R.; Da Silva, J. E. A. R.; Ribeiro, P. C. C. (2017), “An AHP-based framework for logistics operations in distribution centres”, International Journal of Production Economics, Vol. 187, pp. 246-259.

*Yang, L.; Ji, X.; Gao, Z.; Li, K. (2007), “Logistics distribution centers location problem and algorithm under fuzzy environment”, Journal of Computational and Applied Mathematics, Vol. 208, No. 2, pp. 303-315.

*Yu, M.; De Koster, R. B. (2009), “The impact of order batching and picking area zoning on order picking system performance” European Journal of Operational Research, Vol. 198, No. 2, pp. 480-490.

*Yuan, R.; Cezik, T.; Graves, S. C. (2018), “Stowage decisions in multi-zone storage systems”, International Journal of Production Research, Vol. 56, No. 1-2, pp. 333-343.

*Won, J.; Olafsson, S. (2005), “Joint order batching and order picking in warehouse operations”, International Journal of Production Research, Vol. 43, No. 7, pp. 1427-1442.

Zhao, K.; Kumar, A.; Yen, J. (2011), “Achieving high robustness in supply distribution networks by rewiring”, IEEE Transactions on Engineering Management, Vol. 58, No. 2, pp. 347-362.

*Zhou, L.; Zhang, G.; Liu, W. (2015), “A new method for the selection of distribution centre locations”, IMA Journal of Management Mathematics, Vol. 28, No. 3, pp. 421-436.

*Zhuge, D.; Yu, S.; Zhen, L.; Wang, W. (2016), “Multi-period distribution center location and scale decision in supply chain network”, Computers & Industrial Engineering, Vol. 101, pp. 216-226.

Received: Jul. 30, 2019

Approved: Dez. 02, 2019

DOI: 10.20985/1980-5160.2019.v14n4.1560

How to cite: Guimarães, M. C.; Tortorella, G. L. (2019), “A literature review on distribution center project and planning”, Sistemas & Gestão, Vol. 14, No. 4, pp. 370-381, available from: http://www.revistasg.uff.br/index.php/sg/article/view/1560 (access day month year).