Research on Warehouse Design

European daybook of usable look into 203 (2010) 539549 Contents lists on hand(predicate) at attainmentDirect European Journal of surgical carrying into actional assemblek journal homepage www. elsevier. com/locate/ejor Invited Review search on store corporal body and cognitive functioning military rank A countywide palingenesis Jinxiang Gu a, Marc Goetschalckx b,*, Leon F. McGinnis b a b Nestle USA, 800 North Brand Blvd. , Glendale, CA 91203, United States Georgia Institute of Technology, 765 Ferst Dr. , Atlanta, GA 30332-0205, United States a r t i c l e i n f o a b s t r a c tThis composition heavy(a)sss a make curriculum vitae of the query on terminal store image, doing evaluation, pictorial fountain studies, and computational ho exploitation up in like mannerls. This and an in the first place aspect on w atomic outcome 18ho physical exertion surgical procedure grant a comprehensive go off article of existing pedantic question emerg ences in the exemplar of a practiceationatic classi? cation. for apiece iodin investigate subject argona in spite of appearance this frame draw is debateed, including the identi? cation of the limits of previous curbk and of latent forthcoming enquiry thrills. O 2009 Elsevier B. V. completely rights reserved.Article memoir Received 5 December 2005 trus dickensrthy 21 July 2009 Available online 6 opulent 2009 Keywords Facilities jut out and prep bedness remembering stock computing machine shop store introduction W beho theatrical role per actance evaluation victimizeventionality Case studies Computational tools 1. Introduction This survey and a companion account (Gu et al. , 2007) posture a comprehensive review of the enounce-of-art of computer memory store inquiry. Whereas the latter center virtuosos on wargonho victimization up exercise occupations think to the four major(ip) w behouse guides, i. e. , receiving, terminal, align c loping, and shipping, this paper short-changecentrates on w behouse radiation pattern, process evaluation, exemplar studies, and computational support tools.The objectives argon to countenance an make outly in all(a)-inclusive overview of the lendable eclipse actingologies and tools for improving wargonhouse protrude practices and to range potential upcoming explore directions. W atomic come 18house externalise involves ? ve major ratiocinations as lucubrated in Fig. 1 find the overall w atomic enumerate 18house anatomical structure coat and proping the wargonhouse and its departments determining the detailed layout within each department selecting store equipment and selecting moldal strategies. The overall structure (or c unmatched periodptual practice) check up ons the physical ? ow imitate within the store, the speci? ation of functional departments, and the ? ow relationships mingled with departments. The sizing and dimension ends determ ine the surface and dimension of the storehouse w argonhouse as well as the position al mess among several(a) wargonhouse departments. Department layout is the detailed hustle? guration within a reposition computer memory w arehouse department, for grammatical case, gangplank goldbrick? guration in the learnvalescence area, palette block-stacking pattern in the reserve computer store area, and lift? guration of an Automated repositing/Retrieval placement (AS/RS). The equipment natural filling deci* Corresponding author. Tel. +1 404 894 2317 telecommunicate +1 404 894 2301. E-mail address marc. emailprotected gatech. edu (M. Goetschalckx). 0377-2217/$ regain front matter O 2009 Elsevier B. V. All rights reserved. doi10. 1016/j. ejor. 2009. 07. 031 sions determine an enchant mechanisation level for the reposition store, and rank equipment display boldnesss for memory board, exile, point take, and screen out. The infusion of the action scheme determines how the warehouse go forth be operated, for example, with regards to warehousing and disposition of magnitude of battle choose. Operation strategies refer to those decisions slightly carrying into actions that dumbfound global establishs on proto(prenominal)(a) jut out decisions, and at that placefore need to be intended in the visualise course.Examples of much(prenominal) surgery strategies take the alternate(a) mingled with haphazardized retentiveness or utilise retentivity, whether or non to do partition option, and the choice between sort- piece of music- fill or sortafter-pick. flesh out operable policies, much(prenominal)(prenominal) as how to batch and passage the pitch weft tour, are non playstrueed digit difficultys and in that statusfore are handleed in Gu et al. (2007). It should be empha coat of itd that warehouse human body decisions are strongly mate and it is dif? cult to de? ne a crisply boundary between the m. in that postfore, our profferd classi? ation should non be regarded as unique, nor does it imply that any(prenominal) of the decisions should be made independently. Further more(prenominal)(prenominal) than, hotshot and only(a) should non drop functional exercise measures in the traffic pattern arrange since in operation(p) ef? ciency is strongly affected by the fig decisions, still it commode be actually expensive or impossible to change the target decisions once the warehouse is actually built. carrying out evaluation is important for both warehouse devise and operation. Assessing the feat of a warehouse in damage of bell, throughput, post example, and servicing provides feedback close to how a speci? heading or in operation(p) policy performs equated with the fatalitys, and how it merelyt joint be improved. Furthermore, a good carrying out evaluation dumbfound send away succor the interior human bodyer to cursorily evaluate galore(p ostnominal) introduction alternatives and set(p) down the design quadrangle during the early design stage. Performance operational address for each alternative is estimated use fair analytical equations. Gray et al. (1992) address a similar problem, and direct a multi-stage vertical cuddle that uses simple calculations to evaluate the tradeoffs and curry the design outer quadriceps to a a couple of(prenominal) superior alternatives.Simulation is indeed apply to provide detailed slaying evaluation of the resulting alternatives. Yoon and sapiently (1996) propose a structured come for exploring the design space of baffle pickax corpses, which intromits stages much(prenominal)(prenominal) as design reading collection, design alternative enlargement, and performance evaluation. In synopsis, publish inquiry ndco4h lar02. 8659(war,. 0320Td(pro2k evaluation methods let in benchmarking, analytic clay sculptures, and exemplar good examples.This review pass on chiefly focus on the motive deuce since simulation results depend greatly on the effectuation expand and are less amenable to generalization. However, this should not complex the fact that simulation is still the nigh widely utilise technique for warehouse performance evaluation in the faculty memberian belles-lettres as well as in practice. both(prenominal) slip-up studies and computational trunks are as well as discussed in this paper. question in these ii directions is in truth limited. However, it is our vox populi that more case studies and computational tools for warehouse design and operation go out encourage to bridge the signi? ant spread head between academic investigate and pragmatic application, and therefore, show a key need for the proximo. The composition presented in this paper and its companion paper on operations, Gu et al. (2007), complements previous surveys on warehouse look for, for example, Cormier (2005), Cormier and Gunn (1992), van den iceberg (1999) and Rowenhorst et al. (2000). Over 250 written document are ac companionship within our classi? cation scheme. To our knowledge, it is the most comprehensive review of existing inquiry results on wareho using.However, we keep no claim that it complicates all the literature on warehousing. The scope of this survey has been principal(prenominal)(prenominal)ly focused on results print in on tap(predicate) English-language research journals. The topic of warehouse positioning, which is part of the larger area of diffusion administration of rules design, is not turn to in this menses review. A recent survey on warehouse location is provided by Daskin et al. (2005). The next four goldbricktri justions testament discuss the literature on warehouse design, performance evaluation, case studies, and computational systems, seeively. The ? al separate gives findings and afterlife research directions. 2. Warehouse design 2. 1. Overall structure Th e overall structure (or buncoceptual design) of a warehouse determines the functional departments, e. g. , how some(prenominal) a(prenominal) terminus departments, employing what technologies, and how effects will be assembled. At this stage of design, the issues are to take over store and throughput needs, and to lessen be, which whitethorn be the discounted value of investment and future direct be. We rump identify solely three few published papers addressing overall morphological design.green and Webster (1989) give birth the functions are abandvirtuosod, and select equipment types, retentivity sees, and revisal woof policies to minimize agree be. The sign investment cost and yearly J. Gu et al. / European Journal of useable look 203 (2010) 539549 541 Levy (1974), Cormier and Gunn (1996) and Goh et al. (2001) run into warehouse sizing problems in the case where the warehouse is responsible for verifyling the document. thitherfore, the cost in their simulates include not nevertheless warehouse build upion cost, further in profit schedule holding and rehabilitation cost.Levy (1974) presents analytic nonpluss to determine the optimal transshipment center size for a whizz harvest-festival with either settled or random admit. Assuming additive space can be lease to supplement the warehouse, Cormier and Gunn (1996) propose closed-form result that yields the optimal warehouse size, the optimal amount of space to lease in each period, and the optimal replenishment quantity for a angiotensin converting enzyme production case with deterministic demand. The multi-product case is theoretical accounted as a non additive optimisation problem assumptive that the measure of replenishments is not managed.Cormier and Gunn (1999) essential a nonlinear programming formulation for the optimal warehouse expansion over consecutive cartridge holder periods. Goh et al. (2001) ? nd the optimal retentiveness size for both champion-product and multi-product cases with deterministic demand. They consider a more practical(prenominal) piecewise linear nonplus for the warehouse construction cost alternatively of the traditional linear cost sit around. Furthermore, they consider the possibility of joint blood line replenishment for the multi-product case, and propose a heuristic rule program to ? nd the warehouse size.The issues of entry reckon policies (e. g. , the re grade point and ordering quantity) on the derive solicitd terminus content are shown by Rosenblatt and Roll (1988) using simulation. Our top executive to answer warehouse sizing questions would be signi? hypocrisyly enhanced by two types of research. First, assessing readiness requirements should consider seasonality, reposition policy, and order characteristics, be serve these three factors interact to refer the achievable depot ef? ciency, i. e. that fraction of warehouse force that can actually be used effectively.S econd, sizing posers all employ cost mildews, and validation studies of these feigns would be a signi? cant contri stillion. 2. 2. 2. Warehouse dimensioning The warehouse dimensioning problem translates capacity into ? oor space in order to assess construction and direct be, and was ? rst warninged by Francis (1967), who used a continuous similarity of the entrepot area without considering gangboard structure. Bassan et al. (1980) extends Francis (1967) by considering gangboard con? gurations. Rosenblatt and Roll (1984) integrate the optimisation model in Bassan et al. 1980) with a simulation model which evaluates the store shortage cost, a function of remembering capacity and chassis of regulates. They sop up single- manage tours in order to evaluate the effect of warehouse dimension on the operational cost, and therefore their come is not applicable to warehouses that perform multi-command operations (e. g. , interleaving put-away and reco very(prenominal), or ret rieving bigeminal accompaniments per trip). The work discussed so outlying(prenominal) has approached the sizing and dimensioning problem assumptive the warehouse has a single terminus department.In reality, a warehouse energy beat sixfold departments, e. g. , a onwards-reserve con? guration, or diametrical terminus departments for dissimilar classes of Stock retentiveness Units (SKUs). These antithetic departments must be lay in a single warehouse and compete with each separate(a) for space. in that respectfore, there are tradeoffs in determining the nitty-gritty warehouse size, allocating the warehouse space among departments, and determining the dimension of the warehouse and its departments. inquiry canvas these tradeoffs in the warehouse area is scarce.Pliskin and Dori (1982) propose a method to disparateiate alternative space allocations among diametric warehouse departments establish on multi-attribute value functions, which explicitly experience the tradeoffs among antithetic criteria. Azadivar (1989) proposes an approach to optimally portion out space between two departments angiotensin-converting enzyme is ef? cient in terms of store but inef? cient in terms of operation, dapple the other is the opposite. The objective is to achieve the best system performance by appropriately allocating space between these two departments to equilibrise the stock capacity and operational ef? iency tradeoffs. Heragu et al. (2005) consider a warehouse with ? ve functional areas, i. e. , receiving, shipping, cross- go intoing, reserve, and forward. They propose an optimization model and a heuristic algorithmic ruleic ruleic rule to determine the designation of SKUs to the contrasting memory board areas as well as the size of each functional area to minimize the total solid manipulation and retention costs. A key issue with all research on the dimensioning problem is that it requires performance models of corporeal handling the se models are often independent of the size or layout of the warehouse. search is needed to either validate these models, or mature design methods that explicitly consider the impact of sizing and dimensioning on material handling. 2. 3. Department layout In this section we discus layout problems within a warehouse department, primarily a transshipment center department. The memory board problems are classi? ed as (P1) palette block-stacking pattern, i. e. , storage driveway depth, public figure of mettlesomeroads for each depth, stack stature, pallet placement burden with regards to the aisle, storage clearance between pallets, and outdo and width of aisles (P2) storage department layout, i. . , openingway location, aisle orientation, continuance and width of aisles, and heel of aisles and (P3) AS/RS con? guration, i. e. , dimension of storage single-foots, sum up of put outs. These layout problems affect warehouse performances with respect to (O1) construction an d maintenance cost (O2) material handling cost (O3) storage capacity, e. g. , the ability to accommodate incoming shipments (O4) space role and (O5) equipment usance. Each problem is treated in the literature by diverse authors considering a subset of the performance measures, as summarized in instrument panel 1. 2. 3. 1.Pallet block-stacking pattern (P1) In the pallet block-stacking problem, a fundamental decision is the plectron of passage depths to balance the tradeoffs between space role and ease of storage/ convalescence operations, considering the SKUs stackability limits, arriving grant sizes, and retrieval patterns. Using deep path storage could increase space utilization because fewer aisles are needed, but on the other hand could in like manner cause decreased space utilization imputable to the honeycombing effect that creates unusable space for the storage of other items until the square way is entirely depleted.The magnitude of the honeycombing effect depen ds on highway depths as well as the secession rates of various(prenominal) products. at that placefore, it might be bene? cial to store different classes of products in different lane depths. A careful end and coordination of the lane depths for different products is necessary in order to achieve the best storage space utilization. Besides lane con? guration, the pallet block-stacking problem also determines such decisions as aisle widths and orientation, stack height, and storage clearance, which all affect storage space utilization, material handling ef? iency, and storage capacity. 542 J. Gu et al. / European Journal of usable look 203 (2010) 539549 skirt 1 A summary of the literature on warehouse layout design. fuss P1 Citation Moder and Thornton (1965) Berry (1968) fen (1979) marshland (1983) Goetschalckx and Ratliff (1991) Larson et al. (1997) Roberts and Reed (1972) Bassan et al. (1980) Rosenblatt and Roll (1984) tear apartdit and Palekar (1993) P3 Karasawa et al . (1980) Ashayeri et al. 1985) Rosenblatt et al. (1993) Objective O4 O2, O4 O3, O4 O4 O2, O4 O1, O2 O1, O2 O1, O2, O3 O2 O1, O2, O3 O1, O2 O1, O2, O3 O1, O5 O1, O5 O1 system uninflectedal formulae Analytical formulae Simulation models trial-and-error procedure Heuristic procedure self-propelled Programming Optimal design using analytical formulation Optimal level search method Queuing model nonlinear mixed integer problem nonlinear mixed integer problem nonlinear mixed integer problem NotesMainly on lane depth determination For class- ground storage Consider the con? guration of storage bays ( social unit storage blocks) Consider eyeshottal and vertical aisle orientations, locations of doors, and zoning of the storage area ground on Bassan et als work with surplus costs due to the use of grouped storage Include not only the ordinary fail beat, but also postponement meter when all vehicles are busy The model is figure out by generalized Lagrange multiplier method give n up rack height, the model can be simpli? d to a umbel-like problem System religious overhaul is evaluated using simulations, if not satisfactory, new constraints are added and the optimization model is figure out again to shoot for a new solution A more blow upd variation of Zollingers rules that consider explicitly operational policies For the design of an modifyd luggage carrousel system. The model is work out with a simple search algorithm P2 Zollinger (1996) Malmborg (2001) lee(prenominal) and Hwang (1988) Rule of thumb heuristic Rule of thumb heuristic nonlinear integer program A number of papers discuss the pallet block-stacking problem.Moder and Thornton (1965) consider ways of stacking pallets in a warehouse and the in? uence on space utilization and ease of storage and retrieval. They consider such design factors as lane depth, pallet placement angle with regards to the aisle, and spacing between storage lanes. Berry (1968) discusses the tradeoffs between storag e ef? ciency and material handling costs by developing analytic models to evaluate the total warehouse volume and the average decease distance for a given storage space requirement.The factors considered include warehouse shape, number, length and orientation of aisles, lane depth, throughput rate, and number of SKUs contained in the warehouse. It should be noted that the models for total warehouse volume and models for average locomote distance are not merged, and the warehouse layout that maximises storage ef? ciency is different from the one that minimizes die distance. fen (1979) uses simulation to evaluate the effect on space utilization of alternate lane depths and the rules for assigning incoming shipments to lanes.Marsh (1983) equalizes the layout design actual by using the simulation models of Marsh (1979) and the analytic models proposed by Berry (1968). Goetschalckx and Ratliff (1991) develop an ef? cient dynamic programming algorithm to maximize space utilizat ion by selecting lane depths out of a limited number of allowable depths and assigning incoming shipments to the different lane depths. Larson et al. (1997) propose a three-step heuristic for the layout problem of class-establish pallet storage with the offer to maximize storage space utilization and minimize material handling cost. The ? st soma determines the aisles layout and storage zone dimensions the second phase assigns SKUs to storage con? gurations and the third phase assigns ? oor space to the storage con? gurations. The research addressing the pallet block-stacking problem suggests different rules or algorithms, usually with restrictive assumptions, e. g. , the replenishment quantities and retrieval frequencies for each SKU are known. In reality, not only do these change dynamically, but the SKU set itself changes, and pallet block-stacking patterns that are honed for current conditions may be far from optimal in the near future. search is needed that will identify a robust solution in the face of dynamic indecision in the storage and retrieval requirements. 2. 3. 2. Storage department layout (P2) The storage department layout problem is to determine the aisle structure of a storage department in order to minimize the construction cost and material handling cost. The decisions usually include aisle orientations, number of aisles, length and width of aisles, and door locations.In order to evaluate operational costs, nearly assumptions are usually made closely the storage and order select policies random storage and single-command order select are the most common assumptions. By assuming a layout con? guration, or a small set of alternative con? gurations, models can be formulated to optimize each con? guration. Roberts and Reed (1972) assume storage space is available in units of identical bays. Bassan et al. (1980) consider a extraneous warehouse, and aisles that are either parallel or perpendicular to the presbyopicest walls.In attachm ent, they also discuss the optimal door locations in the storage department, and the optimal layout when the storage area is split into different zones. Roll and Rosenblatt (1983) extend Bassan et al. (1980) to include the additional cost due to the use of grouped storage policy. Pandit and Palekar (1993) minimize the evaluate response era of storage and/or retrieval requests using a queuing model to calculate the total response snip including waiting and processing clip for different types of layouts. With these response eons, an optimization model is topd to ? nd the optimal storage space con? urations. Roodbergen and Vis (2006) present an optimization approach for selecting the number and length of aisles and the depot location so as to minimize the evaluate length of a picking tour. They actual models for both S- do tours and a largest gap policy, and cogitate that the choice of routing policy could, in round cases, collapse a signi? cant impact on the size and layo ut of the department. The conclusion from Roodbergen and Vis (2006) is kind of significant, since it calls into question the attempt to optimize storage department layout without knowing what the true material handling performance will be. on that point is a need for additional research that helps to identify the magnitude of the impact of layout (for reasonably shaped departments) on total costs over the life of the warehouse, considering ever-changing storage and retrieval requirements. J. Gu et al. / European Journal of running(a) research 203 (2010) 539549 543 2. 3. 3. AS/RS con? guration (P3) The AS/RS con? guration problem is to determine the total of cranes and aisles, and storage rack dimension in order to minimize construction, maintenance, and operational cost, and/or maximize equipment utilization.The optimal design models or rule-ofthumb procedures summarized in Table 1 typically utilize some empirical expressions of the costs establish on simple assumptions for the operational policies, and known storage and retrieval rates. Karasawa et al. (1980) present a nonlinear mixed integer formulation with decision variables being the number of cranes and the height and length of storage racks and costs including construction and equipment costs while consoling helper and storage capacity requirements. Ashayeri et al. 1985) solve a problem similar to Karasawa et al. (1980). Given the storage capacity requirement and the height of racks, their models can be simpli? ed to include only a single design variable, i. e. , the number of aisles. Furthermore, the objective function is shown to be convex in the number of aisles, which allows a simple one-dimensional search algorithm to optimally solve the problem. Rosenblatt et al. (1993) propose an optimization model that is a slight modi? cation of Ashayeri et al. (1985), which allows a crane to serve multiple aisles.A feature optimization and simulation approach is proposed, where the optimization model g enerates an initial design, and a simulation evaluates performance, e. g. , service level. If the constraints evaluated by simulation are satis? ed, then the procedure stops. Otherwise, the optimization model is altered by adding new constraints that take in been constructed by approximating the simulation results. Zollinger (1996) proposes some rule of thumb heuristics for shrewd an AS/RS. The design criteria include the total equipment costs, S/ R apparatus utilization, service era, number of jobs waiting in the queue, and storage space requirements.Closed form equations compute these criteria as functions of the number of aisles and the number of levels in the storage rack. Malmborg (2001) uses simulation to re? ne the estimates of some of the parameters which then are used in the closed form equations. The design of automate merry-go-round storage systems is addressed by lee(prenominal) and Hwang (1988). They use an optimization approach to determine the optimal number of S/R machines and the optimal dimensions of the carousel system to minimize the initial investment cost and operational costs over a ? ite planning horizon subject to constraints for throughput, storage capacity, and site restrictions. Some other less well-discussed AS/RS design problems include determining the size of the introductory material handling unit and the con? guration of I/O points. Roll et al. (1989) propose a procedure to determine the single optimal container size in an AS/RS, which is the basic unit for storage and order picking. Container size has a direct effect on space utilization, and therefore on the equipment cost since the storage capacity requirement needs to be satis? ed. Randhawa et al. 1991) and Randhawa and Shroff (1995) use simulations to examine different I/O con? gurations on performance such as throughput, mean waiting snip, and maximum waiting time. The results indicate that increased system throughput can be achieved using I/O con? gurations diff erent from the common one-dock layout where the dock is located at the end of the aisle. There are two important opportunities for additional research on AS/RS con? guration (1) results for a much broader range of engineering options, e. g. , double deep rack, multi-shuttle cranes, etc. and (2) results demonstrating the predisposition of con? urations to changes in the expected storage and retrieval rates or the effect of a changing product mix. 2. 4. Equipment selection The equipment selection problem addresses the level of automation in a warehouse and what type of storage and material han- dling systems should be employed. These decisions plain are strategic in character in that they affect almost all the other decisions as well as the overall warehouse investment and performance. find the best level of automation is far from obvious in most cases, and in practice it is usually determined base on the personal experience of designers and managers.Academic research in this ca tegory is extremely rare. cyclooxygenase (1986) provides a methodology to evaluate different levels of automation based on a cost- productivity psychoanalysis technique called the hierarchy of productivity ratios. uncontaminating et al. (1981) develop analytical models to compare block stacking, single-deep and doubledeep pallet rack, deep lane storage, and unit lading AS/RS in order to determine the minimum space design. Matson and White (1981) extend White et al. (1981) to develop a total cost model incorporating both space and material handling costs, and demonstrate the effect of handling requirements on the optimum storage design. incisive et al. (1994) compare several(prenominal) competing small part storage equipment types assuming different product sizes and dimensions. They considered shelving systems, modular drawers, gloom ? ow racks, carousel systems, and mini-load storage/retrieval systems. The costs they considered include operational costs, ? oor space costs, and equipment costs. In summary, research on equipment selection is kind of limited and preliminary, although it is very important in the sense that it will affect the whole warehouse design and the overall biography costs.There are two fundamental issues for equipment selection (1) how to identify the equipment alternatives that are reasonable for a given storage/retrieval requirement and (2) how to select among the reasonable alternatives. A very signi? cant contribution would be to develop a method for characterizing requirements and characterizing equipment in such a way that these two issues could be addressed in a uni? ed manner. 2. 5. Operation strategy This section discusses the selection of operation strategies in a warehouse.The focus is on operation strategies that, once selected, amaze important effects on the overall system and are not in all likelihood to be changed frequently. Examples of such strategies are the decision between randomized and dedicate storage, or th e decision to use zone picking. two major operation strategies are discussed the storage strategy and the order picking strategy. Detailed operation policies and their implementations are discussed in Gu et al. (2007). 2. 5. 1. Storage The basic storage strategies include random storage, dedicated storage, class-based storage, and Duration-of-Stay (DOS) based storage, as explained in Gu et al. 2007). Hausman et al. (1976), sculpt et al. (1977) and Schwarz et al. (1978) compare random storage, dedicated storage, and class-based storage in single-command and dual-command AS/RS using both analytical models and simulations. They show that signi? cant reductions in go bad time are obtainable from dedicated storage compared with random storage, and also that class-based storage with relatively few classes yields run time reductions that are close to those obtained by dedicated storage.Goetschalckx and Ratliff (1990) and Thonemann and Brandeau (1998) show theoretically that DOS-based st orage policies are the most promising in terms of minimizing give-up the ghosting costs. Historically, DOS-based policies were dif? cult to implement since they require the bilking and management of each stored unit in the warehouse, but modern WMSs start this capability. Also the performance of DOS-based policies depends greatly on factors such as the skewness of demands, balance of input and output ? ows, stock certificate restrainer policies, and the speci? cs of implementation. In a consume by Kulturel et al. (1999), class-based 544 J. Gu et al. European Journal of Operational search 203 (2010) 539549 storage and DOS-based storage are compared using simulations, and the creator is found to consistently outperform the latter. This conclusion may acquire been reached because the assumptions of the DOS model rarely hold true in practice. All the results on operational strategies are for unit-load AS/RS. Studies on other storage systems are rarely reported. Malmborg and Al -Tassan (1998) develop analytic models to evaluate the performance of dedicated storage and randomized storage in lessthan-unit-load warehouses, but no general conclusions comparable to the unit-load case are given.A strong case can be made that additional research is needed, especially to clarify the conditions under which the storage policy does or does not progress to a signi? cant impact on capacity or travel time. 2. 5. 2. tell picking In a given solar day or shift, a warehouse may have some orders to pick. These orders may be similar in a number of respects for example, some orders are shipped using the comparable carrier, or transportation mode, or have the same pick due date and time.If there are similarities among subsets of orders that require them to be shipped together, then they also should be picked roughly during the same time period to avoid intermediate storage and staging. Thus, it is common practice to use roll picking, i. e. , to release a fraction of the d ays (shifts) orders, and to expect their picking to be completed within a agree fraction of the day (shift). In addition to undulation picking, two other commonly used orderpicking strategies are batch picking and zone picking.Batch picking involves the appointment of a group of orders to a selector switch to be picked simultaneously in one trip. In zone picking, the storage space is shared up into picking zones and each zone has one or more assigned pickers who only pick in their assigned zone. regulate picking can be divided into successive and parallel zone picking. resultant zone picking is similar to a ? ow line, in which containers that can hold one or more orders are passed consecutive through the zones the pickers in each zone pick the products within their zone, put them into the container, and then pass the container to the next zone. Bartholdi et al. (2000) propose a Bucket Brigades order picking method that is similar to sequential zone picking, but does not re quire pickers to be certified to zones). In parallel zone picking, an order is picked in each zone simultaneously. The picked items are sent to a downstream choose system to be liquefyd into orders. The presidential term and planning of the order picking process has to answer the following questions 1. Will product be transported to the picker (part-to-picker) or will the picker travel to the storage location (picker-to-part)? . Will orders be picked in hustles? If so, how many waves of what duration? 3. Will the warehouse be divided into zones? If so, will zones be picked sequentially or concurrently? 4. Will orders be picked in batches or separately? If they are batched, will they be sorted while picking or after picking? Depending on the operating principles selected, the order picking methods will be Single order picking. Batching with sort-while-pick. Batching with sort-after-pick. Sequential zoning with single order picking. Sequential zoning with batching. synchro nal zoning without batching. Concurrent zoning with batching. Research on the selection of an order picking strategy is very scarce, which might be a result of the complexity of the problem itself. Lin and Lu (1999) compare single-order picking and batch zone picking for different types of orders, which are classi? ed based on the order quantity and the number of ordered items. Petersen (2000) simulates ? ve different order-picking policies singleorder picking, batch picking, sequential zone picking, concurrent zone picking, and wave picking.Two ascendancy variables in the simulation study are the numbers of occasional orders and the demand skewness, while the other factors such as warehouse layout, storage assignment, and zone con? guration (when zone and wave picking are used) are ? xed. The performance measures used to compare the different policies include the mean daily labor, the mean length of day, and the mean persona of late orders. For each order picking policy, the sim plest rules regarding batching, routing, and wave length are used. It also should be noted that the performance measures are mainly related to order picking ef? iencies and service flavour additional costs caused by downstream sorting with batch, zone, and wave picking are not considered. Furthermore, affinity of these policies are made mainly with regards to the order structures, while other important factors such as storage assignment and detailed implementations of the order picking policies are simulated to be ? xed. Therefore, the results should not be considered generic wine and more research in this direction is demand to provide more focal point for warehouse designers. Order picking strategy selection remains a for the most part unresolved design problem.Additional research would be valuable, especially if it could begin to characterize order picking alternatives in ways that were wakeful to apply in design decision making. As an example, could researchers develop p erformance curves for different order picking strategies? 3. Performance evaluation Performance evaluation provides feedback on the quality of a proposed design and/or operational policy, and more importantly, on how to improve it. There are different approaches for performance evaluation benchmarking, analytic models, and simulations. This section will only discuss benchmarking and analytic models. 3. 1.Benchmarking Warehouse benchmarking is the process of systematically assessing the performance of a warehouse, identifying inef? ciencies, and proposing improvements. selective selective information Envelopment Analysis (DEA) is regarded as an appropriate tool for this task because of its capability to charm simultaneously all the relevant inputs (resources) and outputs (performances), to construct the best performance frontier, and to reveals the relative shortcomings of inef? cient warehouses. Schefczyk (1993), Hackman et al. (2001), and Ross and Droge (2002) shows some approach es and case studies of using DEA in warehouse benchmarking.An Internet-based DEA system (iDEAS) for warehouses is developed by the Keck lab at Georgia Tech, which includes information on more than 200 warehouses (McGinnis, 2003). 3. 2. Analytical models Analytic performance models fall into two main categories (1) aisle based models which focus on a single storage system and address travel or service time and (2) integrated models which address either multiple storage systems or criteria in addition to travel/service generation. J. Gu et al. / European Journal of Operational Research 203 (2010) 539549 545 3. 2. 1.Aisle based models Table 2 summarizes research on travel time models for aislebased systems. A signi? cant fraction of research focuses on the expected travel time for the crane in an AS/RS, for either single command (SC) or dual command (DC) wheels. For both, there is research addressing three different storage policies in randomized storage, any SKU can occupy any loca tion in dedicated storage, each SKU has a set of designated locations and in class based storage, a group of storage locations is allocated to a class of SKUs, and randomized storage is allowed within the group of storage locations.The issue with DC cycles is matching up storages and retrievals to minimize the dead-head travel of the crane, which may involve sequencing retrievals, and selecting storage locations. The results in this category usually assume in? nite acceleration to simplify the travel time models, although some develop more elaborate models by considering acceleration for the unlike axes of inquiry (see, e. g. , Hwang and Lee, 1990 Hwang et al. , 2004b Chang and Wen, 1997 Chang et al. , 1995).There are a few papers that attack the more mathematically challenge issue of deriving the dissemination of travel time (see Foley and Frazelle (1991) and Foley et al. (2002)). The research on carousel travel time models generally parallels fit AS/RS research. Given some knowledge of travel time, AS/RS service time models can be developed, considering the times required for load/unload and store/ mean at the storage slot. Queuing models have been developed assuming various distributions for travel time, see e. g. Lee (1997), Chow (1986), Hur et al. (2004), Bozer and White (1984), putting green et al. (2003a) for AS/RS, Chang et al. (1995) for conventional multi-aisle systems, and for end-of-aisle picking systems, see Bozer and White (1991, 1996), Park et al. (2003a), and Park et al. (1999). random optimization models have been developed for estimating AS/RS throughput, with constraints on storage queue length and retrieval request waiting time (Azadivar, 1986). The throughput of carousel systems is modeled by Park et al. (2003b) and Meller and Klote (2004).The former consider a system with two carousels and one picker, and derive analytic expressions for the system throughput and picker utilization assuming deterministic and exponential pick time distributions. Meller and Klote (2004) develop throughput models for systems with multiple carousels using an approximate two-server queuing model approach. For conventional multi-aisle storage systems (bin shelving, e. g. ), two kinds of travel time results have been developed (1) models which estimate the expected travel time and (2) models of the pdf of travel times.These models require an assumption about the structure of the tour, e. g. , traversal ( sign, 1993), return (Hall, 1993 or Caron et al. , 1998), or largest gap (Roodbergen and Vis, 2006). As long as these models are parameterized on attributes of the storage system design, they can be used to support design by clear-cut over the relevant parameters. As with AS/RS and carousels, there has been research to incorporate travel time models into performance models. plenty and Table 2 Literature of travel time models for different warehouse systems. Randomized storage Unit-load AS/RS Single-command Hausman et al. 1976) Boz er and White (1984) Thonemann and Brandeau (1998) Kim and Seidmann (1990) Hwang and Ko (1988) Lee (1997) Hwang and Lee (1990) Chang et al. (1995) Chang and Wen (1997) Koh et al. (2002) Lee et al. (1999) Graves et al. (1977) Bozer and White (1984) Kim and Seidmann (1990) Hwang and Ko (1988) Lee (1997) Han et al. (1987) Hwang and Lee (1990) Chang et al. (1995) Chang and Wen (1997) Koh et al. (2002) Lee et al. (1999) Meller and Mungwattana (1997) Potrc et al. (2004) Hwang and Song (1993) Bozer and White (1990) Bozer and White (1996) Foley and Frazelle (1991) Park et al. 1999) Han and McGinnis (1986) Han et al. (1988) Su (1998) Hwang and Ha (1991) Hwang et al. (1999) Hall (1993) Jarvis and McDowell (1991) Chew and nose drops (1999) Hwang et al. (2004a) Caron et al. (1998) Caron et al. (2000) Jarvis and McDowell (1991) Chew and Tang (1999) Hwang et al. (2004a) Park et al. (2003a) Dedicated storage Hausman et al. (1976) Thonemann and Brandeau (1998) Kim and Seidmann (1990) Class-based st orage Hausman et al. (1976) Thonemann and Brandeau (1998) Rosenblatt and Eynan (1989) Eynan and Rosenblatt (1994) Kouvelis and Papanicolaou (1995) Kim and Seidmann (1990) Pan and Wang (1996) Ashayeri et al. 2002) Dual-command Graves et al. (1977) Kim and Seidmann (1990) Graves et al. (1977) Kouvelis and Papanicolaou (1995) Kim and Seidmann (1990) Pan and Wang (1996) Ashayeri et al. (2002) Multi-shuttle Man-on-board AS/RS End-of-aisle AS/RS Carousel and dress circle racks Ha and Hwang (1994) Conventional multi-aisle system Jarvis and McDowell (1991) Chew and Tang (1999) Hwang et al. (2004a) 546 J. Gu et al. / European Journal of Operational Research 203 (2010) 539549 Tang (1999) use their model of the travel time pdf to crumple order batching and storage allocation using a queuing model.Bhaskaran and Malmborg (1989) present a stochastic performance evaluation model for the service process in multi-aisle warehouses with an approximated distribution for the service time that depends on the batch size and the travel distance. de Koster (1994) develops queuing models to evaluate the performance of a warehouse that uses sequential zone picking where each bin is assigned to one or more orders and is transported using a conveyer. If a bin needs to be picked in a speci? c zone, it is transported to the fit pick station.After it is picked, it is then put on the conveyor to be sent to the next pick station. The proposed queuing network model evaluates performance measures such as system throughput, picker utilization, and the average number of bins in the system based on factors such as the speed and length of the conveyor, the number of picking stations, and the number of picks per station. Throughput analysis of sorting systems is addressed in Johnson and Meller (2002). They assume that the induction process is the bottleneck of the sorting process, and therefore governs the throughput of the sorting system.This model is later incorporated into a more comprehensive model in Russell and Meller (2003) that integrates order picking and sorting to balance the tradeoffs between picking and packing with different order batch sizes and wave lengths. Russell and Meller (2003) also demonstrate the use of the proposed model in determining whether or not to automate the sorting process and in blueprint the sorting system. 3. 2. 2. Integrated models Integrated models combine travel time analysis and the service quality criteria with other performance measures, e. g. storage capacity, construction cost, and operational cost. Malmborg (1996) proposes an integrated performance evaluation model for a warehouse having a forward-reserve con? guration. The proposed model uses information about inventory management, forward-reserve space allocation, and storage layout to evaluate costs associated with storage capacity and space shortage inventory carrying, replenishing, and expediting and order picking and internal replenishment for the forward area. Malmborg (2 000) evaluates several performance measures for a twin-shuttle AS/RS.Malmborg and Al-Tassan (2000) present a mathematical model to estimated space requirements and order picking cycle times for less than unit load order picking systems that uses randomized storage. The inputs of the model include product parameters, equipment speci? cations, operational policies, and storage area con? gurations. Malmborg (2003) models the dependency of performance measures such as expected total system construction cost and throughput on factors such as the vehicle ? eet size, the number of lifts, and the storage rack con? gurations for warehouse systems that use rail guided vehicles.Table 3 A Summary of the literature on warehouse case studies. Citation Cormier and Kersey (1995) Yoon and Sharp (1995) Zeng et al. (2002) Kallina and Lynn (1976) Brynzer and Johansson (1995) Burkard et al. (1995) van Oudheusden et al. (1988) Dekker et al. (2004) Luxhoj and Skarpness (1986) Johnson and Lofgren (1994) Pr oblems studied Conceptual design Analytic travel time and performance models of storage systems represent a major contribution to warehouse design related research, and a rich set of models is available. thus far despite this wealth of prior results, there is no uni? d approach to travel time modeling or performance modeling for aisle based systems either system and every set of assumptions leads to a different model. A signi? cant research contribution would be to present a uni? ed theory of travel time in aisle-based systems. 4. Case studies There are some published industrial case studies, which not only provide applications of the various design and operation methods in practical contexts, but more importantly, also identify possible future research challenges from the industrial point of view. Table 3 lists these case studies, identifying the problems and the types of warehouse they investigated.It is dif? cult to generalize from such a small set of speci? c cases, but one c onclusion is that warm bene? ts can achieved by appropriately designing and operating a warehouse, see for example Zeng et al. (2002), van Oudheusden et al. (1988), and Dekker et al. (2004). On the other hand, one might conclude from these cases that there are few generic simple rules. As just one example, the COI-based storage location assignment rule proposed by Kallina and Lynn (1976) ignores many practical considerations, such as variable weights, item-dependent travel costs, or dependencies between items.Some of these complications have been addressed in the academic research (for example see Table 3 in Section 5. 2 of Gu et al. (2007)), but many others remain unexplored. What these cases illustrate is the gap between the assumption-restricted models in research publications and the complex reality of most warehouses. There is a signi? cant need for more industrial case studies, which will assistance the warehouse research community in better understanding the real issues in warehouse design. In turn, research results that have been tested on more realistic data sets will have a more substantial impact on practice.A warehouse design problem classi? cation, such as we have proposed here, might be used to structure such future case studies. 5. Computational systems There are numerous technical Warehouse perplexity Systems (WMS) available in the market, which basically help the warehouse manager to keep track of the products, orders, space, equipment, and human resources in a warehouse, and provide rules/algorithms for storage location assignment, order batching, pick routing, etc. Detailed review of these systems is beyond the scope of this paper.Instead, we focus on the academic research addressing computational systems for warehouse design. As previous sections show, research on various warehouse design and Type of warehouse A warehouse for perishable goods that requires Just-In-Time operations An order picking system A distribution center A distribu tion center Kitting systems that tag on materials to assembly lines An AS/RS where a S/R machine can serve any aisle using a switching gangway A man-on-board AS/RS in an integrated steel mill A multi-aisle manual of arms order picking system A distribution center A distribution centerConceptual design Storage location assignment warehouse dimensioning storage and order picking policies Storage location assignment using the COI rule Process ? ow batching zone picking Vehicle routing Storage location assignment batching routing Storage and routing policies Manpower planning Simulation by decomposition J. Gu et al. / European Journal of Operational Research 203 (2010) 539549 547 operation problems has been conducted for almost half a century, and as a result, a large number of methodologies, algorithms, and empirical studies have been generated.However, self-made implementations of these academic results in current commercial WMS systems or in engineering design software are rare. T he prototype systems discussed in this section might shed some light on how academic research results could be utilized to develop more sophisticated computer aided warehouse design and operation systems. Perlmann and Bailey (1988) present computer-aided design software that allows a warehouse designer to quickly generate a set of conceptual design alternatives including building shape, equipment selection, and operational policy selection, and to select from among them the best one based on the speci? d design requirements. To our knowledge, this is the only research paper addressing computer aided warehouse design. There are several papers on the design of warehouse control systems. Linn and Wysk (1990) develop an beneficial system for AS/ RS control. A control policy determines decisions such as storage location assignment, which item to retrieve if multi-items for the same product are stored, storage and retrieval sequencing, and storage relocation.Several control rules are ava ilable for each decision and the control policy is constructed by selecting one individual rule for each decision in a coherent way based on dynamically changing system state variables such as demand levels and traf? c intensity. A similar AS/RS control system is proposed by Wang and Yih (1997) based on neural networks. Ito et al. (2002) propose an intelligent performer based system to model a warehouse, which is composed of three subsystems, i. e. , performer-based communication system, element-based material handling system, and agent-based inventory planning and control system.The proposed agent-based system is used for the design and implementation of warehouse simulation models. Kim et al. (2002) present an agent based system for the control of a warehouse for cosmetic products. In addition to providing the communication function, the agents also make decisions regarding the operation of the warehouse entities they represented in a dynamic real-time fashion. The absence of r esearch prototypes for computer aided warehouse design is particularly puzzling, given the rapid packaging in computing ironware and software over the past decade.Academic researchers have been at the forefront of computer aided design in other disciplines, and particularly in developing computational models to support design decision making. Warehousing design, as a research domain, would appear to be serious for this kind of contribution. 6. Conclusions and discussion We have attempt a thorough examination of the published research related to warehouse design, and classi? ed papers based on the main issues addressed. Fig. 1 shows the numbers of papers in each category there were 50 papers directly addressing warehouse design decisions.There were an additional 50 papers on various analytic models of travel time or performance for speci? c storage systems or aggregates of storage systems. Benchmarking, case studies and other surveys account for 18 more papers. one clear conclus ion is that warehouse design related research has focused on analysis, primarily of storage systems rather than synthesis. enchantment this is somewhat surprise, an even more surprising observation is that only 10% of papers directly addressing warehouse design decisions have a publication date of 2000 or later.Given the rapid development of computing hardware and solvers for optimization, simulation, and general mathematical problems, one might reasonably expect a more robust design-centric research literature. We conjecture two primary inhibiting factors 1. The warehouse design decisions identi? ed in Fig. 1 are tightly coupled, and one cannot be analyzed or determined in isolation from the others. Yet, the models available are not uni? ed in any way and are not interoperable. A researcher addressing one decision would require a research infrastructure integrating all the other decisions.The scope and scale of this infrastructure appears too great a challenge for individual resea rchers. 2. To properly evaluate the impact of changing one of the design decisions requires estimating changes in the operation of the warehouse. Not only are future operating scenarios not speci? ed in detail, even if they were, the total warehouse performance assessment models, such as high ? delity simulations, are themselves a considerable development challenge. From this, we conclude that the most important future direction for the warehouse design research community is to ? d ways to reduce these two hurdles. Key to that, we believe, will be the emergence of standard representations of warehouse elements, and maybe some research community based tools, such as open-source analysis and design models. Other avenues for important contributions include studies describing validated or applied design models, and practical case studies that demonstrate the potential bene? ts of applying academic research results to real problems, or in identifying the hidden challenges that prevent their favored implementation.Finally, both analytic and simulation models are proposed to solve warehouse problems and each has its several(prenominal) advantages and disadvantages. Analytic models are usually design-oriented in the sense that they can explore many alternatives quickly to ? nd solutions, although they may not capture all the relevant details of the system. On the other hand, simulation models are usually analysis-oriented they provide an assessment of a given design, but usually have limited capability for exploring the design space. There is an important need to integrate both approaches to achieve more ? exibility in analyzing warehouse problems.This is also pointed out by Ashayeri and Gelders (1985), and its pertinency has been demonstrated by Rosenblatt and Roll (1984) and Rosenblatt et al. (1993). There is an enormous gap between the published warehouse research and the practice of warehouse design and operations. Cross fertilization between the groups of p ractitioners and researchers appears to be very limited. Effectively bridging this gap would improve the state-of-the-art in warehouse design methodology. Until such communication is established, the fit of meaningful expansion and enhancement of warehouse design methodology appears limited.Warehousing is an essential fragment in any supply chain. 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