Andrea Fumi, Laura Scarabotti* And Massimiliano M. Schiraldi
Int. J. Services and Operations Management, Vol. X, No. Y, xxxx1The effect of slot-code optimisation on travel times incommon unit-load warehousesAndrea Fumi, Laura Scarabotti* andMassimiliano M. SchiraldiDepartment of Engineering and Management,‘Tor Vergata’ University of Rome,Via del Politecnico, 00133, Rome, ItalyE-mail: andrea.fumi@uniroma2.itE-mail: laura.scarabotti@uniroma2.itE-mail: schiraldi@uniroma2.it*Corresponding authorAbstract: The main aim of this paper is to estimate material handling timesreductions in one-block unit-load warehouse organised with an optimal slotcode allocation, rather than with a uniform pick/store locations distribution,while comparing single and dual-command cycles from a travel distanceperspective; results are calculated through multiple what-if analysis based onrandom scenarios simulations assuming variable input/output positions andwarehouse shapes. Simulations helped in the effective quantification of traveltimes reductions, gaining a result of extreme importance for thosemanufacturing, distribution and retailing companies which aim at bothdesigning their warehouse and determining the right type and number oftransportation resources. Because of currently used warehouse managementsystems (WMS), companies do not reckon so needful existing literature relyingon uniform pick/store distribution: this paper seems the first to address aprecise estimation of material handling times when fast-movers items are moreor less effectively placed nearby warehouses entrance.Keywords: single command cycles; dual command cycles; travel distance;random product allocation; optimised product allocation; one-block warehouse;unit-load warehouse; material handling; slot-code allocation optimisation;storage assignment.Reference to this paper should be made as follows: Fumi, A., Scarabotti, L.and Schiraldi, M.M. (xxxx) ‘The effect of slot-code optimisation on traveltimes in common unit-load warehouses’, Int. J. Services and OperationsManagement, Vol. X, No. Y, pp.000–000.Biographical notes: Andrea Fumi graduated with honours in ManagementEngineering at ‘Tor Vergata’ University of Rome, is currently attending anMBA and PhD candidate in Engineering and Management. He is also workingwith the Industrial Engineering Research Group of ‘Tor Vergata’s’ UniversityEnterprise Engineering Department as a Research Assistant in ManufacturingSystems Engineering. His main research interests: operations management,production management, project management, logistics and transportation.Laura Scarabotti graduated with honours in Management Engineering at ‘TorVergata’ University of Rome, is currently attending an MBA and PhDcandidate in Engineering and Management. She is also working with theIndustrial Engineering Research Group of ‘Tor Vergata’s’ UniversityCopyright 200x Inderscience Enterprises Ltd.
2A. Fumi et al.Enterprise Engineering Department as a Research Assistant in ManufacturingSystems Engineering. His main research interests: business processmanagement, logistics and transportation, operations management, productionmanagement, project management.Massimiliano M. Schiraldi is an Assistant Professor in OperationsManagement, PhD in Engineering and Management, MBA, he teachesOperations Management courses since 2001. At ‘Tor Vergata’ University inRome, he is the Lecturer of ‘Design of Manufacturing Systems’ and of‘Production Planning and Inventory Control’ courses. Every year he teaches tomore than 200 students in the Engineering School, and up to now he supervisedmore than 160 students in their thesis work on top of 11 PhDs. He is the authorof more than 60 international scientific publications related to operationsmanagement, and one book on inventory management.1Introduction“Good or bad, the warehouse ultimately portrays the efficiency or inefficiency of theentire supply chain” (Frazelle, 2002). Warehousing is a strategic aspect of supply chainmanagement and plays a crucial role in the success, or failure, of businesses (More andBabu, 2009, 2012); thus is decisive that warehouses are well managed to decrease theoverall function cost (Richards, 2011; Pettersson and Segerstedt, 2012; Dharmapriya andKulatunga, 2011). Effective warehouse management is, to a large extent, determined atthe organisational phase (Strack and Pochet, 2010; Gu et al., 2010) and the related costsseem to be mainly influenced by the usage of tracing technologies (Mancini et al., 2012;Wamba and Boeck, 2008; Smith and Offodile, 2009) on top of the a proper itemsallocation (Gagliardi et al, 2007; Grant and Fernie, 2008; Battista et al., 2012). Accordingto literature, the main aims of an efficient warehouse optimisation is to minimise theaverage travel distance and required space to store items: secondarily, other goals aremaximising the utilisation of available space, equipment, labour, as well as theaccessibility to all items (De Koster et al., 2007; Van den Berg, 1999). Order picking haslong been known as the most labour-intensive and time-consuming warehouse activity(Tompkins et al., 2002) and, at the same time, the main candidate for productivityimprovement studies. As De Koster et al. (2007) underlined in 2007, up to the 55% of thetotal warehouse operating cost results from order picking operations. Given the extremerelevance of warehouse operations costs saving, this paper estimate material handlingdistance reductions, while comparing single and dual-command cycles, obtaining aneffective quantification through a what-if analysis based on simulations.2Previous researchSome authors have devoted publications to the modelling of effective approaches toimprove the efficiency of material handling times to achieve better performance in thesupply chain; literature is now full of important scientific contributions to minimisematerial handling costs and storage/retrieval operations, studying new optimisationcriteria (Hou et al., 2010; Kutzelnigg, 2011; Ceylan et al., 2012; Routroy et al., 2011;
The effect of slot-code optimisation on travel times in common unit-load3Sangwan and Kodali, 2009; Zhang et al., 2009). Several authors have focused theirresearches on the general case of order picking cycle that is a fundamental topic in orderto increase operation efficiency and decrease the labour workload (Qiana and Jie, 2011).Some authors have determined the shortest travel distance and the optimal pick tour for agiven set of pick locations in two cross aisles’ warehouses (Ratliff and Rosenthal, 1983)or with more than two cross aisles (Vaughan and Petersen, 1999) with a dynamicprogramming approach. Then, Roodbergen and De Koster (2001a) applied the Ratliff andRosenthal’s algorithm to middle cross aisles’ warehouses. From the non-random storagepolicy perspective instead, Hwang et al. (2004) and Caron et al. (2000) modelled ananalytical expression assuming turnover based storage policies. Some comparisons on theoptimal routing and heuristics for picking problems are also present in literature(Petersen, 1997; De Koster and Van der Poort, 1998; Pan et al., 2012). It seems though,that no closed-form evaluation technique is yet available to estimate the optimal tourlength for a general number of picks (Pohl et al., 2009) and that so far, the simulationrequired to forecast this length has been performed using routing heuristics under theassumption of a random storage policy (Roodbergen and Vis, 2006; Le-Duc andDe Koster, 2007; Roodbergen et al., 2008; Petersen and Aase, 2004). Some authors havealso developed a mechanism for reducing order picking travel distance through a classbased storage method based on integer programming (Muppani and Adil, 2008a).Afterwards, Muppani and Adil, (2008b) tried to reduce order picking operations for classbased storage arrangement developing a non-linear integer programming using the branchand bound algorithm. Ho et al. (2008) proposed a further method focused on developingdistance or area-based rules to minimise the travel distance of pickers; this method relieson order batching techniques for an order-picking warehouse. In the last two years,Burinskiene (2010) suggested some new approaches for optimisation of order pickingprocesses: the volume-based storage method and the usage of correlation between orderpicking efficiency and stock accuracy. Ene and Öztürk (2012) instead used both thebatching and routing problems to minimise travel costs in warehouse operations. Orderpicking can be considered as a general case of ‘dual-command’ procedure: for manualpicking systems, a single-command cycle means that workers travel from a commonpickup/deposit (P/D) or input/output (I/O) point to a single location and back again toexecute a storage or retrieval request (out and back order picking discipline, seeMalmborg et al., 1988; Malmborg and Bhaskaran, 1990); the dual-command cycle,instead, usually includes both a storage and retrieval request, meaning that order pickingvehicles first move pallets from the I/O point to a location, performing a storageoperation, then carry on to a second location picking a pallet before returning to the I/Opoint (interleaving practice, see Malmborg et al., 1988). Interleaving storage and retrievalrequests makes a more efficient use of time, minimising unloaded – and, therefore,unproductive – travel times (Chen and Li, 2011; Salah, 2011; Pohl et al., 2011).Some authors have devoted publications to the single-command travel distanceestimation (Francis, 1967; Bassan et al., 1980), presenting results on optimal warehouseshape and I/O position; however, few contributions seem to be present on thedual-command travel distance estimation in manual pick systems (Mayer, 1961;Malmborg and Krishnakumar, 1987). As a matter of fact, a closed expression to describeoptimal dual-command travel distance under the assumption of random storage policyonly seems to have been recently developed by Pohl et al. (2009): according to theseauthors, concerning single command procedure, dual-command cycles can reduce empty
4A. Fumi et al.forklift travels from 50% of the total travel distance to about 33%. The single-commandand dual-command travel distance/time estimation problem have also been applied toautomated storage and retrieval system (AS/RS): Hwang and Lee (1988) for example,have modelled this problem assuming a crane simultaneously travelling in bothhorizontal and vertical direction, as reasonable; Azzi et al. (2011) instead suggested anew method to estimate the travel time of a new version of multi-shuttle systems usingdifferent scenarios with Monte Carlo simulation. Furthermore, Hausman et al. (1976),Graves et al. (1977), Schwarz et al. (1978) and Hwang and Song (1993) have comparedrandom, dedicated and class-based storage policies in single-command and dualcommand AS/RS using both analytical models and simulations. The aforementionedcontributions are summarised in Table 1.Table 1Literature review of travel distance models for different warehouse systemsSingle commandDual-commandPickingRatliff and Rosenthal(1983), Roodbergen andDe Koster (2001a),Petersen (1997),De Koster and Van derPoort (1998), Vaughanand Petersen (1999),Petersen and Schmenner(1999), Caron et al.(2000), Hall (1993),Roodbergen and Vis(2006), Le-Duc and DeKoster (2007) Hwang etal. (2004), Qiana and Jie,(2011), Pan et al., (2012),Roodbergen et al. (2008),Muppani and Adil(2008a, 2008b), Ho et al.(2008), Burinskiene(2010), Ene and Öztürk,(2012)ManualsystemsFrancis (1967), Bassanet al. (1980), Malmborg(1988)Mayer (1961), Malmborgand Krishnakumar(1987), Pohl et al. (2009,2011), Chen and Li(2011), Salah (2011)AS/RSHwang and Lee (1988),Hausman et al. (1976),Schwarz et al. (1978),Bozer and White (1984),Hwang et al. (2004),Pandit and Palekar(1993)Hwang and Lee (1988),Graves et al. (1977),Schwarz et al. (1978),Bozer and White (1984),Koh et al. (2002), Azziet al. (2011)Nowadays, the majority of industrial companies use warehouse management systems(WMS) to monitor and optimise the position of their stock-keeping-units (SKUs) instorage locations. WMSs are helpful in granting that fast-movers items are located nearbywarehouses I/O points, thus minimising travel times for stacking/picking operations.Though, analytical result of estimated travel times computation assuming randomlydistributed SKUs, only provide an upper bound of the correct value: this upper bound canbe much higher than the actual value performed by the company. Indeed, results ofinvestigations where the randomly-distributed SKUs hypothesis has been made, are notconsidered very useful by industrial companies. The main aim of this work is thus to
The effect of slot-code optimisation on travel times in common unit-load5estimate material handling times reductions using optimal items allocation rather than apick/store locations uniform distribution, while comparing single and dual-commandcycles in terms of travel distance.Results are obtained through multiple what-if analysis based on variable I/O positionsand warehouse shape simulations: the simulation tool has been validated on arandom-picking scenario, both considering single and dual command cycles, thus tocompare it with an already existing analytical formulation (Pohl et al., 2009).3Warehouses, layout design and routing methodsIn a traditional unit-load warehouse, materials or products are shipped in single discreteunits (e.g., pallets) and racks are organised as to create parallel picking aisles, usuallywith one or more orthogonal cross aisles, with the aim of reducing overall internal traveltimes. The warehouse layout design problem has been deeply treated in literature:warehouse design and performance have been analysed respectively by Gu et al. (2010)as well as by Meller and Gue (2009) who showed different aisles design such as ‘FlyingV’ design, ‘Fishbone’ and ‘Chevron’ aisles. Because of their widespread use in industry,this paper specifically focuses on traditional one-block unit-load warehouses (Figure 1).Figure 1One-block warehouse vs. two-block warehouseIn the most general case of single/dual-command cycles – as to general order-picking –workers can pick/store on both sides of the aisle as well as change direction (no one-wayaisles). In order to minimise the internal travel distance with dual-command cycles, aspecific routing policy has to be chosen. Many order-picker routing policies can be alsoapplied to dual-command cycles for retrieval requests: a detailed description of the‘s-shape’, ‘return’, ‘mid-point’, ‘largest gap’, ‘combined’ and ‘optimal’ methods is givenby De Koster et al. (2007). The ‘s-shape’ is one of the easiest heuristics that implies acomplete aisle crossing in case the aisle contains at least one pick; aisles without picksare not entered at all and once performed the last pick workers return to the I/O point. Inthe ‘return’ heuristic instead, workers are able to enter and leave each aisle from the sameside and, as in the ‘s-shape’ method, only aisles where at least one pick is required areentered. According to the ‘midpoint’ methodology, the warehouse is ideally split in twoareas: aisles containing items to pick in the front half are entered from the front crossaisle while picks in the back half are entered from the back cross one. An improvement ofthe ‘midpoint’ strategy is the ‘largest gap’, in which a worker enters an aisle as far as the
6A. Fumi et al.largest gap within the aisle is: the gap is defined as the distance between any two adjacentpick locations, between the first pick and the front aisle, or between the last pick and theback aisle. The ‘largest gap’ part of the aisle is the one not visited by the picker.Figure 2Order-picker routing policiesS-shapeFigure 3ReturnMid-pointLargest gapExample of paths in dual-command cyclesI/OI/OAll of these techniques were originally developed for one-block warehouses but they canbe used for multiple-block ones by implementing specific changes. Vaughan and Petersen(1999), Roodbergen and De Koster (2001a, 2001b) also showed that the combinedheuristic returns the best results in 93% of 80 analysed instances. According to theaforementioned ‘combined’ method, a decision between entirely traverse an aisle with atleast one pick, or enter and leave the aisle from the same side, should be computed usingdynamic programming (Roodbergen and De Koster, 2001a). Considering dual-commandcycles instead of the general order picking case though, there’s no need of dynamicprogramming to choose between the two alternative decisions: after the storage phase,
The effect of slot-code optimisation on travel times in common unit-load7workers access the second location pick from the front or the back cross aisle dependingon the minimum travel distance between the two store/pick locations (Figure 2).Obviously – as in all the heuristics – aisles without picks are not entered.4Simulation methodologyThe aim of this paper is to underline the differences, in terms of storage and retrievaldistances, among the multiple scenarios of optimised slot-code location (OPT) and thecase of random uniform location (RAN) of products inside a warehouse. As it has beenstated, modern information systems tend to grant an optimisation of slot-code allocationand this clearly reduces stacking/picking travel times. However, a precise estimation ofthis reduction entity is considered to be of extreme importance for manufacturing,distribution and retailing companies, since it can help in the design of the warehouse aswell as in determining the most appropriate type and number of handling vehicles.The comparison is performed through simulation, assuming that forklifts operationscan be performed in single (SC) or in dual (DC) command mode. In each scenario, theaverage distance to complete a SC or a DC cycle – respectively DSC and DDC – have beencomputed, along with their standard deviation over a number of 10’000 runs. This paperoffers an original contribution to estimate the cycle time reduction opportunities comingfrom the slot-code location optimisation, since no theoretical formula to compute DDC orDSC exists under this hypothesis. The simulation model has been validated in a random(RAN) location scenario: here, simulations results show a 0.6% gap with theoreticalresults by Bassan et al. (1980) and by Pohl et al. (2009) respectively in the SC and DCcases.4.1 Hypotheses on warehouse layout and input parametersA generic stacking warehouse has been considered. According to literature, therectangular shape is the optimal geometric shape to store pallets (Berry, 1968), thus thestorage area is assumed to be rectangular, with one input and one output point. Be:Xthe storage area longitudinal widthYthe storage area lateral depth(xout; yout) the output coordinates(xin; yin)the input coordinates.In the developed simulator, all these variables are independent and can be varied toperform multiple what-if analysis. Clearly, xout X and yout Y as well as xin X and yin Y. As far as the simulation tool capabilities are concerned, the input and output pointscould even be placed inside the storage area (e.g., to represent an elevator movingloading units inside the warehouse from an upper/lower floor); however, in order toconsider the most common warehouse case, all simulation runs have been performedunder the assumption of a single I/O point located in the middle of the warehouse longside. This choice is also supported by evidences shown by Bassan et al. (1980): indeed,they displayed how this configuration represents the optimal solution to minimisestorage/retrieval travel times in warehouses with a longitudinal width twice as lateral
8A. Fumi et al.depth. Moreover, a middle I/O point configuration has also been used by Goetschalckxand Ratliff (1998), Hall (1993) and Petersen (1999). A corner located depot instead, wasused by Chew and Tang (1999), De Koster et al. (1999), Gibson and Sharp (1992), andRosenwein (1994), while both middle and corner options are shown in Jarvis andMcDowell (1991), Petersen (1997), and Pet
based storage method based on integer programming (Muppani and Adil, 2008a). Afterwards, Muppani and Adil, (2008b) tried to reduce order picking operations for class-based storage arrangement developing a non-linear integer programming using the branch and bound algorithm. Ho et al. (2008) propose
Adrian S Booth Adrian Weinmann Adriana Durian Adriana Smith Adrienne Bloss adrienne hillary . Adrienne Lahtela Adrienne Quill Adrienne R Whiteley Advaith Nair Aeri York . Andrea Thomas Andrea Tschabold Andrea Virnau Andrea Vonau Andrea Ward Andrea Westfall Andrea Yagoda Andrea Zigelsky ANDREA ZUERRER Andreas Bianchi Andreas Bianchi
8 Andrea Riccio, The Descent of the Professor‘s Soul into the Underworld, Louvre 278 9 Andrea Riccio, The Soul‘s arrival in the Elysian Fields, Louvre 279 10 Andrea Riccio, The Triumph of Humanist Virtue, Louvre 280 11 Andrea Riccio, Death Mask of Girolamo Della Torre, The Della Torre Tomb Monument, .
corrector ai21111 andrea eye q's w/ headband 12pc and25110 andrea flair perma med black and25010 andrea flair perma short black and25020 andrea flair perma short . red/gold correc 24/b 24/dp . ai780550 ardell cs unred 0.25 oz ai780566 ardell cs unred 24/dp 0.07 oz ai6146
Laura Elkins, Karen Finley, Katya Grokhovsky, Suzanne Lacy & Andrea Bowers, Cindy Sherman, Martha Wilson January 27 — March 5, 2021 Opening Reception, Wednesday, February 17 from 4-7 P.M. E ASTERN C ONNECTICUT S TATE U NIVERSITY LAURA ELKINS, Self as Greta at Seaside, 2020, oil on canvas, 24x
Beste Freunde 2 - Kursbuch CD 1 Track 2 Lektion 19, 2 Anna: Laura? Hallo? Laura: Hi, Anna! Simon: Huhuuu! Kannst du uns sehen? Anna: Hey, Simon, ja, ich sehe euch. Super! Laura: Und? Wie ist es in Köln? Gefällt es dir? Anna: Na ja, eigentlich ist die Stadt ganz schön. Aber . ihr seid nicht da. Und München ist so weit weg . Laura: Du fehlst uns auch, Anna! Anna: Echt? Simon: Na klar .
Joseph D’Amelio by Gerardo Pepe 25. Richard & Carmel LaSala by Laura & Angelo Andreaottola Saturday Community Mass December 7, at 9: AM. 26. Veronica & Angelina Andreotta by Angelo & Laura Andreottola 27. Gerardo Andreottola by Angelo & Laura Andreottola 28. Francis Glynn Ribiere by Laura
Henrik Schwartzberg Lapsuus ja kouluaika 11 Yliopistossa 16 Pyhäjärvi ja herännäisyys 24 Pappina Pyhäjärvellä 1835-1839 28 Viranomaiset ja herännäisyys 36 Kalajoen käräjien vaikutus Henrik Schwartzbergin elämänkohtaloihin 40 Laura Katarina Stenbäck Laura Stenbäckin koti Herätys Vöyrin pappilassa Häät Henrik ja Laura Schwartzbergin Ensi aika Pyhäjärvellä Sievissä 1839 .
Fjalët kyce : Administrim publik, Demokraci, Qeverisje, Burokraci, Korrupsion. 3 Abstract. Public administration, and as a result all the other institutions that are involved in the spectrum of its concept, is a field of study that are mounted on many debates. First, it is not determined whether the public administration ca be called a discipline in itself, because it is still a heated debate .
