sustainable warehouse logistics: a nip model for non-road

Sustainable Warehouse Logistics: a NIP Model for non-road vehicles and

storage configuration selection

Boenzi F.*, Digiesi S.*, Facchini F.*, Mossa G.*, Mummolo G.*

* Department of Mechanics, Mathematics and Management, - Polytechnic of Bari, - Viale Japigia, 182, Bari, Italy([email protected], [email protected], [email protected],

[email protected], [email protected])

Abstract: In modern competitive market, logistics plays a key role in creating competitive advantage and profitability. In recent years, many firms adopted green supply chain practices (GSCP) in order to improve their environmental performances while also achieving economic goals (Wu et al. 2015). According to European Logistics Association (ELA/AT Kearney 2004), the warehousing activity contributes to about 20% of the total logistics costs. The adoption of sustainable warehouse logistic strategies could lead to achieve a significant reduction of time and costs required to perform internal logistics activities and to increase the environmental performances of logistics systems. Order picking is the most labour-intensive, costly, and energy-consuming activity for almost every warehouse. Depending on the particular application, the process can be designed and managed in order to minimize the throughput time for an order, or maximize the use of the space, or maximize the accessibility to all items, etc. Many different order picking system types are adopted in warehouses. In most cases workers are employed for these activities, in particular in the picker-to-parts systems (De Koster 2004), where the operator (order picker) drives a forklift along the aisles to retrieve items. The aim of this study is to develop a non-linear integer model allowing identifying a strategy, based on picker-to-parts system, with the goal of optimizing the environmental performance of the internal logistic activities in the warehouse. Suitable storage strategies are identified on the basis of the type of the forklifts adopted (internal combustion or electric engine equipped) and the type of storage configuration adopted (storage racks or stackable units).

Keywords: Sustainable logistics, order picking, picker-to-parts, warehouse management

1. Introduction

Green warehousing is a relatively new approach for implementing of ‘greening activities’ into warehouses and distribution centres. There are many elements could be optimized in a warehouse, but in short, each element that reduces energy consumptions or material usage can be considered as a ‘greening element’ (Dukic et. al 2010). The process of warehousing involves a series of sequential activities, namely: reception of the items, put-away, storage, order picking, sortation, unitizing and shipping (Frazelle 2002). According to European Logistics Association (ELA/AT Kearney 2004), the warehousing activity contributes to about 20% of the total logistics costs; between these costs, order picking activities are estimated to count up to 55% of the total warehouse operating expenses. A correct management of warehouse storage and picking actions can significantly impact the success of logistics operations in most manufacturing companies and play a vital role in their survival (Qichang 2005). For these reasons, warehousing experts consider order picking as the highest priority area for productivity improvements.

Order picking involves the process of clustering and scheduling, the customer orders, assigning stock on locations to order lines, releasing orders, picking the articles from storage locations, and the disposal of the picked articles.

There are order-picking systems characterized by different level of automation (Baker and Halim 2007). Although there are examples of highly-automated warehouses (Dotoli et al. 2015), in many cases order picking is still carried out by human operators. In these cases, the picker-to-parts systems are most common.

Two different types of picker-to-parts systems can be implemented:

low-level picking, in which the order picker picksrequested item/s while travelling along the storage aisles;

high-level picking in which the order pickertravel to the pick locations on board of a crane which is automatically stopped in front of the appropriate pick location and waits for the order picker to perform the pick.

As far as concern the organizational variants, the picker–to-parts systems include picking by article (batch picking) or pick by order (discrete picking). In case of picking by article, multiple customer orders, named: “the batch”, are picked by the same order picker. On the other hand, in case of discrete picking, the order picker takes multiple articles and performs a sorting based on orders. Another basic variant is zoning, which means that a logical storage area is split in multiple parts, each of them operated by a different order picker.

XX Summer School "Francesco Turco" - Industrial Systems Engineering

263

In this paper a low level picker-to-parts system employing humans and forklifts (batch picking approach) is considered. According to the experts opinion, these systems form represents the very large majority among picking systems in warehouse worldwide (over 80% of all order picking systems in Western Europe (De Koster et al. 2007).

The order picking in a typical picker-to-part warehouse consists of four main phases, each of them of different time length (fig. 1). Although different case studies have shown that activities other than travel may substantially contribute to the overall order-picking time (Dekker et al. 2004), travel is often the main component. According to Bartholdi and Hackman (2005) ‘travel time is waste. It costs labour hours but does not add value’. It is, therefore, a first candidate for improvement.

Figure 1: Typical distribution of an order picker's time (Tompkins et al., 2003)

Travel distance is often considered as one of the most important parameter to be optimized in warehouses design. In case of forklifts adoption, further parameters to be optimized (minimized) are the travelling and the picking times.

In many cases, this type of optimization is obtained by means of the reduction of the times required to perform the different elementary activities of material handling process, such as: vertical shifting (lifting/lower activities) and horizontal shifting (transfer activity) of the forklift.

Therefore, a high level of efficiency can be mainly reached by means of:

1. the optimization of the forklift routing;2. the adoption of storage policies that allow to

minimize the number of movement for materialhandling;

3. the adoption of the forklift trucks characterizedby low energy consumption.

In scientific literature, many models are available for the optimization of the pickers routing. Models are mainly based on heuristic methods and integer programming approaches. Few models have been defined for systems with multiple aisles and multiple picks route. The adoption of heuristics allows a reduction between 17% and 34% in path of the forklift within the warehouse (De Koster et al. 2007). However, the amount of reduction is strictly related to the particular operating conditions of the system. Moreover in many cases the implementation of

the heuristic methods is complex and not easily adaptable to different warehouse layouts (Dukic et al. 2010). On the other hand, the optimization based on an integer programming approaches allows reducing the order picking travel distance independently form warehouse layouts. Muppani and Adil (2008) developed a method of storage through the implementation of a "Branch and Bound" algorithm. Further studies propose an additional methodology aiming at the development of distance or area-based rules to minimize the travel distance of pickers; this technique is based on order batching procedures for an order- picking warehouse (Ho, Y. et al. 2008). More recently, the volume-based storage method and the usage of correlation between order picking efficiency and stock accuracy have been proposed in order to achieve an optimization of order picking processes (Burinskiene, 2010). The implementation of algorithms based on integer programming, already tested in scientific literature, to warehouses characterized by different conditions (e.g. layouts, dimensions, number of aisles, etc.) is complex. Moreover, the potential savings are not clear in advance (Fumi et al. 2013).

As far as concern the storage policies, there are numerous ways to assign products to storage locations within storage areas; in scientific literature several storage assignment policies are described, such as: random storage, closet open location storage, dedicated storage, full-turnover storage, class based storage, etc. each of them ensuring the optimization of a different target. According to De Koster et al. (2007), the optimizations of forklift path and storage assignment are characterized by decision variables, at the various levels, strongly interdependent. For example, a certain layout or storage assignment may perform well for certain routing strategies, but poorly for others. Therefore, it is very difficult to include all decisions in one model.

The optimization of energy consumption can be obtained by means of the adoption, in the same warehouse, of forklift equipped by different engines (LPG, diesel, electric). Indeed, recently are available on the market new forklifts characterized by low emission and energy use. There are new technologies for liquid propane forklift that allow them to burn cleaner and be more fuel-efficient. Electric hybrid forklifts and non-road vehicle powered by hydrogen fuel cells are also great examples of new forklift technologies that could be used in order to minimize the environmental impact and provide better energy efficiency and/or operational performances. The most popular forklifts engines are the diesel and the LPG ones, representing about half of the world total forklift fleet. The sales of LPG forklifts in US represented about 1/3 of the large forklift sales in 2005 (source: The British Industrial Truck Association). In Europe, the predominant technology is the battery- powered forklift. In the global market, the three types of forklifts have the same weight (see figure 2).

Optimize the material handling activities by means the adoption of the suitable forklift according to storage configuration, in order to reduce the energy consumption and emission. This approach could be lead to sustainable warehousing.


264

Many software are available on the market allowing to evaluate the energy consumption of forklift powered by different engines. However, models implemented are not intended for warehouse storage optimization purpose. To minimize the energy consumption for material handling activities could allow at the same time to reduce time required for pick orders, and improve the economic performance and competitiveness related to warehouse management.

a. forklifts distribution in EU marketb. forklifts distribution in U.S. marketc. forklifts distribution in the worldwide marketd. forklifts distribution for typologies of work environment

Figure 2: Forklifts distribution on the market

Nowadays, both public and private subjects are increasingly paying more attention to environmental effects of logistics. An increasing request for more sustainable solutions of logistic issues able to minimize both internal (material handling, forklift routing, pick orders, etc.) and external costs has been observed (Digiesi et. al. 2012). External costs due to material handling include the costs of accidents, emissions and noise as well as operation and maintenance of public infrastructures. In warehouse management, there is the need to identify ‘sustainable order picking processes’ characterized by better environmental performances. Currently, a gap between practice and academic research is observed, since most picking methods adopted in firms ensure to minimize costs but, in many cases, neglect the environmental impacts due to material handling activities.

The minimization of the energy consumption of the material handling activities may not be sufficient to ensure a ‘green material handling’. Environmental impacts due to handling activities have to be assessed.

In this work, a non-linear integer model that allows an optimization of the inbound logistics activities in terms of both energy consumption and environmental impacts. The model identifies a picker-to-parts strategy (forklift routing and storage policy) minimizing the energy required for material handling activities. Suitable storage strategies are then identified on the basis of the type of the forklifts

adopted (internal combustion engine equipped or electric) and the type of storage configuration adopted (storage racks or stackable units).

The remaining of this paper is structured as follows: in section 2, the model is presented and input and output parameters are detailed; in section 3 the model is applied to a full-scale case study and the results obtained are presented and discussed; conclusions are in section 4.

2. The model

The model developed is based on non-linear integer programming, and allows to evaluate the overall emission due to forklift operation in each phase of the material handling cycle: transport, picking and retrieve of the stored items. The order - picking strategy considered for the warehouse management is based on a low level picker-to-parts system employing workers and forklifts (batch picking approach).

Emissions due to forklifts operations in warehouses characterized by different storage configuration (storage racks or stackable units) are evaluated. In case of storage racks are adopted, for the picking of the single item the repositioning of other items is not necessary. On the contrary, in case of stacks, for retrieving one item the repositioning of other items could be required. The evaluation of the emission due to material handling activities, allows to identify the storage assignment that minimize the external costs of the pollutant emitted.

In the follow, notations and assumptions adopted in the model are listed:

Q number of items stocked in the warehouse [unit]

α storage configuration (storage racks or stackable units)

h, R, e forklift and maneuvering area sizes [m]

nx, ny nz number of items stored according to the x, yand z-axis respectively, defining the storage geometrical configuration [unit]

Lx, Ly distance between loading/unloading and stockpiles areas [m]

D aisles width [m]dx, dy, dz single item sizes [m]

ny,z average number of forks movements required for storing or retrieving one item, in case of a stockpile of (ny, nz) sizes [#]

ht,dz average fork height required for retrieving one item of height dz from a stockpile of t units vertically stored in [m]

,average fork height required for storing one item of height dz from a stockpile of t units

average fork height required for picking up one item in a stockpile of size nz [m]

Hy,z average fork vertical movement required for picking up one item from a stockpile of (ny, nz) sizes [m]

, fork lifting speed (v as subscript) and forklift travel speed (h as subscript) [m/s]


265

, average time spent by forklifts in fork vertical (v as subscript) and horizontal (h as subscript) shifting [s]

, , , engine utilization factors in vertical (v as subscript) and horizontal (v as subscript) forklift engine power rating [kW]

Eh average energy required for the horizontal shifting of one item [kWh/unit]

Ev average energy consumption for the vertical lifting of one item [kWh/unit]

E average overall energy consumption for picking up one item [kWh/unit] emission factor of the p-th pollutant [g/kWh] external cost of the impact due to the p-th pollutant [€/g]

average total external cost of emission for the handling of one unit [€/unit]

As far as concern the assumptions adopted, they are listed below:

warehouse layout considered is depicted in fig. 4; the total number of items stocked in the warehouse is

constant; the material handling in the warehouse is operated by

means of counterbalance forklifts carrying on one itemfor each load/unload cycle;

all items stocked in the warehouse have prismaticform and are characterized by the same sizes (dx, dy,and dz) and weight;

the items are stocked in stockpiles of the same height; the distance between loading/unloading area of the

warehouse and the stockpiles area (Lx, Ly) is constant; each stockpile can be accessed by both sides (FSA and

RSA in fig. 4) for storage configuration characterizedby stackable units, while adopting a rack as storageconfiguration, the stockpile can be accessed only bylateral side (LSA in fig. 4);

the distance between stockpiles (aisles) according tothe x-axis, is equal to zero in case of storage withstackable units (see fig. 4), instead for rack storage thewidth of the aisles is constant and equal to parameterD, where D=dx+h+R+e (see fig. 3);

a storage configuration is univocally identified by twointeger numbers (ny, nz), being nx = Q/(ny·nz).

No constraints about the size and the geometrical characteristics of the warehouse are considered.

Figure 3: Width of the aisles in relation to R, h, and e

The warehouse layout as in fig. 4 is considered in the model; in each stockpile there are ny×nz items, with ny

identifying the number of items stored along y-axes, while nz the number of items vertically overlapped in the same stockpile.

The storage configuration considered in the model are depicted in fig. 5. The configuration without racks, allows to perform an order – picking strategy characterized by minimum forklift routing (D = 0); in case racks are adopted in the warehouse, the forklift routing is increased, but the average number of the movement required for the order – picking activities is reduced. Moreover, the adoption of the racks allows to pick and to retrieve each item independently each from other.

In both cases, the material handling activity considered in the model consists of the following steps: forklift (unloaded) starts from the ALU and reaches the storage area, then picks up one item and comes back to ALU with it. The model considers all the activities (forklift translation, lifting and lower of the items) required for storing and retrieving the items by the stockpile (with or without racks). Based on these parameters, the model allows calculating the average energy required by forklift for the order – picking activities.

The decision variables of the model (output parameters) are the number of items to be stored according to x, y, and z directions of the stockpile (nx, ny, and nz).

ALU: Load/Unload Area Lx/Ly: Distance between ALU to Storage Area D: Aisles width FSA/RSA: Frontal/Rear access to stockpile for material handling activities

Figure 4: Layout of the warehouse considered in the model

The objective function to be minimized is expressed in equation (1), where E is the average overall energy required by the forklift for the material handling activities, while Ev and Eh are the average energy amounts required by the forklift for the lifting / lower and transfer activities.

In equations (2) and (3), Pn is the nominal power of the engine that equips the forklift, ρu,v and ρu,h are the engine utilization factors for vertical shifting (lifting/lower activities) and horizontal shifting (transfer activity) of the

Storage Area

ALU

Lx

Ly

x

y

FSA

RSA

LSA

D


266

forklift; tv and th are the average time for the vertical and horizontal shifting.

5a. Storage configuration with stackable units 5b. Storage configuration with racks

Figure 5: Stockpiles configurations

(1)

, (2)

, (3)

Parameters tv and th depend on the distance and the speed of the shifting; th parameter depends on the distances Lh identified by equation (4) and by the transfer speed of the forklift; tv parameter depends on average vertical distance (Hy,z) due to lifting phase for the storage and retrieval of the items; the average forks vertical movements values are evaluated by means of equation (5).

whit α=1 if the items are stored in racks, α=0 if the items are stored without racks

(4)

, , (5)

Where ny,z is the average number of the material handling movements required for storing or retrieving one item by the stockpile. In case of items are stored in racks, ny,z is equal to 1; in case there are no racks, ny,z is obtained by equations (6 - 7).

As far as concern , it allows evaluating the average forks height required for picking up one item from a stockpile respectively adopting a storage configuration with rack (8) or with stackable units (9).

,1 28

(6)

,1

21

41

21 (7)

1 (8)

1, , (9)

The first term in equations (9) measures the average forks height required for retrieving one item from a stockpile of type (nx, ny, nz), and the second term the corresponding value for storing one item in the same stockpile. Parameter , measures the overall forks vertical movements required for retrieving one item with dimension dz from a stockpile of type (nx, ny=1, t), and must be computed for all the values of t ranging in (1; nz) by means of equation (10). Parameter , is the average forks height required for storing one items in a stockpile of type (nx, ny=1, t ), and is computed by means of equation (11).

,1

1 3 2 (10)

,1

1 (11)

Starting from the average amount of energy required for picking up one item from a stockpile, the related average external costs are evaluated by means of equations (12) and (13) in case of Diesel/LPG engine equipped forklifts and electrical powered forklifts, respectively. In both cases, the external costs are evaluated as the product of the average amount of energy required for picking up one item and the unitary external cost [€/kWh]. In case of Diesel/LPG engine equipped forklifts, the unitary external cost is obtained as the sum of the products of the pollutants emission rates and the related unitary monetary costs (see eq. 12). Generation rate values and monetary costs of emissions assumed in this work are from Gaines et al. (2008) and Spadaro and Rabl (1999) as shown in table 1.

Table 1: Diesel and LPG Engine Emissions

Pollutant Emission Rate ( ) [g/kwh]

Diesel LPGNOx 10.8 15.6CO 45.0 10.9

In case of electrical powered forklifts (see eq. 13), the unit external cost assumed is referred to the Italian mix of power generation: 0.05 [€/kWh] according to ExternE project-series (2012).

/ (12)


267

(13)

For a given forklifts engine type, as well as a given type of storage configuration adopted, the order picking strategy allowing minimizing the environmental impact due to internal logistics activities is searched for.

3. Case study

The counterbalance forklifts considered in this work are produced by OM-Still® and they are identified with the models: RX70-40, RX70-40T, RX60-40/600; each of them is equipped, respectively, by Diesel, LPG and electric engine (the technical characteristics of the forklift are summarized in table 2).

Table 2: Technical specifications of the OM-Still® RX70-40, RX70-40T, and RX60-40/600 forklifts

Specifications Limits

Die

sel

Load capacity [kg] 4000Lift height [mm] 7180Lift speed loaded/unloaded [mm/s] 0.59/0.59Travel speed unloaded [km/h] 21Engine power [kW] 54Weight unloaded [kg] 6076

LP

G

Load capacity [kg] 4000Lift height [mm] 7180Lift speed loaded/unloaded [mm/s] 0.59/0.59Travel speed unloaded [km/h] 21Engine power [kW] 55Weight unloaded [kg] 6076

Ele

ctri

c

Load capacity [kg] 4000Lift height [mm] 7180Lift speed loaded/unloaded [mm/s] 0.43/0.55Travel speed unloaded [km/h] 20Engine power [kW] 40Weight unloaded (batteries excluded) [kg] 6368

In this full case study are considered a series of items characterized by same sizes and weights (see table 3). The overall number of items adopted (Q) for numerical experiment is equal to 10008 [units]; the distance (Lx, Ly) between ALU and storage area is equal to 10 [m] (for both sides).

Table 3: Sizes and weight of the items adopted in full case study

dx [m] dy [m] dz [m] Weight [kg] 1 2.5 0.5 2000

According to safety practices for material handling activities, two restrictions are established:

1. Maximum speed travel of the forklift for material handling is fixed to 11 [km/h];

2. Maximum height of stack is equal to 3 [m].

The energy required for the order picking of the single item is strongly related to its position in warehouse (identified by nx, ny, and nz parameters) and to storage configuration adopted (storage racks or stackable units). In figure 6 are shown the isoenergetic curves in output by

the model using a diesel forklift and adopting the stackable units as configuration storage (fig. 5a). The model identify an order picking strategies characterized by low level of energy for: nx≤ 60 [units], ny≤ 24 [units] and nz=1 ÷ 2 [units]. The other energetic classes (identified on figure 6a by means colors light-grey and dark gray) are characterized by order picking strategies that require higher energy level. On the basis of the same input parameters (see tab. 3) the model allows to identify the most sustainable order picking strategy adopting racks as configuration storage (fig. 5b). In this case the sustainable order picking strategy is identified for nx≤ 12 [units], ny≤ 50 [units] and nz=6 [units].

According to equations 12 and 13, the external costs are strongly related to energy required for material handling, for this reason the solutions identified by the model, for both storage configuration, optimizes the environmental performance of the internal logistic activities in warehouse. It is interesting note that the order picking strategies suggested by the model depends on different parameters, for this reason the output parameters identified (nx; ny; nz) can significantly change on base of logistics parameters (e.g. type of the forklift adopting for order picking activity, size of items to be allocated, etc.).

Figure 6: Isoenergetic curves using a diesel forklift for different order picking strategies and different storage configuration: adopting stackable units (a) or racks (b)

Adopting the best strategies suggested by the model, varying: the number of the items to be allocated (range 1÷10008 [units]), the type of the forklift used (Diesel, LPG, electric), and the system adopted for the storage (storage racks or stackable units); we have calculated the


268

external costs for each order picking strategies (fig. 7). In this analysis the sum of single energies required for the allocation of each item in warehouse is considered.

Figure 7: External cost for different number of items stocked in warehouse, adopting Diesel, LPG or Electric forklift, with or without rack

It is possible to observe that, for the same Q parameter, the adoption of an electric forklift and the lack of rack as storage configuration, ensure the minimum external costs.

In case of the adoption of a rack system and neglecting the limit about height of the stack (nz), the model identifies a high level of energy (see fig. 8a) required for the order picking activity by the item allocated for low values of ny, nz and for high values of nx.

Figure 8: Energy required by the forklift equipped to diesel power for picking the single item adopting a storage configuration with rack (a) or with stackable units (b)

The energy decreases until to minimum level identified for the item allocated in position ny=4 [units], nz=6 [units] and nx=278 [units]. Therefore, for the picking activity of all other items, allocated in ny>4 [units] and nz>6 [units], the energy increases. This type of trend depends mainly by the physical features of the item.

It is possible to observe (fig. 8b) that, as regarding the energy required by the forklift in case of lack the rack, its value increases for high values of ny and nz. Indeed, to pick/retrieve items allocated in positions with high values of ny and nz required the moving of the other items characterized by lowest values of ny and nz. For the same reason the order of magnitude between the energy level required in this case is higher if compared to energy required for the picking of the single item, adopting the racks in warehouse.

As far as concern the amount of the energy required by the others typologies of the forklift (i.e. LPG or Electric power), are characterized by the same trend, but there are significant variations about the values of the energy identified.

4. Conclusion

The model described in this paper is a tool for drive the user in identifying the best logistic solutions in order to minimize the environmental impact of the material handling activities in a low level picker-to-parts system according to a batch picking approach. The external costs of internal logistic activities are affected by the type of the forklifts engine, by the number of the movements required for the material handling, by the forklift path, and by the type of storage configuration adopted; in order to manage a “sustainable warehouse” it is essential minimize the overall impact due to all factors.

The application of the model to a full-scale case study show the model capabilities in identifying optimal logistic strategies ensuring a low environment impact due to internal logistic activities. Results show how it is possible to identify different order picking strategies allowing to obtain a significant reduction of external costs for a given number of stored items (all characterized by the same sizes and weight), by adopting different storage configuration and typologies of forklifts.

The average amount of energy required for picking or retrieving the single item from the warehouse is lower in case of racks are adopted. The adoption of forklifts equipped by different engines leads the model in identifying different solutions of the order picking strategies. Therefore, in case of spatial constraints to be fulfilled, the model could drive in finding both the best storage configuration and the most suitable type of forklift to be adopted.

The main limits of the proposed model are in the single-type of item to be stored considered, since often this is not the case in an industrial warehouse. Moreover, the model has to be further developed in order to include more optimization criteria in its objective function (inventory turnover index, time of loading and unloading, average time for picking/retrieving of the items, etc.). This will led to apply it to more complex scenario, thus ensuring greater flexibility and increasing the number of the industrial environment to be applied.


269

Acknowledgments

The paper has been written within the framework of the project PON04a2_E “Smart Energy Master per il governo energetico del territorio - SINERGREEN - RES NOVAE”. This project is supported by the Italian University and Research National Ministry research and competitiveness program that Italy is developing to promote “Smart Cities Communities and Social Innovation”.

References

Baker P., Halim Z., (2007). An exploration of warehouse automation implementations: cost, service and flexibility issues. Supply Chain Management: An International Journal, 12 (2), 129-138.

Bartholdi J. J., Hackman S. T., (2005). Warehouse & distribution science. Available on line at: http://www2.isye.gatech.edu/~jjb/wh/wh-sci.html [Accessed April 2015].

Burinskiene, A., (2010). Order picking process at warehouses. International Journal of Logistics Systems and Management, 6(2), 162–178.

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, 182, 481–501.

Dekker R., De Koster R., Roodbergen K.J. , Van Kalleveen H., (2004). Improving order-picking response time at Ankor’s warehouse, 34 (4), 303 – 313.

Digiesi S., Mossa G., Mummolo G., (2012). A loss factor based approach for sustainable logistics. Production Planning & Control, 23, 160-170.

Dotoli M., Epicoco N., Falagario M., Costantino N., Turchiano B., (2015). An integrated approach for warehouse analysis and optimization: A case study. Computers in Industry, 70, 56-69.

Dukic G., Cesnik V., Opetuk T., (2010). Order-picking Methods and technologies for Greener Warehousing. Proceedings of Management of Technology Step to Sustainable Production.

Dukic G., Opetuk T., Cesnik V., (2010). Greener warehouses through increased efficiency. Proceedings of International Conference on Industrial Logistics “Logistics and Sustainability”, 273-280. March 8 – 11, Rio de Janeiro (Brazil)

European Logistics Association (ELA)/AT Kearney, (2004). Excellence in Logistics. ELA, Brussels.

ExternE project-series (2012) – IER, Univeristy of Stuttgart. Available from: http://www.externe.info/ [accessed 18 February 2015]

Frazelle E., (2002). World-Class Warehousing and Material Handling. United States: McGraw-Hill.

Fumi A., Scarabotti L., Schiraldi M., (2013). The Effect of Slot-Code Optimization in Warehouse Order Picking. International Journal of Engineering Business Management, 5, 1-10.

Gaines L.L., Elgowainy A., Wang M.Q., (2008). Full Fuel-Cycle comparison of forklift propulsion Systems.

Argonne National Laboratory – Energy Systems Division.

Ho Y., Su T., Shi Z. (2008). Order Batching Methods for an Order-Picking Warehouse with Two Cross Aisles. Computers and Industrial Engineering, 55(2), 321–347.

Muppani V., Adil G., (2008). A Branch and Bound Algorithm for Class-Based Storage-Location Assignment. European Journal of Operations Research, 189(2), 492–507.

Qichang, L., (2005). Logistics distribution center of picking system selection and design. Machinery Industry Press, Beijing.

Spadaro J.V., Rabl A., (1999). Estimates of real damage from air pollution: Site dependence and simple impact indices for LCA. International Journal Life Cycle Assesment, 4 (4), 229-243.

Tompkins J.A., White J.A., Bozer Y.A., Frazelle E.H., Tanchoco J.M.A., (2003). Facilities Planning. John Wiley & Sons, NJ.

Wu K.J., Liao C.J., Tseng M.L., Chiu A.S.F., (2015). Exploring decisive factors in green supply chain practices under uncertain. International Journal of Production Economics, 159 (1), 147-157.


270

sustainable warehouse logistics: a nip model for non-road

Documents