A multi-objective optimization of automated warehouses is discussed and evaluated in the present paper. Since most of the researchers in material handling community had performed optimization of decision variables with single objective function only (usually named with minimum travel time, maximum throughput capacity, minimum cost, maximum energy efficiency, etc.), the multi-objective optimization (cost -travel time -CO 2 emission/ energy efficiency) will be presented. For the optimization of decision variables in objective functions, the method with genetic algorithms was used. To find the Pareto optimal solutions, the NSGA II genetic algorithm was used. The main objective of our contribution is to determine the performance of the system according to the multi-objective optimization technique. The results of the proposed model could be useful tool for the warehouse designer in the early stage of warehouse design.

This paper studies multiple-deep automated vehicle storage and retrieval systems (AVS/RS) known for their high throughput performance and flexibility. Compared to a single-deep system, multiple-deep AVS/RS has a better space area utilisation. However, a relocation cycle occurs, reducing the throughput performance whenever another stock-keeping unit (SKU) blocks a retrieving SKU. The SKU retrieval sequence is undetermined, meaning that the arrangement is unknown, and all SKUs have an equal probability of retrieval. In addition to the shuttle carrier, a satellite vehicle is attached to the shuttle carrier and is used to access storage locations in multiple depths. A discrete event simulation of multiple-deep AVS/RS with a tier captive shuttle carrier was developed. We focused on the dual command cycle time assessment of nine different storage and relocation assignment strategies combinations in the simulation model. The results of a simulation study for (i) Random, (ii) Depth-first and (iii) Nearest neighbour storage and relocation assignment strategies combinations are examined and benchmarked for five different AVS/RS case study configurations with the same number of storage locations. The results display that the fivefold and sixfold deep AVS/RS outperform systems with fewer depths by utilising Depth-first storage and Nearest neighbour relocation assignment strategies.

In the production bread wheat bran is used as a raw material rich in dietary fiber. Therefore, it is necessary to monitor the content of essential and toxic elements in the flour and bran. This paper investigates essential (Zn, Cu, Fe and Mn) and toxic (Pb, Cd, Hg and As) elements in products of milling wheat grown in the whole territory of Banat, the region in Serbia. Inductively Coupled Plasma Mass Spectrometry was used for analysis. The mean contents of the following elements Pb, Cd, Hg, As, Fe, Mn and Zn in wheat kernels were 0.143 mg/kg, 0.007 mg/kg, 0.017 mg/kg, 35.7643 mg/kg, 50.865 mg/kg, and 21.174 mg/kg, respectively. Cluster Analysis (CA) and Principal component analysis (PCA) was applied to discriminate and to group the different samples, according to element content. Quality results show that the first two principal components, accounting for 80.17% of the total variance, can be considered sufficient for data representation and the first two principal components of toxic elements and essential microelements. Cd (15.28%), Zn (17.91%), Cu (17.08), Fe (16.91%) and Mn (17.54%), have been found the most influential for the first factor coordinate calculation, while the contribution of Pb (27.93%) and Hg (61.86%) has been the most important variable for the second factor coordinate calculation. [Projekat Ministarstva nauke Republike Srbije, TRI 46005]

Screw conveyors are used extensively in food, plastics, mineral processing, agriculture and processing industries for elevating and/or transporting bulk materials over short to medium distances. Despite their apparent simplicity in design, the transportation action is very complex for design and constructors have tended to rely heavily on empirical performance data. Screw conveyor performance is affected by its operating conditions (such as: the rotational speed of the screw, the inclination of the screw conveyor and its volumetric fill level). In this paper, horizontal, several single-pitch screw conveyors with some geometry variations in screw blade were investigated for mixing action during transport, using Discrete Element Method (DEM). The influence of geometry modifications on the performance of screw conveyor was examined, different screw designs were compared, and the effects of geometrical variations on mixing performances during transport were explored. During the transport, the particle tumbles down from the top of the helix to the next free surface and that segment of the path was used for auxiliary mixing action. The particle path is dramatically increased with the addition of three complementary helices oriented in the same direction as screw blades (1458.2 mm compared to 397.6 mm in case of single flight screw conveyor). Transport route enlarges to 1764.4 mm, when installing helices oriented in the opposite direction from screw blades. By addition of straight line blade to single flight screw conveyor, the longest particle path is being reached: 2061.6 mm.

A new optimization model of Automated Storage and Retrieval Systems (AS/RS) containing three objective and four constraint functions is presented in this paper. Majority of the researchers and publications in material handling field had performed optimization of different decision variables, but with single objective function only. Most common functions are: minimum travel time, maximum throughput capacity, minimum cost, maximum energy efficiency, etc. To perform the simultaneous optimization of objective functions (minimum: “investment expenses”, “cycle times”, “CO 2 footprint”) the Non-dominated Sorting Genetic Algorithm II (NSGA II) was used. The NSGA II is a tool for finding the Pareto optimal solutions on the Pareto line. Determining the performance of the system is the main goal of our model. Since AS/RS are not flexible in terms of layout and organizational changes once the system is up and running, the proposed model could be a very helpful tool for the warehouse planners in the early stages of warehouse design