Selecting between Sequential Zoning and Simultaneous Zoning for Picker-to-parts Order Picking System Based on Order Cluster and Genetic Algorithm

The existing research of sequential zoning system and simultaneous zoning system mainly focuses on some optimization problems such as workload balance,product assignment and simulation for each system separately.But there is little research on comparative study between sequential zoning and simultaneous zoning.In order to help the designers to choose the suitable zoning policy for picker-to-parts system reasonably and quickly,a systemic selection method is presented.Essentially,both zoning and batching are order clustering,so the customer order sheet can be divided into many unit grids.After the time formulation in one-dimensional unit was defined,the time models for each zoning policy in two-dimensional space were established using filling curves and sequence models to link the one-dimensional unit grids.In consideration of ＂U＂ shaped dual tour into consideration,the subtraction value of order picking time between sequential zoning and simultaneous zoning was defined as the objective function to select the suitable zoning policy based on time models.As it is convergent enough,genetic algorithm is adopted to find the optimal value of order picking time.In the experimental study,5 different kinds of order/stock keeping unit（SKU） matrices with different densities d and quantities q following uniform distribution were created in order to test the suitability of sequential zoning and simultaneous zoning to different kinds of orders.After parameters setting,experimental orders inputting and iterative computations,the optimal order picking time for each zoning policy was gotten.By observing whether the delta time between them is greater than 0 or not,the suitability of zoning policies for picker-to-parts system were obtained.The significant effect of batch size b,zone number z and density d on suitability was also found by experimental study.The proposed research provides a new method for selection between sequential zoning and simultaneous zoning for picker-to-parts system,and improves the rationality and efficiency of selection process in practical design.

A MATHEMATICAL EXPRESSION OF CRYSTAL GROWTH RATE DURING CRYSTALLIZATION OF AMORPHOUS ALLOYS

A mathematical expression of the crystal growth rate during crystallization of the amorphous alloys was derived from the micromechanism of crystallization newly developed by the authors. Thus, the satisfactory explanation of the experimental results obtained by Nunogaki et al., Heimendahl et al. and the authors might be found. It seems also to be modelled with the expression for the crystal growth and the crystal size influenced by time during the crystallization of amorphous Ni-P alloy foil at in situ heating. Based on the expression, the factors influencing the crystal growth rate, such as temperature, time and microstructure of amorphous alloys have been discussed.

STRUCTURE AND GLASS FORMING ABILITY(GFA)OF AMORPHOUS ALLOYS

A new microstructure model is developed for amorphous alloys,so called Cluster medel, in which the amorphous phase is thought of composing of randomly distributed ordered clusters of different sizes.Thermodynamic calculation on this model deduces a parameter describing the glass forming ability of metallic alloys:α_c=(1-2.08/Φ_m)T_g/T_m,where T_g is gass transition temperature,T_m is the melting temperature,and Φ_m is entralpy change of melting.It is believed that easy glass forming alloy systems have larger values of a_c.This new criterion of GFA not only provides the theoretical background for several GFA criteria in the literature cited,but also can predict the GFA of many alloy systems more reasonably and accurately.

Dilute long period stacking/order(LPSO)-variant phases along the composition gradient in a Mg-Ho-Cu alloy

We have systematically investigated the microstructures of as-cast Mg_(97.49)Ho_(1.99)Cu_(0.43)Zr_(0.09)alloy by atomic resolution high-angle annular dark field scanning transmission electron microscopy(HAADF-STEM), revealing the coexistence of 18R, 14H and 24R long period stacking/order(LPSO) phases with fully coherent interfaces along step-like composition gradient in a blocky intermetallic compound distributed at grain boundary. The short-range order(SRO) L1_(2)-type Cu_(6)Ho_(8)clusters embedded across AB’C’A-stacking fault layers are directly revealed at atomic scale. Importantly, the order degree of SRO clusters in the present dilute alloy is significant lower than previous 6M and 7M in-plane order reported in ternary Mg-TM(transition metal)-RE(rare earth) alloys, which can be well matched by 9M in-plane order. This directly demonstrates that SRO in-plane L1_(2)-type clusters can be expanded into more dilute composition regions bounded along the definite TM/RE ratio of 3/4. In addition, the estimated chemical compositions of solute enriched stacking fault(SESF) in all LPSO variants are almost identical with the ideal SESF composition of 9M in-plane order, regardless of the type of LPSO phases. The results further support the viewpoint that robust L1_(2)-type TM_(6)RE_(8)clusters play an important role in governing LPSO phase formation.

FATOC:Bug Isolation Based Multi-Fault Localization by Using OPTICS Clustering

Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are used to localize only a single fault.However,existing clustering algorithms cannot always obtain completely correct clustering results,which is a potential threat for bug isolation based MFL approaches.To address this issue,we first analyze the influence of the accuracy of the clustering on the performance of MFL,and the results of a controlled study indicate that using the clustering algorithm with the highest accuracy can achieve the best performance of MFL.Moreover,previous studies on clustering algorithms also show that the elements in a higher density cluster have a higher similarity.Based on the above motivation,we propose a novel approach FATOC(One-Fault-at-a-Time via OPTICS Clustering).In particular,FATOC first leverages the OPTICS(Ordering Points to Identify the Clustering Structure)clustering algorithm to group failed test cases,and then identifies a cluster with the highest density.OPTICS clustering is a density-based clustering algorithm,which can reduce the misgrouping and calculate a density value for each cluster.Such a density value of each cluster is helpful for finding a cluster with the highest clustering effectiveness.FATOC then combines the failed test cases in this cluster with all passed test cases to localize a single-fault through the traditional spectrum-based fault localization(SBFL)formula.After this fault is localized and fixed,FATOC will use the same method to localize the next single-fault,until all the test cases are passed.Our evaluation results show that FATOC can significantly outperform the traditional SBFL technique and a state-of-the-art MFL approach MSeer on 804 multi-faulty versions from nine real-world programs.Specifically,FATOC’s performance is 10.32%higher than that of traditional SBFL when using Ochiai formula in terms of metric A-EXAM.Besides,the results also indicate that,when checking 1%,3%and 5%statements of all subject programs,FATOC can locate 36.91%,48.50%and 66.93%of all faults respectively,which is also better than the traditional SBFL and the MFL approach MSeer.