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The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

Utilizing electrical discharge machining (EDM) to process micro-holes in superalloys may lead to the formation of remelting layers and micro-cracks on the machined surface. This work proposes a method of composite processing of EDM and ultrasonic vibration drilling for machining precision micro-holes in complex positions of superalloys.

A six-axis computer numerical control (CNC) machine tool was developed, whose software control system adopted a real-time control architecture that integrates electrical discharge and ultrasonic vibration drilling. Among them, the CNC system software was developed based on Windows + RTX architecture, which could process the real-time processing state received by the hardware terminal and adjust the processing state. Based on the SoC (System on Chip) technology, an architecture for a pulse generator was developed. The circuit of the pulse generator was designed and implemented. Additionally, a composite mechanical system was engineered for both drilling and EDM. Two sets of control boards were designed for the hardware terminal. One set was the EDM discharge control board, which detected the discharge state and provided the pulse waveform for turning on the transistor. The other was a relay control card based on STM32, which could meet the switch between EDM and ultrasonic vibration, and used the Modbus protocol to communicate with the machining control software.

The mechanical structure of the designed composite machine tool can effectively avoid interference between the EDM spindle and the drilling spindle. The removal rate of the remelting layer on 1.5 mm single crystal superalloys after composite processing can reach over 90%. The average processing time per millimeter was 55 s, and the measured inner surface roughness of the hole was less than 1.6 µm, which realized the  micro-hole machining without remelting layer, heat affected zone and micro-cracks in the single crystal superalloy.

The test results proved that the key techniques developed in this paper were suite for micro-hole machining of special materials.

In today’s rapidly evolving work landscape, the design of office spaces is a crucial concern for organizations. Companies are redefining offices as collaboration hubs to entice employees back to in-person work. However, the understanding of how employees choose their workspaces, especially for collaborative activities, and how this should inform office design is lacking. Workers’ collaborative activity patterns can help better understand workspace choice behavior (WCB). In two studies, this paper aims to explore which characteristics of collaborative activities to consider when reshaping offices.

Data collected in a cross-sectional study design at a research institution (n = 285) and a university (n = 352) were used for confirmatory factor analyses and regression analysis.

The first study shows that collaborative activities can be classified into three distinct types: coordinative activities (planned and formal), deep collaboration (planned and complex) and spontaneous communication (informal and short encounters). The second study revalidates this classification and reveals patterns impacting WCB. Frequency and location preference of spontaneous communication and work environment satisfaction are strong predictors of on-site work. Personal characteristics like gender, age, managerial position or commute time are less consequential than assumed.

The results pinpoint guidelines for office designers and leaders in shaping effective workspaces and policies.

This paper provides new insights into classifying collaborative activities and personal characteristics, activity characteristics and environmental factors influencing WCB.