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Multi-depot vehicle routing with precedence constraints and selective backhauling, along with practical challenges such as outsourcing, demand splitting, and flexible pickup and delivery formats, is a common issue in the animal feed industry. However, few studies address demand splitting and flexible pickup constraints. The industry struggles with inadequate management, leading to difficulties in efficiently utilizing limited vehicles for both raw material pickup and product delivery. This inefficiency results in higher transportation costs, including subcontractor vehicle expenses. This study proposes the Extended Adaptive Grey Wolf Optimizer to minimize total transportation costs by incorporating essential factors such as multi-depots, outsourcing services, optimization of split delivery and pickup operations, precedence constraints, and selective backhauling. The results show a remarkable 97.6% improvement toward optimality, representing a significant enhancement of 38.1% improvement over current practice. The findings emphasize the benefits to the industry of adopting proper route sequencing and utilizing subcontractor vehicles. [ABSTRACT FROM AUTHOR]

Industry 4.0 is an era in which the manufacturing industry has adopted digital technologies and the Internet to enable smart manufacturing system, machines used in the production now can communicate with each other and exchange information between each other, and the machinery used in the manufacturing process is more modern and precise. Therefore, educational institutions should develop the curriculum to produce qualified graduates with the knowledge required for the Industry 4.0 era, especially Industrial Engineering graduates who are directly related to the industry sector. The purpose of this research is to collect the data for the Master of Industrial Engineering (MSIE) curriculum development. The Analytic Hierarchy Process (AHP) technique is used to rank the indicators of knowledge that is important to the employment of graduates with a master’s degree in Industrial Engineering, and study the gap between the expectations of employers and the ability of the current MSIE students of Khon Kaen University. The results of the study reveal that the first indicators that are most important to the employment of MSIE graduates is the knowledge of Industry 4.0 strategy and the knowledge that the students should have developed are the collaboration of humans and robots, big data analytics, real time data usage and databased decision making.

This paper presents a multi-product vehicle routing problem with cross-docking operations for food industry to ensure products which can be delivered in time and with minimum transportation and hiring costs to perform for both supplier side and customer side. The problem was formulated as a special multi-product vehicle routing problem with cross-docking and time window constraints (MPVRPCDTW). A novel strategy hybrid based on the Bat algorithm (NSHBA) is presented to solve the MPVRPCDTW for the sustainable food industry by minimising the transportation cost and the number of vehicles, using the poultry industry as a case study. Additionally, a new disturbance algorithm (DA) was developed to find the best neighbourhood strategy to increase the efficiency and quality of solutions in the NSHBA. The computational results revealed that the NSHBA-DA outperformed the Bat algorithm, the differential evolution algorithm, and the particle swarm optimisation algorithm. The MATLAB App architecture based on all eight algorithms was designed and developed. The overall result of this study should prove to benefit the logistics staff in making the right decisions on cross-docking transportation by employing real-time data. [ABSTRACT FROM AUTHOR]