This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, including travel costs, time penalty costs, AGV costs, and unloading setup time costs. To solve the MAGVDUST, this paper builds a mixed-integer linear programming model and proposes an improved genetic algorithm (IGA). In the IGA, an improved nearest-neighbor-based heuristic is proposed to generate a high-quality initial solution. Several advanced technologies are developed to balance local exploitation and global exploration of the algorithm, including an optimal solution preservation strategy in the selection process, two well-designed crossovers in the crossover process, and a mutation based on Partially Mapped Crossover strategy in the mutation process. In conclusion, the proposed algorithm has been thoroughly evaluated on 110 instances from an actual electronic factory and has demonstrated its superior performance compared to state-of-the-art algorithms in the existing literature.

Among the well-known scheduling problems, the customer order scheduling problem (COSP) has always been of great importance in manufacturing. To reflect the reality of COSPs as much as possible, this study considers that jobs from different orders are classified in various classes. This paper addresses a tri-criteria single-machine scheduling model with multiple job classes and customer orders on which the measurement minimizes a linear combination of the sum of the ranges of all orders, the tardiness of all orders, and the total completion times of all jobs. Due to the NP-hard complexity of the problem, a lower bound and a property are developed and utilized in a branch-and-bound for solving an exact solution. Afterward, four heuristics with three local improved searching methods each and a water wave optimality algorithm with four variants of wavelengths are proposed. The tested outputs report the performances of the proposed methods.

Since scheduling literature has a wide range of uncertainties, it is crucial to take these into account when solving performance measure problems. Otherwise, the performance may severely be affected in a negative way. In this paper, an algorithm is proposed to minimize the total completion time (TCT) of a two-machine no-wait flowshop with uncertain setup times within lower and upper bounds. The results are compared to the best existing algorithm in scheduling literature: the programming language Python is used to generate random samples with respect to various distributions, and the TCT of the proposed algorithm is compared to that of the best existing one. Results reveal that the proposed one significantly outperforms the best one given in literature for all considered distributions. Specifically, the average percentage improvement of the proposed algorithm over the best existing one is over 90%. A test of hypothesis is conducted to further confirm the results.

Single minute exchange of dies (SMED) is one of the most important tools to achieve lean production system. The main idea of this system is to provide methods and to use creative and innovative solutions for continuous improvement. Due to the importance of this issue and its effect on reducing waste during the production process, this study presents a method to identify and to weight factors in the establishment of a single minute exchange of dies in 14 plastic injection factories. In this study, fourteen factories in injection industry were chosen and the factors influencing the implementation of single minute exchange of dies were identified. Following data collection, decision matrix was formed and the weight of each factor was determined by using Shannon Entropy. Then, in order to determine the readiness of factories, VIKOR method was used to rank companies. The results indicate priorities of the following factors in establishing SMED that include: Senior management support, technical capabilities, technical knowledge of staff and consultants, knowledge of mold design, manufacturing infrastructure, team work, combination of the project team work, benchmarking, training, clear understanding of project objectives, rewards and motivation, proper management expectation, project management, teamwork and organizational culture. Practical implications: Due to the factors, Top manager can make the best decision for implementing of SMED technique. This study develops factors influencing on SMED implementation based on Shannon and VIKOR methods for ranking parameters and plants.