Because the alternative process plans have significant contributions to the production efficiency of a manufacturing system, researchers have studied the integration of manufacturing functions, which can be divided into two groups, namely, integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Although IPPS and SWDDA are well‐known and solved problems in the literature, there are limited works on integration of process planning, scheduling, and due date assignment (IPPSDDA). In this study, due date assignment function was added to IPPS in a dynamic manufacturing environment. And the studied problem was introduced as dynamic integrated process planning, scheduling, and due date assignment (DIPPSDDA). The objective function of DIPPSDDA is to minimize earliness and tardiness (E/T) and determine due dates for each job. Furthermore, four different pure metaheuristic algorithms which are genetic algorithm (GA), tabu algorithm (TA), simulated annealing (SA), and their hybrid (combination) algorithms GA/SA and GA/TA have been developed to facilitate and optimize DIPPSDDA on the 8 different sized shop floors. The performance comparisons of the algorithms for each shop floor have been given to show the efficiency and effectiveness of the algorithms used. In conclusion, computational results show that the proposed combination algorithms are competitive, give better results than pure metaheuristics, and can effectively generate good solutions for DIPPSDDA problems.

Journal of Industrial and Intelligent Information

One of the most critical issues in machine learning is the selection of appropriate hyper parameters for training models. Machine learning models may be able to reach the best training performance and may increase the ability to generalize using hyper parameter optimization (HPO) techniques. HPO is a popular topic that artificial intelligence studies have focused on recently and has attracted increasing interest. While the traditional methods developed for HPO include exhaustive search, grid search, random search, and Bayesian optimization; meta-heuristic algorithms are also employed as more advanced methods. Meta-heuristic algorithms search for the solution space where the solutions converge to the best combination to solve a specific problem. These algorithms test various scenarios and evaluate the results to select the best-performing combinations. In this study, classical methods, such as grid, random search and Bayesian optimization, and population-based algorithms, such as genetic algorithms and particle swarm optimization, are discussed in terms of the HPO. The use of related search algorithms is explained together with Python programming codes developed on packages such as Scikit-learn, Sklearn Genetic, and Optuna. The performance of the search algorithms is compared on a sample data set, and according to the results, the particle swarm optimization algorithm has outperformed the other algorithms.

Logistics, getting the right product in the right place at the right time to the right customer, is one of the most important functions in large companies. Toyota is one of the world's leading companies in many aspects of successful business practice, particularly in logistics. The techniques developed in the company since the 1950s provide a competitive advantage to Toyota and provide efficiency in many business functions with supply chain management. For that reason, it is imperative to understand how lean logistics practices are applied in the Toyota Boshoku Turkey(TBT). This chapter examines practical logistics applications in TBT, one of the suppliers of Toyota located outside of Japan. In addition to theoretical research, it is also important that practical applications in enterprises such as a Toyota plant contribute to the literature. Consequently, as a case study, discussion and explanation of logistics and supply chains of TBT will spark reader interest.

Traditionally process planning, scheduling and due-date assignment functions are applied sequentially and separately. Since these three functions effect each other and if we don’t integrate then they will become poor input for downstream and global performance will be poor. In these competitive era we have to be competitive also. Integrating these functions will improve global performance. In this study we investigated the benefit of integration. We tested different integration level. First we looked at unintegrated results and later we integrated due-date assignment with process planning and finally we integrated these two functions with ATC (Apparent Tardiness Cost) Dispatching. In this study we observed that as integration level increases the solution becomes better. Integrating due-date assignment with process planning improves global performance and if we integrate ATC dispatching with these two functions then we get the best performance. In addition to integration level we also compared benefit of search techniques, especially genetic (directed) search. We compared ordinary solutions (initial populations) with results of random search and genetic search (after some certain number of iterations). We tested three shop floors which are small, medium and large shop floor. We observed that search is always better than ordinary solutions and genetic search outperformed random (undirected) search. At this study every customer has weight and they are scheduled by considering weights to improve performance value.