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"Agar dapat merespon permintaan pelanggan secara cepat perlu disiapkan sejumlah persediaan yang cukup besar, supaya tersedia pada saat dibutuhkan Penelitian ini mengembangkan model penjadwalan yang terdiri dari dua model. Model satu bertujuan melakukan penjadwalan untuk pemenuhan due date pada lingkungan Job Shop Paralel. Model dua bertujuan melakukan penjadwalan untuk penentuan due date. Pada sistem Job Shop Pararel dinamis yang memproses multi-item berstruktur multi-level, berdasarkan kinerja kualitas pada lingkungan produksi Just In Time (JIT). Pendekatan yang digunakan dalam penelitian ini adalah pendekatan mundur (backward approach) untuk pemenuhan due date dan pendekatan random untuk penentuan due date serta menggunakan metode Tabu Search dengan kriteria minimasi total actual flow time dengan memperhatikan tingkat cacat (defect rate) yang terjadi pada setiap tahapan proses yang dilalui. Peningkatan jumlah item dan jumlah level akan memberikan peningkatan jumlah alternatif jadwal yang berdampak pada CPU time yang diperlukan untuk mendapatkan solusi yang dapat memberikan total actual flow time yang minimum, namun dalam penelitian ini hanya terjadi untuk satu level multi item saja. Untuk dua level dan seterusnya tidak memberikan alternatif jadwal yang bervariasi untuk tingkatan item maupun level. Hal ini disebabkan karena inisialisasi solusi tetangga, panjang tabu list dan jumlah iterasi yang ditetapkan diawal membuat pencarian solusi berhenti di titik lokal optimum.

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In order to response the demand from customer quickly, it needs to have a large size of inventory. This research developes two scheduling model. The first models is to fulfill the due date in the paralel job shop manufacturing and the second is to make the due date itself. In the dynamic paralel job shop system which is produce the multi item structured by multi level, according to the quality performance in the just in time manufacturing. The research uses the backward approach to fulfill the due date and the random approach to make the due date using Tabu Search Method which objective is to minimize total actual flow time concern on defect rate. The increase of item and level affect the increase of scheduling alternatif and cpu time, but in this research it only happens for one level and multi item. For two level and so on it doesn?t give any varians scheduling alternative for level or item invrease. It might becuase of the inisialisasion of neighboor, the lenght of tabu list and iteration which made in the beginning and it makes the solution stop at the optimum local search. "

"We always come cross several decision-making problems in our daily life. Such problems are always conflicting in which many different view points should be satisfied. In politics, business, industrial systems, management science, networks, etc. one often encounters such kind of problems. The most important and difficult part in such problems is the conflict between various objectives and goals. In these problems, one has to find the minimum(or maximum) for several objective functions. Such problems are called vector optimization problems (VOP), multi-criteria optimization problems or multi-objective optimization problems. This volume deals with several different topics / aspects of vector optimization theory ranging from the very beginning to the most recent one. It contains fourteen chapters written by different experts in the field of vector optimization.;We always come cross several decision-making problems in our daily life. Such problems are always conflicting in which many different view points should be satisfied. In politics, business, industrial systems, management science, networks, etc. one often encounters such kind of problems. The most important and difficult part in such problems is the conflict between various objectives and goals. In these problems, one has to find the minimum(or maximum) for several objective functions. Such problems are called vector optimization problems (VOP), multi-criteria optimization problems or multi-objective optimization problems. This volume deals with several different topics / aspects of vector optimization theory ranging from the very beginning to the most recent one. It contains fourteen chapters written by different experts in the field of vector optimization.;We always come cross several decision-making problems in our daily life. Such problems are always conflicting in which many different view points should be satisfied. In politics, business, industrial systems, management science, networks, etc. one often encounters such kind of problems. The most important and difficult part in such problems is the conflict between various objectives and goals. In these problems, one has to find the minimum(or maximum) for several objective functions. Such problems are called vector optimization problems (VOP), multi-criteria optimization problems or multi-objective optimization problems. This volume deals with several different topics / aspects of vector optimization theory ranging from the very beginning to the most recent one. It contains fourteen chapters written by different experts in the field of vector optimization."

"Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies."

"This book presents a comprehensive review on latest research and development trends for design optimization of mechanical elements and devices. Using examples of various mechanical elements and devices, the possibilities for design optimization with advanced optimization techniques are demonstrated. Basic and advanced concepts of traditional and advanced optimization techniques are presented, along with real case studies, results of applications of the proposed techniques, and the best optimization strategies to achieve best performance are highlighted. Furthermore, a novel advanced optimization method named teaching-learning-based optimization (TLBO) is presented in this book and this method shows better performance with less computational effort for the large scale problems."