An Improved Hierarchical Production Planning Model For The Multiproduct Parallel Processor Environment

We often come across computational optimization virtually in all branches of engineering and industry. Many engineering problems involve heuristic search and optimization, and, once discretized, may become combinatorial in nature, which gives rise to certain difficulties in terms of solution procedure. Some of these problems have enormous search spaces, are NP-hard and hence require heuristic solution techniques. Another difficulty is the lack of ability of classical solution techniques to determine appropriate optima of non-convex problems. Under these conditions, recent advances in computational optimization techniques have been shown to be advantageous and successful compared to classical approaches. This Volume presents some of the latest developments with a focus on the design of algorithms for computational optimization and their applications in practice. Through the chapters of this book, researchers and practitioners share their experience and newest methodologies with regard to intelligent optimization and provide various case studies of the application of intelligent optimization techniques in real-world applications.This book can serve as an excellent reference for researchers and graduate students in computer science, various engineering disciplines and the industry.

The goal of this work is to propose a multiperiod mixed-integer linear programming model for the design and production planning of multistage multiproduct batch plants integrating, simultaneously, scheduling decisions. The time horizon consists of several periods in which deterministic variations due to seasonal or market fluctuations like prices, products demand limits, costs, and raw materials availability, are taken into account. The plant operates using production campaigns along each time period. The objective function maximizes the net present value considering incomes from product sales and, investment, resources, inventories, operation, waste disposal and late delivery penalties costs. The proposed model allows not only finding the optimal plant structure (unit sizes and its duplication in parallel in each stage), but also the production planning in every time period as well as number and optimal sequence of batches in each campaign.