An efficient genetic method for multi-objective continuous production scheduling in industrial internet of things

Continuous manufacturing is playing an increasingly important role in modern industry, while research on production scheduling mainly focuses on traditional batch processing scenarios. This paper provides an efficient genetic method to minimize energy cost, failure cost, conversion cost and tardiness cost involved in the continuous manufacturing. With the help of Industrial Internet of Things, a multi-objective optimization model is built based on acquired production and environment data. Compared with a conventional genetic algorithm, non-random initialization and elitist selection were applied in the proposed algorithm for better convergence speed. Problem specific constraints such as due date and precedence are evaluated in each generation. This method was demonstrated in the plant of a pasta manufacturer. In experiments of 71 jobs in a one-month window, near-optimal schedules were found with significant reductions in costs in comparison to the existing original schedule

Production Scheduling in Integrated Steel Manufacturing

Steel manufacturing is both energy and capital intensive, and it includes multiple production stages, such as iron-making, steelmaking, and rolling. This dissertation investigates the order schedule coordination problem in a multi-stage manufacturing context. A mixed-integer linear programming model is proposed to generate operational (up to the minute) schedules for the steelmaking and rolling stages simultaneously. The proposed multi-stage scheduling model in integrated steel manufacturing can provide a broader view of the cost impact on the individual stages. It also extends the current order scheduling literature in steel manufacturing from a single-stage focus to the coordinated multi-stage focus. Experiments are introduced to study the impact of problem size (number of order batches), order due time and demand pattern on solution performance. Preliminary results from small data instances are reported. A novel heuristic algorithm, Wind Driven Algorithm (WDO), is explained in detail, and numerical parameter study is presented. Another well-known and effective heuristic approach based on Particle Swarm Optimization (PSO) is used as a benchmark for performance comparison. Both algorithms are implemented to solve the scheduling model. Results show that WDO outperforms PSO for the proposed model on solving large sample data instances. Novel contributions and future research areas are highlighted in the conclusion

Accelerating Level-Value Adjustment for the Polyak Stepsize

The Polyak stepsize formula has been widely used for convex optimization. However, stepsize computations require the generally unknown optimal value. Dynamic estimations of the optimal value are thus usually needed. In this paper, guided by a decision-based procedure through a novel easy-to-solve ``Polyak Stepsize Violation Detector'' (PSVD) linear constraint satisfaction problem, a series of level values is constructed to successively estimate the optimal value to guarantee convergence for subgradient as well as for approximate subgradient methods. Through a series of empirical tests of convex optimization problems with diverse characteristics, we illustrate the practical advantages of our approach as compared to existing methods

Integrated Production-Inventory Models in Steel Mills Operating in a Fuzzy Environment

Despite the paramount importance of the steel rolling industry and its vital contributions to a nation’s economic growth and pace of development, production planning in this industry has not received as much attention as opposed to other industries. The work presented in this thesis tackles the master production scheduling (MPS) problem encountered frequently in steel rolling mills producing reinforced steel bars of different grades and dimensions. At first, the production planning problem is dealt with under static demand conditions and is formulated as a mixed integer bilinear program (MIBLP) where the objective of this deterministic model is to provide insights into the combined effect of several interrelated factors such as batch production, scrap rate, complex setup time structure, overtime, backlogging and product substitution, on the planning decisions. Typically, MIBLPs are not readily solvable using off-the-shelf optimization packages necessitating the development of specifically tailored solution algorithms that can efficiently handle this class of models. The classical linearization approaches are first discussed and employed to the model at hand, and then a hybrid linearization-Benders decomposition technique is developed in order to separate the complicating variables from the non-complicating ones. As a third alternative, a modified Branch-and-Bound (B&B) algorithm is proposed where the branching, bounding and fathoming criteria differ from those of classical B&B algorithms previously established in the literature. Numerical experiments have shown that the proposed B&B algorithm outperforms the other two approaches for larger problem instances with savings in computational time amounting to 48%. The second part of this thesis extends the previous analysis to allow for the incorporation of internal as well as external sources of uncertainty associated with end customers’ demand and production capacity in the planning decisions. In such situations, the implementation of the model on a rolling horizon basis is a common business practice but it requires the repetitive solution of the model at the beginning of each time period. As such, viable approximations that result in a tractable number of binary and/or integer variables and generate only exact schedules are developed. Computational experiments suggest that a fair compromise between the quality of the solutions and substantial computational time savings is achieved via the employment of these approximate models. The dynamic nature of the operating environment can also be captured using the concept of fuzzy set theory (FST). The use of FST allows for the incorporation of the decision maker’s subjective judgment in the context of mathematical models through flexible mathematical programming (FMP) approach and possibilistic programming (PP) approach. In this work, both of these approaches are combined where the volatility in demand is reflected by a flexible constraint expressed by a fuzzy set having a triangular membership function, and the production capacity is expressed as a triangular fuzzy number. Numerical analysis illustrates the economical benefits obtained from using the fuzzy approach as compared to its deterministic counterpart

Optimizing Block-Stacking Operations with Relocation

The focus of the dissertation is developing the optimization problem of finding the minimum-cost operational plan of block stacking with relocation as well as devising a solution procedure to solve practical-sized instances of the problem. Assuming changeable row depth instead of permanent row depth, this research is distinguished from conventional block stacking studies. The first contribution of the dissertation is the development of the optimization problem under the assumption of deterministic demand. The problem is modeled using integer programming as a variation of the unsplittable multi-commodity flow problem. To find a good feasible solution of practical-sized instances in reasonable time, we decompose the original problem into a series of generalized assignment problems. In addition, to establish a good lower bound on the optimal objective function value, we apply a relaxation based upon Lagrangean decomposition in which the relaxed problem separates into a set of shortest path problems and a set of binary knapsack problems. The second contribution of the dissertation is the development of the optimization problem under the assumption of stochastic demand. The problem is formulated as a discrete time finite horizon Markov decision process model, incorporating the recursive daily situation of determining the assignment of product lots to storage areas for a day based on uncertain daily demand and observed system information. To tackle computational intractability in solving practical-sized instances, we develop a heuristic solution approach taking an on-line manner by instantly determining an action for a single observed state rather than an off-line manner by predetermining an action for every state

Reports to the President

A compilation of annual reports for the 1981-1982 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року