Optimization by Heuristic procedure of Scheduling Constraints in Manufacturing System

This article provides an insight for optimization the output considering combinations of scheduling constraints by using simple heuristic for multi-product inventory system. Method so proposed is easy to implement in real manufacturing situation by using heuristic procedure machines are arranged in optimal sequence for multiple product to manufacture. This reduces manufacturing lead-time thus enhance profit. Copyright © www.iiste.org Keywords: Optimization, Scheduling, Heuristic, Inventory, Layout

Optimizing Plant-line Schedules and an Application at Hidden Valley Manufacturing Company

Interfaces, 32, pp. 1-14.A plant line schedule specifies a plant's sustained batch operations over time with detail sufficient to manage all activities. Plantwide considerations include restrictions on how production centers can be formed from production lines, packaging lines, conveyers, and so forth; the cost and time of product-package item setups, changeovers, and shutdowns; honoring instock service levels, minimum inventory, and committed shipments; recognizing efficiency gains with longer batch runs; respecting crew constraints; and the costs of materials, labor, and carrying inventory. We developed a cost-minimizing optimization model, PROFITS, that features multiple independent time streams for various categories of events that mimic existing periodic reviews of operations. PROFITS is embedded in a graphical user interface that eases the grueling aspects of scheduling, preparing data, controlling scenarios, and visualizing results. At Hidden Valley Manufacturing Company, completing an eight-week plant-line schedule takes about an hour. This is much faster than manual scheduling was-and the schedules are better

Forecasting and planning for a multi-product seasonal production facility

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 63-65).With increasing cost pressure on commodity vaccine products, Novartis Vaccines & Diagnostics is continually looking for ways to improve operating efficiencies and decrease costs. As the largest drug product manufacturing site for Novartis flu vaccine products, Rosia Aseptic Operations experiences dramatic swings in required man-hours throughout the year to accommodate the seasonal nature of flu demand. This challenge is further exasperated by long training lead times for new aseptic operators and substantial severance costs for a permanent employee headcount reduction in Italy. With over 50% of the aseptic operators in Rosia on temporary contracts, management spends at least 25 hours per month reviewing headcount in order to make assessments on contract renewals and expirations. Therefore, this thesis investigates the hypothesis that understanding resource needs can decrease labor costs as well as save management time. A labor resource model based on a demand forecast, operational input data, and a scheduling optimization was developed and validated. The outputs of the model support decisions on overall staffing levels by department as well as provide tools to analyze the appropriate mix of temporary and permanent employee contracts and to understand the time lag associated with staffing decisions. Additionally, sensitivity analysis can be performed to see the effect of changes in policies and shift structures. The model reduces costs and saves management time in the Rosia Aseptic Organization through the longer-term depiction of headcount needs, the cost analysis structure and tools, insights from the production scheduling optimization, and the automatic, pre-crafted graphs and tables. Further discussion of the concepts of aggregate production planning, reveals additional opportunities for Novartis to reduce overall production costs through enabling strategies to match capacity with demand.by Dannielle Sita.S.M.M.B.A