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AbstractWe study the problem of recovering a production plan after a disruption, where the disruption may be caused by incidents such as power failure, market change, machine breakdown, supply shortage, worker no‐show, and others. The new recovery plan we seek after has to not only suit the changed environment brought about by the disruption, but also be close to the initial plan so as not to cause too much customer unsatisfaction or inconvenience for current‐stage and downstream operations. For the general‐cost case, we propose a dynamic programming method for the problem. For the convex‐cost case, a general problem which involves both cost and demand disruptions can be solved by considering the cost disruption first and then the demand disruption. We find that a pure demand disruption is easy to handle; and for a pure cost disruption, we propose a greedy method which is provably efficient. Our computational studies also reveal insights that will be helpful to managing disruptions in production planning. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.

[EN] Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure. ; This research has been partially financed by the project: "Lonja de Impresión 3D para la Industria 4.0 y la Empresa Digital (LONJA3D)" funded by the Regional Government of Castile and Leon and the European Regional Development Fund (ERDF, FEDER) with grant VA049P17. ; De Antón, J.; Senovilla, J.; González, J.; Acebes, F.; Pajares, J. (2020). Production planning in 3D printing factories. International Journal of Production Management and Engineering. 8(2):75-86. https://doi.org/10.4995/ijpme.2020.12944 ; OJS ; 75 ; 86 ; 8 ; 2 ; Canellidis, V., Giannatsis, J., & Dedoussis, V. (2013). Efficient parts nesting schemes for improving stereolithography utilization. CAD Computer Aided Design, 45(5), 875-886. https://doi.org/10.1016/j.cad.2012.12.002 ; Chergui, A., Hadj-Hamou, K., & Vignat, F. (2018). Production scheduling and nesting in ...

Presents an analytic framework for processing planning in industries where fixed batch sizes are common. The overall optimum processing plan is shown to be located on an envelope between the optimum JIT plan and the optimum level plan. These concepts provide the framework for understanding the overall optimum plan, and the framework leads to an efficient heuristic. The approach is practical, illustrated by a case study from the food industry, which shows the place of overall optimum planning within the company's planning system and its implications for company performance.

Production planning is the main aspect for a manufacturer affecting the income of a company. Correct production planning policy chosen for the right product at the right moment in the product life cycle (PLC) reduces production, storing and other related costs. This leads to such problems to be solved as defining the present PLC phase of a product and also determining the transition point – a moment of time when the PLC phase changes. The paper presents the Agent‐Based Production Planning Support system meant for supporting a production manager in his/her production planning decisions. The developed system is based on the analysis of historical demand for products and on the information about transitions between phases in life cycles of those products. The system creates a model and uses it to forecast a transition point for a product that only begins its life on the market. The architecture of the developed system is presented and an analysis of testing on real‐world data results is given. Article in English. Agentinė gamybos planavimo paramos sistema Santrauka.Gamybos planavimas – pagrindinis aspektas, nuo kurio priklauso bendroves pajamos. Teisingos gamybos planavimo politikos pasirinkimas konkretaus gaminio gyvavimo ciklo (GGC) metu mažina gamybos, sandeliavimo ir kitas išlaidas. Reikia nustatyti esama gaminio gyvavimo ciklo etapą ir perėjimo tašką – laiko momentą, kai pasikeičia gyvavimo ciklo etapas. Pateikiama agentinė gamybos planavimo paramos sistema, skirta gamybos vadovo planavimo sprendimams pagrįsti. Sukurtoji sistema pagrista retrospektyvine produktų paklausos analizę ir informaciją apie tų produktų gyvavimo ciklo fazių pokyčius. Sistema kuria modelį ir taiko jį prognozuojant produkto, kuris tik pradeda savo gyvavima rinkoje, perėjimo tašką. Pateikiama sukurtos sistemos struktūra ir bandymų su realiais duomenimis analizes rezultatai. Reikšminiai žodžiai:agentai,duomenų gavimas,sprendimų parama,gamybos planavimas,produkto gyvavimo ciklas,perėjimo tašku prognozavimas. First published online: 10 Feb 2011

AbstractShipbuilding as currently practiced in U.S. commercial shipyards employs little quantitative modeling or analysis in production planning. This paper presents a brief discussion of the shipbuilding process and focuses on one major component which is referred to as outfitting. The outfit planning problem is described in detail and then formally modeled as a generalization of the resource constrained project scheduling problem. The value of the approach as well as barriers to its adoption are also discussed.

Purpose– The focus is the interplay of cognitive capabilities (mathematical understanding and heuristic problem solving) and learning from feedback. Furthermore, the authors analyze the role of individual factors in designing appropriate feedback systems for complex decision-making situations. Based on a learning model the purpose of this paper is to present an experimental study analyzing the feedback effectiveness in a repeated complex production planning task. Referring to individual characteristics in terms of educational background and problem solving capabilities of the decision maker the authors compare different forms of feedback systems.Design/methodology/approach– The authors performed four experiments bi-weekly based on a realistic production planning situation. Participants received – depending on the treatment – different types of feedback concerning the final outcomes of the production plans. For testing the hypotheses, the authors conducted ANCOVAs and additionalpost hoctests for each subgroup to explore the effects of different types of feedback on the subgroups' decision-making performance.Findings– The authors show that feedback information is not always helpful, but due to acquired knowledge and problem solving capabilities can even be harmful. The authors also show that, depending on the decision maker's individual characteristics and her past performance, the type of feedback is crucial for the learning process.Practical implications– The study provides important information about feedback design taking individual characteristics of decision makers (educational background, work experience) into account. Applying the results of the study can increase decision-making performance and enhance learning of production planning tasks.Originality/value– The findings extend previous literature reporting that the performance in complex decision-making tasks depends on educational background and on the ability to cope with the phenomena of cognitive load, working memory limitations and the capability to utilize relevant heuristics to prevent information overload. Some of our results, e.g., the negative impact of non-financial feedback of high-performing economists, contradict the general findings in the literature.