The objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) award is to develop digital technologies for shop floor planning and scheduling issues. Collaborating with the industrial partner, Kimberly-Clark, the PI will investigate plant-wide planning and scheduling models which include monthly lot-sizing issues, weekly shipment coordination issues, and daily production scheduling problems. Currently, most shop-floor scheduling is conducted either manually or using spreadsheets. Such a disconnect between top floor and shop floor in manufacturing means that people do not understand the true impact of their decisions on production. That lack of insight typically leads to significant inefficiencies across the production system. The PI plans to address this issue by decomposing the enterprise-wide planning problem into multiple layered models according to the time frame of planning, solve the models in a coordinated manner, and recompose the solutions to obtain an integrated plan. The main reason for building coordination from top level enterprise-wide management to lower level shop-floor scheduling is the need for feasible scheduling decisions to support and improve the broader operational and economic objectives. By establishing top floor to shop floor communication, industries will be able to significantly improve their production efficiency while achieving a faster response to changes and disturbances in a manufacturing environment. This research will advance the science of solving shop floor planning and scheduling optimization. The digital technologies will establish a connection between shop floor and top floor to align with demand and plan of inbound supply, production and outbound goods. This methodology is expected to push the boundaries of solving shop floor planning and scheduling optimization problems in commercial manufacturing applications. To ensure that this broader impact is realizable, the PI will collaborate closely with Kimberly-Clark to validate these technologies.

To solve the planning and scheduling problems, two new optimization methods are planned: the Nested-Two-Bounds (NTB) and the Peak-Under-Threshold (PUT) method. The NTB method realizes the benefits of incorporating a lower bound into this framework while utilizing the expert knowledge of a given domain (upper bounds). The PUT method utilizes classical extreme value theory which suggests that exceedance values below a high threshold value can be approximated by a generalized Pareto distribution. This threshold value contains key information on the likelihood of the actual lower bound exceeding a certain value. Thus, the PUT method is expected to be very efficient in finding optimal solutions as demonstrated in our preliminary work. The PI plans to investigate both NTB and PUT methods for efficiently solving monthly lot-sizing, weekly shipment coordination and daily scheduling problems. The NTB and PUT approaches will significantly advance the understanding of large-scale planning and scheduling optimization in empirical situations. The research findings in the project have applications to problems common in many manufacturing production systems and the resulting methodology will be applicable to plant-wide optimization problems. The combination of optimization technique and statistical theory is expected to have a broader impact in the research community as other such combinations may benefit from this work. This research spans computational and theoretical aspects in operations research, engineering, computer science and statistics. It is thus expected that the research will generate significant academic interest in several communities.

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University of Wisconsin Madison
United States
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