Tactical Allocation of Machining Resources in Make-to-Order companies with Functional Workshops: Mathematical Modeling, Analysis, and Case Study

Sunney Fotedar


Allocating resources effectively among competing activities is a crucial optimization problem in numerous domains, including manufacturing, healthcare, and finance. Manufacturing, Planning, and Control (MPC) have garnered significant interest from practitioners and operations research experts alike, as it has shown a positive impact on efficiency for companies worldwide. This talk focuses on the tactical resource allocation problem (TRAP) within the context of a multi-item, multi-level, capacitated production planning problem for GKN Aerospace, our case company. The objectives (often conflicting and with no a priori preference) pursued are minimizing resource loading beyond a predefined threshold, qualification costs, and inventory costs. The two main partners in this research are Chalmers University of Technology and GKN Aerospace AB, and for one of the papers we collaborate with Fraunhofer-Chalmers Center for Industrial Mathematics in Gothenburg, Sweden. I mainly present two of my works.

The first work involves the development of a deterministic bi-objective discrete optimization model and a specialized solution approach (https://doi.org/10.1111/itor.13180), while the second work addresses a robust bi-objective optimization model with uncertain qualification costs (https://doi.org/10.1007/s10458-022-09564-8). Mathematical properties of the model formulation will be discussed, along with computational results highlighting the efficacy of the proposed approaches. Furthermore, if time permits I will also provide some details of my recent work where we incorporate a third objective of inventory cost and develop a criterion space decomposition approach to solve this tri-objective discrete optimization problem effectively (https://doi.org/10.1007/s10287-023-00442-6).

As urbanization increases, municipalities across the world have become aware of the negative impacts of road-based transportation, which include traffic congestion and air pollution. As a result, several cities have introduced tolling schemes to discourage vehicles from entering the inner city. However, little research has been done to examine the impact of tolling schemes on the routing of commercial fleets, especially on the resulting costs and emissions. In this study, we investigate a vehicle routing problem considering different congestion charge schemes for several city types. We design comprehensive computational experiments to investigate whether different types of tolling schemes work in the way municipalities expect and what factors affect the performance of the congestion charge schemes. We compare the impact on a company’s total costs, fuel usage (which drives emissions), and delivery tour plans. Our experimental results demonstrate that some congestion pricing schemes may even increase the emi