AdONE Seminar - Monday, October 28, 2024: Prof. Danja Sonntag (Lund University)

From a linear supply chain to a circular economy: The impact of sustainable sourcing labeling regulations

Across the clothing industry accounting for 60% of the world's textile waste, less than 1% of the used products undergo closed-loop recycling enabling the use for equal-value applications. This highlights the problem of a highly linear supply chain in the textile industry, where textiles are produced, used and mainly disposed of. This project contributes to the current debate on transforming the textile industry into a circular economy. Taking into account the challenges of a highly seasonal industry with high demand uncertainties, we combine inventory management practices to reduce waste with a systematic analysis of the economic and environmental impacts of different sustainable sourcing labeling regulations. In the analysis, we incorporate the effect of the manufacturer’s purchasing decisions and governmental regulations about sustainable sourcing labeling on consumer demand driven by consumer environmental awareness. We derive the manufacturer’s optimal sourcing and production quantities and analyze their impact on economic and environmental key performance indicators. 

Date: October 28, 2024

Time: 5 pm, s.t.

Place: Arcisstr. 21, room 0507.EG.790

 

AdONE Seminar - Monday, November 4, 2024: Prof. Pascal Lenzner (Universität Augsburg)

 

Date: November 4, 2024

Time: 5 pm, s.t.

Place: Garching, room tba

 

AdONE Seminar - Monday, November 18, 2024: Prof. Merve Bodur (University of Edinburgh)

Markov Chain-based Policies for Multi-stage Stochastic Integer Linear Programming with an Application to Disaster Relief Logistics

Multi-stage stochastic integer linear programs (MSILPs) arise in many practical applications, including logistics planning. We introduce a novel aggregation framework to address MSILPs with mixed-integer state variables and continuous local variables. Our framework imposes additional structure to the integer state variables by leveraging the information of the underlying stochastic process, which is modelled as a Markov chain (MC). We present an exact solution method to the aggregated MSILP, which can also be used in an approximation form to obtain dual bounds and implementable feasible solutions. Moreover, we apply two-stage linear decision rule approximations to obtain high-quality decision policies with significantly reduced computational effort. We test the proposed methodologies in a novel MSILP for hurricane disaster relief logistics planning. We illustrate the effectiveness of the proposed approaches, analyze the trade-offs between various MC-based policies, and extract problem-specific insights from the solution behaviours. 

Date: November 18, 2024

Time: 5 pm, s.t.

Place: Arcisstr. 21, room 0507.EG.790

 

AdONE Seminar - Monday, December 16, 2024: Prof. Joren Gijsbrechts (ESADE Business School Barcelona)

Speeding up Policy Simulation in Supply Chain RL 

Simulating a single trajectory of a dynamical system under some state-dependent policy is a core bottleneck in policy optimization algorithms. The many inherently serial policy evaluations that must be performed in a single simulation constitute the bulk of this bottleneck. To wit, in applying policy optimization to supply chain optimization (SCO) problems, simulating a single month of a supply chain can take several hours. We present an iterative algorithm for policy simulation, which we dub Picard Iteration. This scheme carefully assigns policy evaluation tasks to independent processes. Within an iteration a single process evaluates the policy only on its assigned tasks while assuming a certain ‘cached’ evaluation for other tasks; the cache is updated at the end of the iteration. Implemented on GPUs, this scheme admits batched evaluation of the policy on a single trajectory. We prove that the structure afforded by many SCO problems allows convergence in a small number of iterations independent of the horizon. We demonstrate practical speedups of 400x on large-scale SCO problems even with a single GPU, and also demonstrate practical efficacy in other RL environments. 

Link: https://arxiv.org/pdf/2406.01939

Date: December 16, 2024

Time: 4 pm, s.t.

Place: Arcisstr. 21, room 0507.EG.790