Optimization of Activity Batching Policies in Business Processes

2025-07-21

AI Summary

This paper addresses the multi-objective trade-off among waiting time, processing cost, and resource consumption in activity batching strategies for business processes. To tackle this challenge, we propose an intervention-guided Pareto optimization framework that integrates three metaheuristic methods—hill climbing, simulated annealing, and reinforcement learning—to dynamically update the Pareto front while balancing solution diversity and convergence. A simulation-driven intervention effect evaluation mechanism enables iterative strategy refinement. Experimental results demonstrate that our approach significantly outperforms baseline methods without heuristic guidance in terms of convergence speed to the Pareto-optimal set, spread of the obtained solutions, and reduction in cycle time. The framework provides an interpretable and scalable paradigm for multi-objective optimization in process batching decisions.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

In business processes, activity batching refers to packing multiple activity instances for joint execution. Batching allows managers to trade off cost and processing effort against waiting time. Larger and less frequent batches may lower costs by reducing processing effort and amortizing fixed costs, but they create longer waiting times. In contrast, smaller and more frequent batches reduce waiting times but increase fixed costs and processing effort. A batching policy defines how activity instances are grouped into batches and when each batch is activated. This paper addresses the problem of discovering batching policies that strike optimal trade-offs between waiting time, processing effort, and cost. The paper proposes a Pareto optimization approach that starts from a given set (possibly empty) of activity batching policies and generates alternative policies for each batched activity via intervention heuristics. Each heuristic identifies an opportunity to improve an activity's batching policy with respect to a metric (waiting time, processing time, cost, or resource utilization) and an associated adjustment to the activity's batching policy (the intervention). The impact of each intervention is evaluated via simulation. The intervention heuristics are embedded in an optimization meta-heuristic that triggers interventions to iteratively update the Pareto front of the interventions identified so far. The paper considers three meta-heuristics: hill-climbing, simulated annealing, and reinforcement learning. An experimental evaluation compares the proposed approach based on intervention heuristics against the same (non-heuristic guided) meta-heuristics baseline regarding convergence, diversity, and cycle time gain of Pareto-optimal policies.

Problem

• Optimizing trade-offs between waiting time, processing effort, and cost in activity batching
• Discovering optimal batching policies via Pareto optimization and intervention heuristics
• Comparing meta-heuristics for policy convergence, diversity, and cycle time improvement

Innovation

• Pareto optimization for batching policy discovery
• Intervention heuristics to improve batching metrics
• Meta-heuristics like hill-climbing and reinforcement learning