Optimization of Activity Batching Policies in Business Processes

TL;DR

This paper introduces a Pareto optimization method using intervention heuristics and meta-heuristics like hill-climbing, simulated annealing, and reinforcement learning to discover optimal batching policies in business processes, balancing cost, waiting time, and effort.

Contribution

It proposes a novel approach combining intervention heuristics with meta-heuristics to optimize batching policies in business processes, improving trade-offs between key performance metrics.

Findings

The approach effectively balances waiting time and cost in batching policies.

Meta-heuristics outperform non-guided methods in convergence and diversity.

Reinforcement learning shows promising results in policy optimization.

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.