Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes

Abstract

The allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.

Pareto-Optimal Trace Generation from Declarative Process Models

“…Burattin et al propose a method to check trace conformance and compliance in business processes [13]. Additionally, López-Pintado et al introduces an approach involving Pareto efficiency to optimize resource allocation [14]. Compared to these works, our contribution is novel in several key aspects.…”

“…Compared to these works, our contribution is novel in several key aspects. First, our primary focus lies in discovering optimal executions (traces) for activities that have fixed resource costs rather than allocating resources to activities for optimization purposes, as observed in [8,9,10,11,14]. Second, we adopt a declarative approach using DCR graphs, whereas most works excepting [12,13] work on imperative process models.…”

“…Another aspect is that we are using a constraint-based model to generate traces and to specify the requirements needed for a trace to be valid and compliant with a given DCR graph, which differs from the conformance checking approach used in [13], which essentially looks at the compliance and informativeness of traces against a model. Finally, our pruning method to accelerate reaching the Pareto frontier is different than the ranking method used by [14].…”

AI-augmented Business Process Management Systems: A Research Manifesto

“…The objective of the ABPMS is to strike optimal tradeoffs between the competing performance measures of its process, such as operational cost and waiting times [53]. Therefore, it autonomously decides what needs to be done to achieve such tradeoffs.…”