Pattern mining‐based pruning strategies in stochastic local searches for scheduling problems

Abstract

Scheduling problems are a subclass of combinatorial problems consisting of a set of tasks/activities/jobs to be processed by a set of resources usually to minimize a time criterion. Some optimization methods used to solve these problems are hybridized with knowledge discovery techniques to extract information during the optimization process and enhance it. However, most of these hybrid techniques are custom-designed and lack generalization. In this paper a module for knowledge extraction in Stochastic Local Searches is designed, aiming to be problem independent and plugged into optimisation methods that relies on multiple Stochastic Local Search replications. The objective is to prune parts of the search space for which the exploration is likely to lead to poor solutions. This is performed through the extraction of high-quality patterns occurring in locally optimal solutions. Benchmarked on two well-known scheduling problems, the Job-shop Problem and the Resource Constrained Project Scheduling Problem, the results show both a speed up in the convergence and the reaching of better local optima solutions.

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

“…As projects have become ever‐more‐common structures for organizing work in modern enterprises, issues involving the simultaneous management of multiple projects have become more pervasive (He et al., 2022). A generalization of the resource‐constrained project scheduling problem (RCPSP) (Blazewicz et al., 1983; Chakrabortty et al., 2020; Laurent et al., 2022; Liu et al., 2023b), the resource‐constrained multiproject scheduling problem (RCMPSP) has been widely studied in recent years (Van Eynde and Vanhoucke, 2020; Cui et al., 2021; Bredael and Vanhoucke, 2023; Hartmann and Briskorn, 2022). A centralized management method is the most commonly used to tackle RCMPSP (Li et al., 2021), that is, integrating all the activities of multiple projects into a super‐ or metanetwork, and then a unified decision‐maker with complete information on multiple projects makes a scheduling plan and allocates resources.…”