Refined Iterated Pareto Greedy for Energy-aware Hybrid Flowshop Scheduling with Blocking Constraints

2025-10-03

AI Summary

This paper addresses the blocking hybrid flowshop scheduling problem (BHFS), aiming to jointly optimize makespan (i.e., maximum completion time) and total energy consumption—particularly relevant for energy-intensive industries such as automotive and pharmaceutical manufacturing. As an NP-hard multi-objective optimization problem, BHFS is formulated via a novel multi-objective mixed-integer programming (MO-MIP) model. To solve it effectively, we propose an enhanced ε-constraint method and a refined iterative Pareto greedy algorithm (RIPG), which integrates Pareto dominance mechanisms, an improved local search procedure, and augmented constraint handling to strike a superior balance between solution quality and computational efficiency. Experimental results demonstrate that RIPG significantly outperforms state-of-the-art algorithms on small- and medium-scale instances, rapidly generating high-quality Pareto-optimal solution sets. The approach thus provides an efficient, scalable decision-support tool for green intelligent manufacturing.

Technology Category

• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

The scarcity of non-renewable energy sources, geopolitical problems in its supply, increasing prices, and the impact of climate change, force the global economy to develop more energy-efficient solutions for their operations. The Manufacturing sector is not excluded from this challenge as one of the largest consumers of energy. Energy-efficient scheduling is a method that attracts manufacturing companies to reduce their consumption as it can be quickly deployed and can show impact immediately. In this study, the hybrid flow shop scheduling problem with blocking constraint (BHFS) is investigated in which we seek to minimize the latest completion time (i.e. makespan) and overall energy consumption, a typical manufacturing setting across many industries from automotive to pharmaceutical. Energy consumption and the latest completion time of customer orders are usually conflicting objectives. Therefore, we first formulate the problem as a novel multi-objective mixed integer programming (MIP) model and propose an augmented epsilon-constraint method for finding the Pareto-optimal solutions. Also, an effective multi-objective metaheuristic algorithm. Refined Iterated Pareto Greedy (RIPG), is developed to solve large instances in reasonable time. Our proposed methods are benchmarked using small, medium, and large-size instances to evaluate their efficiency. Two well-known algorithms are adopted for comparing our novel approaches. The computational results show the effectiveness of our method.

Problem

• Minimizing makespan and energy in hybrid flowshop scheduling
• Addressing conflicting objectives with multi-objective optimization
• Solving blocking constraint scheduling problems using metaheuristic algorithms

Innovation

• Refined Iterated Pareto Greedy metaheuristic algorithm
• Multi-objective mixed integer programming model
• Augmented epsilon-constraint Pareto optimization method