1763. Machine leasing management for welfare-oriented companies using improved invasive weed optimization
Invited abstract in session TC-12: Application of scheduling models, stream Scheduling and Project Management.
Tuesday, 12:30-14:00Room: Clarendon SR 1.02
Authors (first author is the speaker)
| 1. | Chunfeng Liu
|
| School of Management, Hangzhou Dianzi University | |
| 2. | Emrah Demir
|
| Cardiff Business School, Cardiff University |
Abstract
In service-oriented manufacturing, equipment manufacturers can extend their role beyond leasing by offering optimized production scheduling to improve equipment utilization and customer satisfaction. This study focuses on leasing strategies for welfare-oriented companies, where the objective is to maximize the participation of disabled operators in production while minimizing leasing costs. To address this, we propose a bi-objective nonlinear programming model that determines optimal equipment selection, production scheduling, and operator assignment to balance the interests of both lessors and lessees. Given the complexity of the problem, we develop a Discrete Invasive Weed Optimization with a Priority rule-based heuristic (DIWOP) to efficiently generate high-quality solutions. The performance of DIWOP is evaluated against Discrete Invasive Weed Optimization (DIWO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) using three metrics to assess the Pareto solution set. Extensive numerical experiments and statistical t-tests confirm that DIWOP achieves superior convergence, better diversity, and higher solution quality within the same computational time compared to DIWO and NSGA-II. The findings offer valuable insights into optimizing machine leasing management for welfare-oriented companies, demonstrating the potential benefits of integrating advanced algorithms into service-oriented manufacturing.
Keywords
- Manufacturing
- Service Operations
- Metaheuristics
Status: accepted
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