Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling

Author(s)

Fuchigami, Helio Yochihiro ^[1] ; Sarker, Ruhul ^[2] ; Rangel, Socorro ^[3]

Affiliation

^[1] Fed Univ Goias UFG, Fac Sci & Technol FCT, BR-74968755 Aparecida De Goiania - Brazil

^[2] UNSW, SEIT, Canberra, ACT 2610 - Australia

^[3] Univ Estadual Paulista UNESP, Inst Biociencias Letras & Ciencias Exatas IBILCE, BR-19014020 Sao Paulo - Brazil

• Document type: Journal article
• Source: ALGORITHMS; v. 11, n. 4 APR 2018.
• Web of Science Citations: 1

Abstract

The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average) while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines), with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. They prove to be better than adaptations of the NEH heuristic (well-known for providing very good solutions for makespan minimization in flow shop) for the considered problem. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 16/01860-1 - Cutting, packing, lot-sizing, scheduling, routing and location problems and their integration in industrial and logistics settings
• Grantee: Reinaldo Morabito Neto
• Support Opportunities: Research Projects - Thematic Grants