A PERFORMANCE COMPARISON AND EVALUATION OF METAHEURISTICS FOR A BATCH SCHEDULING PROBLEM IN A MULTI-HYBRID CELL MANUFACTURING SYSTEM WITH SKILLED WORKFORCE ASSIGNMENT


Yilmaz Ö. F., Durmuşoğlu M. B.

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, vol.14, no.3, pp.1219-1249, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 3
  • Publication Date: 2018
  • Doi Number: 10.3934/jimo.2018007
  • Journal Name: JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1219-1249
  • Keywords: Batch scheduling, skilled workforce assignment, dual-resource constrained, hybrid manufacturing cells, metaheuristics, ONE-PIECE FLOW, GENETIC ALGORITHM, HEURISTIC ALGORITHMS, RESOURCE FLEXIBILITY, SETUP TIMES, ALLOCATION, OPTIMIZATION, INDUSTRY, SEARCH, DESIGN
  • Karadeniz Technical University Affiliated: Yes

Abstract

This paper focuses on the batch scheduling problem in multi-hybrid cell manufacturing systems (MHCMS) in a dual-resource constrained (DRC) setting, considering skilled workforce assignment (SWA). This problem consists of finding the sequence of batches on each cell, the starting time of each batch, and assigning employees to the operations of batches in accordance with the desired objective. Because handling both the scheduling and assignment decisions simultaneously presents a challenging optimization problem, it is difficult to solve the formulated model, even for small-sized problem instances. Three metaheuristics are proposed to solve the batch scheduling problem, namely the genetic algorithm (GA), simulated annealing (SA) algorithm, and artificial bee colony (ABC) algorithm. A serial scheduling scheme (SSS) is introduced and employed in all metaheuristics to obtain a feasible schedule for each individual. The main aim of this study is to identify an effective metaheuristic for determining the scheduling and assignment decisions that minimize the average cell response time. Detailed computational experiments were conducted, based on real production data, to evaluate the performance of the metaheuristics. The experimental results show that the performance of the proposed ABC algorithm is superior to other metaheuristics for different levels of experimental factors determined for the number of batches and the employee flexibility.