Solving Integrated Process Planning, Dynamic Scheduling, and Due Date Assignment Using Metaheuristic Algorithms

Abstract

Because the alternative process plans have significant contributions to the production efficiency of a manufacturing system, researchers have studied the integration of manufacturing functions, which can be divided into two groups, namely, integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Although IPPS and SWDDA are well-known and solved problems in the literature, there are limited works on integration of process planning, scheduling, and due date assignment (IPPSDDA). In this study, due date assignment function was added to IPPS in a dynamic manufacturing environment. And the studied problem was introduced as dynamic integrated process planning, scheduling, and due date assignment (DIPPSDDA). The objective function of DIPPSDDA is to minimize earliness and tardiness (E/T) and determine due dates for each job. Furthermore, four different pure metaheuristic algorithms which are genetic algorithm (GA), tabu algorithm (TA), simulated annealing (SA), and their hybrid (combination) algorithms GA/SA and GA/TA have been developed to facilitate and optimize DIPPSDDA on the 8 different sized shop floors. The performance comparisons of the algorithms for each shop floor have been given to show the efficiency and effectiveness of the algorithms used. In conclusion, computational results show that the proposed combination algorithms are competitive, give better results than pure metaheuristics, and can effectively generate good solutions for DIPPSDDA problems.

Garment production line optimization using production information based on real‐time power monitoring data

“…Because of its robustness and flexibility, GAs are the most widely used technique for assembly-line optimization. [19][20][21] GA-based process optimization has long been studied using machine learning techniques. 22 Cheshmehgaz et al analyzed the relocation of cellular personnel using a GA. 23 Li et al proposed an active learning GA. 24 Barathwaj et al considered the application of a GA for reducing the number of workers in mixed-model assembly lines.…”

“…The method proposed in this study includes a genetic algorithm (GA) as an optimization technique. Because of its robustness and flexibility, GAs are the most widely used technique for assembly‐line optimization 19–21 . GA‐based process optimization has long been studied using machine learning techniques 22 .…”

A Tabu Search and Hybrid Evolutionary Strategies Algorithms for the Integrated Process Planning and Scheduling with Due-date Agreement

“…Demir and Erden (2020) tried to optimize the dynamic environment with ant colony algorithm in their work. DIPPSDDA, which was previously improved with the genetic algorithm and some metaheuristic algorithms (Erden et al, 2019), was a new field of study.…”

Hybrid Evolutionary Strategy and Simulated Annealing Algorithms for Integrated Process Planning, Scheduling and Due-Date Assignment Problem

“…Following works can be given as studies on Scheduling With Due-Window Assignment (SWDWA) problem; and Yang et al [17]. Some of the works on the IPPSDDA problem can be given as Demir and Taskin [18], Demir and Erden [19]- [21], [21], [22].…”