Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic

Zhang, Sicheng; Wong, TN

doi:10.1080/00207543.2015.1098786

Cited by 18 publications

(8 citation statements)

References 54 publications

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“…Manufacturing scheduling to optimize configurations, either directly or indirectly, has been an important issue for different types of industries, including plastic, textile, and chemical industries, as well as for some service areas [3,7,9,[19][20][21][22][23][24][25].…”

Section: General Overviewmentioning

confidence: 99%

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

et al. 2022

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Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems.

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Section: General Overviewmentioning

confidence: 99%

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

et al. 2022

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“…Equation (5) constrains the relationship between the start time and completion time for job J i at stage Oper j . Equation (6) represents that job J i needs to complete the current stage before proceeding to the next stage. The general constraints of flexible flow shops are still valid for the multi-queue limited buffers scheduling problem in a flexible flow shop with setup times.…”

Section: General Constraint Of Flexible Flow Shops Schedulingmentioning

confidence: 99%

“…In recent years, due to the awareness of both the importance of production preparation and the necessity of separating the setup time and processing time, the scheduling problem considering the setup time has gained a lot of attention from the academic and industrial circles. Zhang et al [6] employed an enhanced version of ant colony optimization (E-ACO) algorithm to solve flow shop scheduling problems with setup times. Shen et al [7] presented a Tabu search algorithm with specific neighborhood functions and a diversification structure to solve the job shop scheduling problem with limited buffers.…”

Section: Introductionmentioning

confidence: 99%

An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

et al. 2019

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Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can improve the local dispatching process for the multi-queue limited buffer. We evaluate our method through the real data from a bus manufacture production line. The results show that the proposed ICGA with local dispatching rules and is very efficient and outperforms other existing methods.

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“…Luo et al [6] addressed the multi-objective integrated process planning and scheduling (MOIPPS) problem, in that a production system required multiple objectives to be taken into account in the more realistic decision-making process. Zhang and Wong [7] studied three different models of IPPS problems, including setup times. They used a customized ant colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

A modified integer and categorical PSO algorithm for solving integrated process planning, dynamic scheduling and due date assignment problem

2021

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Particle Swarm Optimization (PSO) has many successful applications in solving continuous optimization problems. It has been adapted to solve discrete optimization problems using different variants, such as integer PSO (IPSO), discrete PSO (DPSO) and integer and categorical PSO (ICPSO). ICPSO, a recent PSO variant, uses probability distributions instead of the solution values. In this study, we applied ICPSO algorithm to solve dynamic integrated process planning, scheduling and due date assignment (DIPPSDDA) problem which is a higher integration level of well-known problems which are integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Briefly, due date assignment function is integrated to IPPS problem as the third manufacturing function in DIPPSDDA. Furthermore, DIPPSDDA performs scheduling function in a dynamic environment where jobs arrive at the shop floor at any time. The objective of DIPPSDDA problem is to minimize the earliness, tardiness and given due dates length. Since the experimental results show that ICPSO converges, crossover and mutation operators used in genetic algorithms were implemented to ICPSO, namely modified ICPSO (MICPSO). Finally, experimental results indicate that the proposed MICPSO provides better performance as compared to genetic algorithms, ICPSO and modified discrete PSO.

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Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic

Cited by 18 publications

References 54 publications

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

A modified integer and categorical PSO algorithm for solving integrated process planning, dynamic scheduling and due date assignment problem

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