Robust and Fuzzy Optimisation Models for a Flow shop Scheduling Problem with Sequence Dependent Setup Times: A real case study on a PCB assembly company

Gholami-Zanjani, Seyed Mohammad; Hakimifar, Mohammadmehdi; Nazemi, Najmesadat; Jolai, Fariborz

doi:10.1080/0951192x.2016.1187293

Cited by 29 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gholami‐Zanjani et al. (2017) introduced robust optimization and fuzzy optimization to model the uncertainty of the input data, and minimized weighted mean completion time in a PFSP considering setup and nondeterministic processing times. Cui et al.…”

Section: Related Workmentioning

confidence: 99%

A simheuristic algorithm for the stochastic permutation flow‐shop problem with delivery dates and cumulative payoffs

VILLARINHO

Panadero

Pessôa

et al. 2020

Int Trans Operational Res

View full text Add to dashboard Cite

This paper analyzes the permutation flow-shop problem with delivery dates and cumulative payoffs (whenever these dates are met) under uncertainty conditions. In particular, the paper considers the realistic situation in which processing times are stochastic. The main goal is to find the permutation of jobs that maximizes the expected payoff. In order to achieve this goal, the paper first proposes a biased-randomized heuristic for the deterministic version of the problem. Then, this heuristic is extended into a metaheuristic by encapsulating it into a variable neighborhood descent framework. Finally, the metaheuristic is extended into a simheuristic by incorporating Monte Carlo simulations. According to the computational experiments, the level of uncertainty has a direct impact on the solutions provided by the simheuristic. Moreover, a risk analysis is performed using two well-known metrics: the value-at-risk and conditional value-at-risk.

show abstract

Section: Related Workmentioning

confidence: 99%

A simheuristic algorithm for the stochastic permutation flow‐shop problem with delivery dates and cumulative payoffs

VILLARINHO

Panadero

Pessôa

et al. 2020

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…As for the flow shop, Kouvelis et al [15] show that a two-machine flow shop robust scheduling problem with uncertain processing time and min-max regret criterion is NP-hard. Gholami-Zanjani et al [16] study a robust flow shop scheduling with interval processing times and sequence-dependent setup times with the objective of min-max weighted flow time. They analyze the robust counterpart of the model and apply it to a real-case in PCB assembly company.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Optimization on Unrelated Parallel Machine Scheduling with Setup Times

Gao¹,

Wang²,

Shi³

2020

Preprint

View full text Add to dashboard Cite

The parallel machine scheduling problem has been a popular topic for many years due to its theoretical and practical importance. This paper addresses the robust makespan optimization problem on unrelated parallel machine scheduling with sequence-dependent setup times, where the processing times are uncertain, and the only knowledge is the intervals they take values from. We propose a robust optimization model with min-max regret criterion to formulate this problem. To solve this problem, we prove that the worst-case scenario with the maximum regret for a given solution belongs to a finite set of extreme scenarios. Based on this theoretical analysis, the procedure to obtain the maximum regret is proposed and an enhanced regret evaluation method (ERE) is designed to accelerate this process. A multi-start decomposition-based heuristic algorithm (MDH) is proposed to solve this problem. High-quality initial solutions and an upper bound are examined to help better solve the problem. Computational experiments are conducted to justify the performance of these methods. * Since s 0 is a worst-case scenario for schedule π, s f is also a worst-case scenario for π.

show abstract

“…For example, Lee et al [3] introduced a scheduling study of a manufacturing system that produces and assembles the body and vehicle of a fire engine, and Potts et al [4] dealt with a scheduling problem in make-to-order manufacturing systems, such as PC assembly. In addition, there are scheduling problems with assembly operations arising from various manufacturing systems (refrigerators [5], clothing [6], food [7], and semiconductors [8][9][10][11]).…”

Section: Introductionmentioning

confidence: 99%

Heuristics for a Two-Stage Assembly-Type Flow Shop with Limited Waiting Time Constraints

Han

Lee

2021

Applied Sciences

View full text Add to dashboard Cite

This study investigates a two-stage assembly-type flow shop with limited waiting time constraints for minimizing the makespan. The first stage consists of m machines fabricating m types of components, whereas the second stage has a single machine to assemble the components into the final product. In the flow shop, the assembly operations in the second stage should start within the limited waiting times after those components complete in the first stage. For this problem, a mixed-integer programming formulation is provided, and this formulation is used to find an optimal solution using a commercial optimization solver CPLEX. As this problem is proved to be NP-hard, various heuristic algorithms (priority rule-based list scheduling, constructive heuristic, and metaheuristic) are proposed to solve a large-scale problem within a short computation time. To evaluate the proposed algorithms, a series of computational experiments, including the calibration of the metaheuristics, were performed on randomly generated problem instances, and the results showed outperformance of the proposed iterated greedy algorithm and simulated annealing algorithm in small- and large-sized problems, respectively.

show abstract

Robust and Fuzzy Optimisation Models for a Flow shop Scheduling Problem with Sequence Dependent Setup Times: A real case study on a PCB assembly company

Cited by 29 publications

References 36 publications

A simheuristic algorithm for the stochastic permutation flow‐shop problem with delivery dates and cumulative payoffs

A simheuristic algorithm for the stochastic permutation flow‐shop problem with delivery dates and cumulative payoffs

Robust Optimization on Unrelated Parallel Machine Scheduling with Setup Times

Heuristics for a Two-Stage Assembly-Type Flow Shop with Limited Waiting Time Constraints

Contact Info

Product

Resources

About