A Multi-objective Variable Neighborhood Search algorithm for solving the Hybrid Flow Shop Problem

Siqueira, Eduardo Camargo de; Souza, Marcone Jamilson Freitas; Souza, Sérgio Ricardo de

doi:10.1016/j.endm.2018.03.012

Cited by 14 publications

(10 citation statements)

References 4 publications

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“…(2017), de Siqueira et al. (2018), Dios et al. (2018), Khare and Agrawal (2019), Harbaoui and Khalfallah (2020), Aqil and Allali (2020), Costa et al.…”

Section: An Overview Of Shop Scheduling Problems Characteristicsmentioning

confidence: 99%

Shop scheduling in manufacturing environments: a review

Márquez

Ribeiro

2022

Int Trans Operational Res

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We review the literature about shop scheduling problems in manufacturing systems, revealing the concepts and methodologies that most impact the usage of scheduling theory in manufacturing environments. We focus our attention on the job shop and flow shop problems and their variants. We emphasize the interactions between the scheduling functions and manufacturing paradigms such as Industry 4.0, Computer Integrated Manufacturing, Computer‐Aided Process Planning, Advanced Planning and Scheduling, and Integrated Process Planning and Scheduling. We describe the main components and characteristics of the scheduling ecosystem, and we discuss how the scheduling interacts with the components that make it up and how it is affected by them. The metadata collected from the digital libraries on which the review was based (ScienceDirect, Scopus, and Elsevier) made it possible to characterize the historical evolution of the main concepts of the scheduling ecosystem in terms of scientific publications and research trends in the period 2000–2020.

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“…(2017), de Siqueira et al. (2018), Dios et al. (2018), Khare and Agrawal (2019), Harbaoui and Khalfallah (2020), Aqil and Allali (2020), Costa et al.…”

Section: An Overview Of Shop Scheduling Problems Characteristicsmentioning

confidence: 99%

Shop scheduling in manufacturing environments: a review

Márquez

Ribeiro

2022

Int Trans Operational Res

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“…To minimize makespan and mean flow time, Solano-Charris et al [20] presented an ACO; Marichelvam et al [21] proposed a discrete firefly algorithm. To minimize makespan and mean tardiness, Mundim and Queiroz [22] and De Siqueira et al [23] developed a variable neighborhood search algorithm(VNS) respectively; Ying et al [24] proposed an iterated pareto greedy algorithm, Tran and Ng [25] proposed a hybrid water flow algorithm; Asefi et al [26] considered the no-wait constraint and proposed a hybrid NSGA-II and VNS algorithm; Cho et al [27] developed a Pareto genetic algorithm while considering reentrant. To minimize the total flow time and the number of tardy jobs, Wang et al [28] developed a NSGAII.…”

Section: B Multi-objective Hfs Scheduling Problemsmentioning

confidence: 99%

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

Song

2021

IEEE Access

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Efficient scheduling benefits productivity promotion, energy savings and the customer's satisfaction. In recent years, with a growing concern about the energy saving and environmental impact, energy oriented scheduling is going to be a hot issue for sustainable manufacturing. In this study, we investigate an energy-oriented scheduling problem deriving from the hybrid flow shop with unrelated parallel machine. First, we formulate the scheduling problem with a mixed integer linear programming (MILP) model, which considers two objectives including minimizing the completion time and energy consumption. Second, a hybrid multi-objective teaching-learning based optimization (HMOTLBO) algorithm based on decomposition is proposed. In the proposed HMOTLBO, a new solution presentation and five decoding rules are designed for mining the optimal solution. To reduce the standby energy consumption and turning on/off energy consumption, a greedy shifting algorithm is developed without changing the completion time of a scheduling. To improve the converge speed of the algorithm, a weight matching strategy is designed to avoid randomly matching weight vectors with students. To enhance the exploration and exploitation capacities of the algorithm, A teaching operator based on crossover and a self-learning operator based on a variable neighborhood search(VNS) are proposed. Finally, fourth different experiments are performed on 15 cases, the comparison result verified the effectiveness and the superiority of the proposed algorithm.INDEX TERMS Hybrid flow shop scheduling, teaching and learning based optimization, multi-objective, makespan, energy consumption.

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“…As far as possible, we set the parameters according to Ruiz et al [2] and de Siqueira et al [3], to support a more homogeneous notation for machine scheduling studies. The following assumptions hold for our model.…”

Section: Description Of the Basic Modelmentioning

confidence: 99%

“…De Siqueira et al [3] modify a variable neighbourhood search of Geiger [20] for the problem FHM, ((RM (k) ) c k=1 ) | M j , skip | C max . To change the solutions, one of the six neighborhood strategies is applied randomly:…”

Section: Related Workmentioning

confidence: 99%

Scheduling Algorithms for a Hybrid Flow Shop under Uncertainty

Schumacher

Buchholz

2020

Algorithms

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In modern production systems, scheduling problems have to be solved in consideration of frequently changing demands and varying production parameters. This paper presents a approach combining forecasting and classification techniques to predict uncertainty from demands, and production data with heuristics, metaheuristics, and discrete event simulation for obtaining machine schedules. The problem is a hybrid flow shop with two stages, machine qualifications, skipping stages, and uncertainty in demands. The objective is to minimize the makespan. First, based on the available data of past orders, jobs that are prone to fluctuations just before or during the production phase are identified by clustering algorithms, and production volumes are adjusted accordingly. Furthermore, the distribution of scrap rates is estimated, and the quantiles of the resulting distribution are used to increase corresponding production volumes to prevent costly rescheduling resulting from unfulfilled demands. Second, Shortest Processing Time (SPT), tabu search, and local search algorithms are developed and applied. Third, the best performing schedules are evaluated and selected using a detailed simulation model. The proposed approach is validated on a real-world production case. The results show that the price for a very robust schedule that avoids underproduction with a high probability can significantly increase the makespan.

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A Multi-objective Variable Neighborhood Search algorithm for solving the Hybrid Flow Shop Problem

Cited by 14 publications

References 4 publications

Shop scheduling in manufacturing environments: a review

Shop scheduling in manufacturing environments: a review

A Hybrid Multi-Objective Teaching-Learning Based Optimization for Scheduling Problem of Hybrid Flow Shop With Unrelated Parallel Machine

Scheduling Algorithms for a Hybrid Flow Shop under Uncertainty

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