A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels

Huang, Jing; Süer, Gürsel A.

doi:10.1016/j.cie.2014.12.001

Cited by 52 publications

(17 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ingimundardottir and Runarsson [9] created an autoselection of combined DR by converting them into measurable contribution factors in the optimizing process of scheduling problems. Huang and Süer [10] adopted GA to explore the best combination of DR and used a "Hold" strategy for multi-objective JSS.…”

Section: Literature Reviewmentioning

confidence: 99%

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Aqel

Gao

2019

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

The flexible job shop scheduling problem (FJSP) is considered as an important problem in the modern manufacturing system. It is known to be an NP-hard problem. Most of the algorithms used in solving FJSP problem are categorized as metaheuristic methods. Some of these methods normally consume more CPU time and some other methods are more complicated which make them difficult to code and not easy to reproduce. This paper proposes a modified iterated greedy (IG) algorithm to deal with FJSP problem in order to provide a simpler metaheuristic, which is easier to code and to reproduce than some other much more complex methods. This is done by separating the classical IG into two phases. Each phase is used to solve a sub-problem of the FJSP: sequencing and routing sub-problems. A set of dispatching rules are employed in the proposed algorithm for the sequencing and machine selection in the construction phase of the solution. To evaluate the performance of proposed algorithm, some experiments including some famous FJSP benchmarks have been conducted. By compared with other algorithms, the experimental results show that the presented algorithm is competitive and able to find global optimum for most instances. The simplicity of the proposed IG provides an effective method that is also easy to apply and consumes less CPU time in solving the FJSP problem.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

Aqel

Gao

2019

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…Kurdi [32] proposed a new hybrid island model GA to solve the production planning problem with the objective of makespan minimization, and a new naturally inspired selfadaptation phase strategy was proposed which was capable of striking a better balance between diversification and intensification of the search process; in the proposed selfadaptation phase strategy, the best individuals were recruited to perform a local search using Tabu Search. Huang and Süer [33] proposed a dispatching rule based genetic algorithm with fuzzy satisfaction levels to solve the multiobjective manufacturing production planning problem, and the objective was to develop a decision making platform which appropriately handles conflicts among different performance measures in a manufacturing system. Rahman et al [34] considered a make-to-stock production system, where three related issues must be considered: the length of a production cycle, the batch size of each product, and the order of the products in each cycle.…”

Section: Literature Reviewmentioning

confidence: 99%

“…While it is the most important point to achieve the best profit under the condition of satisfying the customers' demand, the product plan makers have to try their best to cut down the manufacturing cost (material cost, processing cost, delay cost, and so on) in maximum extent. In order to make a feasible production planning which could meet the resource allocation circumstances at a lowest cost [33,34], there are two parameters that must be considered. One is resource allocation quantity (Raq), which contains facility resource, human resource, time resource, and so on.…”

Section: Problem Description and Optimization Modelmentioning

confidence: 99%

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

Guo

Liu

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

show abstract

“…Figure 3 shows the concept of the Fab-level backward capacity-filtering procedure. The production and Sadowski 1995Sadowski , 1997Pritsker and Snyder 1997;Ho and Li 2004), genetic algorithm-based approaches (Kurz and Askin 2004;Huang and Suer 2015;Kurdi 2016), and time-bucketing approaches (Shimoyashiro, Isoda, and Awane 1984;Ragatz and Mabert 1988;Bobrowski 1989;Cigolini and Portioli-Staudacher 2002;Saad, Pickett, and Kittiaram 2004). Although the use of simulations is a powerful technique for the evaluation of various alternative solutions, it has limitations regarding the generation of suitable solutions on the first step.…”

Section: Introductionmentioning

confidence: 99%

Backward capacity-filtering for electronics Fabs

Seo

Chung

Kim³

et al. 2016

Production Planning & Control

View full text Add to dashboard Cite

In electronics Fabs such as semiconductor Fabs and liquid crystal display (LCD) Fabs, which are capitalintensive, finite-capacity planning is critical to achieving full-capacity production and on-time production. However, existing finite-capacity planning methods do not adequately reflect the actual capacity profiles of an electronics Fab. In this paper, we propose a new Fab-level capacity-filtering procedure to generate finite-capacity plan: a backward capacity-filtering procedure for generating finite-capacity release plan. When developing the Fab-level filtering procedures, we use stage-level capacity-filtering algorithms as a key building block. In this study, we enhance the previous capacity-filtering algorithms proposed by one of the authors in order to facilitate the practical requirements of electronics Fabs. We apply the proposed capacity-filtering method to a modern LCD-TFT Fab in Korea. Performance analyses demonstrate that the proposed method is superior to existing methods. ARTICLE HISTORYFinite-capacity planning; flexible flow line; electronic Fab; lcd and semiconductor Fab

show abstract

A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels

Cited by 52 publications

References 30 publications

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

A Modified Iterated Greedy Algorithm for Flexible Job Shop Scheduling Problem

A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

Backward capacity-filtering for electronics Fabs

Contact Info

Product

Resources

About