Many-objective genetic programming for job-shop scheduling

Masood, Atiya; Yi, Ming; Chen, Gang; Zhang, Mengjie

doi:10.26686/wgtn.14344058

Cited by 5 publications

(6 citation statements)

References 28 publications

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“…Specifically, three typical test instances of the FT test set (i.e., FT06, FT10, and FT20) and all 40 test instances of the LA test set (i.e., LA01-LA40) are selected to evaluate the algorithm as they have different dimension sizes vary from 36 (i.e., J = 6 and M = 6) to 300 (i.e., J = 30 and M = 10). According to [16], the due date D j of job j is calculated as…”

Section: A Experimental Designmentioning

confidence: 99%

“…The third category is the many-objective JSSP (MaJSSP) model with more than three objectives. Specifically, a fourobjective model (i.e., mean flow time, maximum flow time, mean weighted tardiness, and maximum weighted tardiness) is constructed in [16]- [18]. To solve this model, a genetic programming-based nondominated sorting GA approach [16], a reference point adaption method [17], and a fitness-based selection technique [18] are proposed.…”

Section: Introductionmentioning

confidence: 99%

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Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Liu

Chen

Zhan

et al. 2023

IEEE Trans. Cybern.

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The job-shop scheduling problem (JSSP) is a challenging scheduling and optimization problem in the industry and engineering, which relates to the work efficiency and operational costs of factories. The completion time of all jobs is the most commonly considered optimization objective in the existing work. However, factories focus on both time and cost objectives, including completion time, total tardiness, advance time, production cost, and machine loss. Therefore, this article first time proposes a many-objective JSSP that considers all these five objectives to make the model more practical to reflect the various demands of factories. To optimize these five objectives simultaneously, a novel multiple populations for multiple objectives (MPMO) frameworkbased genetic algorithm (GA) approach, called MPMOGA, is proposed. First, MPMOGA employs five populations to optimize the five objectives, respectively. Second, to avoid each population only focusing on its corresponding single objective, an archive sharing technique (AST) is proposed to store the elite solutions collected from the five populations so that the populations can obtain optimization information about the other objectives from the archive. This way, MPMOGA can approximate different parts of the entire Pareto front (PF). Third, an archive update strategy

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Section: A Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Liu

Chen

Zhan

et al. 2023

IEEE Trans. Cybern.

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“…Update the HM as X w = X new , if f(X new ) < f(X w ) (16). If NI is completed, return the best harmony vector X B in the HM; otherwise, go back to step (4) Where HMS, HMCR, PAR, BW and NI stand for the Harmony Memory Size, Harmony Memory Consideration Rate, Pitch Adjustment Rate, Bandwidth of Pitch Adjustment and Number of Iterations, respectively. v is the variable symbol and n is the number of the variable.…”

Section: An Overview Of the Harmony Search Algorithmmentioning

confidence: 99%

“…Flowline manufacturing cell scheduling is the application and scheduling of flow shop production mode in the cell manufacturing environment, that is, the task family of each production cell and the tasks within the family are processed through all machines of the cell according to a certain production technology, and a reasonable processing sequence is arranged to make one or some performance achieve the optimal. The common performance indicators include the minimum maximum completion time (makespan) [2], total tardiness [3] and mean flow time [4].…”

Section: Introductionmentioning

confidence: 99%

Scheduling Optimization in Flowline Manufacturing Cell Considering Intercell Movement with Harmony Search Approach

Huang

Yang

2020

Mathematics

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Based on the non-permutation property of intercell scheduling in flowline manufacturing cells, a hybrid harmony search algorithm is proposed to solve the problem with the makespan criterion. On the basis of the basic harmony search algorithm, the three key elements of memory consideration, pitch adjustment and random selection are discretized and improved to adapt to the operation-based encoding. To compare the performance, different scale cases are generated in both the overall solution and the two-stage solution with the proposed algorithm, the hybrid particle swarm optimization algorithm and the hybrid genetic algorithm. The relative deviation is taken as the performance index. The compared results show that a better solution can be obtained with the proposed algorithm in both the overall solution and the two-stage solution, verifying the superior performance of the proposed algorithm.

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“…Masood et al [90] proposed a many-objective GP approach to handle a DJSS problem with up to four objectives. A many-objective optimisation problem is a subset of multi-objective optimisation problems that have more than three objectives [38].…”

Section: Gp Search Mechanismmentioning

confidence: 99%

Evolving Dispatching Rules for Dynamic Job Shop Scheduling Problems using Genetic Programming

Park¹

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<p>Job shop scheduling (JSS) problems are difficult combinatorial optimisation problems that have been studied over the past 60 years. The goal of a JSS problem is to schedule the arriving jobs as effectively as possible on the limited machine resources on the shop floor. Each job has a sequence of operations that need to be processed on specific machines, but the machines can only process one job at a time. JSS and other types of scheduling are important problems in manufacturing systems, such as semiconductor manufacturing. In particular, this thesis focuses on dynamic JSS (DJSS) problems, where unforeseen events occur during processing that needs to be handled by the manufacturer. Examples of dynamic events that occur in DJSS problems are dynamic or unforeseen job arrivals, machine breakdowns, uncertain job processing times, and so on. A prominent method of handling DJSS problems is to design effective dispatching rules for the DJSS problem handled by the manufacturer. Dispatching rules are local decision makers that determine what job is processed by a machine when the machine finishes processing the previous job and becomes available. Dispatching rules have been investigated extensively by both academics and industry experts due to their simplicity, interpretability, low computational cost and their ability to cope effectively in dynamic environments. However, dispatching rules are designed for a specific DJSS problem and have no guarantee that they retain their effectiveness on other DJSS problems. In a real-world scenario, the properties of a manufacturing system can change over time, meaning that previously effective dispatching rule may longer be effective. Therefore, a manufacturer may need to redesign a dispatching rule to maintain a competitive edge on the market. However, designing an effective dispatching rule for a specific DJSS problem is expensive, and typically requires a human expert and extensive trial-and-error process to verify their effectiveness. To circumvent the manual design of dispatching rules, researchers have proposed hyper-heuristic approaches to automate the design of dispatching rules. In particular, various genetic programming based hyper-heuristic (GP-HH) approaches have been proposed in the literature to evolve effective dispatching rules for scheduling problems, including DJSS problems. However, there are many potential directions that have not been fully investigated. The overall goal of this thesis is to develop new and effective GP-HH approaches to designing high-quality dispatching rules for DJSS problems that aims to improve beyond the standard GP approach while maintaining computational efficiency. The focus will be on developing approaches which can decompose complex JSS problems down to simpler subcomponents, evolving multiple heuristics to handle the subcomponents, and developing GP-HH approaches that can handle complex DJSS problems by exploiting the problem properties. This thesis is the first to develop ensemble GP approaches that evolve ensembles of dispatching rules using cooperative coevolution. In addition, the thesis also investigates different combination schemes for one of the ensemble GP approaches to combine the ensemble member outputs effectively. The results show that ensemble GP approach evolves rules that perform significantly better than the rules evolved by the benchmark GP approach. This thesis provides the first investigation into applying GP-HH to a DJSS problem with dynamic job arrivals and machine breakdowns. In addition, the thesis also develops machine breakdown GP approach to the DJSS problem by incorporating machine breakdown GP terminals. The results show that the standard GP do not generalise well over the DJSS problem. The best rules from the machine breakdown GP approach do perform better than the best rule from the standard GP approach, and the analysis shows that the rules behaviour is similar to the shortest processing time rule in certain decision situations. This thesis is the first to develop a multitask GP approach to evolve a portfolio of dispatching rules for a DJSS problem with dynamic job arrivals and machine breakdowns. The multitask GP approach improve on the standard GP approach either in terms of the effectiveness of the output rules or the computation time required to evolve the rules. The analysis shows that the difference between DJSS problem having no machine breakdowns and having machine breakdowns is a more significant factor than the difference between two DJSS problems with different frequencies of machine breakdown investigated.</p>

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Many-objective genetic programming for job-shop scheduling

Cited by 5 publications

References 28 publications

Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Scheduling Optimization in Flowline Manufacturing Cell Considering Intercell Movement with Harmony Search Approach

Evolving Dispatching Rules for Dynamic Job Shop Scheduling Problems using Genetic Programming

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