Integrated process planning and scheduling by an agent-based ant colony optimization

Leung, CW; Wong, TN; Mak, K. L.; Fung, Richard Y. K.

doi:10.1016/j.cie.2009.09.003

Cited by 142 publications

(56 citation statements)

References 25 publications

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“…Then, there will be more pheromone on the shortest path, influencing later ants to follow this path, after their completion of one tour. By time, the path which has high level of pheromone will be most often selected and considered as the shortest route (Leung et al 2010). …”

Section: Ant Colony Optimisationmentioning

confidence: 99%

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Küçükkoç

Zhang

2014

International Journal of Production Research

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Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed by Ozcan et al. (Balancing parallel two-sided assembly lines, International Journal of Production Research, 48 (16), 4767-4784, 2010). The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles explained and discussed.Keywords: mixed-model parallel two-sided assembly lines; assembly line balancing; agent based ant colony optimisation; meta-heuristics; artificial intelligence

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Section: Ant Colony Optimisationmentioning

confidence: 99%

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Küçükkoç

Zhang

2014

International Journal of Production Research

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“…Data set for Experiment 4 is adapted from Leung et al [37]. The problem was originally formulated by Lee and DiCesare [38].…”

Section: Methodsmentioning

confidence: 99%

Integrated process planning and scheduling using genetic algorithms

Chaudhry

Usman

2017

Teh. vjesn.

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INTEGRATED PROCESS PLANNING AND SCHEDULING USING GENETIC ALGORITHMS Imran Ali Chaudhry, Muhammad UsmanOriginal scientific paper Process planning and scheduling are two of the most important functions in any manufacturing system. Traditionally process planning and scheduling are considered as two separate functions. In this paper a Genetic Algorithm (GA) for integrated process planning and scheduling is proposed where selection of the best process plan and scheduling of jobs in a job shop environment are done simultaneously. In the proposed approach a domain independent spreadsheet based approach is presented to solve this class of problems. The precedence relations among job operations are considered in the model, based on which implicit representation of a feasible process plans for each job can be done. To verify the performance and feasibility of the presented approach, the proposed algorithm has been evaluated against a number of benchmark problems that have been adapted from the previously published literature. The experimental results show that the proposed approach can efficiently achieve optimal or near-optimal solutions for the problems adopted from literature. It is also demonstrated that the proposed algorithm is of general purpose in application and could be used for the optimisation of any objective function without changing the model or the basic GA routine. Keywords: integrated process planning and scheduling (IPPS); genetic algorithms; job shop Integracija projektiranja tehnoloških procesa i planiranja primjenom genetičkih algoritamaIzvorni znanstveni članak Projektiranje tehnoloških procesa i planiranje predstavljaju dvije najvažnije funkcije svakog proizvodnog procesa. Tradicionalno se one smatraju dvjema odvojenim funkcijama. U ovom se radu predlaže Genetički Algoritam (GA) za integraciju ovih aktivnosti, gdje se simultano odvija izbor najboljeg tehnološkog procesa i vremenski plan poslova u pogonu. U radu se za rješavanje te vrste problema predstavlja pristup zasnovan na proračunskoj tablici neovisnog područja. U modelu se razmatraju odnosi prvenstva u izvođenju poslova na temelju kojih se donosi implicitno predstavljanje mogućih planova za izvršenje svakog posla. Zbog provjere izvršenja i ostvarivosti predstavljenog pristupa, predloženi se algoritam provjeravao na nizu referentnih problema prilagođenih iz ranije objavljene literature. Eksperimentalni rezultati pokazuju da se predloženim pristupom mogu učinkoviti postići optimalna ili njima blizu rješenja za probleme prilagođene iz literature. Također je pokazano da predloženi algoritam ima opću namjenu i može se primijeniti za optimizaciju bilo koje objektivne funkcije bez promjene modela ili osnovne GA rutine. Ključne riječi: integracija projektiranja tehnoloških proces i planiranja (IPPS); genetički algoritami; radna mjesta

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“…The implementation of ant colony algorithm in scheduling domain is the subject of many works [8,9,10,11]. In many cases authors proposes some extensions dedicated for a special kind of scheduling task's model: flow configuration, constraints etc.…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimisation for scheduling of flexible job shop with multi-resources requirements

et al. 2017

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Abstract. The paper presents application of ant colony optimisation algorithm for scheduling multi-resources operations in flexible job shop type of production systems. Operations that require the participation of two or more resources are common in industrial practice, when planning are subject not only machines, but also other additional resources (personnel, tools, etc.). Resource requirements of operation are indicated indirectly by resource groups. The most important parameters of the resource model and resource groups are also described. A basic assumptions for ant colony algorithm used for scheduling in the considered model with multiresources requirements of operations is discussed. The main result of the research is the schema of metaheuristic that enables searching best-score solutions in manufacturing systems satisfying presented constraints.

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Integrated process planning and scheduling by an agent-based ant colony optimization

Cited by 142 publications

References 25 publications

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Integrated process planning and scheduling using genetic algorithms

Ant colony optimisation for scheduling of flexible job shop with multi-resources requirements

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