An object-coding genetic algorithm for integrated process planning and scheduling

Zhang, Luping; Wong, TN

doi:10.1016/j.ejor.2015.01.032

Cited by 96 publications

(57 citation statements)

References 33 publications

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“…Genetic or evolutionary algorithms are widely used in solving IPPS. Morad and Zalzala [9], Zhao and Wu [10], Moon et al [11], Kim et al [12], Drstvensek and Balic , Moon et al [13], Shao et al [14], Li et al [15], Li et al [16], Seker et al [17], and Zhang and Wong [18] are some examples on this area. It is seen that solving integrated problems are harder according to the literature.…”

Section: Background and Literature Surveymentioning

confidence: 99%

Proses planlama ve ağırlıklı teslim tarihi atama ile birlikte ağırlıklı çizelgeleme probleminin bazı saf ve melez meta-sezgisel yöntemler ile çözümü

Demir¹,

Erden

2017

SAÜ Fen Bilimleri Enstitüsü Dergisi

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If we search literature for integrated process planning and scheduling problem and for scheduling with due date assignment problem we can find hundreds of researches made on these problems. But integration of the three important manufacturing functions are not addressed much in the literature. In this study process planning, weighted scheduling and weighted due date assignment functions are integrated and solved using some pure and hybrid metaheuristics. We studied eight shop floors using random, evolutionary strategies, genetic algorithms and some hybrid searches. We tried to observe how search techniques improve solutions as iterations go on and how evolutionary strategies, genetic algorithms and hybrid search performs well compared to the random search. We also observed that hybrid searches are also powerful search techniques as genetic search and evolutionary strategies.Keywords: process planning, weighted scheduling, weighted due date assignment, evolutionary strategies, genetic algorithm, hybrid metaheuristics, random search Proses planlama ve ağırlıklı teslim tarihi atama ile birlikte ağırlıklı çizelgeleme probleminin bazı saf ve melez meta-sezgisel yöntemler ile çözümü ÖZ Entegre süreç planlama ve çizelgeleme probleminin ve entegre teslim tarihi atama ile birlikte çizelgeleme probleminin literatürüne baktığımızda, literatürde bu konularda yüzlerce araştırma bulabiliriz. Fakat, üç önemli üretim fonksiyonlarının entegrasyonu konusu literatürde ele alınmayan bir alandır. Bu çalışmada süreç planlama, ağırlıklı çizelgeleme ve ağırlıklı teslim tarihi atama fonksiyonları entegre edilmiş ve problem bazı saf ve melez meta-sezgisel yöntemler kullanılarak çözülmüştür. Bu çalışmada biz 8 farklı atölyeyi rassal, evrimsel stratejiler, genetic algoritmalar ve bazı melez aramaları kullanarak çalıştık. Biz arama yöntemlerinin çözümü iterasyonlar devam ederken nasıl iyileştirdiğini ve evrimsel stratejiler, genetic algoritmalar ve melez aramaların rassal aramalara göre daha üstün sonuçlar verdiğini gözlemledik. Ayrıca melez aramaların genetic arama ve evrimsel stratejiler gibi güçlü arama teknikleri olduğunu gözlemledik.Anahtar Kelimeler: süreç planlama, ağırlıklı planlama, ağırlıklı teslim tarihi atama, evrimsel stratejiler, genetik algoritma, melez meta-sezgiseller, rassal arama

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Section: Background and Literature Surveymentioning

confidence: 99%

Proses planlama ve ağırlıklı teslim tarihi atama ile birlikte ağırlıklı çizelgeleme probleminin bazı saf ve melez meta-sezgisel yöntemler ile çözümü

Demir¹,

Erden

2017

SAÜ Fen Bilimleri Enstitüsü Dergisi

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“…Mohapatra et al [20] propose an improved controlled elitist non-dominated sorting GA for IPPS where the objective function is the minimisation of machining cost, makespan and idle time of machines. Zhang and Wong [21] also present an object-coding GA for IPPS. The proposed GA uses machining operations for the representation of chromosomes and corresponding genetic operations.…”

Section: Related Researchmentioning

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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“…Clearly, there are a total of seven features to finish this part. Due to technical constraints, e.g., primary surfaces prior to secondary surfaces and rough machining operations prior to finish machining operations, the example part can be finished with six operations with a certain precedence relationships between operations [26]. One operation is responsible for one or two feature(s); corresponding features are given near the operation in Figure 1b.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…To present a complete set of feasible and alternative manufacturing resources and precedence relationships between operations for each part, jobs to be processed in the process planning problem are represented by a network graph as illustrated in Figure 1b [5,25,26]. This network graph represents the manufacturing procedure of a screw given in Figure 1a.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

2017

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Abstract:Process planning is an important function in a manufacturing system; it specifies the manufacturing requirements and details for the shop floor to convert a part from raw material to the finished form. However, considering only economical criterion with technological constraints is not enough in sustainable manufacturing practice; formerly, criteria about low carbon emission awareness have seldom been taken into account in process planning optimization. In this paper, a mathematical model that considers both machining costs reduction as well as carbon emission reduction is established for the process planning problem. However, due to various flexibilities together with complex precedence constraints between operations, the process planning problem is a non-deterministic polynomial-time (NP) hard problem. Aiming at the distinctive feature of the multi-objectives process planning optimization, we then developed a hybrid non-dominated sorting genetic algorithm (NSGA)-II to tackle this problem. A local search method that considers both the total cost criterion and the carbon emission criterion are introduced into the proposed algorithm to avoid being trapped into local optima. Moreover, the technique for order preference by similarity to an ideal solution (TOPSIS) method is also adopted to determine the best solution from the Pareto front. Experiments have been conducted using Kim's benchmark. Computational results show that process plan schemes with low carbon emission can be captured, and, more importantly, the proposed hybrid NSGA-II algorithm can obtain more promising optimal Pareto front than the plain NSGA-II algorithm. Meanwhile, according to the computational results of Kim's benchmark, we find that both of the total machining cost and carbon emission are roughly proportional to the number of operations, and a process plan with less operation may be more satisfactory. This study will draw references for the further research on green manufacturing in the process planning level.

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An object-coding genetic algorithm for integrated process planning and scheduling

Cited by 96 publications

References 33 publications

Proses planlama ve ağırlıklı teslim tarihi atama ile birlikte ağırlıklı çizelgeleme probleminin bazı saf ve melez meta-sezgisel yöntemler ile çözümü

Proses planlama ve ağırlıklı teslim tarihi atama ile birlikte ağırlıklı çizelgeleme probleminin bazı saf ve melez meta-sezgisel yöntemler ile çözümü

Integrated process planning and scheduling using genetic algorithms

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

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