A Heuristic Algorithm for the <i>n</i> Job, <i>m</i> Machine Sequencing Problem

Campbell, Herbert George; Dudek, R. A.; Smith, Milton L.

doi:10.1287/mnsc.16.10.b630

Cited by 795 publications

(269 citation statements)

References 8 publications

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“…Beberapa algoritma pure flow shop yang telah diteliti adalah Johnson [10], Campbell, et al [11], Gupta [12], Dannenbring [13], Logendran and Nudtasomboon [14], Pour [15], Nawaz, et al [16] [17], simulated annealing [4] dan Branch and Bound [18]. Penelitian tersebut umumnya menggunakan kriteria minimasi makespan.…”

Section: Pendahuluanunclassified

Improve Algoritma Hodgson Untuk Meminimasi Jumlah Job Terlambat Pada Penjadwalan Flow shop

Widodo¹

2018

JTI

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PendahuluanPenjadwalan merupakan pengaturan waktu pada mesin yang mencakup kegiatan mengalokasikan fasilitas dan peralatan. Penjadwalan berguna untuk menentukan urutan pekerjaan [1]. Fungsi penjadwalan yaitu memproses produk sesuai dengan waktu yang diinginkan, memprediksi kesiapan setiap sumber daya yang diperlukan [2]. Berdasarkan aliran produksi, penjadwalan di klasifikasi menjadi flow shop dan job shop. Harto, et al. [3] menyatakan bahwa pada penjadwalan job shop umumnya menggunakan algoritma aktif dan non delay. Husen, et al. [4] menyatakan bahwa pada penjadwalan job shop dapat menggunakan algoritma simulated annealing. Penjadwalan flow shop adalah penjadwalan di mana semua job yang akan dikerjakan melalui urutan proses operasi yang sama [1]. Umumnya performansi dari penjadwalan yaitu untuk meminimalkan flow time, makespan, maximum tardiness, job tardy dan lateness [5]. Pengertian dari flow time itu sendiri ialah waktu yang dibutuhkan sebuah pekerjaan untuk berada di dalam sebuah sistem produksi. Sedangkan waktu yang dibutuhkan untuk menyelesaikan seluruh pekerjaan (job) adalah makespan [6]. Dalam

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Section: Pendahuluanunclassified

Improve Algoritma Hodgson Untuk Meminimasi Jumlah Job Terlambat Pada Penjadwalan Flow shop

Widodo¹

2018

JTI

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“…Reeves (2003) had stated that 'seeding' the initial population with known good solutions can help the genetic algorithm find better solutions more quickly compared to random start. Hence, initial population of every generation was seeded with solutions from heuristics by Campbell et al (1970) and Nawaz et al (1983).…”

Section: Hybrid Gamentioning

confidence: 99%

A hybrid genetic algorithm for a complex cost function for flowshop scheduling problem

Bhowmick¹,

Maniyan²,

Saxena³

et al. 2011

IJET

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Abstract:Supply chain excellence has a real impact on business strategy. Manufacturing is an integral part of this strategy represents one of the most exciting opportunities to create value and one of the most challenging tasks for the policy makers. In this paper, we consider a performance criterion for the flowshop scheduling problem that aims to minimise a complex cost function, i.e., the sum of weighted tardiness and weighted flow-time costs. A heuristic and hybrid genetic algorithms are proposed and experimental results are provided. We address this trade-off and propose solution techniques that are easy for the shop-floor manager to implement. As scheduling function is an integral part of supply chain, the proposed solution minimises the opportunity losses and improves cost based supply chain performance. This paper addresses this interesting and challenging domain.

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“…─ PAL: a slope index heuristic by Palmer, see [15] ─ CDS: a best choice heuristic by Campbell, Dudek and Smith, see [16] ─ GUP: a slope index heuristic by Gupta, see [17] ─ DAN: a heuristic by Dannenbring, see [18] ─ NEH: a constructive heuristic by Nawaz, Enscore and Ham, see [19] All heuristics try to minimise the makespan of a given problem instance. They are taken as reference algorithms to evaluate the new developed method.…”

Section: Reference Heuristicsmentioning

confidence: 99%

An Autonomous Control Concept for Production Logistics

Rekersbrink¹,

Scholz‐Reiter²,

Zabel³

2010

Advanced Manufacturing and Sustainable Logistics

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Abstract. The German Collaborative Research Centre 637 'AutonomousCooperating Logistic Processes' tries to make a paradigm shift from central planning to autonomous control in the field of logistics. Among other things, autonomous routing algorithms based on internet routing protocols are developed. The Distributed Logistics Routing Protocol (DLRP) was originally designed for transport networks to match goods and vehicles and to continuously make route decisions. Now the protocol was transferred to production logistics as a promising autonomous control method. The DLRP enables the abilities for logistic objects, orders and machines, to make own decisions with the information actually and locally available. In contrast to common scheduling algorithms, the DLRP is not a planning, but a control method with the capability for multiple, user defined optimization goals. The new autonomous control concept for production logistics will be presented in this paper and a first evaluation with common scheduling heuristics will be given.

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A Heuristic Algorithm for the n Job, m Machine Sequencing Problem

Cited by 795 publications

References 8 publications

Improve Algoritma Hodgson Untuk Meminimasi Jumlah Job Terlambat Pada Penjadwalan Flow shop

Improve Algoritma Hodgson Untuk Meminimasi Jumlah Job Terlambat Pada Penjadwalan Flow shop

A hybrid genetic algorithm for a complex cost function for flowshop scheduling problem

An Autonomous Control Concept for Production Logistics

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