38 research outputs found
A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring
The Artificial Bee Colony (ABC) is the name of an optimization algorithm that
was inspired by the intelligent behavior of a honey bee swarm. It is widely
recognized as a quick, reliable, and efficient methods for solving optimization
problems. This paper proposes a hybrid ABC (HABC) algorithm for graph
3-coloring, which is a well-known discrete optimization problem. The results of
HABC are compared with results of the well-known graph coloring algorithms of
today, i.e. the Tabucol and Hybrid Evolutionary algorithm (HEA) and results of
the traditional evolutionary algorithm with SAW method (EA-SAW). Extensive
experimentations has shown that the HABC matched the competitive results of the
best graph coloring algorithms, and did better than the traditional heuristics
EA-SAW when solving equi-partite, flat, and random generated medium-sized
graphs
Integrated Batching and Lot Streaming with Variable Sublots and Sequence-Dependent Setups in a Two-Stage Hybrid Flow Shop
Consider a paint manufacturing firm whose customers typically place orders for two or more products simultaneously: liquid primer, top coat paint, and/or undercoat paint. Each product belongs to an associated product family that can be batched together during the manufacturing process. Meanwhile, each product can be split into several sublots so that overlapping production is possible in a two-stage hybrid flow shop. Various numbers of identical capacitated machines operate in parallel at each stage. We present a mixed-integer programming (MIP) to analyze this novel integrated batching and lot streaming problem with variable sublots, incompatible job families, and sequence-dependent setup times. The model determines the number of sublots for each product, the size of each sublot, and the production sequencing for each sublot such that the sum of weighted completion time is minimized. Several numerical example problems are presented to validate the proposed formulation and to compare results with similar problems in the literature. Furthermore, an experimental design based on real industrial data is used to evaluate the performance of proposed model. Results indicate that the computational cost of solving the model is high
Penjadwalan Produksi Flow Shop untuk Meminimasi Makespan dengan Menggunakan Metode Lot Streaming (Studi Kasus PT. XYZ)
Pada setiap permasalahan penjadwalan, pesanan customer harus diselesaikan berdasarkan kesepakatan antara perusahaan dan customer. Perbedaan pola kedatangan pesanan, jenis produk pesanan dan variasi jumlah mesin untuk memproduksi dapat menyebabkan keterlambatan penyelesaian pesanan dan melanggar due date yang disepakati. Penelitian ini bertujuan untuk memberikan usulan penjadwalan yang meminimasi makespan menggunakan metode Lot Streaming. Kondisi awal menunjukkan waktu penyelesaian pesanan sebesar 9802,7 menit dengan keterlambatan 2 pesanan. Penelitian ini dikembangkan menggunakan 3 skenario dengan cara membagi job menjadi beberapa sublot yaitu 2 lot dan 3 lot dengan ukuran yang berbeda pada tiap bagian lot. Pada skenario 1 didapatkan hasil terbaik dengan Penjadwalan Per Job dengan Lot Streaming 3 Lot (8953,7 menit). Pada skenario 2 didapatkan hasil terbaik dengan Penjadwalan Per Lot dengan Lot Streaming 3 Lot (8884,3 menit). Pada skenario 3 didapatkan hasil terbaik dengan Penjadwalan Per Lot tukar urutan dengan Lot Streaming 3 Lot (9157,3 menit). Hasil menunjukkan bahwa skenario 2 adalah hasil terbaik dengan persentase perbaikan sebesar 9,37% dibandingkan dengan penjadwalan kondisi awal dan pengurangan jumlah keterlambatan menjadi tidak ada keterlambatan
Modelling and Scheduling Lot Streaming Flexible Flow Lines
Although lot streaming scheduling is an active research field, lot streaming flexible flow lines problems have received far less attention than classical flow shops. This paper deals with scheduling jobs in lot streaming flexible flow line problems. The paper mathematically formulates the problem by a mixed integer linear programming model. This model solves small instances to optimality. Moreover, a novel artificial bee colony optimization is developed. This algorithm utilizes five effective mechanisms to solve the problem. To evaluate the algorithm, it is compared with adaptation of four available algorithms. The statistical analyses showed that the proposed algorithm significantly outperformed the other tested algorithms
Modelo de subloteo considerando el efecto aprendizaje en configuraciones productivas flow-shop
El subloteo (lot streaming) es una de las metodologías más estudiadas en los problemas de secuenciamiento en configuraciones productivas tipo flowshop, aunque no lo es el impacto del subloteo sobre el procesamiento propio de los trabajos. Es por eso, que en este trabajo, se estudia el impacto del efecto aprendizaje en base a la aplicación de subloteo. El efecto de aprendizaje aplicado considera el trabajo acumulado de los sublotes ya procesados del mismo producto. De esta forma no se sobredimensiona el aprendizaje. Se experimentó con instancias de mediana complejidad y se resolvió con GAMS/CPLEX. Se demuestra que el aprendizaje que se genera como consecuencia del subloteo es significativo en términos del makespan. Este resultado es más cercano a la realidad de los sistemas en los que el factor humano tiene importante injerencia.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Modelo de subloteo considerando el efecto aprendizaje en configuraciones productivas flow-shop
El subloteo (lot streaming) es una de las metodologías más estudiadas en los problemas de secuenciamiento en configuraciones productivas tipo flowshop, aunque no lo es el impacto del subloteo sobre el procesamiento propio de los trabajos. Es por eso, que en este trabajo, se estudia el impacto del efecto aprendizaje en base a la aplicación de subloteo. El efecto de aprendizaje aplicado considera el trabajo acumulado de los sublotes ya procesados del mismo producto. De esta forma no se sobredimensiona el aprendizaje. Se experimentó con instancias de mediana complejidad y se resolvió con GAMS/CPLEX. Se demuestra que el aprendizaje que se genera como consecuencia del subloteo es significativo en términos del makespan. Este resultado es más cercano a la realidad de los sistemas en los que el factor humano tiene importante injerencia.Sociedad Argentina de Informática e Investigación Operativa (SADIO
The problem of uninterrupted hybrid flow shop scheduling with regard to the fuzzy processing time
Purpose: In this paper, an uninterrupted hybrid flow shop scheduling problem is modeled under uncertainty conditions. Due to the uncertainty of processing time in workshops, which is due to delays in receiving raw materials or machine failure, fuzzy programming method has been used to control the processing time parameter. In the proposed model, there are several jobs that must be processed by machines in sequence. The main purpose of the proposed model is to determine the correct sequence of operations and assign operations to each machine at each stage, so that the total completion time (Cmax) is minimized.
Methodology: In this paper, the fuzzy programming method is used to control the uncertain parameter. Also, The GAMS software and CPLEX solver have also been used to solve the sample problems.
Findings: The results of solving the problem in small and medium size show that with increasing the rate of uncertainty, the amount of processing time increases and therefore the completion time of the whole work increases. On the other hand, with the increase in the number of machines in each stage due to the high efficiency of the machines, the completion time of all works has decreased.
Originality/Value: The most important innovation of this article is the design of uninterrupted hybrid flow shop scheduling with regard to the fuzzy processing time
Bičių spiečių imitavimas sprendžiant optimizavimo uždavinius
Straipsnyje nagrinėjami klausimai, susiję su naujoviškų metodų taikymu sprendžiant optimizavimo uždavinius. Šiuo konkrečiu atveju diskutuojama apie bičių spiečių elgsenos imitavimą ir galimą jo taikymą kombinatorinio (diskretinio) tipo optimizavimo uždaviniams. Straipsnio pradžioje aptariami konceptualūs aspektai ir bendroji bičių spiečių imitavimo algoritmų idėja. Aprašoma bičių spiečiaus imitavimo algoritmo realizacija atskiram nagrinėjamam atvejui – kvadratinio paskirstymo uždaviniui, kuris yra vienas iš aktualių ir sudėtingų kombinatorinio optimizavimo uždavinių pavyzdžių. Straipsnyje pateikiami ir su realizuotu algoritmu atliktų eksperimentų rezultatai, kurie iliustruoja skirtingų veiksnių (parametrų) įtaką gaunamų sprendinių kokybei ir patvirtina aukštą algoritmo efektyvumo lygį.Bee Swarm Intelligence in (Combinatorial) OptimizationAlfonsas Misevičius, Jonas Blonskis, Vytautas Bukšnaitis
SummaryIn this paper, we discuss some issues related to the innovative intelligent optimization methods. More precisely, we are concerned with the bee colony optimization approach, which is inspired by the behaviour of natural swarms of honey bees. Both the conceptual methodological facets of the swarm intelligence paradigm and the aspects of implementation of the artificial bee colony algorithms are considered. In particular, we introduce an implementation of the artificial bee colony optimization algorithm for the well-known combinatorial optimization problem of quadratic assignment (QAP). The results of computational experiments with different variants of the implemented algorithm are also presented and discussed. Based on the obtained results, it is concluded that the proposed algorithm may compete with other efficient heuristic techniques.