An ant colony algorithm for job shop scheduling problem with tool flow

Zhu, Rui; Wang, Shilong; Zhu, Zhe-qi; Yi, Lili

doi:10.1177/0954405413514398

Cited by 15 publications

(15 citation statements)

References 27 publications

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“…The proposed algorithm contained a new pheromone initialization procedure and a local search method. Rui et al (2014) presented an ant colony algorithm to solve the integrated job shop scheduling problem with tool flow in flexible manufacturing system. Huang et al (Huang et al 2015) proposed a no-wait FSMP problem with time window constraint which a new ant colony optimization (ACO), known as ant colony optimization with flexible update (ACOFU), is presented to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid ant colony algorithm for scheduling of no-wait flowshop

Riahi

Kazemi

2016

Oper Res Int J

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In this paper, the no-wait flow shop scheduling problem under makespan and flowtime criteria is addressed. The no-wait flowshop is a variant of the wellknown flowshop scheduling problem where all processes follow the previous one without any interruption for operations of a job. Owing to the problem is known to be NP-hard for more than two machines, a hybrid meta-heuristic algorithm based on ant colony optimization (ACO) and simulated annealing (SA) algorithm is improved. First, at each step, due to the characteristic of ACO algorithm that include solution construction and pheromone trail updating, some different areas of search space are checked and best solution is selected. Then, to enhance the quality and diversity of the solution and finding best neighbor of this solution, a novel SA is presented. Moreover, a new principle is applied for global pheromone update based on the probability function like SA algorithm. The proposed approach solution is compared with several the state-of-the-art algorithms in the literature. The reported results show that the proposed algorithms are effective and the new approach for local search in ACO algorithm is efficient for solving the no-wait flow shop problem. Then, we employed another hybrid ACO algorithm based on hybridization of ACO with variable neighborhood search (VNS) and compare the results given by two proposed algorithms. These results show that our new hybrid provides better results than ACO-VNS algorithm.

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Section: Introductionmentioning

confidence: 99%

A new hybrid ant colony algorithm for scheduling of no-wait flowshop

Riahi

Kazemi

2016

Oper Res Int J

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“…The job shop scheduling problem (JSSP) is a combinatorial optimization problem as well as nondeterministic polynomial-time (NP)-hard, and it is one of the most typical and complex among various production scheduling problems. [1][2][3][4] In a classical JSSP, n-jobs J = {J1, J2, ..., Jn} are processed on m-machines M = {M1, M2, ..., Mm}, and each job has specific operation order. 5 Each job can be processed by at most one machine at a time, and each machine can process at most one operation at a time.…”

Section: Introductionmentioning

confidence: 99%

“…2 The common objectives in JSSPs are to minimize some performance measures such as makespan, mean flow time, mean tardiness, number of tardy jobs, mean setup time and mean number of setups. 4,5,[9][10][11][12] JSSPs can be solved by using (1) exact methods, (2) hybrid methods and (3) heuristics. 13 Exact methods such as branch and bound (B&B) approach, dynamic programming (DP) and mixed integer linear programming (MILP) can only solve small-sized scheduling problems.…”

Section: Introductionmentioning

confidence: 99%

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A review on job shop scheduling with setup times

Sharma

Jain

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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Job shop scheduling problems with setup times arise in many practical situations such as aerospace industries, fabrication industries, printing industries and semiconductor manufacturing industries. This is the first article which provides a comprehensive review of the literature on job shop scheduling research involving setup times. The literature is classified into two broad categories, namely, job shop scheduling problems with non-batch (job) setup times and job shop scheduling problems with batch setup times. The two categories are subcategorized into problems with sequence-dependent setup times and sequence-independent setup times and review the exact, hybrid and heuristic solution methods used for each subcategory. Furthermore, it summarizes the existing research and identifies directions for future research.

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“…Thus, undirected searching techniques or branch and bound methods, in the worst case, take an unacceptable computational time to produce a solution close to the optimum; therefore, presentation of the most efficient heuristic algorithm has been the main concern of the scheduling research area. 1 Today's manufacturing environment is quite different from the traditional one. It is characterized by decreasing lead time, exacting standards of quality, larger part variety and competitive costs.…”

Section: Introductionmentioning

confidence: 99%

Multipart setup planning through integration of process planning and scheduling

Haddadzade

Razfar

Zarandi

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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The importance of integration of process planning and scheduling is evident in today's competitive manufacturing system. Meanwhile, the setup planning plays an important role for integration, because setup planning is done after process planning, just before scheduling. In this article, a new approach for setup planning for multiparts is introduced to realize integration of process planning and scheduling with more details of manufacturing constraints for setup planning. For this purpose, the necessary and unnecessary (preferred) constraints are defined for setup formation and a new matrix is presented to distinguish these and to specify the precedence of operations. The simulated annealing is used in setup planning for N parts simultaneously based on multi-technical and scheduling objectives. The proposed system is based on the machining operation instead of features, to select machine tools more accurately. Another factor for the selection of machine tools is tolerances that are considered in this article and greatly affected the machining cost. An illustrative example is presented to show the effectiveness of the operation-based system and the second example is presented to demonstrate the influence of multipart setup planning versus single-part setup planning. The results show that considering multiparts improves the scheduling objectives such as makespan.

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An ant colony algorithm for job shop scheduling problem with tool flow

Cited by 15 publications

References 27 publications

A new hybrid ant colony algorithm for scheduling of no-wait flowshop

A new hybrid ant colony algorithm for scheduling of no-wait flowshop

A review on job shop scheduling with setup times

Multipart setup planning through integration of process planning and scheduling

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