Scheduling in a flexible job shop followed by some parallel assembly stations considering lot streaming

Daneshamooz, Fatemeh; Hosseini, Seyed Mohammad Hassan

doi:10.1080/0305215x.2021.1887168

Cited by 17 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2016; Karakostas et al. , 2020; Daneshamooz et al. , 2021) is adapted and used for the sequencing sub-problem.…”

Section: Solution Approach and Proposed Algorithmsmentioning

confidence: 99%

“…At first, the part assignment to factories is carried out in the first phase, while parts sequencing in each factory is dedicated to the second phase. In this way, a VNS with a selfadaptive mechanism introduced and used by (Wang et al, 2012;Stenger et al, 2013;Todosijevi c et al, 2016;Karakostas et al, 2020;Daneshamooz et al, 2021) is adapted and used for the sequencing sub-problem. Moreover, the neighborhood structures get a self-adapting mechanism according to the selection probability.…”

Section: Two-level Self-adaptive Variable Neighborhood Search (Tlsavns)mentioning

confidence: 99%

See 1 more Smart Citation

Distributed assembly permutation flow-shop scheduling problem with non-identical factories and considering budget constraints

Hosseini

2022

View full text Add to dashboard Cite

PurposeThis paper aims to address a distributed assembly permutation flow-shop scheduling problem (DAPFSP) considering budget constraints and factory eligibility. The first stage of the considered production system is composed of several non-identical factories with different technology levels and so the factories' performance is different in terms of processing time and cost. The second stage is an assembly stage wherein there are some parallel work stations to assemble the ready parts into the products. The objective function is to minimize the maximum completion time of products (makespan).Design/methodology/approachFirst, the problem is formulated as mixed-integer linear programing (MIP) model. In view of the nondeterministic polynomial (NP)-hard nature, three approximate algorithms are adopted based on variable neighborhood search (VNS) and the Johnsons' rule to solve the problem on the practical scales. The proposed algorithms are applied to solve some test instances in different sizes.FindingsComparison result to mathematical model validates the performance accuracy and efficiency of three proposed methods. In addition, the result demonstrated that the proposed two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm outperforms the other two proposed methods. Moreover, the proposed model highlighted the effects of budget constraints and factory eligibility on the makespan. Supplementary analysis was presented by adjusting different amounts of the budget for controlling the makespan and total expected costs. The proposed solution approach can provide proper alternatives for managers to make a trade-off in different various situations.Originality/valueThe problem of distributed assembly permutation flow-shop scheduling is traditionally studied considering identical factories. However, processing factories as an important element in the supply chain use different technology levels in the real world. The current paper is the first study that investigates that problem under non-identical factories condition. In addition, the impact of different technology levels is investigated in terms of operational costs, quality levels and processing times.

show abstract

“…, 2016; Karakostas et al. , 2020; Daneshamooz et al. , 2021) is adapted and used for the sequencing sub-problem.…”

Section: Solution Approach and Proposed Algorithmsmentioning

confidence: 99%

Section: Two-level Self-adaptive Variable Neighborhood Search (Tlsavns)mentioning

confidence: 99%

Distributed assembly permutation flow-shop scheduling problem with non-identical factories and considering budget constraints

Hosseini

2022

View full text Add to dashboard Cite

show abstract

“…Daneshamooz et al [40] proposed an algorithm based on a variable neighborhood search to minimize the makespan for a lot streaming problem in a flexible job shop followed by a parallel assembly station. An FJS-LS problem in an Engineer to order environment was presented in [41].…”

Section: Pure Flow Shop Lot Streaming (Pfs-ls)mentioning

confidence: 99%

“…The latest finish-time of the sublots of a job is its departure time dj , which was discussed previously. With these variables defined, the sublot finish separation time of a job, ĥj , is the difference dj − ĝj , enforced by the constraint in Equation (40). The constraint in Equation (41) and the objective function will enforce ĥmax to assume the value max ∀j { ĥj }.…”

Section: Makespan (Z 1 )mentioning

confidence: 99%

A Two-Stage Multi-Objective Genetic Algorithm for a Flexible Job Shop Scheduling Problem with Lot Streaming

Rooyani

Defersha

2022

Algorithms

View full text Add to dashboard Cite

The work in this paper is motivated by a recently published article in which the authors developed an efficient two-stage genetic algorithm for a comprehensive model of a flexible job-shop scheduling problem (FJSP). In this paper, we extend the application of the algorithm to solve a lot streaming problem in FJSP while at the same time expanding the model to incorporate multiple objectives. The objective function terms included in our current work are the minimization of the (1) makespan, (2) maximum sublot flowtime, (3) total sublot flow time, (4) maximum job flowtime, (5) total job flow time, (6) maximum sublot finish-time separation, (7) total sublot finish-time separation, (8) maximum machine load, (9) total machine load, and (10) maximum machine load difference. Numerical examples are presented to illustrate the greater need for multi-objective optimization in larger problems, the interaction of the various objective function terms, and their relevance in providing better solution quality. The ability of the two-stage genetic algorithm to jointly optimize all the objective function terms is also investigated. The results show that the algorithm can generate initial solutions that are highly improved in all of the objective function terms. It also outperforms the regular genetic algorithm in convergence speed and final solution quality in solving the multi-objective FJSP lot streaming. We also demonstrate that high-performance parallel computation can further improve the performance of the two-stage genetic algorithm. Nevertheless, the sequential two-stage genetic algorithm with a single CPU outperforms the parallel regular genetic algorithm that uses many CPUs, asserting the superiority of the two-stage genetic algorithm in solving the proposed multi-objective FJSP lot streaming.

show abstract

“…In the most research on the HFSP [7], each job is treated as a whole, and the job cannot proceed to the next stage before the completion of the processing at a given stage [8]. In the actual production scenario [9,10], a job, called a lot in the following, usually consists of a number of identical items. When the lot is large, items already processed completely on a machine need to wait a long time in the output buffer of this machine, whereas the downstream machine may be idle.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm

Zhang

Han

et al. 2022

Mathematics

View full text Add to dashboard Cite

Energy conservation, emission reduction, and green and low carbon are of great significance to sustainable development, and are also the theme of the transformation and upgrading of the manufacturing industry. This paper concentrates on studying the energy-efficient hybrid flowshop scheduling problem with consistent sublots (HFSP_ECS) with the objective of minimizing the energy consumption. To solve the problem, the HFSP_ECS is decomposed by the idea of “divide-and-conquer”, resulting in three coupled subproblems, i.e., lot sequence, machine assignment, and lot split, which can be solved by using a cooperative methodology. Thus, an improved cooperative coevolutionary algorithm (vCCEA) is proposed by integrating the variable neighborhood descent (VND) strategy. In the vCCEA, considering the problem-specific characteristics, a two-layer encoding strategy is designed to represent the essential information, and a novel collaborative model is proposed to realize the interaction between subproblems. In addition, special neighborhood structures are designed for different subproblems, and two kinds of enhanced neighborhood structures are proposed to search for potential promising solutions. A collaborative population restart mechanism is established to ensure the population diversity. The computational results show that vCCEA can coordinate and solve each subproblem of HFSP_ECS effectively, and outperform the mathematical programming and the other state-of-the-art algorithms.

show abstract

Scheduling in a flexible job shop followed by some parallel assembly stations considering lot streaming

Cited by 17 publications

References 27 publications

Distributed assembly permutation flow-shop scheduling problem with non-identical factories and considering budget constraints

Distributed assembly permutation flow-shop scheduling problem with non-identical factories and considering budget constraints

A Two-Stage Multi-Objective Genetic Algorithm for a Flexible Job Shop Scheduling Problem with Lot Streaming

Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm

Contact Info

Product

Resources

About