An improved model and a heuristic for capacitated lot sizing and scheduling in job shop problems

Poursabzi, Omid; Mohammadi, Mohammad; Naderi, Bahman

doi:10.24200/sci.2017.20016

Cited by 6 publications

(5 citation statements)

References 31 publications

(49 reference statements)

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“…There is a plenty of different heuristic algorithms (including Tabu search, GA, SA, PSO, Ant and Bee colony, and etc.) among which SA and Tabu search algorithms based on their merits are employed extensively for optimization of different problems and processes [13][14][15][16][17][18]. Easy to program and converge fast are the significant advantages of using TS algorithm.…”

Section: Designing the Solution Methodsmentioning

confidence: 99%

Multi-criteria optimization of multi product assembly line using hybrid Tabu-SA algorithm

Tavakoli

2020

SN Appl. Sci.

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In this paper, a multi-criteria optimization problem has been addressed including minimizing three important objectives: (a) total amount of fines for untimely production of orders; (b) total carrier rails' time including the preparation times of the stations; and (c) total cost of feeding outside the work station plan. In order to consider the relative importance of the stated goals, all costs/fines have been weighed. The proposed problem is one the nondeterministic polynomial complex problems, for which no comprehensive solution has been presented. Hence, to find the optimal answer in a reasonable time, Tabu search (TS) and simulated annealing (SA) algorithms have been used. Furthermore, to obtain the merits of the stated algorithms, a combination of both has been employed (Tabu-SA algorithm). To illustrate the performance of the proposed method, two numerical examples have been solved. The computational results indicate the high speed of integrated search algorithm and the superiority of the quality of its solutions. Based on the results, the proposed hybrid Tabu-SA algorithm has a remarkable advantage in comparison with TS and SA algorithms in terms of convergence rate. The total cost has been reduced at 24% using the proposed hybrid Tabu-SA algorithm. Besides, the computing time was improved at 29% and 46% using the proposed procedure respectively.Keywords Multi-criteria optimization · Multi modal assembly lines (MMAL) · Tabu search (TS) algorithm · Simulated annealing (SA) algorithm * Ahmad Tavakoli, tavakoli-a@um.ac.ir |

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Section: Designing the Solution Methodsmentioning

confidence: 99%

Multi-criteria optimization of multi product assembly line using hybrid Tabu-SA algorithm

Tavakoli

2020

SN Appl. Sci.

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“…JSSP is a NP-hard problem [42]. Exact methods exhibit impractical computational times for medium and large instances, therefore, it is inevitable to apply proper and efficient solution methods for solving these types of problems.…”

Section: -5 Solution Representationmentioning

confidence: 99%

A novel bi-objective model for a job shop scheduling problem with consideration of Fuzzy parameters, modified learning effects and multiple preventive maintenance activities

2021

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This paper aims at introducing a novel bi-objective model for a Job Shop Scheduling Problem (JSSP) in order to minimize makespan and maximum tardiness simultaneously. Some realistic assumptions, i.e. Fuzzy processing times and due dates involving triangular possibility distributions, transportation times, availability constraints, modified position-based learning effects on processing times, and sum-of-processing-time based learning effects on duration of maintenance activities have been considered, to provide a more general and practical model for the JSSP. Based on the learning effects, Processing times decrease as a machine performs an operation frequently, and workers gain working skills and experiences. In this paper based on DeJong's learning effect a novel and modified formulation has been proposed for this effect. According to the above-mentioned assumptions, a novel mixed-integer linear programming (MILP) model for the JSSP is suggested. The proposed model is first converted to an auxiliary crisp model, given that model is a possibilistic programming, it is then solved by the TH and εconstraint methods for small instances, and the results are compared. For medium and large instances, five metaheuristic algorithms, including NSGA-ΙΙΙ, PESA-ΙΙ, SPEA-ΙΙ, NSGA-ΙΙ, and MOEA/D are utilized, and the results are finally compared on the basis of three performance metrics.

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“…Although the studies in the literature have performed better than the heuristics, they have been found to be less performance than the meta-heuristic ones. It has been stated that the issue is an open area to develop [40]. For this purpose, a new model is proposed in order to find solutions to large-scale problems and to make the obtained results compete with other metaheuristics.…”

Section: The Best Parameter Levels According To the Size Of The Problemmentioning

confidence: 99%

A Data Mining Based Solution Method for Flow Shop Scheduling Problems

2020

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The scheduling theme which determines where and when the manufacturing actions will be performed is important to perform the activities in time, efficiently and cost-effectively. In this paper, an algorithm is proposed for the solution of a flow shop scheduling problem which holds an important place in the scheduling literature. The path relinking algorithm and data mining are used together for the solution of the flow shop scheduling problem studied here. While data mining is used for globally searching the solution space, path relinking is used for local search. Data Mining is a method which is used to extract the embedded information in a cluster that includes implicit information. Path relinking is an algorithm which advances by making binary displacements in order to convert the initial solution to the guiding solution and repeats by assigning the best obtained solution within this process as to the starting point. The efficiency of the model was tested on Taillard's flow shop scheduling problems. Consequently, it has been possible to solve the large-size problem without the considerable mathematical background. The obtained results show that the proposed method is in the competitiveness level with the other metaheuristic methods.

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An improved model and a heuristic for capacitated lot sizing and scheduling in job shop problems

Cited by 6 publications

References 31 publications

Multi-criteria optimization of multi product assembly line using hybrid Tabu-SA algorithm

Multi-criteria optimization of multi product assembly line using hybrid Tabu-SA algorithm

A novel bi-objective model for a job shop scheduling problem with consideration of Fuzzy parameters, modified learning effects and multiple preventive maintenance activities

A Data Mining Based Solution Method for Flow Shop Scheduling Problems

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