scholarly journals Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model

Computers ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Eduardo Guzman ◽  
Beatriz Andres ◽  
Raul Poler

This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses.

2020 ◽  
Vol 164 ◽  
pp. 03019 ◽  
Author(s):  
Anton Shabaev ◽  
Anton Sokolov ◽  
Alexander Urban ◽  
Dmitry Pyatin

An approach to the optimal timber transport scheduling is described in the paper. A description of this problem is given, a multi-criteria mathematical model is created. It is noted that the problem belongs to the class of General vehicle routing problems (GVRP) associated with the job-shop scheduling. A hybrid algorithm for solving this problem based on the decomposition method using the simplex method and the genetic algorithm is developed. Testing of the proposed approach using real data from wood harvesting enterprises showed its effectiveness. The algorithm was implemented in “Opti-Wood” decision support system for wood harvesting planning and management, developed by Opti-Soft company (Russia).


1995 ◽  
Vol 3 (3) ◽  
pp. 267-298 ◽  
Author(s):  
Terry Warwick ◽  
Edward P. K. Tsang

The car sequencing problem (CarSP) was seen as a challenge to artificial intelligence. The CarSP is a version of the job-shop scheduling problem, which is known to be NP-complete. The task in the CarSP is to schedule a given number of cars (of different types) in a sequence to allow the teams in each workstation on the assembly line to fit the required options (e.g. radio, sunroof) on the cars within the capacity of that workstation. In unsolvable problems, one would like to minimize the penalties associated with the violation of the capacity constraints. Previous attempts to tackle the problem either have been unsuccessful or have been restricted to solvable CarSPs only. In this paper, we report on promising results in applying a generic genetic algorithm, which we call GAcSP, to tackle both solvable and unsolvable CarSPs.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Huafeng Quan ◽  
Dacheng Liu ◽  
Shu Rao ◽  
...  

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