An integrative heuristic method for detailed operations scheduling in assembly job shop systems

The job shop scheduling problem with blocking constraints and total tardiness minimization represents a challenging combinatorial optimization problem of high relevance in production planning and logistics. Since general-purpose solution approaches struggle with finding even feasible solutions, a permutation-based heuristic method is proposed here, and the applicability of basic scheduling-tailored mechanisms is discussed. The problem is tackled by a local search framework, which relies on interchange- and shift-based operators. Redundancy and feasibility issues require advanced transformation and repairing schemes. An analysis of the embedded neighborhoods shows beneficial modes of implementation on the one hand and structural difficulties caused by the blocking constraints on the other hand. The applied simulated annealing algorithm generates good solutions for a wide set of benchmark instances. The computational results especially highlight the capability of the permutation-based method in constructing feasible schedules of valuable quality for instances of critical size and support future research on hybrid solution techniques.

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A New GT Heuristic for Solving Multi Objective Job Shop Scheduling Problems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.591.184 ◽

2014 ◽

Vol 591 ◽

pp. 184-188

Author(s):

D. Lakshmipathy ◽

M. Chandrasekaran ◽

T. Balamurugan ◽

P. Sriramya

Keyword(s):

Production Scheduling ◽

Job Shop ◽

Job Shop Scheduling ◽

Heuristic Method ◽

Flow Time ◽

Scheduling Problems ◽

Mean Flow ◽

Practical Applications ◽

Shop Scheduling ◽

Mean Flow Time

The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems in manufacturing system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. New Game theory based heuristic method (GT) is used for finding optimal makespan, mean flow time, mean tardiness values of different size problems. The results show that the GT Heuristic is an efficient and effective method that gives better results than Genetic Algorithm (GA). The proposed GT Heuristic is a good problem-solving technique for job shop scheduling problem with multi criteria.

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Hybrid Quantum Annealing Heuristic Method for Solving Job Shop Scheduling Problem

Lecture Notes in Computer Science - Computational Science – ICCS 2020 ◽

10.1007/978-3-030-50433-5_39 ◽

2020 ◽

pp. 502-515

Author(s):

Krzysztof Kurowski ◽

Jan Wȩglarz ◽

Marek Subocz ◽

Rafał Różycki ◽

Grzegorz Waligóra

Keyword(s):

Job Shop ◽

Job Shop Scheduling ◽

Heuristic Method ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Quantum Annealing ◽

Shop Scheduling

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Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

The Scientific World JOURNAL ◽

10.1155/2013/418396 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

M. Frutos ◽

M. Méndez ◽

F. Tohmé ◽

D. Broz

Keyword(s):

Buffer Capacity ◽

Job Shop ◽

Production Systems ◽

Pareto Frontier ◽

Multiobjective Evolutionary Algorithms ◽

Machine Availability ◽

Operations Scheduling ◽

Performance Indexes ◽

Availability Constraints ◽

Multiobjective Algorithms

Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

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517 A heuristic method for Job-shop scheduling problem considering critical path

The Proceedings of Conference of Hokkaido Branch ◽

10.1299/jsmehokkaido.2012.51.175 ◽

2012 ◽

Vol 2012.51 (0) ◽

pp. 175-176

Author(s):

Soichiro YOKOYAMA ◽

Masahito YAMAMOTO ◽

Masashi FURUKAWA ◽

Ikuo SUZUKI

Keyword(s):

Job Shop ◽

Job Shop Scheduling ◽

Critical Path ◽

Heuristic Method ◽

Scheduling Problem ◽

Job Shop Scheduling Problem ◽

Shop Scheduling

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A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling

Evolutionary Computation ◽

10.1162/evco_a_00183 ◽

2016 ◽

Vol 24 (4) ◽

pp. 609-635 ◽

Cited By ~ 23

Author(s):

Emma Hart ◽

Kevin Sim

Keyword(s):

Job Shop ◽

Job Shop Scheduling ◽

Heuristic Method ◽

Ensemble Method ◽

Divide And Conquer ◽

Dispatching Rules ◽

Programming Algorithm ◽

Benchmark Problems ◽

Large Set ◽

Shop Scheduling

We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyper-heuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.

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