Ant colony algorithm for multi-criteria job shop scheduling to minimize makespan, mean flow time and mean tardiness

Udomsakdigool, Apinanthana; Khachitvichyanukul, Voratas

doi:10.1080/17509653.2011.10671153

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…In the actual production, many problems can be taken as a job shop scheduling problem, such as workshop scheduling in the industry, departure and arrival times of logistic problems, the delivery times of orders in a company, and so on. In the manufacturing field, JSP aims to determine the processing order between jobs on each machine to acquire good production performance, e.g., makespan [1,2], total weighted tardiness [3,4], average flow time [5,6], etc. In previous researches, most work about JSP only consider the time-related indicators, rather than the environmental factors, such as energy consumption, CO 2 emissions and carbon footprint, etc.…”

Section: Instructionmentioning

confidence: 99%

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Energy-Efficient Scheduling for a Job Shop Using Grey Wolf Optimization Algorithm with Double-Searching Mode

Jiang

Zhang

Zhu

et al. 2018

Mathematical Problems in Engineering

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Workshop scheduling has mainly focused on the performances involving the production efficiency, such as times and quality, etc. In recent years, environmental metrics have attracted the attention of many researchers. In this study, an energy-efficient job shop scheduling problem is considered, and a grey wolf optimization algorithm with double-searching mode (DMGWO) is proposed with the objective of minimizing the total cost of energy-consumption and tardiness. Firstly, the algorithm starts with a discrete encoding mechanism, and then a heuristic algorithm and the random rule are employed to implement the population initialization. Secondly, a new framework with double-searching mode is developed for the GWO algorithm. In the proposed DMGWO algorithm, besides of the searching mode of the original GWO, a random seeking mode is added to enhance the global search ability. Furthermore, an adaptive selection operator of the two searching modes is also presented to coordinate the exploration and exploitation. In each searching mode, a discrete updating method of individuals is designed by considering the discrete characteristics of the scheduling solution, which can make the algorithm directly work in a discrete domain. In order to further improve the solution quality, a local search strategy is embedded into the algorithm. Finally, extensive simulations demonstrate the effectiveness of the proposed DMGWO algorithm for solving the energy-efficient job shop scheduling problem based on 43 benchmarks.

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Section: Instructionmentioning

confidence: 99%

“…Constraint (5) is the completion time of machine . Constraint (6) represents the workload of machine . Constraints (7) and (8) represent 0-1 variables.…”

Section: Problem Descriptionmentioning

confidence: 99%

Energy-Efficient Scheduling for a Job Shop Using Grey Wolf Optimization Algorithm with Double-Searching Mode

Jiang

Zhang

Zhu

et al. 2018

Mathematical Problems in Engineering

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“…The goal of this experimentation is to analysis the performances of MOACO algorithms, which are given in the previous Section 2, by changing the number of ants and number of objectives. In most of the previous studies [19,20], the number of ants was set equal to the number of operations, when applying ant colony optimization algorithms for the job shop scheduling problem. i.e.…”

Section: Experimentationmentioning

confidence: 99%

A Performance Study for the Multi-objective Ant Colony Optimization Algorithms on the Job Shop Scheduling Problem

Ariyasingha¹,

Fernando²

2015

IJCA

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Most of the research on job shop scheduling problem are concerned with minimization of a single objective. However, the real world applications of job shop scheduling problems are involved in optimizing multiple objectives. Therefore, in recent years ant colony optimization algorithms have been proposed to solve job shop scheduling problems with multiple objectives. In this paper, some recent multi-objective ant colony optimization algorithms are reviewed and are applied to the job shop scheduling problem by considering two, three and four objectives. Also in this study, four criteria: makespan, mean flow time, mean tardiness and mean machine idle time are considered for simultaneous optimization. Two types of models are used by changing the number of ants in a colony and each multi-objective ant colony optimization algorithm is applied to sixteen benchmark problem instances of up to 20 jobs × 5 machines, for evaluating the performances of these algorithms. A detailed analysis is performed using the performance indicators, and the experimental results have shown that the performance of some multi-objective ant colony optimization algorithms depend on the number of objectives and the number of ants.

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“…Udomsakdigool and Khachitvichyanukul proposed [22] an ACO algorithm, solving multiobjective JSP (MOJSP) with the sum of weighted normalized values of makespan, mean flow time and mean tardiness as an objective function. Ants use different heuristic information based on priority dispatching rule to diversify the search.…”

Section: Multi-objective Casementioning

confidence: 99%

Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

Rudy¹,

Å»elazny²

2014

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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In this paper the job shop scheduling problem (JSP) with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP) were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO) and genetic algorithm (GA) for MOJSP. Both of those methods employ certain technique, taken from the multicriteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.

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Ant colony algorithm for multi-criteria job shop scheduling to minimize makespan, mean flow time and mean tardiness

Cited by 12 publications

References 13 publications

Energy-Efficient Scheduling for a Job Shop Using Grey Wolf Optimization Algorithm with Double-Searching Mode

Energy-Efficient Scheduling for a Job Shop Using Grey Wolf Optimization Algorithm with Double-Searching Mode

A Performance Study for the Multi-objective Ant Colony Optimization Algorithms on the Job Shop Scheduling Problem

Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

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