scholarly journals Many-objective genetic programming for job-shop scheduling

2021 ◽  
Author(s):  
Atiya Masood ◽  
Yi Mei ◽  
Gang Chen ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Fitness Function ◽  
Dispatching Rules ◽  
Shop Scheduling ◽  
Conflicting Objectives ◽  
The Many ◽  
Tardy Jobs ◽  
Single Objective

In Job Shop Scheduling (JSS) problems, there are usually many conflicting objectives to consider, such as the makespan, mean flowtime, maximal tardiness, number of tardy jobs, etc. Most studies considered these objectives separately or aggregated them into a single objective (fitness function) and treat the problem as a single-objective optimization. Very few studies attempted to solve the multi-objective JSS with two or three objectives, not to mention the many-objective JSS with more than three objectives. In this paper, we investigate the many-objective JSS, which takes all the objectives into account. On the other hand, dispatching rules have been widely used in JSS due to its flexibility, scalability and quick response in dynamic environment. In this paper, we focus on evolving a set of trade-off dispatching rules for many-objective JSS, which can generate non-dominated schedules given any unseen instance. To this end, a new hybridized algorithm that combines Genetic Programming (GP) and NSGA-III is proposed. The experimental results demonstrates the efficacy of the newly proposed algorithm on the tested job-shop benchmark instances. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Many-objective genetic programming for job-shop scheduling

2021 ◽  
Author(s):  
Atiya Masood ◽  
Yi Mei ◽  
Gang Chen ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Fitness Function ◽  
Dispatching Rules ◽  
Shop Scheduling ◽  
Conflicting Objectives ◽  
The Many ◽  
Tardy Jobs ◽  
Single Objective

In Job Shop Scheduling (JSS) problems, there are usually many conflicting objectives to consider, such as the makespan, mean flowtime, maximal tardiness, number of tardy jobs, etc. Most studies considered these objectives separately or aggregated them into a single objective (fitness function) and treat the problem as a single-objective optimization. Very few studies attempted to solve the multi-objective JSS with two or three objectives, not to mention the many-objective JSS with more than three objectives. In this paper, we investigate the many-objective JSS, which takes all the objectives into account. On the other hand, dispatching rules have been widely used in JSS due to its flexibility, scalability and quick response in dynamic environment. In this paper, we focus on evolving a set of trade-off dispatching rules for many-objective JSS, which can generate non-dominated schedules given any unseen instance. To this end, a new hybridized algorithm that combines Genetic Programming (GP) and NSGA-III is proposed. The experimental results demonstrates the efficacy of the newly proposed algorithm on the tested job-shop benchmark instances. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling

2020 ◽  
Author(s):  
Binzi Xu ◽  
Yi Mei ◽  
Yan Wang ◽  
Zhicheng Ji ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Dynamic Environment ◽  
Accurate Information ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Conflicting Objectives

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this paper, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

scholarly journals Minimizing Tardiness Penalty Costs in Job Shop Scheduling under Maximum Allowable Tardiness

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1398
Author(s):  
Jae-Gon Kim ◽  
Hong-Bae Jun ◽  
June-Young Bang ◽  
Jong-Ho Shin ◽  
Seong-Hoon Choi
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Service Industries ◽  
Dispatching Rules ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Breach Of Contract ◽  
Shop Scheduling ◽  
Penalty Costs ◽  
Tardy Jobs

In many manufacturing or service industries, there exists maximum allowable tardiness for orders, according to purchase contracts between the customers and suppliers. Customers may cancel their orders and request compensation for damages, for breach of contract, when the delivery time is expected to exceed maximum allowable tardiness, whereas they may accept the delayed delivery of orders with a reasonable discount of price within maximum allowable tardiness. Although many research works have been produced on the job shop scheduling problem relating to minimizing total tardiness, none of them have yet considered problems with maximum allowable tardiness. In this study, we solve a job shop scheduling problem under maximum allowable tardiness, with the objective of minimizing tardiness penalty costs. Two kinds of penalty costs are considered, i.e., one for tardy jobs, and the other for canceled jobs. To deal with this problem within a reasonable time at actual production facilities, we propose several dispatching rules by extending well-known dispatching rules for the job shop scheduling problem, in cooperation with a probabilistic conception of those rules. To evaluate the proposed rules, computational experiments were carried out on 300 test instances. The test results show that the suggested probabilistic dispatching rules work better than the existing rules and the optimization solver CPLEX, with a time limit.

scholarly journals Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling

2020 ◽  
pp. 1-31
Author(s):  
Binzi Xu ◽  
Yi Mei ◽  
Yan Wang ◽  
Zhicheng Ji ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Dynamic Environment ◽  
Accurate Information ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Conflicting Objectives ◽  
Mean Tardiness

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e., the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this article, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and most accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

scholarly journals A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem

2020 ◽  
Author(s):  
S Nguyen ◽  
Mengjie Zhang ◽  
M Johnston ◽  
K Chen Tan
Keyword(s):  
Genetic Programming ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Computational Study ◽  
Dispatching Rules ◽  
Scheduling Problem ◽  
Learning Approaches ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Designing effective dispatching rules is an important factor for many manufacturing systems. However, this time-consuming process has been performed manually for a very long time. Recently, some machine learning approaches have been proposed to support this task. In this paper, we investigate the use of genetic programming for automatically discovering new dispatching rules for the single objective job shop scheduling problem (JSP). Different representations of the dispatching rules in the literature are newly proposed in this paper and are compared and analysed. Experimental results show that the representation that integrates system and machine attributes can improve the quality of the evolved rules. Analysis of the evolved rules also provides useful knowledge about how these rules can effectively solve JSP. © 1997-2012 IEEE.

scholarly journals An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling

2021 ◽  
Author(s):  
John Park ◽  
Yi Mei ◽  
Su Nguyen ◽  
Gang Chen ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Linear Combination ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Majority Voting ◽  
Dispatching Rules ◽  
Shop Scheduling ◽  
Weighted Majority ◽  
Dynamic Job Shop ◽  
Weighted Majority Voting

Genetic programming based hyper-heuristic (GP-HH) approaches that evolve ensembles of dispatching rules have been effectively applied to dynamic job shop scheduling (JSS) problems. Ensemble GP-HH approaches have been shown to be more robust than existing GP-HH approaches that evolve single dispatching rules for dynamic JSS problems. For ensemble learning in classification, the design of how the members of the ensembles interact with each other, e.g., through various combination schemes, is important for developing effective ensembles for specific problems. In this paper, we investigate and carry out systematic analysis for four popular combination schemes. They are majority voting, which has been applied to dynamic JSS, followed by linear combination, weighted majority voting and weighted linear combination, which have not been applied to dynamic JSS. In addition, we propose several mea-sures for analysing the decision making process in the ensembles evolved by GP. The results show that linear combination is generally better for the dynamic JSS problem than the other combination schemes investigated. In addition, the different combination schemes result in significantly different interactions between the members of the ensembles. Finally, the analysis based on the measures shows that the behaviours of the evolved ensembles are significantly affected by the combination schemes. Weighted majority voting has bias towards single members of the ensembles. © This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

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