Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling

Many real-world production scheduling problems involve the simultaneous optimization of multiple conflicting objectives that are challenging to solve without the aid of powerful optimization techniques. This includes the multi-objective Job-shop Scheduling Problem (JSP), which is among the most difficult to solve owing to the existence of an intractably large, highly complex solution space. Particle Swarm Optimization (PSO) is a population-based metaheuristic that possesses many advantages compared to other metaheuristics in solving scheduling problems. However, due to the complex nature of the multi-objective JSP, a single approach like PSO is not sufficient to explore the search space effectively owing to its shortcoming such as the tendency to become trapped in local optima. Besides, since PSO operates in the continuous domain, it cannot be applied directly to solve a discrete problem like the JSP efficiently. This research first proposes an improved continuous MOPSO to address the rapid clustering problem that exists in the basic PSO algorithm using three improvement strategies: re-initialization of particles, systematic switch of best solutions and mutation on global best selection. In order to establish an efficient mapping between the particle’s position in the continuous MOPSO and the scheduling solution in the JSP, this research proposes the JSP to be adopted within a discrete MOPSO through a modified solution representation using the permutation-based representation and a modified setup of the particle’s position and velocity. The discrete MOPSO also includes the modified maximin fitness function to promote solution diversity in the selection of global best solutions. In order to accomplish better performance by improving the search quality and efficiency of the discrete MOPSO, this research proposes a hybrid with the Diversification Generation Method in Scatter Search, the non-dominated sorting mechanism in non-dominated sorting Genetic Algorithm II (NSGA-II) and the local search mechanism in Tabu Search. The experimentations of the proposed algorithm are conducted using existing benchmark instances and a published case study on an energy-efficient job-shop model. The computational results are evaluated against other optimization techniques published in the literature. From the results, it is found that the proposed improved algorithm is effective in solving the benchmark instances compared to when no improvement is implemented and with a reasonable increase in computational costs. It is also discovered that the hybrid-discrete MOPSO (HD-MOPSO) algorithm manages to obtain higher values in the performance metrics consisting of non-dominance ratio and hypervolume compared to the competing algorithms. For the non-dominance ratio, HD-MOPSO is able to contribute 89% to 100% of solutions to the reference Pareto front. For the hypervolume values, HD-MOPSO manages to obtain a minimum of 1.0172 to 1.2862 out of the optimum value of 1.44. As higher values of metrics indicate better performance, HD-MOPSO thus outperforms the competing algorithms in solving the benchmark instances and the published case study. For these types of problems, the proposed algorithm is demonstrated to be capable of producing higher percentages of solutions in the overall non-dominated set with better quality in terms of convergence and diversity than those obtained by the competing algorithms

Performance Evaluation Of Different Types Of Particle Representation Procedures Of Particle Swarm Optimization In Job-Shop Scheduling Problems

This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP

Intelligent vision-based navigation system for mobile robot: A technological review

Vision system is gradually becoming more important. As computing technology advances, it has been widely utilized in many industrial and service sectors. One of the critical applications for vision system is to navigate mobile robot safely. In order to do so, several technological elements are required. This article focuses on reviewing recent researches conducted on the intelligent vision-based navigation system for the mobile robot. These include the utilization of mobile robot in various sectors such as manufacturing, warehouse, agriculture, outdoor navigation and other service sectors. Multiple intelligent algorithms used in developing robot vision system were also reviewed

Performance evaluation of different types of particle representation procedures of Particle Swarm Optimization in Job-shop Scheduling Problems

This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP

A Study on Multi-Objective Particle Swarm Optimization in Solving Job-Shop Scheduling Problems

Particle Swarm Optimization (PSO) is a population-based metaheuristic that was modelled based on the social interaction and communication of organisms, such as a flock of birds or a school of fishes. It is widely applied to solve a single-objective function in existing research, but this is not suitable for cases in the real world, which normally consist of multiple-objective criteria. Such cases encompass the Job-shop Scheduling Problem (JSP), where it is a typical production scheduling problem and belongs to one of the most difficult problems of combinatorial optimization. Subsequently, the multi-objective Particle Swarm Optimization (MOPSO) was established to accommodate the requirement of multiple-objective cases encountered in real-world production systems. Nevertheless, research works on solving JSP with multiple objectives using MOPSO are still limited compared to the single objective. In this study, comparison and discussion of existing works, in terms of objective functions, test problems, multi-objective optimization methods, scheduling constraints, strategies and performances are conducted. This study also highlights current MOPSO improvement strategies and the aims of their implementation in solving JSP. Finally, this study proposes a MOPSO model in solving JSP that consolidates these aspects of improvement strategies, which would set the path for future directions of research provided in the final part of the paper

A Study On Multi-Objective Particle Swarm Optimization In Solving Job-Shop Scheduling Problems

Particle Swarm Optimization (PSO) is a population-based metaheuristic that was modelled based on the social interaction and communication of organisms, such as a flock of birds or a school of fishes. It is widely applied to solve a single-objective function in existing research, but this is not suitable for cases in the real world, which normally consist of multiple-objective criteria. Such cases encompass the Job-shop Scheduling Problem (JSP), where it is a typical production scheduling problem and belongs to one of the most difficult problems of combinatorial optimization. Subsequently, the multi-objective Particle Swarm Optimization (MOPSO) was established to accommodate the requirement of multiple-objective cases encountered in real-world production systems. Nevertheless, research works on solving JSP with multiple objectives using MOPSO are still limited compared to the single objective. In this study, comparison and discussion of existing works, in terms of objective functions, test problems, multi-objective optimization methods, scheduling constraints, strategies and performances are conducted. This study also highlights current MOPSO improvement strategies and the aims of their implementation in solving JSP. Finally, this study proposes a MOPSO model in solving JSP that consolidates these aspects of improvement strategies, which would set the path for future directions of research provided in the final part of the paper

Performance Evaluation of Continuous and Discrete Particle Swarm Optimization in Job-Shop Scheduling Problems

The Particle Swarm Optimization (PSO) is an optimization method that was modeled based on the social behavior of organisms, such as bird flocks or swarms of bees. It was initially applied for cases defined over continuous spaces, but it can also be modified to solve problems in discrete spaces. Such problems include scheduling problems, where the Job-shop Scheduling Problem (JSP) is among the hardest combinatorial optimization problems. Although the JSP is a discrete problem, the continuous version of PSO has been able to handle the problem through a suitable mapping. Subsequently, its modified model, namely the discrete PSO, has also been proposed to solve it. In this paper, the performance of continuous and discrete PSO in solving JSP are evaluated and compared. The benchmark tests used are FT06 and FT10 problems available in the OR-library, where the goal is to minimize the maximum completion time of all jobs, i.e. the makespan. The experimental results show that the discrete PSO outperforms the continuous PSO for both benchmark problems

Classification of weld bead defects based on image segmentation method

Defect is an imperfection that could impair the worth and utility of a finished good. The defects show some disorder of the product and it is opposite the standard or criteria that have been stated. In defining and detecting the defects occur, many ways have been discussed and observed. However, the techniques or ways are not appropriate or not suitable for some condition and situation. In addition, welding process is one of the critical processes in detecting and defining defect to ensure the quality of the weld bead. To overcome the problem in detecting and defining defects, image processing is one of the methods in improving the process of detecting defects. The defects are classified based on automatic thresholding method that automates detecting and defining the defects. This study proposes a Decision Tree-based classification of weld bead defects through segmentation of image. The result obtained shows that the classification is effective in identifying the weld bead defects with 89% accuracy. For future work, the focus will be made to improve the detection accuracy by integrating suitable filters

Intelligent Vision-Based Navigation System For Mobile Robot: A Technological Review

Vision system is gradually becoming more important. As computing technology advances, it has been widely utilized in many industrial and service sectors. One of the critical applications for vision system is to navigate mobile robot safely. In order to do so, several technological elements are required. This article focuses on reviewing recent researches conducted on the intelligent vision-based navigation system for the mobile robot. These include the utilization of mobile robot in various sectors such as manufacturing, warehouse, agriculture, outdoor navigation and other service sectors. Multiple intelligent algorithms used in developing robot vision system were also reviewed