Algorithm For Job Shop Scheduling Problem Research Articles

Performance Analysis of Opposition Based Particle Swarm Optimization with Cauchy Distribution in Minimizing Makespan Time in Job Shop Scheduling

In the contemporary circumstances, manual solving of job shop scheduling problem (JSSP) is quite time consuming and inaccurate. The main intention of this paper is to analyze the performance of various optimization techniques in JSSP in order to minimize makespan time. This paper aims to analyze four optimization techniques viz, particle swarm optimization (PSO), genetic algorithm (GA), opposition based genetic algorithm(OGA) and opposition based particle swarm optimization with Cauchy distribution (OPSO CD) in addition to the existing optimization techniques applied in various research papers on combinatorial optimization problems viz., JSSP. A comparative study of these optimization techniques were conducted and analyzed to find out the most effective optimization technique on solving JSSP. Results show that OPSO CD is found to possess minimum makespan time in comparison with other algorithms for JSSP.

A hybrid grey cuckoo search algorithm for job-shop scheduling problems under fuzzy conditions

A hybrid grey cuckoo search algorithm for job-shop scheduling problems under fuzzy conditions

Beer froth artificial bee colony algorithm for job-shop scheduling problem

Job-shop scheduling problem (JSSP) is a vital combinatorial optimization problem in the field of machine scheduling. The high complexity of JSSP is attracting researchers since the past few decades and many swarm intelligence (SI) based algorithms have been presented to solve it. Artificial bee colony algorithm (ABC) has been proven to be an efficient technique in the field of SI based algorithms. ABC algorithm is attracting researchers because of its performance available in literature in the area of solving real-world optimization problems. This article presents a modified ABC algorithm to solve JSSP. Here, in the onlooker bee phase of ABC, to maintain a proper harmony amid exploration and exploitation capabilities, beer froth phenomenon inspired position update is incorporated. The proposed strategy is named as Beer froth artificial bee colony algorithm (BeFABC). The BeFABC has been assessed on 25 benchmark test problems and compared with other state-of-art algorithms. Further, it is applied to solve 62 well-known instances of discrete JSSP. The obtained numerical results and statistical analysis depict that the proposed algorithm is competent in dealing with the discrete real-world JSSP.

An adaptive multi-population genetic algorithm for job-shop scheduling problem

Job-shop scheduling problem (JSP) is a typical NP-hard combinatorial optimization problem and has a broad background for engineering application. Nowadays, the effective approach for JSP is a hot topic in related research area of manufacturing system. However, some JSPs, even for moderate size instances, are very difficult to find an optimal solution within a reasonable time because of the process constraints and the complex large solution space. In this paper, an adaptive multi-population genetic algorithm (AMGA) has been proposed to solve this problem. Firstly, using multi-populations and adaptive crossover probability can enlarge search scope and improve search performance. Secondly, using adaptive mutation probability and elite replacing mechanism can accelerate convergence speed. The approach is tested for some classical benchmark JSPs taken from the literature and compared with some other approaches. The computational results show that the proposed AMGA can produce optimal or near-optimal values on almost all tested benchmark instances. Therefore, we can believe that AMGA can be considered as an effective method for solving JSP.

Forward VNS, Reverse VNS, and Multi-VNS Algorithms for Job-Shop Scheduling Problem

This paper proposes a number of forward VNS and reverse VNS algorithms for job-shop scheduling problem. The forward VNS algorithms are the variable neighborhood search algorithms applied to the original problem (i.e., the problem instance with the original precedence constraints). The reverse VNS algorithms are the variable neighborhood search algorithms applied to the reversed problem (i.e., the problem instance with the reversed precedence constraints). This paper also proposes a multi-VNS algorithm which assigns an identical initial solution-representing permutation to the selected VNS algorithms, runs these VNS algorithms, and then uses the best solution among the final solutions of all selected VNS algorithms as its final result. The aim of the multi-VNS algorithm is to utilize each single initial solution-representing permutation most efficiently and thus receive its best result in return.

Efficient local search algorithms for job-shop scheduling problems

To solve job-shop scheduling problems, this paper develops four local search algorithms based on an input parameter of controlling the maximum allowable delay times of all machines. The use of this parameter in different values can change the size in numbers of the algorithm's solution space. The first proposed algorithm fixes the mentioned parameter's value and generates only forward schedules. The second algorithm reduces the parameter value iteratively and generates only forward schedules. The third algorithm reduces the parameter value iteratively; in addition, each operation-based permutation generated by this algorithm is transformed into both forward and backward schedules simultaneously. The fourth algorithm in the first-half iterations reduces the parameter value iteratively as well as generating only forward schedules; after that, this algorithm in the second-half iterations increases the parameter value iteratively as well as generating only backward schedules.

Efficient local search algorithms for job-shop scheduling problems

Efficient local search algorithms for job-shop scheduling problems

A tabu search/path relinking algorithm to solve the job shop scheduling problem

We present an algorithm that incorporates a tabu search procedure into the framework of path relinking to generate solutions to the job shop scheduling problem (JSP). This tabu search/path relinking (TS/PR) algorithm comprises several distinguishing features, such as a specific relinking procedure to effectively construct a path linking the initiating solution and the guiding solution, and a reference solution determination mechanism based on two kinds of improvement methods. We evaluate the performance of TS/PR on almost all of the benchmark JSP instances available in the literature. The test results show that TS/PR obtains competitive results compared with state-of-the-art algorithms for JSP in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, TS/PR is able to improve the upper bounds for 49 out of the 205 tested instances and it solves a challenging instance that has remained unsolved for over 20years.

Variable neighbourhood search algorithms applied to job-shop scheduling problems

This research attempts to find the neighbourhood structures of the variable neighbourhood search (VNS) algorithm well fitting to job-shop scheduling problem (JSP). So as to achieve this objective, this research adapts the seven neighbourhood structures given by the travelling salesman problem’s literature to create the seven VNS algorithms for JSP. The performances of these proposed VNS algorithms are then compared to the performances of several existing algorithms, i.e., ACO, PSO and VNS algorithms taken from previously published literature. The comparison indicates that all proposed VNS algorithms perform better than the ACO and PSO algorithms with the significance level of 0.05. This paper finally reveals the neighbourhood structures performing well to JSP.

The Effect of Crossover and Mutation Operators on Genetic Algorithm for Job Shop Scheduling Problem

Job Shop Scheduling Problem (JSSP) is a famous NP-hard problem in scheduling field. The concentration of JSSP is to find a feasible scheduling plan to figure out the earliest completion time under machine and processing sequence constraints. At present, genetic algorithm has been widely adopted in varies of operation research problems including JSSP, and good performance have been achieved. However, few work have stress the selection of varies operators when implemented for JSSP. Using benchmark problems, this paper compares the effect of crossover and mutation operators on genetic algorithm for JSSP.

System Development of a Simulated Annealing Algorithm for Job-Shop Scheduling Problem Based on Delphi

The job-shop scheduling is the key element of a manufacturing execution system (MES). It is significant for enterprises to utilize resources rationally, enhance product quality, shorten production cycle, reduce production cost, and improve its market competitiveness. In the paper, Simulated Annealing (SA) algorithm is adopted to solve the job-shop scheduling problem. SA algorithm is a random search method proposed to solve large-scale combinatorial optimization problems. As an efficient and general method, the optimization and convergence performance of SA algorithm is mainly affected by the problem and several factors. In order to facilitate use’s employment, a job-shop scheduling system based on SA algorithm is developed using Delphi. The system developed is verified by an example. The results show that it is an effective solution to the job-shop scheduling problems.

Genetic Algorithm with Local Search for Job Shop Scheduling Problem

The job shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem. This paper presents a hybrid genetic algorithm for the JSSP with the objective of minimizing makespan. A new generation alternation model of genetic algorithm for JSSP is designed. Every pair of randomly selected parents must pass either crossover or mutation, which are deployed in parallel. In the inner structure of crossover, mutation is partially embedded. Schedules are constructed using a procedure that generates full active schedules. In each generation, a local search heuristic based on the neighborhood structure proposed by Nowicki and Smutnicki is applied to improve the solutions. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns

The job-shop scheduling problem (JSSP) is considered to be one of the most complex combinatorial optimisation problems. In our previous attempt, we hybridised a Genetic Algorithm (GA) with a local search technique to solve JSSPs. In this research, we propose an improved local search technique, Shifted Gap-Reduction (SGR), which improves the performance of GAs when solving relatively difficult test problems. We also modify the new algorithm for JSSPs with machine unavailability and breakdowns. We consider two scenarios of machine unavailability. First, where the unavailability information is available in advance (predictive) and, secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules.

Hybrid Algorithm for JSP with Discretely Controllable Processing Times

Job-shop scheduling problem with discretely controllable processing times(JSP-DCPT) is a generalization of the classical Job-shop scheduling problem(JSP).To avoid solving the approximate problem of JSP-DCPT by using polynomial time approximation schemes,a hybrid algorithm is proposed to solve JSP-DCPT directly.Based on a decomposition approach,both the hybrid algorithm of tabu search and simulated annealing(TSSA) and the fast elitist non-dominated sorting genetic algorithm(NSGA-II) are nested within the proposed algorithm in order to effectively and efficiently solve the JSP and the discrete time-cost tradeoff problems decomposed from JSP-DCPT.Computational instance of different problem size is separately constructed on the basis of JSP benchmark FT06,FT10 and FT20.Experimental simulation results demonstrate that constrained Pareto frontiers can be obtained by using the proposed hybrid algorithm.

An effective TPA-based algorithm for job-shop scheduling problem

The job-shop scheduling problem (JSSP) is one of the most general and difficult of all traditional scheduling problems. Many different approaches have been applied to JSSP and a rich harvest has been obtained. However, some JSSP, even with moderate size, cannot be solved to guarantee optimality. In this paper, a computationally effective team process algorithm (TPA) for solving the minimum makespan problem of job-shop scheduling is used. In the TPA system, the team numbers are divided into the elite and plain groups. The manipulations of learning and exploring are properly defined, and the member renewal rules are reasonably established. It makes the algorithm possesses the potential of global, local and directional search. Numerical results verify that the modified TPA has the properties of simple implementation, high success rate for global optimization, fast convergence, and better than the modified particle swarm optimization (PSO) with GA and GA alone.

A PARTICLE SWARM OPTIMIZATION ALGORITHM ON JOB-SHOP SCHEDULING PROBLEMS WITH MULTI-PURPOSE MACHINES

This paper is a contribution to the research which aims to provide an efficient optimization algorithm for job-shop scheduling problems with multi-purpose machines or MPMJSP. To meet its objective, this paper proposes a new variant of particle swarm optimization algorithm, called GLN-PSOc, which is an extension of the standard particle swarm optimization algorithm that uses multiple social learning topologies in its evolutionary process. GLN-PSOc is a metaheuristic that can be applied to many types of optimization problems, where MPMJSP is one of these types. To apply GLN-PSOc in MPMJSP, a procedure to map the position of particle into the solution of MPMJSP is proposed. Throughout this paper, GLN-PSOc combined with this procedure is named MPMJSP-PSO. The performance of MPMJSP-PSO is evaluated on well-known benchmark instances, and the numerical results show that MPMJSP-PSO performs well in terms of solution quality and that new best known solutions were found in some instances of the test problems.

Multiple colony ant algorithm for job-shop scheduling problem

Ant colony optimization (ACO) is a metaheuristic that takes inspiration from the foraging behaviour of a real ant colony to solve the optimization problem. This paper presents a multiple colony ant algorithm to solve the Job-shop Scheduling Problem with the objective that minimizes the makespan. In a multiple colony ant algorithm, ants cooperate to find good solutions by exchanging information among colonies which are stored in a master pheromone matrix that serves the role of global memory. The exploration of the search space in each colony is guided by different heuristic information. Several specific features are introduced in the algorithm in order to improve the efficiency of the search. Among others is the local search method by which the ant can fine-tune their neighbourhood solutions. The proposed algorithm is tested over set of benchmark problems and the computational results demonstrate that the multiple colony ant algorithm performs well on the benchmark problems.

An improved genetic algorithm for job-shop scheduling problems using Taguchi-based crossover

A Taguchi-based genetic algorithm (TBGA) is proposed as an improved genetic algorithm to solve the job-shop scheduling problems (JSP). The TBGA combines the powerful global exploration capabilities of conventional genetic algorithm (GA) with the Taguchi method that exploits optimal offspring. The latter method is used as a new crossover and is incorporated in the crossover operation of a GA. The reasoning ability of the Taguchi-based crossover can systematically select the better genes to achieve crossover and, consequently, enhance the GA. Furthermore, mutation is designed to have the neighbor search technique of performing the fine-tuning on the positions of jobs for the JSP. Therefore, the proposed TBGA approach possesses the merits of global exploration and robustness. The proposed TBGA approach is effectively applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. In these studied problems, there are numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed evolutionary approaches. The computational experiments show that the proposed TBGA approach can obtain both better and more robust results than those evolutionary methods reported recently.

A local search algorithm for jobshop scheduling problems with alternative operations and sequence-dependent setups

Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. This paper addresses a jobshop scheduling problem with these factors simultaneously considered. In it, a mixed integer program model of the problem is presented, along with a local search scheme. The local search scheme utilizes a property identified to reduce computation time. In addition, a property that establishes a performance bound is presented. Results from a comparative computational study using benchmark data show that the scheme significantly enhances the performance of several greedy-based dispatching rules for the problem under consideration.

An efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate

In this paper, by considering the imprecise or fuzzy nature of the data in real-world problems, job-shop scheduling problems with fuzzy processing time and fuzzy duedate are formulated and a genetic algorithm which is suitable for solving the formulated problems is proposed. On the basis of the agreement index of fuzzy duedate and fuzzy completion time, the formulated fuzzy job-shop scheduling problems are interpreted so as to maximize the minimum agreement index. For solving the formulated fuzzy job-shop scheduling problems, an efficient genetic algorithm is proposed by incorporating the concept of similarity among individuals into the genetic algorithms using the Gannt chart. As illustrative numerical examples, both 6×6 and 10×10 job-shop scheduling problems with fuzzy duedate and fuzzy processing time are considered. Through the comparative simulations with simulated annealing, the feasibility and effectiveness of the proposed method are demonstrated.