Minimizing Multiple Objective Function for Scheduling Machine Problems (original) (raw)
Related papers
2020
In this paper, two of the local search algorithms are used (genetic algorithm and particle swarm optimization), in scheduling number of products (n jobs) on a single machine to minimize a multi-objective function which is denoted as (total completion time, total tardiness, total earliness and the total late work). A branch and bound (BAB) method is used for comparing the results for (n) jobs starting from (5-18). The results show that the two algorithms have found the optimal and near optimal solutions in an appropriate times.
Exact Methods for Solving Multi-Objective Problem on Single Machine Scheduling
Iraqi Journal of Science
In this paper, one of the Machine Scheduling Problems is studied, which is the problem of scheduling a number of products (n-jobs) on one (single) machine with the multi-criteria objective function. These functions are (completion time, the tardiness, the earliness, and the late work) which formulated as . The branch and bound (BAB) method are used as the main method for solving the problem, where four upper bounds and one lower bound are proposed and a number of dominance rules are considered to reduce the number of branches in the search tree. The genetic algorithm (GA) and the particle swarm optimization (PSO) are used to obtain two of the upper bounds. The computational results are calculated by coding (programing) the algorithms using (MATLAP) and the final results up to (18) product (jobs) in a reasonable time are introduced by tables and added at the end of the research.
A united search particle swarm optimization algorithm for multiobjective scheduling problem
Applied Mathematical Modelling, 2010
The performance of a scheduling system, in practice, is not evaluated to satisfy a single objective, but to obtain a trade-off schedule regarding multiple objectives. Therefore, in this research, I make use of multiple objective decision-making method, a global criterion approach, to develop a multi-objective scheduling problem model with different due-dates on parallel machines processes, in which consider three performance measures, namely minimum run time of every machine, earlierness time (no tardiness) and process time of every job, simultaneously. According to this special multi-objective scheduling problem, the method of reverse order drawing GATT will be proposed, at the same time, bring forward a united search particle swarm optimization algorithm (USPSOA) solves this multi-objective scheduling problem. The validity and adaptability of the USPSOA is investigated through experimental results.
Al-Mustansiriyah Journal of Science
This paper present algorithms for solving a single machine scheduling problem to minimize the sum of total completion times, total tardiness,maxim-um tardiness,and maximum earliness.The single machine total tardiness problem is already NP-hard, so the consider problem is strongly NP-hard, and several algorithms are used to solve it. Branch and bound algorithmwith dominance ruleand local search algorit- hms are proposed for the problem. For the Branch and bound algorithm results- show that using dominance rule improve the performance of the algorithm in both computation times and optimal values,but it need longer times.Thus we tackle the problemof large sizes with local search algorit- hms descent method, simulated annealing and tabusearch. The perfomance of these algorithms is evaluated on a large set of test problems and the results are compared.The computational results show that simulated annealing algorithm and Tabu search algorithm are better than Descent method with preferenc...
An Approach to Multi-Objective Job Shop Scheduling Using Hybrid Particle Swarm Optimization
A job shop scheduling (JSS) problem is one of the most famous and popular scheduling problems. It is very important in the fields of both production management and combinatorial optimization. The JSS problem is to allocate jobs successively to multiple machines, that have different functions and production capabilities, and its objective is to find a schedule to complete processes in the minimal time span. In this paper, we propose hybrid algorithms using particle swarm optimization (PSO) for multi-objective job shop scheduling problems. The objective is to minimize makespan, total tardiness and total earliness of jobs simultaneously. In fact, we doubled the penalty of delay jobs because it is larger than other penalties. The results obtained by the proposed hybrid algorithms are compared with the results of standard PSO. Numerical experiments show that proposed hybrid algorithms can obtain more reasonable results.
A Particle Swarm Optimization Algorithm on Job-Shop Scheduling Problems with Multi-Purpose Machines
Asia-pacific Journal of Operational Research, 2009
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. 161 162 P. Pongchairerks & V. Kachitvichyanukul scheduling problems are complex and cannot be solved to optimality in polynomial time. One of those is the job-shop scheduling problem with multi-purpose machines (MPMJSP). The problem normally comes with a given set of jobs where each job consists of a chain of operations. For this entire process, there is a set of multi-purpose machines which is equipped with different tools that enable it to function for more than one purpose. Associated with each operation, there is a set of machines which can process an operation where each operation must be processed during an uninterrupted time period of a given length. MPMJSP attempts to minimize the latest completion time of the given jobs. This latest completion time will be called makespan throughout this paper.
Using Heuristic and Branch and Bound Methods to Solve a Multi-Criteria Machine Scheduling Problem
Iraqi Journal of Science
In this paper, we investigate some methods to solve one of the multi-criteria machine scheduling problems. The discussed problem is the total completion time and the total earliness jobs To solve this problem, some heuristic methods are proposed which provided good results. The Branch and Bound (BAB) method is applied with new suggested upper and lower bounds to solve the discussed problem, which produced exact results for in a reasonable time.
Dominance rules for single machine scheduling to minimize weighted multi objective functions
Nucleation and Atmospheric Aerosols, 2023
In this paper we studied, the problem of scheduling jobs on a single machine to minimize the multiple objective function and family setup time. This objective function is (total discount completion time and maximum tardiness respectively) which formulated as ݏ/1 /∑ ∑ (1 − ி ୀଵ ୀଵ ݁ ି ೕ) + ܶ ௫. for solving this problem, we derived a lower bound to be used in a branch and bound algorithm. We also proposed heuristic method in order to get an upper bound (near optimal solution). the proposed is number of dominance rules are considered to reduce the number of branches in the search tree. The genetic algorithm (GA) is used to obtain one of the upper bounds is used. The computational results are calculated by coding (programing) the algorithms using (MATLAP) and the final results up to (17) product (jobs) in a reasonable time are introduced by tables and added at the end of the research.
Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem
International Journal of Intelligent Systems Technologies and Applications
Hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO) and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP). Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.