Alireza Rahimi-Vahed

A hybrid multi-objective immune algorithm for a flow shop scheduling problem with bi-objectives: Weighted mean completion time and weighted mean tardiness

This paper investigates a novel multi-objective model for a no-wait flow shop scheduling problem that minimizes both the weighted mean completion time (C¯w) and weighted mean tardiness (T¯w). Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new hybrid multi-objective algorithm based on the features of a biological immune system (IS) and bacterial optimization (BO) to find Pareto optimal solutions for the given problem. To validate the performance of the proposed hybrid multi-objective immune algorithm (HMOIA) in terms of solution quality and diversity level, various test problems are examined. Further, the efficiency of the proposed algorithm, based on various metrics, is compared against five prominent multi-objective evolutionary algorithms: PS-NC GA, NSGA-II, SPEA-II, MOIA, and MISA. Our computational results suggest that our proposed HMOIA outperforms the five foregoing algorithms, especially for large-sized problems.

A multi-objective scatter search for a dynamic cell formation problem

Cellular manufacturing system-an important application of group technology (GT)-has been recognized as an effective way to enhance the productivity in a factory. Consequently, a multi-objective dynamic cell formation problem is presented in this paper, where the total cell load variation and sum of the miscellaneous costs (machine cost, inter-cell material handling cost, and machine relocation cost) are to be minimized simultaneously. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for finding locally Pareto-optimal frontier. To demonstrate the efficiency of the proposed algorithm, MOSS is compared with two salient multi-objective genetic algorithms, i.e. SPEA-II and NSGA-II based on some comparison metrics and statistical approach. The computational results indicate the superiority of the proposed MOSS compared to these two genetic algorithms.

A multi-objective scatter search for a mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems.

A multi-objective particle swarm for a flow shop scheduling problem

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective particle swarm (MOPS), exploiting a new concept of the Ideal Point and a new approach to specify the superior particles position vector in the swarm, is designed and used for finding locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOPS performs better than the genetic algorithm, especially for the large-sized problems.

Multi-criteria sequencing problem for a mixed-model assembly line in a JIT production system

Mixed-model assembly lines (MMAL) are a type of production lines where a variety of products models similar to product characteristics are assembled in a just-in-time (JIT) production system. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of these lines. In this paper, we consider three objectives simultaneously: (i) total utility work cost, (ii) total production rate variation cost, and (iii) total setup cost. In this study, these three objectives are first weighted by their relative importance weights and then a new mathematical model is presented. To solve this model, a memetic algorithm (MA) is proposed to determine suitable sequences. The performance of the MA is compared with the Lingo 6 software. A number of test problems are carried out to verify the good ability of the proposed MA in terms of the solution quality and computational time. The computational results reveal that the MA finds promising results, especially in the case of large-sized problems.

A multi-objective scatter search for a bi-criteria no-wait flow shop scheduling problem

The flow shop problem as a typical manufacturing challenge has gained wide attention in academic fields. This article considers a bi-criteria no-wait flow shop scheduling problem (FSSP) in which weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a FSSP has been proved to be NP-hard in a strong sense, a new multi-objective scatter search (MOSS) is designed for finding the locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm (GA), i.e. SPEA-II. The computational results show that the proposed MOSS performs better than the above GA, especially for the large-sized problems.

A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem

The sequencing of products for mixed-model assembly line in Just-in-Time manufacturing systems is sometimes based on multiple criteria. In this paper, three major goals are to be simultaneously minimized: total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Due to the NP-hardness of the problem, a new multi-objective particle swarm (MOPS) is designed to search locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms (MOGAs), i.e. PS-NC GA, NSGA-II, and SPEA-II. Comparison shows that MOPS provides superior results to MOGAs.

Solving a Bi-Criteria Permutation Flow Shop Problem Using Immune Algorithm

A flow shop problem as a typical manufacturing challenge has gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, in which the weighted mean completion time and the weighted mean tardiness are to be minimized simultaneously. Since a flow shop scheduling problem has been proved to be NP-hard in strong sense, an effective multi-objective immune algorithm (MOIA) is proposed for searching locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved and the efficiency of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm, i.e. SPEA-II. The computational results show that the proposed MOIA performs better than the above genetic algorithm, especially for large-sized problems

A Memetic Algorithm for Multi-Criteria Sequencing Problem for a Mixed-Model Assembly Line in a JIT Production System

This paper presents a new mathematical model of mixed-model assembly lines (MMAL) to find the best sequences of product models in a just-in-time (JIT) production system. The objective is to minimize three criteria with their importance weights: (i) total utility work cost, (ii) total production rate variation cost, and (iii) total setup cost. Due to its NP-hardness, a memetic algorithm (MA) is proposed and its performance is compared with the Lingo 6 software. To validate the proposed model, a number of test problems are solved to verify the good ability of the proposed MA in terms of the solution quality and computational time. The results reveal that the MA finds promising results, especially in the case of large-sized problems.

A New Approach for Mixed-Model Assembly Line Sequencing

This paper presents a fuzzy goal programming approach for solving a multi-objective mixed- model assembly line sequencing problem in a just-in-time production system. A mixed-model assembly line is a type of production line that is capable of diversified small lot production and is able to respond promptly to sudden demand changes for a variety of models. Determining the sequence of introducing models to such an assembly line is of particular importance for the efficient implementation of just-in-time (JIT) systems. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. Because of existence conflicting objectives, we propose a fuzzy goal programming based approach to solve the model. This approach is constructed based on the desirability of decision maker (DM) and tolerances considered on goal values. To illustrate the behavior of the proposed model, some of instances are solved optimally and computational results reported.