A hybrid genetic local and global search algorithm for solving no-wait flow shop problem with bi criteria

This paper addresses the m-machine no-wait Flow Shop Scheduling with Setup Times (NW-FSSWST). Two performance measures: total flow time and makespan are considered. The objective is to find a sequence that minimizing total flow time ($$\sum C_{j}$$ ∑ C j ) and makespan ($$C_{j}$$ C j ) simultaneously. A Hybrid Genetic Local and Global Search Algorithm (HGLGSA) is proposed to solve the NW-FSSWST for two performance criteria. The hybrid genetic algorithm is constructed by insert-search and self-repair algorithm with self-repair function. The proposed HGLGSA is tested on 192 benchmark problems of NW-FSSWST in the literature. A full factorial experimental design is made for determined the best parameter sets that improve the performance of the proposed algorithm. The computational results are compared with the benchmark solutions from the literature. The experimental results demonstrate the effectiveness and efficiency of the proposed HGLGSA for solving NW-FSSWST.

Optimizing Lot-Sizes and Scheduling in Terms of Mass Customization

The goal of mass customisation is to offer products tailored to the specific needs of the customers. Even though customers are aware that manufacturers need a certain time to produce and deliver customised products, the companies might guarantee that their products will arrive on time. Then, the objective of manufacturing managers is to minimise the total flow time of parts through the shop. One of the effective ways to reach this objective is to optimise schedules in order to satisfy the due date criterion, which plays a crucial role in the mass customisation environment. This paper, in the first part, outlines methodological tools to tackle the problem of shortening delivery times through scheduling and management of resources. In the second part of the paper, the proposed methodology framework through the theoretical example is applied.

Solution for Multi-Objective Single Row Facility Layout Problem Using PSO Algorithm

An optimized facility layout design helps to ensure a high level of machine usability along with minimum cost and good performance. However, facility layout is a multi-objective design optimization problem, and as such, it is difficult to solve. This original research work aims to design an optimal linear machine sequence using particle swarm optimization algorithm that minimizes the following: the total investment cost of machines, the total number of machines in the final sequence, the total flow time of the products, and the total flow distance of the products. The effectiveness of the proposed algorithm is demonstrated with a reasonable number of problems. Maximum of 19.1% reduction in total flow distance of products, 12.8% reduction in total investment cost of machines, 28.4% reduction in total flow time of products, and reduction of two numbers of machines in the layout are achieved by proposed method compared with the previous approaches.