Discrete Particle Swarm Optimization for Flexible Flow Line Scheduling

Previous research on scheduling flexible flow lines (FFL) to minimize makespan has utilized approaches such as branch and bound, integer programming, or heuristics. Metaheuristic methods have attracted increasing interest for solving scheduling problems in the past few years. Particle swarm optimization (PSO) is a population-based metaheuristic method which finds a solution based on the analogy of sharing useful information among individuals. In the previous literature different PSO algorithms have been introduced for various applications. In this research we study some of the PSO algorithms, continuous and discrete, to identify a strong PSO algorithm in scheduling flexible flow line to minimize the makespan. Then the effectiveness of this PSO algorithm in FFL scheduling is compared to genetic algorithms. Experimental results suggest that discrete particle swarm performs better in scheduling of flexible flow line with makespan criteria compared to continuous particle swarm. Moreover, combining discrete particle swarm with a local search improves the performance of the algorithm significantly and makes it competitive with the genetic algorithm (GA)

A Memetic Algorithm for Hybrid Flowshops with Flexible Machine Availability Constraints

This paper considers the problem of scheduling hybrid flowshops with machine availability constraints (MAC) to minimize makespan. The paper deals with a specific case of MAC caused by preventive maintenance (PM) operations. Contrary to previous papers considering fixed or/and conservative policies, we explore a case in which PM activities might be postponed or expedited while necessary. Regarding this flexibility in PM activities, we expect to obtain more efficient schedule. A simple technique is employed to schedule production jobs along with the flexible MACs caused by PM. To solve the problem, we present a high performing metaheuristic based on memetic algorithm incorporating some advanced features. To evaluate the proposed algorithm, the paper compares the proposed algorithm with several wellknown algorithms taken from the literature. Finally, we conclude that the proposed algorithm outperforms other algorithms

A memetic algorithm to minimize the total sum of earliness tardiness and sequence dependent setup costs for flow shop scheduling problems with job distinct due windows

The research considers the flow shop scheduling problem under the Just-In-Time (JIT) philosophy. There are n jobs waiting to be processed through m operations of a flow shop production system. The objective is to determine the job schedule such that the total cost consisting of setup, earliness, and tardiness costs, is minimized. To represent the problem, the Integer Linear Programming (ILP) mathematical model is created. A Memetic Algorithm (MA) is developed to determine the proper solution. The evolutionary procedure, worked as the global search, is applied to seek for the good job sequences. In order to conduct the local search, an optimal timing algorithm is developed and inserted in the procedure to determine the best schedule of each job sequence. From the numerical experiment of 360 problems, the proposed MA can provide optimal solutions for 355 problems. It is obvious that the MA can provide the good solution in a reasonable amount of time

HYBRID GENETIC AND PENGUIN SEARCH OPTIMIZATION ALGORITHM (GA-PSEOA) FOR EFFICIENT FLOW SHOP SCHEDULING SOLUTIONS

This paper presents a novel hybrid approach, fusing genetic algorithms (GA) and penguin search optimization (PSeOA), to address the flow shop scheduling problem (FSSP). GA utilizes selection, crossover, and mutation inspired by natural selection, while PSeOA emulates penguin foraging behavior for efficient exploration. The approach integrates GA's genetic diversity and solution space exploration with PSeOA's rapid convergence, further improved with FSSP-specific modifications. Extensive experiments validate its efficacy, outperforming pure GA, PSeOA, and other metaheuristics

Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method

AbstractThe aim of most supply chain optimization problems is to minimize the total cost of the supply chain. However, since environmental protection is of concern to the public, a green supply chain, because of its minimum effect on nature, has been seriously considered as a solution to this concern. This paper addresses the modeling and solving of a supply chain design for annual cost minimization, while considering environmental effects. This paper considers the cost elements of the supply chain, such as transportation, holding and backorder costs, and also, the environmental effect components of the supply chain, such as the amount of NO2, CO and volatile organic particles produced by facilities and transportation in the supply chain. Considering these two components (cost and environmental effects), we propose a multi-objective optimization problem. In this model, the facilities and transportation options have a capacity constraint and, at each level of the chain, we have several transportation options with different costs. We utilize a memetic algorithm in combination with the Taguchi method to solve this complex model. We also propose a novel decoding method and priority based algorithm for coding the solution chromosome. The performance of the proposed solution method has been examined against the hybrid genetic Taguchi algorithm (GATA) on a set of numeric instances, and results indicate that the proposed method can effectively provide better results than previous solution procedures

効果的な知識を組み込んだ遺伝的アルゴリズムによる代替機械を有する生産システムのための高性能スケジューリング

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems

Flow shop scheduling deals with the determination of the optimal sequence of jobs processing on machines in a fixed order with the main objective consisting of minimizing the completion time of all jobs (makespan). This type of scheduling problem appears in many industrial and production planning applications. This study proposes a new bi-objective mixed-integer programming model for solving the synchronous flow shop scheduling problems with completion time. The objective functions are the total makespan and the sum of tardiness and earliness cost of blocks. At the same time, jobs are moved among machines through a synchronous transportation system with synchronized processing cycles. In each cycle, the existing jobs begin simultaneously, each on one of the machines, and after completion, wait until the last job is completed. Subsequently, all the jobs are moved concurrently to the next machine. Four algorithms, including non-dominated sorting genetic algorithm (NSGA II), multi-objective simulated annealing (MOSA), multi-objective particle swarm optimization (MOPSO), and multi-objective hybrid vibration-damping optimization (MOHVDO), are used to find a near-optimal solution for this NP-hard problem. In particular, the proposed hybrid VDO algorithm is based on the imperialist competitive algorithm (ICA) and the integration of a neighborhood creation technique. MOHVDO and MOSA show the best performance among the other algorithms regarding objective functions and CPU Time, respectively. Thus, the results from running small-scale and medium-scale problems in MOHVDO and MOSA are compared with the solutions obtained from the epsilon-constraint method. In particular, the error percentage of MOHVDO’s objective functions is less than 2% compared to the epsilon-constraint method for all solved problems. Besides the specific results obtained in terms of performance and, hence, practical applicability, the proposed approach fills a considerable gap in the literature. Indeed, even though variants of the aforementioned meta-heuristic algorithms have been largely introduced in multi-objective environments, a simultaneous implementation of these algorithms as well as a compared study of their performance when solving flow shop scheduling problems has been so far overlooked

A water flow algorithm for optimization problems

Ph.DDOCTOR OF PHILOSOPH