Scheduling can be described as a decision-making process. It is applied in various applications, such as manufacturing, airports, and information processing systems. More so, the presence of symmetry is common in certain types of scheduling problems. There are three types of parallel machine scheduling problems (PMSP): uniform, identical, and unrelated parallel machine scheduling problems (UPMSPs). Recently, UPMSPs with setup time had attracted more attention due to its applications in different industries and services. In this study, we present an efficient method to address the UPMSPs while using a modified harris hawks optimizer (HHO). The new method, called MHHO, uses the salp swarm algorithm (SSA) as a local search for HHO in order to enhance its performance and to decrease its computation time. To test the performance of MHHO, several experiments are implemented using small and large problem instances. Moreover, the proposed method is compared to several state-of-art approaches used for UPMSPs. The MHHO shows better performance in both small and large problem cases.

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

Permutation flow shop scheduling problems (PFSPs) with many objectives are highly important scheduling problems in operations research. They have attracted substantial attention from enterprise operators and researchers in recent years. In this article, a membership function is used to connect the function values of many-objective optimization problems with a fuzzy membership degree. An approach for mapping function values to fuzzy sets is presented. Thus, the fuzzy relative entropy (FRE) can be employed in many-objective PFSPs. A many-objective PFSP model with four objectives, namely, the makespan, total tardiness, inventory holding cost, and energy consumption cost, is established. The optimal foraging algorithm (OFA) with FRE (OFA/FRE) is proposed for solving many-objective PFSPs. FRE is used as the fitness strategy of OFA. OFA/FRE is evaluated in three classification experiments and compared with five algorithms on the Walking-Fish-Group test suite, PFSP benchmark instances, and a practical PFSP for machine tool components. The results demonstrate that OFA/FRE is a promising tool for many-objective PFSP. The FRE can be used in scheduling problems and in many-objective optimization problems.

Optimal Foraging Algorithm That Incorporates Fuzzy Relative Entropy for Solving Many-Objective Permutation Flow Shop Scheduling Problems

In any organization and business, efficient scheduling cause increased efficiency, reducing the time required to complete jobs and increasing an organization’s profitability in a competitive environment. Also, the flow-shop scheduling problem is a vital type of scheduling problem with many real-world applications. Flow-shop scheduling has numerous exciting applications in various manufacturing and industrial domains. During the past eras, the growing interests in the arrangement of flow shops with diverse objective functions (for example, minimizing the makespan and flow-time) were observed. The permutation flow-shop is formulated as mixed-integer programming, and it is an NP-Hard problem. Therefore, in this paper, a novel method is provided to decrease the makespan and completion time. Since parallel algorithms use some computing elements to accelerate the search and present a new exploration pattern that is frequently suitable to enhance the quality of the results, in this research, a parallel ant colony optimization algorithm is employed to solve the mentioned problem. The Matlab simulation setting in Net Beans IDE 8.0.2 and Java to simulate the introduced method is applied. According to the obtained results, the suggested procedure has more efficiency than the previous methods. The Matlab simulator outcomes have indicated that the average response time has been improved compared to the PSO-SA and HBC algorithms. Also, the makespan is improved in comparison to GA and MOACSA.

A new approach for solving the flow‐shop scheduling problem using a parallel optimization algorithm

/pdf/whale-optimization-algorithms-for-multi-objective-flowshop-191p1rpc.pdf

Whale Optimization Algorithms for Multi-Objective Flowshop Scheduling Problems

This article addresses the permutation flowshop scheduling problem with the objective of minimizing the total completion time subject to a makespan constraint. The makespan is related to system utilization while the total completion time is related to the waiting time, and hence to the work in process (WIP); in real contexts, focusing on both total completion time and makespan allows a good trade-off between WIP and utilization to be found. Two local search algorithms are developed in the paper and, by using an extensive computational experience on literature benchmark instances, they are proved to be able to find good solutions both for regular and no-wait flowshops. 

An effective approach for total completion time minimization subject to makespan constraint in permutation flowshops

This paper proposes, a multi-objective optimization of minimizing makespan and total flow time of jobs for permutation flow shop scheduling is considered. Bi-objective issues are comprehended by doling out uniform weight to every objective function in view of its preference or determining every competent solutions. In flow shop scheduling environment, many meta-heuristic algorithms have been used to find optimal or near-optimal solutions due to the computational cost of determining exact solutions. This work provides a hybridization of genetic algorithm and simulated annealing algorithm (HGASA) based multi-objective optimization algorithm for flow shop scheduling. HGASA could be a simple and proficient algorithm that is utilized to determine for every single and multi-objective problem in flow shop scheduling shop environment. This algorithm can works simply for realistic manufacturing system applications. The proposed hybrid algorithm searches the optimal solution for objectives by considering the makespan and total flow time. The performance of the proposed HGASA was tested on standard flow shop benchmark problems to calculate its performance. The test results show that the HGASA algorithm performed better in terms of searching quality and efficiency than other meta-heuristic algorithms.

/pdf/a-hybrid-algorithm-for-multi-objective-optimization-of-2osli0bhzy.pdf

A Hybrid Algorithm for Multi-Objective Optimization of Minimizing Makespan and Total Flow Time in Permutation Flow Shop Scheduling Problems.

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A Hybrid Algorithm for Multi-Objective Optimization of Minimizing Makespan and Total Flow Time in Permutation Flow Shop Scheduling Problems.