Algorithm For Flow Shop Scheduling Research Articles

Improved Jaya algorithm for energy-efficient distributed heterogeneous permutation flow shop scheduling

Improved Jaya algorithm for energy-efficient distributed heterogeneous permutation flow shop scheduling

A bi-objective Genetic Algorithm for flexible flow shop scheduling: A real-world application in the electrical industry

The electrical sector forces manufacturing companies of electrical solutions to continually innovate and implement new processes for greater efficiency. The growing demand for electrical energy, as well as the need to adapt to hybrid operations that combine multi-project operation models with continuous production models, requires efficient workflow management. Accordingly, this article proposes a Genetic Algorithm (GA) approach for solving the scheduling problem in a Flexible Hybrid Flow Shop (FHFS) environment considering a transfer batch approach to minimize makespan and total tardiness. The approach is inspired by a real-world application in the electrical industry and also accounts for unrelated parallel machines, precedence, release times, and due dates for jobs at each production center as key constraints. Three real-data scenarios were generated and evaluated. In the first scenario, a 7 % improvement in makespan was observed compared to real execution times. In Scenario 2, the makespan improved significantly by 33 %, and only 17.4 % of jobs were delayed, compared to 96 % in the real data. Likewise, GA showed a lightly better performance over Tabu Search (TS) in 3.01 % for makespan while the delayed jobs found by GA were 25 % below those obtained by TS. These results highlight the potential of the proposed method to improve overall production efficiency, not only in the electrical sector but also in similar industries.

Viral mutation-inspired evolutionary algorithm for permutation flowshop scheduling

Viral mutation-inspired evolutionary algorithm for permutation flowshop scheduling

Green manufacturing electrical network scheduling using Slime Mould and Interior search algorithms

The strategic reduction of power consumption in electrical machines, particularly within manufacturing networks, is critical for achieving energy efficiency and sustainability. Bio-inspired algorithms have demonstrated remarkable potential for optimization, with their adaptability to specific problem domains being a key factor in their success. This study employs discrete optimization algorithms tailored for scheduling in an electrical manufacturing network modeled as a hybrid flow shop, where energy efficiency is paramount. Experimental evaluations were conducted on a customized hybrid flow shop benchmark comprising approximately 45 cases and 900 instances, utilizing a random distribution of hourly energy consumption and a specific tariff structure for monthly usage. The findings demonstrate the efficacy of the proposed discretized metaheuristics in significantly reducing power consumption costs while maintaining a minimized total makespan. This work highlights the potential of bio-inspired algorithms for green hybrid flow shop scheduling within electrical networks, contributing to enhanced operational efficiency, sustainability, and energy conservation in modern manufacturing environments.

A learning-based memetic algorithm for energy-efficient distributed flow-shop scheduling with preventive maintenance

In manufacturing systems, implementing preventive maintenance (PM) is essential for ensuring sustainable production since the inevitable wear and tear of machines can significantly affect production efficiency. In today’s decentralized economy, distributed shop scheduling has emerged within the framework of distributed manufacturing to reduce costs, enhance efficiency, and strengthen competitiveness. Thus, this paper proposes a learning-based memetic algorithm (LMA) for addressing the energy-efficient distributed flow-shop scheduling problem with preventive maintenance (EDFSP-PM) to minimize both makespan and total energy consumption simultaneously. First, a mathematical model is formulated and encoding and decoding methods are developed to map solutions to schedules with consideration of PM operations. Second, two heuristics are employed to generate high-quality solutions and various problem-specific operators are designed for different sub-problems and objectives. Third, a hierarchical learning mechanism is proposed via employing multi-layer Q-learning to select appropriate operators for solutions with diverse characteristics. Fourth, a feedback learning mechanism with solution pool is devised to reintegrate solutions from the pool into the search process to enhance search efficiency. Finally, numerical experiments are conducted to verify the effectiveness of the designed mechanisms. The comparative results demonstrate superior performance of the proposed LMA in terms of convergence and diversity.

A knowledge-driven many-objective algorithm for energy-efficient distributed heterogeneous hybrid flowshop scheduling with lot-streaming

More enterprises are enhancing their productive capacity to keep up with the rapidly shifting market demands. Meanwhile, with increasing environmental consciousness, sustainable manufacturing has drawn greater attention. In this paper, the many-objective energy-efficient distributed heterogeneous hybrid flowshop scheduling problem with lot-streaming (DHHFSPLS) is investigated, where the number of sublots is variable. To settle this challenging issue, a mathematical model is established, and a knowledge-driven many-objective optimization evolutionary algorithm (KDMaOEA) is proposed for minimizing makespan, total earliness, total tardiness and total energy consumption. In the KDMaOEA, a knowledge-driven multiple populations collaborative search strategy is devised to strengthen exploitation capabilities. Specifically, the population is divided into five subpopulations, where four superior subpopulations are employed to facilitate the optimization of each objective and one inferior subpopulation learns from four superior subpopulations to effectively utilize the optimization knowledge. Furthermore, an adaptive switching-based environmental selection strategy is fulfilled to guarantee the distribution and convergence of the solution set. Finally, extensive numerical simulations are undertaken to verify the effectiveness of the KDMaOEA in solving the many-objective energy-efficient DHHFSPLS.

A Q-learning-based multi-population algorithm for multi-objective distributed heterogeneous assembly no-idle flowshop scheduling with batch delivery

Distributed heterogeneous factory environments have become the mainstream in real-world manufacturing enterprises. Scheduling the assembly no-idle flowshops in such distributed heterogeneous environments is significant for practitioners. This problem takes into account the heterogeneity between different factories and the batch delivery process. To minimize inventory and tardiness costs of this new problem, a novel Q-learning-based multi-population multi-objective evolutionary algorithm is proposed. The algorithm incorporates five problem-specific properties to design the solution representation and calculate objectives. To obtain high-quality initial dual populations, a collaborative initialization technique combining four NEH heuristics and a completely random approach is proposed. To enhance algorithm’s search efficiency, a Q-learning-based selection method is developed to coordinate four job-related and four product-related operators. The dual-cooperation strategy is introduced to improve the global search capability. Experimental results demonstrate the effectiveness of all the designed components, indicating that the proposed algorithm outperforms five existing algorithms. It is well-suited for addressing real-world distributed heterogeneous scheduling scenarios by the proposed algorithm.

Parallel shifting bottleneck algorithms for non-permutation flow shop scheduling

Parallel shifting bottleneck algorithms for non-permutation flow shop scheduling

A Multi-Objective Non-Dominated Sorting Gravitational Search Algorithm for Assembly Flow-Shop Scheduling of Marine Prefabricated Cabins

Prefabricated cabin modular units (PMCUs) are a widespread type of intermediate products used during ship or offshore platform construction. This paper focuses on the scheduling problem of PMCU assembly flow shops, which is summarized as a multi-objective, fuzzy-blocking hybrid flow-shop-scheduling problem based on learning and fatigue effects (FB-HFSP-LF) to minimize the maximum fuzzy makespan and maximize the average fuzzy due-date agreement index. This paper proposes a multi-objective non-dominated sorting gravitational search algorithm (MONSGSA) to solve it. In the proposed MONSGSA, the ranked-order value is used to convert continuous solutions to discrete solutions. Multi-dimensional Latin hypercube sampling is used to enhance initial population diversity. Setting up an external archive to maintain non-dominated solutions while introducing an adaptive inertia factor and a trap avoidance operator to guide individual positional updates. The results of multiple sets of experiments show that Pareto solutions of MONSGSA have better distribution and convergence compared to other competitors. Finally, the instance of PMCU manufacturer is used for validation, and the results show that MONSGSA has better applicability to practical problems.

Three-way decision based island harmony search algorithm for robust flow-shop scheduling with uncertain processing times depicted by big data

This paper discusses an uncertain two-machine permutation flow-shop scheduling problem (2PFSP) with total weighted tardiness and common due date. Uncertain processing times are described by a large set of discrete scenarios, which is a type of big data. The objective is to minimize the schedule performance under the worst-case scenario. Identifying the worst-case scenario for each evaluated schedule is quite time-consuming in the situation that the scenario set size is large so that the objective evaluation might be computationally expensive. To handle this difficulty, three-way decision is used to preprocess the large-size scenario set to get a reduced scenario set so that the concept of surrogate worst-case scenario is adopted. A hybrid harmony search algorithm of combining three-island framework and the scenario-based local search is developed to solve the discussed problem. Based on the single-scenario knowledge of 2PFSP, a problem-specific scenario-dependent neighborhood structure is constructed under the surrogate worst-case scenario. An extensive experiment was carried out. The computational results show that the application of surrogate worst-case scenario based on three-way decision is effective in reducing the time consuming while keeping schedule performance evaluation. Being compared to the worst-case scenario objective evaluation, for an example in the case of the middle bad-scenario ratio, the surrogate worst-case scenario objective evaluation made the solution algorithm save 12.95 % in average CPU time for all instances while the relative performance difference is only 1.809 % in average. Being compared to possible alternative algorithms derived from the state-of-the-art algorithms, the developed algorithm is advantageous for the addressed problems.

A Q-learning memetic algorithm for energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities

Most studies on distributed assembly permutation flowshop scheduling do not consider product priorities and factory heterogeneity. This causes delays in critical products and cannot reflect the real-world production situation. This paper focuses on the energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities (EHDAPFS-P) to minimize total tardiness and total energy consumption simultaneously. Unlike traditional models, factory heterogeneity and product priorities are considered to better reflect the production environment and customer satisfaction in real-world situations. Then, a Q-learning memetic algorithm (QLMA) is proposed to solve this problem: (i) a high-quality initial population is obtained using a hybrid initialization strategy that combines four problem-specific heuristics; (ii) six efficient neighborhood structures are tailored to guide the population to converge to the promising areas; (iii) the most useful neighborhood structure is selected among the six structures using the Q-learning algorithm to accelerate the convergence, thus maximizing the cumulative and future improvements according to the population state; and (iv) an energy-saving strategy is developed to optimize the total energy consumption without deteriorating the total tardiness. The proposed QLMA is compared with seven state-of-the-art algorithms on 261 benchmark instances to demonstrate its superiority or at least competitiveness.

A cooperative adaptive genetic algorithm for reentrant hybrid flow shop scheduling with sequence-dependent setup time and limited buffers

This paper deals with a reentrant hybrid flow shop problem with sequence-dependent setup time and limited buffers where there are multiple unrelated parallel machines at each stage. A mathematical model with the minimization of total weighted completion time is constructed for this problem. Considering the complexity of the problem at hand, an effective cooperative adaptive genetic algorithm (CAGA) is proposed. In the algorithm, a dual chain coding scheme and a staged-hierarchical decoding approach are, respectively, designed to encode and decode each solution. Six dispatch heuristics and a dynamic adjustment method are introduced to define initial population. To balance the exploration and exploitation abilities, three effective operations are implemented: (1) two new crossover and mutation operators with collaborative mechanism are imposed on genetic algorithm; (2) an adaptive adjustment strategy is introduced to re-optimize better solutions after mutation operations, where ant colony search algorithm and modified greedy heuristic are intelligently switched; (3) a reset strategy with dynamic variable step strategy is embedded to re-generate some non-improved solutions. A Taguchi method of design of experiment is adopted to calibrate the parameter values in the presented algorithm. Comparison experiments are executed on test instances with different scale problems. Computational results show that the proposed CAGA is more effective and efficient than several well-known algorithms in solving the studied problem.

Deep Reinforcement Learning Based Optimization Algorithm for Permutation Flow-Shop Scheduling

As a new analogy paradigm of human learning process, reinforcement learning (RL) has become an emerging topic in computational intelligence (CI). The synergy between the RL and CI is an emerging way to develop efficient solution algorithms for solving complex combinatorial optimization (CO) problems like machine scheduling problem. In this paper, we proposed an efficient optimization algorithm based on Deep RL for solving permutation flow-shop scheduling problem (PFSP) to minimize the maximum completion time. Firstly, a new deep neural network (PFSPNet) is designed for the PFSP to achieve the end-to-end output without limitation of problem sizes. Secondly, an actor-critic method of RL is used to train the PFSPNet without depending on the collection of high-quality labelled data. Thirdly, an improvement strategy is designed to refine the solution provided by the PFSPNet. Simulation results and statistical comparison show that the proposed optimization algorithm based on deep RL can obtain better results than the existing heuristics in similar computational time for solving the PFSP.

A multi-objective discrete differential evolution algorithm for energy-efficient two-stage flow shop scheduling under time-of-use electricity tariffs

In response to the appeal for environmental protection and the efficient utilization of energy, local governments in China actively promote time-of-use (TOU) electricity tariffs for manufacturing enterprises. Motivated by a real-life industrial scenario of large special-purpose pressure vessel production, this paper addresses an energy-efficient two-stage flow shop scheduling problem under TOU tariffs to minimize the total electricity cost and the mean tardiness. Since the problem is computationally intractable, we focus on developing a multi-objective discrete differential evolution (MDDE) algorithm. Specifically, based on the optimal properties of the problem, we tailor an encoding scheme that consists of two job sequences and an idle time vector. The novel mutation and crossover operators are designed to generate the trail individuals, and the hypervolume contribution indicator is incorporated into the bi-criteria selection operator to measure the quality of the solution. Furthermore, two neighborhood structures are designed to iteratively improve the non-dominated solutions in the external archive set. We evaluate the performance of the MDDE algorithm via extensive computational experiments. The experimental results indicate that the MDDE algorithm can obtain the good approximate Pareto front, and it outperforms the well-known NSGA-II, SPEA2 and MOEA/D algorithms in solution quality.

A novel iterated greedy algorithm for no-wait permutation flowshop scheduling to minimize weighted quadratic tardiness

This article addresses the no-wait permutation flowshop layout where the jobs must be processed continuously in the available machines without interruption. The objective function is weighted quadratic tardiness minimization. Since the problem under study is NP-hard, an iterated greedy algorithm is introduced where the parameters are selected according to a variable neighbourhood descent framework. In the authors' innovative algorithm, in the first iteration of the search process, the parameters are initialized with a fixed number. In the remaining iterations, such parameters are reduced deterministically, aiming to explore distinct values throughout the search process. The proposal is compared with five other algorithms proposed for closely related problems, considering two performance measures: the Relative Deviation Index (RDI) and the Success Rate (SR). The proposed algorithm produced average values of RDI and SR of 1.2% and 86.9%, respectively. The computational results demonstrated that the authors' proposal outperformed all the other five algorithms under comparison.

Island neighboring heuristics harmony search algorithm for flow shop scheduling with blocking

• Island Neighboring Heuristics Harmony Search (INHS) for Flow Shop Scheduling is proposed. • Island model concept utilized in INHS to enhance solutions diversity. • Three neighboring heuristics are utilized in INHS to improve the local exploitation. • Two selection memory consideration methods are utilized: roulette-wheel and global-best. • Constraints are maintained during the search through repair strategy. This paper proposes an island neighboring heuristics harmony search algorithm (INHS) to tackle the blocking flow-shop scheduling problem. The island model is used to diversify the population and thus enhance the algorithm performance. The proposed method distributes the individuals in the population into different islands or sub-population. Then the harmony search algorithm iterates to look for and to develop a new solution enhanced by using neighboring heuristics. A migration process is applied, after a predefined number of iterations, to perform an exchange between some individuals in islands. The proposed algorithm is evaluated using 12 real-world datasets, each with 10 instances. A sensitivity analysis of the island model parameters is conducted to choose values that minimize the total flow time. The evaluation is conducted using two criteria, the number of evolutions and the Central Processing Unit (CPU) time using six comparative methods and sixteen comparative methods, respectively. For the first criterion, the proposed algorithm excels other comparative algorithms when solving instances of four datasets. For the second criterion, the proposed algorithm outperforms other comparative methods in six out of the twelve datasets. In conclusion, The obtained results prove the efficiency and competitiveness of the proposed algorithm when tackling the blocking flow-shop scheduling problem.

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

The manufacturing sector consumes most of the global energy and had been in focus since the outbreak of the energy crisis. One of the proposed strategies to overcome this problem is to implement appropriate scheduling, such as Hybrid Flow Shop Scheduling. Therefore, this study aims to create a Hybrid Archimedes Optimization Algorithm (HAOA) for solving the Energy-Efficient Hybrid Flow Shop Scheduling Problem (EEHFSP). It is hoped that this helps to provide new insights into advanced HAOA methods for resolving the EEHFSP as the algorithm has the potential to be a more efficient alternative. In this study, three stages of EEHFSP were considered in the problem as well as a sequence-dependent setup and removal times in the second stage. Experiments with three population variations and iterations were presented for testing the effect of HAOA parameters on energy consumption. Furthermore, ten job variations are also presented to evaluate the performance of the HAOA algorithm and the results showed that HAOA iteration and the population did not affect the removal and processing of energy consumption, but impacted that of setup and idle. The comparison of these ten cases revealed that the proposed HAOA produced the best total energy consumption (TEC) when compared to the other algorithms.

Comparison of cooling strategies in simulated annealing algorithms for flow-shop scheduling

Flow-shop scheduling is considered. The order of operations must be the same for each job to minimize the maximum completion time. The Simulated Annealing algorithm is a standard approximate solution method in scheduling and optimization in general. Since the algorithm depends on cooling as a heuristic to generate better approx- imations, choosing the strategy with which the temperature decreases can affect the final result.

Data structures and simulation algorithms for flow-shop scheduling with resource availability constraints

Flow-shop scheduling problems are classic examples of multi-resource and multi-operation optimization problems. This paper demonstrates our model and simulation for solving Flow-shop scheduling optimization problems with resource availability constraints to minimize makespan. Working hours, transfer times and setup times are taken into account. Working hours could be interpreted in two ways. In the first option, operations must fit into a single available time slot. In the second option, the simulation could cut operations across multiple time slots. We propose a simulation algorithm that accounts for both scenarios and could be used with any permutation-based exact, heuristic, or search algorithm.

Improved algorithms for proportionate flow shop scheduling with due-window assignment

Improved algorithms for proportionate flow shop scheduling with due-window assignment