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Hybrid flow shop scheduling problem (HFSP) has been extensively considered, however, some real-life conditions are seldom investigated. In this study, HFSP with no precedence between some stages is solved and an adaptive shuffled frog-leaping algorithm (ASFLA) is developed to optimize makespan. A new solution representation and a decoding procedure are presented, an adaptive memeplex search and dynamical population shuffling are implemented together. Many computational experiments are implemented. Computational results prove that the new strategies of ASFLA are effective and ASFLA is very competitive in solving HFSP with no precedence between some stages.

The casting production process typically involves single jobs and small batches, with multiple constraints in the molding and smelting operations. To address the discrete optimization challenge of casting production scheduling, this paper presents a multi-objective batch scheduling model for molding and smelting operations on unrelated batch processing machines with incompatible job families and non-identical job sizes. The model aims to minimise the makespan, number of batches, and average vacancy rate of sandboxes. Based on the genetic algorithm, virus optimization algorithm, and two local search strategies, a hybrid algorithm (GA-VOA-BMS) has been designed to solve the model. The GA-VOA-BMS applies a novel Batch First Fit (BFF) heuristic for incompatible job families to improve the quality of the initial population, adopting the batch moving strategy and batch merging strategy to further enhance the quality of the solution and accelerate the convergence of the algorithm. The proposed algorithm was then compared with multi-objective swarm optimization algorithms, namely NSGA-II, SPEA-II, and PESA-II, to evaluate its effectiveness. The results of the performance comparison indicate that the proposed algorithm outperforms the others in terms of both quality and stability.

Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings. However, the distributed two-stage hybrid flow shop scheduling problem (DTHFSP) with fuzzy processing time is seldom investigated in multiple factories. Furthermore, the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP. In the current study, DTHFSP with fuzzy processing time was investigated, and a novel Q-learning-based teaching-learning based optimization (QTLBO) was constructed to minimize makespan. Several teachers were recruited for this study. The teacher phase, learner phase, teacher’s self-learning phase, and learner’s self-learning phase were designed. The Q-learning algorithm was implemented by 9 states, 4 actions defined as combinations of the above phases, a reward, and an adaptive action selection, which were applied to dynamically adjust the algorithm structure. A number of experiments were conducted. The computational results demonstrate that the new strategies of QTLBO are effective; furthermore, it presents promising results on the considered DTHFSP.

Digital rural agricultural engineering is the most widely representative innovative application of digital technology in the field of modern China’s new agricultural technology and economic development and the practice of China’s new rural economy and society under the background of the process of modern social knowledge networking, intelligent information society, and the digital sharing of social information resources. Using the method of literature research, this paper analyzes the problems existing in the current logistics circulation system of agricultural products. Through the co-occurrence analysis of technical keywords, this paper studies the current situation in the field of agricultural product logistics circulation systems and puts forward countermeasures and measures to promote the innovation and development of agricultural product logistics circulation systems. Finally, it is concluded that we should explore and innovate to build a fresh circulation supply chain system of new smart agricultural products in the new era of agriculture driven by rural big data.

The assembly scheduling problem (ASP) and distributed assembly scheduling problem (DASP) have attracted much attention in recent years; however, the transportation stage is often neglected in previous works. Factory eligibility means that some products cannot be manufactured in all factories. Although it extensively exists in many real-life manufacturing processes, it is hardly considered. In this study, a distributed three-stage ASP with a DPm→1 layout, factory eligibility and setup times is studied, and a Q-learning-based artificial bee colony algorithm (QABC) is proposed to minimize total tardiness. To obtain high quality solutions, a Q-learning algorithm is implemented by using eight states based on population quality evaluation, eight actions defined by global search and neighborhood search, a new reward and an adaptive ε−greedy selection and applied to dynamically select the search operator; two employed bee swarms are obtained by population division, and an employed bee phase with an adaptive migration between them is added; a new scout phase based on a modified restart strategy is also presented. Extensive experiments are conducted. The computational results demonstrate that the new strategies of QABC are effective, and QABC is a competitive algorithm for the considered problem.

Energy-efficient hybrid flow shop scheduling problem has attracted much attention in deterministic case; however, uncertainty is seldom considered in previous works. In this paper, energy-efficient interval hybrid flow shop scheduling problem (EIHFSP) is investigated, and a new imperialist competitive algorithm with empire grouping (EGICA) is proposed to minimize total energy consumption and makespan simultaneously. Groups of empires are obtained by defining normalized cost and normalized total cost in interval case, constructing initial empires, and grouping all empires. Assimilation is implemented in a new way, and an adaptive revolution is adopted in each group. Two-phase imperialist competition is newly proposed, and an adaptive search of member from archive is adopted. A number of computational experiments are conducted. The computational results demonstrate that the EGICA has promising advantages on solving the EIHFSP.

A number of related works have been done on multi-objective flexible job shop scheduling problem, however, many-objective flexible job shop scheduling problems (MaOFJSP) with at least four objectives are seldom considered. In this paper, an imperialist competitive algorithm with the diversified operators (DOICA) is proposed for MaOFJSP with the minimization of makespan, total tardiness, total workload, and total energy consumption. In DOICA, the diversified assimilation and revolution are used according to the features of empires, a novel imperialist competition is implemented by excluding the strongest empire and doing multiple neighborhood search of a solution in the strongest empire. The extensive experiments are conducted by using a number of benchmark instances to test the impact of strategies of DOICA on its performance and compare DOICA with other algorithms from literature finally. The computational results validate that the new strategies of DOICA are effective and DOICA can provide promising results for the considered MaOFJSP.

This paper presents a hybrid method of Simulated Annealing (SA) algorithm and Sine Cosine Algorithm (SCA) to solve unrelated parallel machine scheduling problems (UPMSPs) with sequence-dependent and machine-dependent setup times. The proposed method, called SASCA, aims to improve the SA algorithm using the SCA as a local search method. The SCA provides a good tool for the SA to avoid getting stuck in a focal point and improving the convergence to an efficient solution. SASCA algorithm is used to solve UPMSPs by minimizing makespan. To evaluate the performance of SASCA, a set of experiments were performed using 30 tests for 4 problems. Moreover, the performance of the proposed method was compared with other meta-heuristic algorithms. The comparison results showed the superiority of SASCA over other methods in terms of performance dimensions.

Unrelated parallel machine scheduling problem (UPMSP) is a typical scheduling one and UPMSP with various real-life constraints such as additional resources has been widely studied; however, UPMSP with additional resources, maintenance, and energy-related objectives is seldom investigated. The Artificial Bee Colony (ABC) algorithm has been successfully applied to various production scheduling problems and demonstrates potential search advantages in solving UPMSP with additional resources, among other factors. In this study, an energy-efficient UPMSP with additional resources and maintenance is considered. A dynamical artificial bee colony (DABC) algorithm is presented to minimize makespan and total energy consumption simultaneously. Three heuristics are applied to produce the initial population. Employed bee swarm and onlooker bee swarm are constructed. Computing resources are shifted from the dominated solutions to non-dominated solutions in each swarm when the given condition is met. Dynamical employed bee phase is implemented by computing resource shifting and solution migration. Computing resource shifting and feedback are used to construct dynamical onlooker bee phase. Computational experiments are conducted on 300 instances from the literature and three comparative algorithms and ABC are compared after parameter settings of all algorithms are given. The computational results demonstrate that the new strategies of DABC are effective and that DABC has promising advantages in solving the considered UPMSP. [ABSTRACT FROM AUTHOR]

This study focuses on the scheduling problem of unrelated parallel batch processing machines (BPM) with release times, a scenario derived from the moulding process in a foundry. In this process, a batch is initially formed, placed in a sandbox, and then the sandbox is positioned on a BPM for moulding. The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume. To minimize the makespan, a new cooperated imperialist competitive algorithm (CICA) is introduced. In CICA, the number of empires is not a parameter, and four empires aremaintained throughout the search process. Two types of assimilations are achieved: The strongest and weakest empires cooperate in their assimilation, while the remaining two empires, having a close normalization total cost, combine in their assimilation. A new form of imperialist competition is proposed to prevent insufficient competition, and the unique features of the problem are effectively utilized. Computational experiments are conducted across several instances, and a significant amount of experimental results show that the new strategies of CICA are effective, indicating promising advantages for the considered BPM scheduling problems. [ABSTRACT FROM AUTHOR]

Bottleneck stage and reentrance often exist in real-life manufacturing processes; however, the previous research rarely addresses these two processing conditions in a scheduling problem. In this study, a reentrant hybrid flow shop scheduling problem(RHFSP) with a bottleneck stage is considered, and an elite-class teaching-learning-based optimization (ETLBO) algorithm is proposed to minimize maximum completion time. To produce high-quality solutions, teachers are divided into formal ones and substitute ones, and multiple classes are formed. The teacher phase is composed of teacher competition and teacher teaching. The learner phase is replaced with a reinforcement search of the elite class. Adaptive adjustment on teachers and classes is established based on class quality, which is determined by the number of elite solutions in class. Numerous experimental results demonstrate the effectiveness of new strategies, and ETLBO has a significant advantage in solving the considered RHFSP. [ABSTRACT FROM AUTHOR]