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Rescheduling in a hybrid flowshop holds significant importance in modern industries that face uncertain events. Moreover, real-world manufacturing scenarios often utilise lot streaming to enhance market competitiveness. In light of escalating energy demands and their consequential environmental impacts, contemporary manufacturing companies are placing a heightened emphasis on energy efficiency. This study addressed a green hybrid flowshop rescheduling problem with consistent sublots (GHFRP_CS) in the context of urgent lot insertion. Initially, we establish an optimisation model aimed at minimising the makespan, total energy consumption, and system stability. To tackle this NP-hard multi-objective optimization problem, we develop a constructive heuristic generating promising solutions based on lot split, sequence, and local search rules. Further improvement is achieved through a multi-objective discrete artificial bee colony algorithm (MDABC). MDABC decomposes the problem into sub-problems, initiating solutions with the constructive heuristic and refining them through employed bee, onlooker bee, and scout bee phases. Computational experiments compare MDABC with other multi-objective evolutionary algorithms (MOEAs) on small- and large-scale problems. Results demonstrate MDABC's superiority, achieving fourfold accuracy and efficiency enhancement for small-scale instances and sixfold improvement for large-scale problems at low cost compared to other MOEAs. [ABSTRACT FROM AUTHOR]

As a pivotal component of rural revitalization, effective management of rural courtyard spaces is crucial for improving environmental quality and economic development. Utilizing the 2023 "Most Beautiful Courtyard" design competition in Guangze County as a backdrop, this study investigates the specific circumstances and practical challenges encountered during the courtyard transformation process. Based on a comprehensive literature review, this research establishes a unified indicator evaluation system; investigates the perspectives of villagers, designers, and managers; and conducts an in-depth analysis of the challenges faced in rural courtyard transformation practice. The goal is to offer substantial reference points for policy formulation and practical implementation, alongside recommendations for effective courtyard transformation. At the construction management level, the government should develop detailed operational guidelines for rural courtyard transformation, closely monitor construction progress, manage funds scientifically, and ensure efficient communication among the three groups. At the localization level, villagers' daily production and life should be integrated with village cultural symbols, respecting and exploring localization factors. Adequate consideration of the ecological environment and climatic conditions is crucial to promoting the sustainable development of rural courtyards. [ABSTRACT FROM AUTHOR]

Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario. In this study, we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem (TSP) with life-strength constraints. To address this problem, we proposed an improved iterated greedy (IIG) algorithm. First, a push-forward insertion heuristic (PFIH) strategy was employed to generate a high-quality initial solution. Second, a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm's exploration ability. Furthermore, three problem-specific swap operators were developed to improve the algorithm's exploitation ability. Additionally, an improved simulated annealing (SA) strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima. To verify the effectiveness of the proposed algorithm, the Solomon dataset was extended to generate 27 instances for simulation. Finally, the proposed IIG was compared with five state-of-the-art algorithms. The parameter analysis was conducted using the design of experiments (DOE) Taguchi method, and the effectiveness analysis of each component has been verified one by one. Simulation results indicate that IIG outperforms the compared algorithms in terms of the number of rescue survivors and convergence speed, proving the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the development of global and decentralised economies, distributed production emerges in large manufacturing firms. A distributed production model exists with hybrid flowshops. As an extension of the hybrid flowshop scheduling problem (HFSP), the distributed hybrid flowshop scheduling problem (DHFSP) with sequence dependent setup times (SDST) is a new challenging project. The DHFSP involves three sub-problems: the first one is to allocate a factory for each job; the second one is to determine job sequence in each factory; the third one is to allocate a machine for each job at each stage. This paper presents a machine position-based mathematical model and a discrete artificial bee colony algorithm (DABC) for the DHFSP-SDST to optimise the makespan. The proposed DABC employs a two-level encoding to ensure an initiative scheduling. Decoding method combines with the earliest available machine and earliest completion time rule for feasible schedules. The proposed DABC also employ effective solutions update techniques: the hybrid neighbourhood operators, and many times of Critical Factory Swap to enhance exploitation. 780 benchmarks in total are generated. Extensive experiments are carried out to test the performance of the DABC. Computational results and statistical analyses validate that the DABC outperforms the best performing algorithm in the literature. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and related softwares for mathematical models, mathematical modelling of scheduling problems is receiving growing attention from researchers. In this work, the hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the objective aimed to minimise the makespan is studied. According to the characteristics of the HFSP-UPM, eight mixed integer linear programming (MILP) models are formulated in order to obtain optimal solutions based on different modelling ideas. Then, these models are extended to solve HFSP-UPM with sequence-dependent setup times (HFSP-UPM-SDST), no-wait HFSP-UPM (HFSP-UPM-NW) and HFSP-UPM with blocking (HFSP-UPM-B). All the proposed models and the existing model are detailedly compared and evaluated under three aspects namely modelling process, size complexity and computational complexity. Numerical experiments show that MILP models dependent on diverse modelling ideas perform very differently. The model developed based on stage precedence is the best one and should be given preference in future applications. In addition, the proposed models of HFSP-UPM-NW and HFSP-UPM-B improve several best known solutions for the test instances in the existing literature. [ABSTRACT FROM AUTHOR]

This paper investigates an energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines (HFSP-UPM) with the energy-saving strategy of turning off and on. We first analyse the energy consumption of HFSP-UPM and formulate five mixed integer linear programming (MILP) models based on two different modelling ideas namely idle time and idle energy. All the models are compared both in size and computational complexities. The results show that MILP models based on different modelling ideas vary dramatically in both size and computational complexities. HFSP-UPM is NP-Hard, thus, an improved genetic algorithm (IGA) is proposed. Specifically, a new energy-conscious decoding method is designed in IGA. To evaluate the proposed IGA, comparative experiments of different-sized instances are conducted. The results demonstrate that the IGA is more effective than the genetic algorithm (GA), simulating annealing algorithm (SA) and migrating birds optimisation algorithm (MBO). Compared with the best MILP model, the IGA can get the solution that is close to an optimal solution with the gap of no more than 2.17% for small-scale instances. For large-scale instances, the IGA can get a better solution than the best MILP model within no more than 10% of the running time of the best MILP model. [ABSTRACT FROM AUTHOR]