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In the mixed-model assembly line balancing and sequencing problem (MALBSP), workstations are assumed to be constantly available. The failure of any workstation will make the entire assembly line stop working. Preventive maintenance (PM) is a way to maintain the workstation before its failure, reduce unexpected downtime, and prolong its useful life. Previous studies have considered PM scenarios (PMS) in the simple and U-shaped assembly line to improve production efficiency and smoothness effectively, but not in the mixed-model assembly line. This paper fills this research gap, and the MALBSP considering PMS (MALBSP_PMS) is studied in this paper. A mixed-integer linear programming model is proposed to minimize makespan and task alteration. A migrating birds optimization algorithm is improved (IMBO) to obtain well-distributed Pareto frontier solutions. This algorithm designs a restart mechanism and an intra-population crossover operator to avoid falling into the local optimal and enhance its searchability. Experimental results demonstrate the effectiveness of two improvements and the IMBO algorithm. In addition, a real-world case study is introduced to illustrate the importance of considering PM scenarios in MALBSP. [ABSTRACT FROM AUTHOR]

While solving the shunting scheduling of EMU first-level maintenance (SSEFM), most existing literature assumed a single maintenance route for all trains and considered only a through depot. It neglects the problem-specific characteristics in terms of varied maintenance routes and a stub-end depot, causing the infeasibility of the generated schedule in such particular circumstances. Therefore, the SSEFM problem with flexible maintenance routes in a stub-end depot with a transversal yard configuration is considered in this work. First, a multi-objective mixed-integer linear programming (MILP) model is formulated to maximize the reservation time in the storage area, and minimize the overstay time in the cleaning and inspecting areas. The relationship between constraints including flexible maintenance routes, train shunting conflicts, track occupation conflicts, and train arrival/departure times, are coordinated. Subsequently, a heuristic-based enhanced particle swarm optimization algorithm (EPSO) with two improvements is proposed to tackle this NP-hard problem. Specifically, three heuristic rules about the depth-first operation track allocation, the conflict-free bottleneck track allocation, and the right-shift track occupancy repair are designed to ensure the feasibility of the shunting schedule. Accordingly, a three-level decoding mechanism is designed to achieve a near-optimal shunting schedule with great train and route sequences. Two improvements on crossover and mutation operators are developed to enhance the exploration and exploitation ability. Finally, a real-world instance in China is solved to verify the effectiveness and efficiency of the proposed model and algorithm. Experimental results show that EPSO is relatively more effective than all the compared algorithms. [ABSTRACT FROM AUTHOR]

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate. [ABSTRACT FROM AUTHOR]

In this paper, flexible preventive maintenance (PM) activities are incorporated into two-stage assembly flow shop scheduling where m dedicated machines in the fabrication stage and one machine in the assembly stage. The operational status of each machine is described by a continuous variable, maintenance level. The maintenance level value is inversely proportional to the processing time. Once a PM activity is performed, this value will return to the initial value. Different from the PM at fixed predefined time intervals, flexible PM can be carried out at any time point, but the maintenance levels are not less than 0. Hence, a MILP model with maintenance level constraints is formulated to minimize the total completion time and maintenance time. Regarding the methods, a latest PM decision strategy is proposed to determine the execution time of PM activities. This new strategy is embedded into 15 constructive heuristics and 7 meta-heuristics (three variants of iterated local search, three variants of Q-learning-based ant colony system with local search and a Q-learning-based hyper-heuristics) to address this new problem. The final experimental analysis demonstrates the significance of the integrated model and the effectiveness of the proposed constructive heuristics and meta-heuristics. [ABSTRACT FROM AUTHOR]

The fast and accurate estimation of makespan is essential for the determination of the delivery date and the sustainable development of the enterprise. In this paper, a high-quality training dataset is constructed and an adaptive ensemble model is proposed to achieve fast and accurate makespan estimation. First, both the logistics features extracted by the Pearson correlation coefficient and the new meaningful nonlinear combination features dug out by gene expression programming are first involved in this paper for constructing a high-quality dataset. Secondly, an improved clustering with elbow criterion and a resampling operation are applied simultaneously to generate representative subsets; and correspondingly, several back propagation neural network (BPNN) with the architecture optimized by genetic algorithm are trained by these subsets respectively to generate effective diverse learners; and then, a K-nearest neighbor based dynamic weight combination strategy which is sensitive to current testing sample is proposed to make full use of the learner's positive effects and avoid its negative effects. Finally, the results of effective experiments prove that both the newly involved features and the improvements in the proposed ensemble are effective. In addition, comparison experiments confirm that the proposed enhanced ensemble of BPNNs outperforms significantly the prevailing approaches, including single, ensemble and hybrid models. And hence, the proposed model can be utilized as a convenient and reliable tool to support customer order acceptance. [ABSTRACT FROM AUTHOR]

• RMALBSP considering PM scenarios promotes productivity and smoothness. • Mathematical model generate allocation and sequencing plans for scenarios. • Intra-population crossover facilitates the exchange of information. • Four neighborhood structures are designed to replace repetitive solutions. • Results indicate that MOCDE outperforms the other eight algorithms. Considering preventive maintenance scenarios (PMS) in assembly line balancing problems can significantly influence machine reliability and production continuity. However, no research considers PMS in robust mixed-model assembly line balancing and sequencing problems (RMALBSP) with interval processing times. This paper designs a robust mathematical model to solve RMALBSP considering PMS (RMALBSP_PMS) with two types of objectives: makespan and task alteration. Then, it is linearized by duality to be directly solved by Cplex. We also develop a multi-objective cooperative differential evolution algorithm (MOCDE) to solve large-scale instances. In this algorithm, we design a coevolutionary mechanism to partition the RMALBSP and optimize each part separately. The priority-based encoding and decoding methods are proposed to represent solutions in each population. Evolutionary operators for populations and the archive are designed to explore the solution space. Four neighbor search operators are developed to replace repetitive solutions. The first experimental result illustrates the model can obtain the Pareto solutions of small-scale instances, and the MOCDE is more suitable for solving large-scale instances. The remaining two experiments demonstrate that the designed coevolutionary mechanism and improvements are effective, and the MOCDE algorithm outperforms eight state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

1 A MILP model is developed for an energy-aware mixed shop scheduling problem. 2 Bi-criterion selection is designed to improve diversity and convergence of MOEA. 3 Q-learning-based hyper-heuristic is used for full exploration and exploitation. 4 Statistical results indicate QHH-BS with two improvements outperforms others. Owning to diverse customer demands and enormous product varieties, mixed shop production systems are applied in practice to improve responsiveness and productivity along with energy-saving. This work addresses a mixture of job-shop and flow-shop production scheduling problem with a speed-scaling policy and no-idle time strategy. A mixed-integer linear programming model is formulated to determine the speed level of operations and the sequence of job-shop and flow-shop products, aiming at the simultaneous optimization of production efficiency and energy consumption. Then, a multi-objective Q-learning-based hyper-heuristic with Bi-criteria selection (QHH-BS) is developed to obtain a set of high-quality Pareto frontier solutions. In this algorithm, a new three-layer encoding is designed to represent the production sequence of job-shop and flow-shop products; the Pareto-based and indicator-based selection criteria are sequentially implemented to encourage diversity and convergence; Q-learning with a multi-objective metric-based reward mechanism is applied to select an optimizer from three prominent optimizers in each iteration for better exploration and exploitation. Three conclusions are drawn from extensive experiments: (1) Bi-criteria selection is superior to single-criterion selections; (2) Q-learning-based hyper-heuristic is more effective and robust than single optimizer-based algorithms and simple hyper-heuristics; (3) QHH-BS outperforms the existing state-of-the-art multi-objective algorithms in convergence and diversity. [ABSTRACT FROM AUTHOR]

With the increase of mass customization, flexible job shop scheduling problem considering assembly stage has widely existed in many manufacturing industries, such as die-casting mould factories. This problem is to find a reasonable machine assignment and operation sequence both in fabrication and assembly stages and simultaneously maximize production efficiency. In reality, energy shortages and environmental pollution have given an impetus to the development of energy-aware production scheduling problems. In this study, we address an energy-aware flexible assembly job shop scheduling problem (EFAJSP) with the objectives of minimizing flow time and energy consumption and first develop a mixed-integer linear programming (MILP) model to solve EFAJSP problem. Then, the model-specific characteristics are extracted and applied to a matheuristic decoding method for exploring the Pareto optimal solution. Due to the complexity of EFAJSP problem, a matheuristic and learning-oriented multi-objective artificial bee colony algorithm (MLABC), which combines the advantages of mathematical programming, reinforcement learning and meta-heuristic algorithm, is proposed. In addition, an initialization, destruction/construction operator and population update operator are proposed and work together to improve the exploration and exploitation performance of the proposed MLABC. Finally, numerical experimental results demonstrate the effectiveness of the proposed MILP model and the superiority of the MLABC over other algorithms in the literature. [ABSTRACT FROM AUTHOR]

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study. [ABSTRACT FROM AUTHOR]