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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]

Energy-saving, one of the most significant strategies for green manufacturing, has become the focus of more and more scholars and enterprise managers. Hence, this work addresses the minimization of energy and cost requirements on mixed-model multi-manned assembly line balancing with multi-skilled workers. A new mixed-integer linear programming model is proposed to define the problem. Additionally, a novel multi-objective co-evolutionary algorithm is designed to achieve the trade-off between energy and cost requirements. This algorithm includes a two-layer solution representation to achieve full coverage of the solution space and a new decoding mechanism with idle time reduction. A collaborative initialization as the first stage of the algorithm is extended to get high-quality and great-diversity initial solutions. A self-learning evolution for each sub-population with four problem-specific evolutionary operators is developed to explore task or worker assignment sequences, and a dual-cooperation strategy is proposed to enhance the interaction between sub-populations. The final experiments, based on 269 instances, demonstrate that the improvement components are effective and the proposed algorithm is superior to seven latest multi-objective evolutionary algorithms from numerical, statistical and differential analyses. • Define the MALBP-MW by a MILP model to minimize the energy and cost requirements. • Design a new decoding with idle time reduction and a collaborative initialization. • Propose four problem-specific evolutionary operators for MOCEA. • Develop MOCEA with a self-learning selection and dual-cooperation. [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]

In enterprises, machines are subject to some unavailable periods resulting in production halts. The consideration of preventive maintenance (PM) and resource assignment in straight and U-shaped assembly lines is of paramount importance. Hence, this paper aims to tackle the integration of PM and balancing problems in straight and U-shaped resource-dependent assembly lines via two new mixed-integer linear programming (MILP) models and a memetic algorithm. In the MILP models, the optimal PM intervals of all machines and all PM scenarios are determined according to the maximization of the availability. In the memetic algorithm, a multi-attribute solution representation, including heuristic rule matrix, task separator set and machine assignment permutation, is designed to build a feasible solution. For this multi-attribute solution representation, this algorithm respectively designs different crossover, mutation and local search operators to exploit the space around the solutions. Finally, five comparison experiments are conducted to prove the effectiveness of the MILP models in small-scale instances and the superiority of the improvements and memetic algorithm in all instances. • All maintenance scenarios are determined according to the maximization of the availability. • Two new mixed-integer linear programming models for SRDALB_PM and URDALB_PM are formulated. • An attempt is made to solve the new problems by memetic algorithm. • The proposed algorithm designs multi-attribute solution representation and multi-operators. [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]

Frequently occurring and unavoidable perturbations in railway systems cause a reduction in performance and even the infeasibility of the original schedule. In addition, existing related studies assume that the maintenance routes are fixed in a deterministic situation. Therefore, it is necessary to focus on robust scheduling of electric multiple units first-level maintenance with flexible maintenance routes to defend against stochastic uncertainties. Firstly, a mixed-integer linear programming model with uncertain parameters in objective functions and constraints is built for the first time. Multiple constraints are formulated to consider flexible maintenance routes, train shunting conflicts, and track occupation conflicts in a stub-end depot. A robust optimization approach is adopted to obtain a deterministic robust counterpart model with uncertainties in processing/arrival/departure times. In this model, uncertainty and reliability levels are introduced to describe and quantify the disturbance degree of processing/arrival/departure times and the allowable violation degree of resource constraints respectively. Moreover, an adaptive iterative local search is proposed by embedding heuristic rules for the initialization. The proposed algorithm includes problem-specific neighborhood structures to improve the evolutionary ability, a variable neighborhood descent method to guide and drive the search toward promising areas of the search space, and an adaptive perturbation mechanism to enable the algorithm to jump out of local optimum. Numerical results from China's railway system validate the proposed model and quantitatively demonstrate the merit of the proposed algorithm. Further analysis shows that our approach can achieve an appropriate trade-off between robustness and efficiency for various uncertainty and reliability levels. • The SSEFM problem under stochastic uncertainties is addressed. • A multi-objective MILP model with deterministic robust equivalent counterpart is defined. • An adaptive iterative local search is proposed to solve this problem. • The algorithm is enhanced with a variable neighborhood descent and adaptive perturbation. [ABSTRACT FROM AUTHOR]