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Given that a U-shaped assembly line is usually regarded as a serial flow production system, preventive maintenance (PM) on machines in any station of the line may lead to stoppage of the whole line and sequentially cause great loss of production cost. To improve production continuity during PM, this paper proposes multiple alternative assignment plans with interchangeable abilities to ascertain the production recovery from changes of line structure. A mixed-integer mathematical model is further formulated to make decisions on alternative assignment plans. In this model, cycle time and total assignment plan alteration cost are minimised simultaneously. And an enhanced JAYA algorithm is developed to obtain well-distributed Pareto frontier solutions effectively and efficiently. The proposed algorithm suggests a similarity-based selection and various crossover/neighbourhood search operators to enhance its search ability for global optima. Numerical experiment results prove the effectiveness of the proposed model and enhanced JAYA. This novel methodology of integration optimisation bridges assembly line balancing and PM, and thus promotes production continuity and achieves economic benefits under maintenance within large-scaled assembly lines in real situations. Furthermore, a real-world case study is conducted to illustrate the significance of this novel methodology. [ABSTRACT FROM AUTHOR]

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]

In multi-manned assembly lines, more than one operator is allowed on the same workstation. Limited research has been carried out on sequence-dependent set-up times. Therefore, this article optimizes the multi-manned assembly line balancing problem with sequence-dependent set-up times using an enhanced migrating birds optimization (EMBO) algorithm. Sequence-dependent set-up times are embedded in two ways: forward and backward set-ups. EMBO designs a mixed neighbourhood search and acceptance criteria to help it escape from local optima, and a diversification mechanism to enhance the global exploration ability. Three types of experiment and two scenarios, with low and high set-up times, are conducted to test the performance of the mixed-integer linear programming (MILP) model and EMBO. The first experiment compares three MILP models (no, low and high set-up times) to illustrate the significance of set-up times. The final two experiments demonstrate the effectiveness of EMBO by comparison with the MILP model and six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and productivity improvement on U-shaped robotic assembly lines. This paper first develops a nonlinear multi-objective mixed-integer programming model, reformulates it into a linear form by linearising the multiplication of two binary variables, and then refines the weight of multiple objectives so as to achieve a better approximation of true Pareto frontiers. In addition, Pareto artificial bee colony algorithm (PABC) is extended to tackle this new complex problem. This algorithm stores all the non-dominated solutions into a permanent archive set to keep all the good genes, and selects one solution from this set to overcome the strong local minima. Comparative experiments based on a set of newly generated benchmarks verify the superiority of the proposed PABC over four multi-objective algorithms in terms of generation distance, maximum spread, hypervolume ratio and the ratio of non-dominated solution. [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]

• 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]

Considering preventive maintenance (PM) scenarios in assembly line balancing (ALB) problem has been proven to be an effective way to promote line efficiency. However, previous research only focused on the single-model assembly line and ignored the mixed-model assembly line, resulting in the line cannot meet the growing customization needs of customers. This paper addresses the mixed-model ALB considering PM scenarios (MALB_PMs). A mixed-integer mathematical formulation is proposed to optimize the cycle time and task alteration. The cooperative co-evolutionary algorithm is introduced to simplify the large-scaled cases via the powerful divide-and-conquer architecture and is enhanced with four improvements. An archive is generated to save complete solutions with better performance and evaluate the fitness of solutions. A mixed-model variable step-size decoding method is designed to speed up the decoding process of the algorithm. One inter-population crossover operator is designed to enhance the communication among sub-problems. Four objective-oriented neighbor search operators are proposed to promote the convergence performance of the algorithm. Finally, three small-scale instances are employed to illustrate the application of the mathematical model, and 39 cases are designed to test the performance of the developed algorithm. Experiment results demonstrate that the proposed mathematical formulation can obtain the Pareto solutions of small-scaled instances; The developed algorithm outperforms other algorithms on three criteria; The Pareto front obtained by this algorithm is closer to the True Pareto front. • MALBP considering PM scenarios promotes productivity and smoothness. • An archive is generated to transfer useful collaborative information. • A mixed model decoding method is designed to reduce the running time. • Four neighbor search operators are designed to expedite the convergence speed. • Statistical results indicate ECCEA with four improvements outperforms others. [ABSTRACT FROM AUTHOR]

• ALBP considering PM scenarios promotes the productivity and smoothness. • Multi-task optimization benefits the simultaneous optimization of all scenarios. • The matrix reduction method is proposed to reduce the decoding time. • The neighborhood search operator is designed to expedite the convergence speed. • Statistical results indicate IMO-MFEA with two improvements outperforms others. Assembly line balancing considering regular production and preventive maintenance scenarios (ALBP_RP/PM) is a new and effective way to simultaneously handle the assembly production and equipment maintenance. It generates multiple allocation plans in advance. If a workstation is to be maintained, the corresponding allocation plan is applied to bypass this workstation and enable production continuity within the assembly line. However, the ALBP_RP/PM was generally solved as a single-task optimization problem, implying the coupling relationship of allocation plans under different scenarios was essentially ignored. For this reason, it is hereafter treated as a multi-task optimization problem under the premise that the assembly line balancing under each scenario is an independent task. On this basis, an improved multi-objective multifactorial evolutionary algorithm with two extra improvements is proposed to minimize cycle times and operation alterations. Specifically, the native evolutionary operators are reformulated to encourage the inter-scenario sharing of effective knowledge fragments among allocation plans to accelerate the simultaneous optimization of all the tasks; a matrix reduction method is proposed to reduce the decoding time in generating operation sequence; a neighborhood search operator is designed to strengthen the convergence performance of the algorithm. Comprehensive computational results demonstrate that the inter-scenario sharing mechanism in the multi-task optimization expedites the convergence speed and improves the operation allocation plans effectively. Besides, the proposed algorithm clearly outperforms the other five single-task optimization algorithms and four multi-task ones under the same computational limits. [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]

Current dynamic markets require manufacturing industries to organize a robust plan to cope with uncertain demand planning. This work addresses the mixed-model multi-manned assembly line balancing under uncertain demand and aims to optimize the assembly line configuration by a robust mixed-integer linear programming (MILP) model and a robust solution generation mechanism embedded with dispatching rules. The proposed model relaxes the cycle time constraint and designs robust sequencing constraints and objective functions to ensure the line configuration can meet all the demand plans. Furthermore, two solution generation mechanisms, including a task-operator-sequence and an operator-task-sequence, are designed. To quickly find a suitable line configuration, a gene expression programming (GEP) approach with multi-attribute representation is proposed to obtain efficient dispatching rules which are ultimately embedded into the solution generation mechanisms. Experimental results show that solving the proposed MILP model mathematically is effective when tackling small and medium-scale instances. However, for large instances, the dispatching rules generated by the GEP have significant superiority over traditional heuristic rules and those rules mined by a genetic programming algorithm. • Uncertain demand are first considered into mixed-model multi-manned assembly line balancing problems. • A novel robust MILP model with cycle time relaxation and robust objective function is developed. • Two robust heuristic solution generation embedded with evolutionary-generated rules are proposed to obtain robust solutions. • A gene expression programming with multi-attribute representation to mine efficient dispatching rules. [ABSTRACT FROM AUTHOR]

Considering preventive maintenance (PM) scenarios in the assembly line balancing problem (ALBP) have been proved effective and can be used to increase production efficiency. All these studies assumed that the processing time of tasks was fixed. However, many factors can affect the processing time in the actual production process and make the original task allocation plans infeasible. The robust method is introduced to solve the uncertain processing time, and the robust ALBP considering PM scenarios (RALBP_PMs) is studied in this paper. In this problem, three objectives are optimized: the cycle time under the regular scenario, the maximum cycle time, and the maximum task alteration under all PM scenarios. A robust mixed-integer optimization model is proposed to hedge against uncertainty, and then it is linearized by duality to make it solvable. A Q-learning-based variable neighborhood search (QVNS) algorithm with several improvements is designed to solve this problem. Several heuristic rules are designed to improve the performance of the initial population. Roulette wheel selection based on crowding distance is designed to select a solution with more potential. Eleven problem-specific neighborhood structures are designed to improve search efficiency. These structures are sorted by the Q-learning-based sorting method to find a good solution quickly. Experiment results demonstrate that considering PM scenarios in RALBP is necessary. The proposed mathematical model can obtain the Pareto solutions of small-scale instances, and the proposed QVNS algorithm is more suitable for solving large-scale instances. • RALBP considering PM scenarios promotes productivity and smoothness. • A mathematical model is designed to generate allocation plans for scenarios. • Eleven neighborhood structures are designed to generate a new solution. • Q-learning-based sorting method is proposed to sort neighborhood structures. • Results indicate that QVNS outperforms the other six algorithms. [ABSTRACT FROM AUTHOR]

For assembly line balancing problem considering preventive maintenance scenarios (ALBP-PM), the production managers' preferences should be concerned and hence the derived Pareto-optimal solution set (POS) should be advanced towards the corresponding region-of-interest. Otherwise, production managers might select a final solution far from their interests, and then obviously reducing the quality and efficiency of decision-making. Thus, a preference-based multi-objective optimization problem is formulated to simultaneously minimize the cycle times under different scenarios and total task adjustments among scenarios. An improved variable neighborhood search algorithm (IVNS) with novel ar -dominance and three modifications is proposed to obtain a preferred POS. Specifically, ar -dominance is integrated into it to guide the advancement direction of the preferred POS. Enhanced neighborhood structures based on critical stations are proposed to generate better-quality neighbor solutions whose cycle times are mathematically proven smaller. On this basis, a local search strategy is designed to randomly select a neighbor solution to guarantee quality and decrease the computational cost. A problem-specific adaptive restart mechanism is developed to escape from the local optimum. Computational results suggest that the IVNS obtains a better preferred POS than other eight state-of-the-art algorithms in terms of convergence, distribution and closeness to the preferences, and furthermore, incorporating preferences into the ALBP-PM helps production managers make final decisions in a more precise and faster way. • A novel ALBP-PM concerning production managers' preferences is tackled. • The ar -dominance advances Pareto-optimal solutions towards the region-of-interest. • Enhanced neighborhood structures generate neighbor solutions with smaller cycle time. • Problem-specific restart mechanism sets threshold adaptively to escape local optimum. • Statistical results indicate IVNS outperforms all other comparison algorithms. [ABSTRACT FROM AUTHOR]

The current assembly line balancing studies ignore the preventive maintenance (PM) of machines in some workstations, implying that the already-known PM information has been completely missed. Moreover, PM may bring about a production stoppage for a considerable time. Hence, this paper considers PM scenarios into the assembly line balancing problem to improve the production efficiency and smoothness simultaneously. For this multi-objective problem, a heuristic rule relying on the tacit knowledge is dug up via gene expression programming to obtain an acceptable solution quickly. Then, an enhanced grey wolf optimizer algorithm with two improvements is proposed to achieve Pareto front solutions. Specifically, a variable step-size decoding mechanism accelerates the speed of the algorithm; the specially-designed neighbor operators prevent the algorithm from trapping in local optima. Experiment results demonstrate that the discovered heuristic rule outperforms other existing rules; the joint of improvements endows the proposed meta-heuristic with significant superiority over three variants and other six well-known algorithms. Besides, a real-world case study is conducted to validate the discovered rule and the proposed meta-heuristic. • ALBP considering PM scenarios promotes the productivity and smoothness. • The heuristic rule mined by GEP can obtain an acceptable solution quickly. • The variable step-size decoding method can speed up the solution procedure. • Specially-designed neighbor operators can balance exploration and exploitation. • Statistical results indicate EGWO with two improvements outperforms others. [ABSTRACT FROM AUTHOR]