This paper proposes a novel framework for applying reinforcement learning (RL) within agent-based models (ABMs) to study labor market dynamics in labor economics. ABMs provide a flexible platform for simulating economic systems, particularly by modelling heterogeneous agents and their complex interactions, which effectively capture non-linear and emergent phenomena in labor markets. We extend an existing labor market model by integrating it into an RL framework, using the Deep Deterministic Policy Gradient (DDPG) algorithm to train agents to maximize profits, and comparing their performance with bounded-rational agents governed by predefined policies. Our findings show that RL agents, depending on the level of competition and rationality in the market, spontaneously learn distinct strategies, which significantly impact outcomes, such as unemployment and wage distribution. This work underscores the importance of ABMs in analyzing labor market dynamics and illustrates how RL-equipped agents can learn optimal strategies in evolving economic environments, offering a robust tool for policy analysis and exploring the complexities of labor market behavior.

The multi-row facility layout problem is a prevalent and significant planning challenge in manufacturing workshops. This problem requires distributing facilities with pairwise transport weights among several rows to attain a layout with minimal logistics costs. However, the significance of aisles in multi-row facility layout has frequently been overlooked. An efficient aisle structure can result in a smooth transportation path and reduced material-handling costs. This paper contributes to the existing literature by introducing a new multi-row facility layout problem that considers long-straight aisles. First, mathematical formulas for the actual transportation distance between facilities through aisles are defined, and a mixed-integer programming model is constructed. Second, a hybrid algorithm based on an intelligent algorithm and a mathematical model is proposed. This method utilizes an improved teaching-learning-based optimization algorithm as a framework for optimizing the discrete facility sequence, and two decoding methods based on linear programming are designed to obtain the facility locations and transportation paths. Experimental results demonstrate that the two decoding strategies have their own advantages in terms of solution quality, efficiency, and area utilization. Moreover, improvement strategies for teaching-learning-based optimization algorithms are observed to be effective. Finally, we present two actual workshop examples of multi-row layout designs. The comparison of different algorithms reveals that the proposed algorithm has significant advantages in terms of solution quality and stability. Note to Practitioners—In multi-row layouts within manufacturing workshops, the strategic placement of facilities is crucial for reducing logistics costs. Essential factors such as the through-aisle structure and precise facility location are significant considerations. To more accurately reflect the actual material flow in such layouts, this paper proposes a new multi-row layout that takes into account the interaction of logistics aisles and the exact positioning of facilities. This problem is addressed by developing a mathematical model and proposing a novel hybrid method that combines teaching-learning-based optimization and linear programming to tackle real-life scenarios. The experimental findings suggest that the hybrid method is effective in generating superior solutions for the problem, and the optimization of the continuous and precise locations of facilities within the multi-row layout problem can significantly reduce material-handling costs. Our approach is shown to be feasible in accurately reflecting the logistics cost and layout scheme efficiency, thereby helping to achieve better production efficiency and cost reduction.

Disassembly production lines that employ shared stations with multiple workers and robots are ideal for addressing obsolete products with complex structures and hazardous parts. In this study, a multiproduct multi-man–robot disassembly line balancing problem (MPMMR-DLBP) is developed and its mixed-integer programming model (MIPM) is established to minimize the number of stations, idle balancing index of operators (workers and robots), and the number of operators. In addition, a problem-oriented group evolutionary (POGE) algorithm is proposed to efficiently solve the MPMMR-DLBP. The proposed POGE develops a new “1+3” encoding mode and a heuristic decoding strategy based on the shortest time to complete tasks to construct a one-to-one correspondence between encoding sequences and disassembly schemes. Moreover, a reassociation evolution operation (REO) and a mapping crossover operation (MCO) are designed to generate new solutions and allow the population to evolve to the global optimum. Subsequently, the correctness of MIPM and the performance of POGE are verified using two small-scale cases. Finally, an actual MPMMR-DLBP for the mixed disassembly of refrigerators, microwave ovens, and dishwashers is optimized by POGE. A comparison of the optimized results with the other three common algorithms shows that the POGE is superior in the large-scale MPMMR-DLBP, and multiple optimized disassembly schemes are provided for decision makers.

To reasonably arrange disassembly facilities and plan enterprise space, we propose a parallel disassembly line balancing problem (PW-PDLBP) with parallel workstations. Additionally, a mixed-integer nonlinear programming (MINLP) model that minimizes the line length, number of workstations, idle time balancing index, and energy consumption is established based on the problem characteristics and is solved using the GUROBI optimizer. Furthermore, a multiobjective enhanced differential evolution algorithm (MEDE) is developed to obtain high-quality disassembly schemes for PW-PDLBP. The correctness of encoding and decoding and the solving performance of MEDE are verified by comparing with the MINLP model and four existing algorithms. Then, an instance consisting of two different types of end-of-life TVs is optimized. Finally, the effectiveness of PW-PDLBP in improving enterprise space utilization is validated by comparing it with the parallel line layout without parallel workstations.