The mixed-model assembly line balancing problem (MMALBP) in multi-demand scenarios is investigated, which addresses demand fluctuations for each product in each scenario. The objective is to minimize the sum of costs associated with tasks allocation, workstation activation, and penalty costs for unbalanced workloads. A mixed integer programming model is developed to consider the constraint of workstation space capacity. A phased heuristic algorithm is designed to solve the problem. The computational results show that considering demand fluctuations in multiple demand scenarios leads to more balanced workstation loads and improved assembly line production efficiency. Finally, sensitivity analysis of important parameters is conducted to summarize the impact of parameter changes on the results and provide practical management insights.

As a supply chain integrator, a fourth party logistics (4PL) typically does not have its own logistics facilities, so the 4PL needs to match third party logistics (3PLs) and customers to meet customers' logistics service demands. An effective matching method can not only improve the efficiency of 4PL supply chain management, but also establish more long-term and stable cooperative relationships with customers and 3PLs. Therefore, we propose a novel two-sided logistics matching method considering the trading psychology and matching effort of matching subjects under the 4PL. First, based on considering the trading psychology, the concepts of blocking pair and stable matching are redefined. Then, based on the public values and matching effort of customers and 3PLs, the evaluation values of customers and 3PLs are calculated. And the trading possibilities of customers and 3PLs are calculated by considering the fairness threshold. Next, we consider different stable matching demands of customers and 3PLs and develop a bi-objective matching model to maximize the trading possibilities of both customers and 3PLs. Furthermore, the properties of the proposed method are discussed. Finally, a numerical example and comparison analysis are provided to prove the feasibility and effectiveness of the proposed method.

This study aims to find a way to alleviate or eliminate the negative impact of showrooming on brick-and-mortar retailers. Therefore, under careful consideration of the effects of intershowrooming and intrashowrooming, this study explores whether retailers can effectively solve the negative impact of showrooming by opening online channels. Conduct a comparative study on the decision-making of dual/multi-channel supply chain members before and after the retailer opens an online channel and analyze the influence. In addition, we also explored the impact of factors such as the market scale expansion effect and internet market power structure. Research has found that regardless of the market scale expansion effect generated, it is effective for the retailer to increase profits by opening an online channel. The impact of market scale expansion is not entirely beneficial to the retailer. Under the intrashowrooming, the effect of market scale expansion may benefit the manufacturer. But what is more noteworthy is that for the manufacturer, the impact of intrashowrooming is not necessarily the greater, the better, and the manufacturer's profit may decrease as this effect increases.

With a view to overcoming a few shortcomings resulting from the kernel-based SVM models, these kernel-free support vector machine (SVM) models are newly promoted and researched. With the aim of deeply enhancing the classification accuracy of present kernel-free quadratic surface support vector machine (QSSVM) models while avoiding computational complexity, an emerging kernel-free ν-fuzzy reduced QSSVM with L1 norm regularization model is proposed. The model has well-developed sparsity to avoid computational complexity and overfitting and has been simplified as these standard linear models on condition that the data points are (nearly) linearly separable. Computational tests are implemented on several public benchmark datasets for the purpose of showing the better performance of the presented model compared with a few known binary classification models. Similarly, the numerical consequences support the more elevated training effectiveness of the presented model in comparison with those of other kernel-free SVM models. What`s more, the presented model is smoothly employed in lung cancer subtype diagnosis with good performance, by using the gene expression RNAseq-based lung cancer subtype (LUAD/LUSC) dataset in the TCGA database.

The challenge presented by simultaneous buffer and service rate allocation in manufacturing systems represents a difficult non-deterministic polynomial problem. Previous studies solved this problem by iteratively utilizing a generative method and an evaluative method. However, it typically takes a long computation time for the evaluative method to achieve high evaluation accuracy, while the satisfactory solution quality realized by the generative method requires a certain number of iterations. In this study, a data-driven hybrid approach is developed by integrating a tabu search–non-dominated sorting genetic algorithm II with a whale optimization algorithm–gradient boosting regression tree to maximize the throughput and minimize the average buffer level of a manufacturing system subject to a total buffer capacity and total service rate. The former algorithm effectively searches for candidate simultaneous allocation solutions by integrating global and local search strategies. The prediction models built by the latter algorithm efficiently evaluate the candidate solutions. Numerical examples demonstrate the efficacy of the proposed approach. The proposed approach improves the solution efficiency of simultaneous allocation, contributing to dynamic production resource reconfiguration of manufacturing systems.

In multi-stage machining-assembly production, collaborative scheduling for multiple production lines can effectively improve the execution efficiency of production planning and increase the effective output of the production system. In this paper, a production scheduling mathematical model was constructed for the collaborative scheduling problem of machining-assembly multi-production lines with kitting constraints, with the optimization objectives of minimizing assembly completion time and tardiness time. For the scheduling model, the product assembly process is constrained by the machining sequence of the jobs on the machining lines. Only by collaborating on the production scheduling schemes of the machine line and the assembly line as a whole can the output efficiency of the product on the assembly line be improved. An improved hybrid multi-objective optimization algorithm named SMOEA/D is designed to solve this scheduling model. The algorithm uses adaptive parents’ selection and mutation rate strategies and integrates the Tabu search strategy for the search process in the solution space when the solution of the sub-problem has not been improved after specified search generations, to improve the local search ability and search accuracy of MOEA/D algorithm. To verify the performance of the SMOEA/D algorithm in solving machining-assembly collaborative scheduling problems in production systems with different resource configurations and scales, two sets of numerical experiments were designed, corresponding to situations where the number of operations on each production line is equal or unequal. The running results of the proposed algorithm were compared with three other well-known multi-objective algorithms. The comparison results indicate that the SMOEA/D algorithm is effective and superior for solving such problems.

This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, including travel costs, time penalty costs, AGV costs, and unloading setup time costs. To solve the MAGVDUST, this paper builds a mixed-integer linear programming model and proposes an improved genetic algorithm (IGA). In the IGA, an improved nearest-neighbor-based heuristic is proposed to generate a high-quality initial solution. Several advanced technologies are developed to balance local exploitation and global exploration of the algorithm, including an optimal solution preservation strategy in the selection process, two well-designed crossovers in the crossover process, and a mutation based on Partially Mapped Crossover strategy in the mutation process. In conclusion, the proposed algorithm has been thoroughly evaluated on 110 instances from an actual electronic factory and has demonstrated its superior performance compared to state-of-the-art algorithms in the existing literature.

In modern industrial manufacturing, there are uncertain dynamic disturbances between processing machines and jobs which will disrupt the original production plan. This research focuses on dynamic multi-objective flexible scheduling problems such as the multi-constraint relationship among machines, jobs, and uncertain disturbance events. The possible disturbance events include job insertion, machine breakdown, and processing time change. The paper proposes a conv-dueling network model, a multidimensional state representation of the job processing information, and multiple scheduling objectives for minimizing makespan and delay time, while maximizing the completion punctuality rate. We design a multidimensional state space that includes job and machine processing information, an efficient and complete intelligent agent scheduling action space, and a compound scheduling reward function that combines the main task and the branch task. The unsupervised training of the network model utilizes the dueling-double-deep Q-network (D3QN) algorithm. Finally, based on the multi-constraint and multi-disturbance production environment information, the multidimensional state representation matrix of the job is used as input and the optimal scheduling rules are output after the feature extraction of the conv-dueling network model and decision making. This study carries out simulation experiments on 50 test cases. The results show the proposed conv-dueling network model can quickly converge for DQN, DDQN, and D3QN algorithms, and has good stability and universality. The experimental results indicate that the scheduling algorithm proposed in this paper outperforms DQN, DDQN, and single scheduling algorithms in all three scheduling objectives. It also demonstrates high robustness and excellent comprehensive scheduling performance.

The previous research relating to CSR and mindfulness leadership, for the purposes of this study, provides an intersection where both backgrounds come together to invite an opportunity for collaboration. The most critical factor in the implementation of CSR is the development of leaders for a sustainable global society as mindfulness should be relevant to the achievement of organizational and leadership objectives. It is here that we find the inspiration for the current study and the relevance of the research query endeavoring to discover if the framework of mindfulness leadership could prove an effective driver for CSR? For this reason, 20 in-depth interviews were conducted from the middle level managers working in an emerging economy - Pakistan. The conceptual framework of leadership theory guided this study’s investigation as it brought to the forefront certain challenges facing organizational leaders endeavoring to facilitate CSR objectives; and explored if the application of mindfulness leadership could prove an effective leadership framework for successful CSR engagement. This supporting data coupled with the study’s interview of executive leaders, across industry lines, familiar with CSR initiatives, lent to certain analytical insight that identified mindfulness leadership as a framework for organizations to facilitate the accomplishment of CSR objectives.

Production plans based on Material Requirement Planning (MRP) frequently fall short in reflecting actual customer demand and coping with demand fluctuations, mainly due to the rising complexity of the production environment and the challenge of making precise predictions. At the same time, MRP is deficient in effective adjustment strategies and has inadequate operability in plan optimization. To address material management challenges in a volatile supply-demand environment, this paper creates a make-to-stock (MTS) material production planning model that is based on customer demand and the demand-driven production planning and control framework. The objective of the model is to optimize material planning output under resource constraints (capacity and storage space constraints) to meet the fluctuating demand of customers. To solve constrained optimization problems, the demand-driven material requirements planning (DDMRP) management concept is integrated with the grey wolf optimization (GWO) algorithm and proposed the DDMRP-GWO algorithm. The proposed DDMRP-GWO algorithm is used to optimize the inventory levels, shortage rates, and production line capacity utilization simultaneously. To validate the effectiveness of the proposed approach, two sets of customer demand data with different levels of volatility are used in experiments. The results demonstrate that the DDMRP-GWO algorithm can optimize the production capacity allocation of different types of parts under the resource constraints, enhance the material supply level, reduce the shortage rate, and maintain a stable production process.