As the simple assembly line balancing problem type 1 (SALBP1) has been proven to be NP-hard, heuristic and metaheuristic approaches are widely used for solving middle to large instances. Nevertheless, the characteristics (fitness landscape) of the problem’s search space have not been studied so far and no rigorous justification for implementing various metaheuristic methods has been presented. Aiming to fill this gap in the literature, this study presents the first comprehensive and in-depth Fitness Landscape Analysis (FLA) study for SALBP1. The FLA was performed by generating a population of 1000 random solutions and improving them to local optimal solution, and then measuring various statistical indices such as average distance, gap, entropy, amplitude, length of the walk, autocorrelation, and fitness-distance among all solutions, to understand the complexity, structure, and topology of the solution space. We solved 83 benchmark problems with various cycle times taken from Scholl’s dataset which required 83000 local searches from initial to optimal solutions. The analysis showed that locally optimal assembly line balances in SALBP1 are distributed nearly uniformly in the landscape of the problem, and the small average difference between the amplitudes of the initial and optimal solutions implies that the landscape was almost plain. In addition, the large average gap between local and global solutions showed that global optimum solutions in SALBP1 are difficult to find, but the problem can be effectively solved using a single-solution-based metaheuristic to near-optimality. In addition to the FLA, a new mathematical formulation for the entropy (diversity) of solutions in the search space for SALBP1 is also presented in this paper.

In this paper, an electric vehicle routing problem with time windows and under travel time uncertainty (U-EVRW) is addressed. The U-EVRW aims to find the optimal proactive routing plan of the electric vehicles under the travel time uncertainty during the route of the vehicles which is rarely studied in the literature. Furthermore, customer time windows, limited loading capacities and limited battery capacities constraints are also incorporated. A new mixed integer programming (MIP) model is formulated for the proposed U-EVRW. In addition to the commercial CPLEX Optimizer version 20.1.0, a modified Clustering Search based Genetic algorithm (MCSGA) is developed as a solution method. Numerical tests are conducted on the one hand to validate the effectiveness of the proposed MCSGA and on the other hand to analyze the impact of travel time uncertainty of the electric vehicle on the solutions quality.

A multi-objective dynamic demand split delivery emergency material distribution model is developed to enhance the efficiency of emergency material distribution and facilitate the smooth progress of safety rescue operations during unconventional emergencies. This model incorporates the psychological view of those affected by disasters. The issue of dynamic demand may be transformed into a static demand problem by dividing the distribution time window into time domains of equal length. The optimization process is thereafter executed in real-time with the timed batch methodology. A refined ant colony method has been developed to address the model by integrating the attributes of the mathematical model, followed by doing an arithmetic case analysis. The findings indicate that the algorithm and mathematical model suggested in this study are efficacious in addressing the emergency material distribution issue, offering valuable decision-making advice and reference.

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 the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale.

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.