As the scope and complexity of logistics outsourcing services expand, logistics activity has become a separate market service. It has become a critical issue to determine how to appropriately price logistics services. In this study, we systematically review the scientific literature on the pricing of logistics services based on content analysis and bibliometric methods, focusing on the application of game theory. We compare and analyze the literature in terms of three dimensions: logistics scenarios, game models, and influencing factors. This study identifies the main players and key research scenarios of logistics pricing, analyzes6aa the most appropriate and commonly used game models, and clarifies the main influencing factors of logistics pricing. Finally, we suggest future research directions to fill gaps in existing knowledge. This study conducts a systematic review of the current state of empirical research in the field of logistics pricing, which aids in the development of new models. The results of this study help to advance logistics services from a pricing standpoint, thereby increasing the economic and environmental sustainability of logistics activities.

With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time.

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

Hybrid flow-shop scheduling based on the parallel sequential movement mode (HFSP-PSMM) is an extended application of hybrid flow-shop scheduling that ensures that the equipment works continuously during the processing cycle. However, current research has only investigated the flow-shop scheduling of single-equipment processing, and ignores the effect of auxiliary time. Therefore, this paper investigates a multi-equipment hybrid flow-shop scheduling problem based on the parallel sequential movement mode and considers the setup time and handling time. The mathematical model of the HFSP-PSMM was developed with the handling and makespan numbers as the optimization objectives. The NSGA-II-V based on the NSGA-II was designed by combining the problem characteristics. New crossover, mutation, and selection strategies were proposed and variable neighborhood search operations were implemented for the optimal set of Pareto solutions. Finally, through an algorithm comparison, performance testing, and an example simulation, the effectiveness of the NSGA-II-V for solving the HFSP-PSMM was verified.

As a green, efficient, and feasible solution, logistics resource sharing has received increasing attention in urban last-mile delivery. Instability in cooperation and unequal income distribution are significant constraints to logistics resource sharing. In this paper, we investigate the logistics resource sharing decision-making process among express delivery companies. First, according to the characteristics of the express delivery companies, symmetric and asymmetric game models based on evolutionary game theory are proposed, respectively. We examine the express delivery company’s choice of strategy and the major determinants of collaboration. Then, we examine the income distribution problem for subjects sharing logistics resources and propose an improved Raiffa solution that takes enterprise scale into account. Finally, certain management insights are offered for the express delivery companies to support the realization of logistics resource sharing. The results show that the evolution direction of the model is influenced by the initial state, enterprise scale, income distribution coefficient, and default penalty coefficient. Furthermore, the improved Raiffa solution takes into account the asymmetry of resource contribution of participating subjects and is more reasonable.

Application of dynamic cellular manufacturing system (DCMS) is a well-known strategy to improve manufacturing efficiency in the production environment with high variety and low volume of production. Often, neither the trade-off of inter and intra-cell material movements nor the trade-off of hiring and firing of operators are examined in details. This paper presents simulation results of an integrated mixed-integer model including sensitivity analysis for several numerical examples. The comprehensive model includes cell formation, inter and intracellular materials handling, inventory and backorder holding, operator assignment (including resource adjustment) and flexible production routing. The model considers multi-production planning with flexible resources (machines and operators) where each period has different demands. The results verify the validity and sensitivity of the proposed model using a genetic algorithm.