Single-machine due-window assignment scheduling with delivery times and variable processing times is investigated, where the variable processing time of a job means that the processing time is a function of its position in a sequence and its resource allocation. Currently, there are multiple optimization objectives for the due-window assignment problem, and there is a small amount of research on optimization problems where the window starting time, the rejected cost and the optimal scheduling are jointly required. The goal of this paper is to minimize the weighed sum of scheduling cost, resource consumption cost and outsourcing measure under the optional job outsourcing (rejection). Under two resource allocation models (i.e., linear and convex resource allocation models), the scheduling cost is the weighted sum of the number of early–tardy jobs, earliness–tardiness penalties and due-window starting time and size, where the weights are positional-dependent. The main contributions of this paper include the study and data simulation of single-machine scheduling with learning effects, delivery times and outsourcing cost. For the weighed sum of scheduling cost, resource consumption cost and outsourcing measure, we prove the polynomial solvability of the problem. Under the common and slack due-window assignments, through the theoretical analysis of the optimal solution, we reveal that four problems can be solved in $O\left(n^6\right)$ time, where n is the number of jobs.

We study single-machine group green scheduling considering group setup time deterioration effects and job-processing time learning effects, where the setup time of a group is a general deterioration function on its starting setup time and the processing time of a job is a non-increasing function on its position. We focus on confirming the job schedule for each group and group schedule for minimizing the total weighted completion time. It is proved that this problem is NP-hard. According to the problem’s NP-hardness, we present some optimal properties (including lower and upper bounds) and then propose a branch-and-bound algorithm and two heuristic algorithms (including the modified Nawaz–Enscore–Ham algorithm and simulated annealing algorithm). Finally, numerical simulations are provided to indicate the effectiveness of these algorithms, which demonstrates that the branch-and-bound algorithm can solve random instances of 100 jobs and 14 groups within reasonable time and that simulated annealing is more accurate than the modified Nawaz–Enscore–Ham algorithm.

This study investigates the evolution and stability of China’s carbon emission trading system, addressing disparities between China’s carbon market development and international practices. By constructing a three-party evolutionary game model involving the government, enterprises, and consumers, this paper identifies key factors influencing system stability and offers policy recommendations. The main contributions include: (1) Developing a dynamic model that incorporates consumer behavior, including the herd effect, to reveal the interplay between demand-side dynamics and market evolution. (2) Demonstrating that government regulatory costs, consumer preferences, and enterprise investment costs significantly impact market stability and green transitions. (3) Highlighting that carbon prices alone have limited influence on enterprise participation, underscoring the importance of comprehensive incentives. This work provides actionable insights to optimize policy design, enhance market efficiency, and promote low-carbon transformation in China.

This work studies single-machine scheduling with general position-dependent deterioration, where job processing times are general non-decreasing functions dependent on their positions in a sequence. The goal is to find a job sequence such that makespan is minimized. The problem can be extended to deal with green scheduling environment where processing time increases due to additional carbon-reduction procedure. Under some optimal properties, we prove that the problem is solved by the largest processing time (denoted by LPT) first rule.

In this paper, we study a single-machine green scheduling problem with learning effects and past-sequence-dependent delivery times. The problem can be properly applied to tackle green manufacturing where production and delivery time are variable and highly subject to process-reengineering. Our goal is to determine the optimal sequence such that total weighted completion time and maximum tardiness are minimized. For the general case, we provide the analysis procedure of lower bound, and also propose the heuristic and branch-and-bound algorithms. Furthermore, computational experiments are conducted to demonstrate the effectiveness of our algorithms.

Green scheduling that aims to enhance efficiency by optimizing resource allocation and job sequencing concurrently has gained growing academic attention. To tackle such problems with the consideration of scheduling and resource allocation, this paper considers a single-machine group scheduling problem with common/slack due-date assignment and a controllable processing time. The objective is to decide the optimized schedule of the group/job sequence, resource allocation, and due-date assignment. To solve the generalized case, this paper proves several optimal properties and presents a branch-and-bound algorithm and heuristic algorithms. Numerical experiments show that the branch-and-bound algorithm is efficient and the heuristic algorithm developed based on the analytical properties outruns the tabu search.