In this paper, two new approaches are proposed for extracting composite priority rules for scheduling problems. The suggested approaches use simulation and gene expression programming and are able to evolve specific priority rules for all dynamic scheduling problems in accordance with their features. The methods are based on the idea that both the proper design of the function and terminal sets and the structure of the gene expression programming approach significantly affect the results. In the first proposed approach, modified and operational features of the scheduling environment are added to the terminal set, and a multigenic system is used, whereas in the second approach, priority rules are used as automatically defined functions, which are combined with the cellular system for gene expression programming. A comparison shows that the second approach generates better results than the first; however, all of the extracted rules yield better results than the rules from the literature, especially for the defined multi-objective function consisting of makespan, mean lateness and mean flow time. The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems.

In this paper, we propose a novel approach to the development of green public policies. We emphasize the significance of the system of systems (SoSs) methodology, which concentrates on understanding and managing complex systems by contemplating their interdependencies and interactions as a single integrated system. Based on this perspective, we consider green public policy as a system interacting with the energy, health, pollutants, and economic systems of the SoSs. As a method of systematic structural analysis, we use both decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) techniques to understand the relationships between the systems listed above. We provide a literature review on the application of these soft operation research (SOR) techniques. We note that SOR methods are suitable for the formulation of effective public policies. Using an example, we demonstrate the primary advantage of DEMATEL and ISM, which is the establishment of easily comprehendible causal relationships between the systems in the SoSs.

Green contracting mechanisms are utilized to integrate sustainable and environmentally protective goals into business objectives. This study proposes a framework for the design of green contract mechanisms in forestry supply chain management. We assumed that there was an applicant for harvesting timber in a forest, and that the owner tried to evaluate different scenarios to design a green contracting mechanism. We also assumed that the owner of a forest cared about green goals, such as carbon dioxide absorption, in the forest. We regarded the interests of the parties, such as the profit of the applicant as well as the green goals of the forest owner. We used multi-attribute decision-making techniques such as the weighted sum, normalized weighted sum, TOPSIS, and VIKOR to evaluate the various scenarios. In the literature, another approach was taken to solve a similar problem based on multi-objective techniques and the Pareto optimality concept. We compared the outcomes of the determined framework with the outputs of the previously employed methods. The recommended framework can provide more interpretable results since it considers the interests of different sides. The framework can assist businesses in designing contracts that promote sustainable operations and support compliance with the United Nations’ Sustainable Development Goals.

This paper compares the non-dominated sorting genetic algorithm (NSGA-II) and NSGA-III to solve multiobjective sectorization problems (MO-SPs). We focus on the effects of the parameters of the algorithms on their performance and we use statistical experimental design to find more effective parameters. For this purpose, the analysis of variance (ANOVA), Taguchi design and response surface method (RSM) are used. The criterion of the comparison is the number of obtained nondominated solutions by the algorithms. The aim of the problem is to divide a region that contains distribution centres (DCs) and customers into smaller and balanced regions in terms of demands and distances, for which we generate benchmarks. The results show that the performance of algorithms improves with appropriate parameter definition. With the parameters defined based on the experiments, NSGA-III outperforms NSGA-II.