• Energy Research

With the background of implementing carbon peaking and carbon neutralization, identifying methods to realize energy-saving and carbon reduction effectively has become an important issue in the intelligent energy-conservation manufacturing industry. During the process of achieving this goal, determining an optimal location for a low-carbon and intelligent manufacturing industrial park is a foremost decision-making problem for manufacturing corporations’ energy-efficient development. The article established a multi-criteria decision framework to assist manufacturing companies when selecting suitable industrial park sites. To begin with, an evaluation criteria framework is confirmed by literature search. Then, a fuzzy optimization model, which combines the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is presented, where fuzzy TOPSIS is used to determine the decision-maker criteria weights. Then, criteria weights are calculated by the optimization model with construction of a Lagrange function. Moreover, the fuzzy VIKOR method is applied to sort alternatives and choose the best alternative location. In addition, five alternative sites for a manufacturing company are evaluated and ranked according to the values of the ranking index as a numerical case to demonstrate the proposed framework’s application. Finally, a comprehensive analysis of diverse methods and sensitivity analyses for the volatility in criteria weights and decision-maker weights is illustrated to confirm that the framework is practicable for the problem of intelligent and sustainable manufacturing industrial park-site selection.

The evaluation of manufacturing component suppliers is focused on economic indicators, with insufficient emphasis on green indicators and no consideration of the correlation between indicators. Firstly, indicators related to green production are incorporated into the supplier evaluation system. Then, for the problem that attributes in decision making can be divided into different categories and there are interrelationships between attributes of the same category, a multi-attribute decision-making (MADM) method based on the partitioned Maclaurin symmetric mean operator (PMSM) is proposed. Finally, the proposed MADM method was applied to the evaluation of component suppliers considering green production. Comparing popular decision methods with the newly proposed method for validation, it was demonstrated that the proposed multi-attribute decision method is highly flexible and versatile. Furthermore, the newly proposed aggregation operator can not only handle the correlation between multiple attributes, but also be converted to other general aggregation operators through parameter adjustment.

In this paper, a vehicle routing problem (VRP) model considering delivery time windows and variable service time is established for the delivery problem in community group purchasing. A solution model for an improved ant colony algorithm (ACA) is proposed by improving the initial feasible solution and the neighbourhood search mechanism of the ant colony algorithm. The algorithm of the improved ant colony and the commonly used algorithm are solved for real cases and publicly available benchmark datasets, respectively, for comparative analysis. The results show that the improved ACA has stronger optimization capability, faster convergence speed, and has advantages in solving VRPTW problems with variable service time. The computational efficiency is also improved by 41% over the genetic algorithm (GA) in the solution of the benchmark dataset, which provides a certain reference for solving the community group distribution problem.