# Simheuristics Approaches for Efficient Decision-Making Support in Materials Trading Networks
## 3 Optimisation Approaches  
### Evolutionary Algorithms  
Evolutionary algorithms are metaheuristic algorithms developed to solve optimisation problems. In evolutionary algorithms (EAs), solutions are represented as individuals in a population. The individual's definition and encoding depend on the optimisation problem to be solved [43]. The individual might be presented as a string of binary numbers or real numbers. A fitness value assigned to each individual represents the objective function value associated with it. These individuals evolve in each generation and form a new population. The fittest individuals evolve inspired by the theory of evolution defined by [PERSON]. The selection of the individuals to evolve is facilitated by a biased selection, such as roulette-wheel or tournaments [44; 45]. In addition to \"individual\", \"population\", and \"fitness\" terms used in the EA, other terms are used, such as \"offsprings\", \"crossover\", and \"mutation\" [7; 45; 43]. Offsprings are individuals from the population that are reproduced using crossover and mutation. In a crossover, parts of the individuals' genes are exchanged between two individuals. The crossover form depends on the number of exchange points along the individual's length, for example, one-point crossover, two-point crossover, and uniform crossover. In uniform crossover, multiple crossing points are defined along the individual's length. Mutation modifies one individual to reproduce an offspring. One or more parts of the individual are changed. Both crossover and mutation can be customised based on the optimisation problem. The evolution of the individuals continues until a termination criterion is met, such as reaching a specified number of generations or stagnation [7; 45]. Stagnation is defined when the best-found solution is not changed by the algorithm over a specific number of generations. Evolutionary algorithms can handle optimisation problems in different domains. For example, an EA was used to minimise the service costs in marine container terminal operations [46]. [PERSON] et al. [47] compared an EA and other algorithms for solving the vehicle routing problem with a \"factory-in-a-box\" concept. The EA found the nearest solution to the optimum in small-scale problem instances and better solutions than other algorithms in large-scale problem instances. Evolutionary algorithms can detect promising regions in the search space and be utilised in the learning process while solving optimisation problems [48; 49]. In online optimisation, the problems' features change over time and are unknown in advance. A learning-based EA was proposed to handle this problem [49]. Additionally, EAs were used to solve combinatorial optimisation problems, such as the vehicle routing problem and the travelling salesman problem [7; 48]. Evolutionary algorithms can handle multi-objective optimisation problems [50]. In these problems, the non-dominated solutions, which outperform other solutions at least in one of the objective functions, are stored, and the algorithm looks for solutions along the Pareto front. [PERSON] [48] used a multi-objective evolutionary algorithm to minimise the number of vehicles and the total travelled distance in the vehicle routing problem. Researchers have utilised EAs in optimisation problems, including minimisation of energy consumption. For example, [PERSON] et al. [51] solved a multi-objective green express cabinet assignment problem in urban last-mile logistics using probability-guided EA. They minimised total costs and energy consumption. The total makespan and total energy consumption were minimised in the flow shop scheduling problem with sequence-dependent setup times [52]. [PERSON] and [PERSON] [43] stated that EAs for multi-objective problems provide promising solutions in problems with less than four objective functions. Other approaches proposed combining an EA and DES to solve optimisation problems, such as optimising lead time and total inventory holding costs in job sequencing and buffer size optimisation problems [53].