Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review
2. Overview of Managerial Economics
3. Overview of Heuristic Optimization and Simulation Methods
- Continuous simulation refers to the modeling of a system with random variables that change continuously over time. This methodology makes use of differential equations and frequently employs numerical approaches for their resolution (Amaran et al. 2016).
- Discrete-event simulation points out to the modeling of a system with random variables that can vary over time (i.e., dynamic systems) but just in a discrete way (when specific events happen during a time). This approach is traditionally utilized to analyze the behavior of time-dependent network systems, such as a supply chain (Fishman 2013).
- Monte Carlo simulation (MCS) employs random sampling for addressing stochastic problems that do not evolve over time (i.e., static systems). This method is extensively used for dealing with not analytically tractable stochastic optimization problems (Bianchi et al. 2009).
- Optimization integrated with simulation: In this approach, the optimization is used to evaluate a specific problem. Then, the result comes back to the simulation, which then re-starts its activity. For instance, the ordering of jobs could be re-scheduled according to the current state of the simulation (Dias et al. 2018).
- Simulation as objective function: In this case, the optimization provides a feasible solution, evaluated through the simulation. Then, these results are used by the optimization in order to generate alternative solutions. This, for instance, can be applied for determining the staff assigned to a given project (Zhang et al. 2019).
- Simulation results as a start for optimization: Using this combination, the simulation is conducted before the optimization, and is the one that provides the initialization parameters for the optimization process. For instance, the determination of the required staff for a certain production process and, afterward, using the optimization to allocate this staff (Rezaeiahari and Khasawneh 2020).
- Optimization for configuring simulation: Here, the simulation is employed to evaluate the feasibility of a solution found by the optimization (Yang et al. 2019).
4. Applications in Competitive Markets
5. Applications in Imperfect Markets
5.1. Monopolies and Monopsonies
5.2. Monopolistic Competition
5.3. Oligopolies and Oligopsonies
6. Public Sector and Public-Private Partnerships
- Monopoly power: (a) breaking up existing monopolies, (b) preventing monopolistic practices, (c) preventing mergers that reduce competition, and (d) preventing collusion.
- Negative externalities: (a) taxes, (b) government standards, (c) permits (either tradable or not), (d) liability rules, and bargaining among affected parties.
- Positive externalities: (a) promoting literacy and education, ) improving health, (c) promoting research, (d) patent system, and copyright.
- Imperfect information: (a) banning some drugs, (b) taxing unhealthy products, (c) mandating compulsory education, and (d) requiring producers to provide information (e.g., warning labels on cigarettes).
6.2. Public Goods
7. Common Trends and Open Challenges
- The first challenge is the adaptability of the used techniques. In fact, a particular decision-making problem can be answered by different methodologies (Tsao et al. 2020) and, in other cases, a particular methodology can provide a solution for different sets of decision-making requirements. That is, within the same industry, firms are most likely to face similar problems or have similar goals. However, and independently of their goals, their necessities cannot be answered by the same methodology, since the firm environment can be diametrically opposite and can vary over time (Salamat et al. 2016). For example, collaborators (i.e., they may be based in local, national, or international region), target customers (i.e., with different demographics and living patterns) and market environment (i.e., perfect or imperfect competition), among others. Thus, it is in these cases where an intelligent design that allows adaptation and evolution of decision-making mechanisms play a fundamental role in the competitiveness of firms. In a similar line, it is also necessary to develop dynamic procedures that include uncertainty (Kumar and Chatterjee 2015; Mathur et al. 2017).
- The second challenge is the reliability of some of the simulation outputs. Some authors, like Zhang et al. (2019), point out about the discrepancies between the simulation outputs and the observed data in practice, due to multiple nuances that were not incorporated in the models in complex environments. This statement is particularly true in those cases where consumers’ behavior may intervene (e.g., hospitals). As a consequence, the aforementioned discrepancies in output may affect the reliability of the model insights, especially when those insights are implemented in high-risk practice.
- The third challenge is the applicability, i.e., the horizon of repercussion to which the decision to make belongs. The previous literature review shows that firms and researchers are making an attempt to design and implement possible simulation and metaheuristic algorithms for better decision, but in most of the studies referenced in this work, the utilization of the aforementioned techniques is limited to the strategic decision level (considering the importance of these decisions for the survival of the firm). However, there is no impediment to use them as a support system in all decision making processes. Reinforcing this idea, some authors, such as Mendoza-Gómez et al. (2018), advocate its massive implementation as a support technique in the decision-making process in regular basis (i.e., tactical and operational levels).
- Finally, the fourth challenge refers to the dissemination of these techniques as a decision tool, the uniqueness in the solution. The procedures described in this study can provide more than one good and feasible solution, such that, according to Tang et al. (2017) this can generate controversy when making a decision. It is possible that in this scenario, in which two equally good solutions are available, the managers of the firms doubt the effectiveness of these tools and dismiss it as effective tools by not providing a single solution.
8. Potential use of Simheuristics and Learnheuristics
- Pricing strategies in oligopolies or monopolistic competition usually are subject to incomplete/ uncertain information, such as the pricing strategies and cost of competitors or characteristics of potential entrants. Taking into account this uncertainty is key for the economical sustainability of an organization.
- The best market segment strategy may depend on the strategies of the competitors, current and future technologies, and customer demands, among many other factors. All these factors are impossible to predict perfectly and may change over time very fast.
- Public transportation has many relevant positive externalities. To design efficient systems is required to estimate the number of users, but such predictions tend to have a high margin error. Moreover, the number of users of a given system, for instance a shuttle bus service, may depend on many factors that may change over time: prices in the car market, environmental awareness, characteristics of other public alternatives, etc.
- Public/private portfolio design is usually a multiobjective optimization problem with many restrictions. Completion times, cost and even the preferences of decision-makers and available funding may be fuzzy and change over time.
Conflicts of Interest
|ACO||Ant colony optimization|
|ICA||Imperialist competitive algorithm|
|ILS||Iterated local search|
|MCS||Monte Carlo simulation|
|PSO||Particle swarm optimization|
|VNS||Variable neighbourhood search|
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Calvet, L.; de la Torre, R.; Goyal, A.; Marmol, M.; Juan, A.A. Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review. Adm. Sci. 2020, 10, 47. https://doi.org/10.3390/admsci10030047
Calvet L, de la Torre R, Goyal A, Marmol M, Juan AA. Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review. Administrative Sciences. 2020; 10(3):47. https://doi.org/10.3390/admsci10030047Chicago/Turabian Style
Calvet, Laura, Rocio de la Torre, Anita Goyal, Mage Marmol, and Angel A. Juan. 2020. "Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review" Administrative Sciences 10, no. 3: 47. https://doi.org/10.3390/admsci10030047