# Maritime Supply Chain Optimization by Using Fuzzy Goal Programming
###### Abstract
A shipping company that follows a low-cost strategy competes on price, i.e., freight rate or other equivalents. In this case, it competes by obtaining economies of scale in operations, centralizing facilities and functions, adopting strict financial accounting, and minimizing service features [12]. Leading maritime companies in the supply chain should move away from classical approaches. Considering supply chain management in one piece, it should reduce total costs and explore ways to increase competition by adding more values. The real competition is not between maritime transport companies but among the supply chains [13]. Supply chain management can be defined as a process that maritime companies carry out and plan to achieve mutual benefits and goals in transportation operations. To mention several advantages of supply chain management, integrating the supply chain to maritime companies leads to reduced transport and transaction costs, improved access to new markets and improved service quality. However, little attention has been paid to the integration of supply chain management in the maritime industry [14]. There are uncertainties in supply chain parameters from different viewpoints such as quality, quantity, delivery time. These uncertainties can be evaluated in three different groups such as demand, process, and supply [15; 16]. Uncertainty in supply is the uncertainty caused by delays or errors in deliveries [17]. In short, delay in delivery time may result from errors such as quantity errors in delivery or product damage. The uncertainty in the process is due to the low reliability of the production process. Finally, the uncertainty in demand is the most important one. In other words, volatility in demand and uncertainty caused by erroneous demand forecasts [18]. The demands of the end user can be stated in some linguistic expressions. These linguistic and verbal variables can be expressed at certain intervals. For example, when expressing the request, the following statements can be used: It may be less than a specific request for \(t_{1}\), or \(t_{2}\) may be more, or it may be approximately equal to the demand for \(t_{3}\). These statements can be shown with membership functions in fuzzy logic. Uncertainty is the most important factor affecting activity and coordination in the supply chain management decision process as well as affecting the operating performance of the system [19]. In many studies, uncertainties in the supply chain have been modeled by probability distributions. Probability distributions are generally obtained by examining the situations recorded in the past [20]. However, when statistical data are unreliable or incomplete, stochastic models may not be a good choice [21]. In this case, imprecise parameters can be determined based on experience and subjective managerial judgment [22]. Generally, the relevant expert can say the value range for a parameter and make an estimate of what value this parameter will most likely receive in that range. The fuzzy set theory, which requires simpler and less data than the probability theory used for the uncertainties in the supply chain, maybe an alternative approach [23; 24]. Fuzzy set theory is used to model systems that are difficult to define precisely [25; 26]. Fuzzy set theory is introduced as a method that adds variability and subjectivity to solution processes [27; 28]. It is seen that fuzzy modeling is also common in the field of supply chain modeling [29; 30]. In this study, a network optimization model is created mathematically for any maritime supply chain problem operating under uncertain demand conditions. Fuzzy goal programming is implemented. A problem has been created that minimizes container shipping costs and damage to cargoes throughout special cargo. This model was analyzed, the results were examined, and its appropriateness was evaluated. In Section 2, fuzzy goal programming is given. Section 3 provides the proposed model of maritime supply chain. Section 4 discusses the results and current status of maritime industry.