Induced Interval-Valued Intuitionistic Fuzzy Hybrid Aggregation Operators with TOPSIS Order-Inducing Variables

Two induced aggregation operators with novelly designed TOPSIS order-inducing variables are proposed: Induced Interval-valued Intuitionistic Fuzzy Hybrid Averaging (I-IIFHA) operator and Induced Interval-valued Intuitionistic Fuzzy Hybrid Geometric (I-IIFHG) operator. The merit of two aggregation operators is that they can consider additional preference information of decision maker’s attitudinal characteristics besides argument-dependent information and argument-independent information. Some desirable properties of I-IIFHA and I-IIFHG are studied and theoretical analysis also shows that they can include a wide range of aggregation operators as special cases. Further, we extend these operators to form a novel group decision-making method for selecting the most desirable alternative in multiple attribute multi-interest group decision-making problems with attribute values and decision maker’s interest values taking the form of interval-valued intuitionistic fuzzy numbers, and application research to real estate purchase selection shows its practicality

Optimization-Based Simulation of Container Terminal Productivity using Yard Truck Double Cycling

ABSTRACT The growth of global trade transiting over the ocean has been continually increasing. A new generation of large vessels has recently been introduced to the transhipment system. These large vessels can carry more than 16000 twenty-foot equivalent container units (TEUs), maximizing shipping productivity. Container terminals must improve their productivity to meet the rapid increases in trade demand and to keep pace with developments in the shipbuilding industry. Reducing vessel turnaround time in container terminals increases the capacity for world trade. This time reduction can be achieved by improving one or more container terminal major resources or factors. The objective of this research is to maximize container terminal productivity by minimizing vessel turnaround time within reasonable hourly and unit costs. A new strategy is introduced, employing double cycling to reduce the empty travel of yard trucks. This double-cycling strategy still requires the use a single-cycle strategy before the trucks can be incorporated into double-cycle scheduling. The single-cycle start-up is necessary in order to create enough space to begin loading a vessel if there is no other space. The strategy is based on combining the efforts of two quay cranes (Unloading and Loading quay cranes) to work as a unit. The technique optimizes the number of trucks in terms of time and cost, minimizing yard truck cycles by minimizing single cycle routes and maximizing double cycle trips. This requires five steps. First, a good knowledge base of a container terminal’s operation and of the behaviours of the Quay cranes (QCs), Yard trucks, and Yard cranes needs to be constructed. Second, analysis of the collected data is required to simulate the container terminal operation and to implement the Genetic algorithm. Third, the double cycling truck strategy is simulated, tested and verified. Fourth, sensitivity analysis is performed to rank and select the best alternatives. Optimization of the selected alternatives in terms of productivity and cost as well as verifying the results using real case studies comprises the fifth step. Genetic Algorithm is used to optimize the results. Some selection approaches are implemented on the set of the nearest optimum solutions to rank and select the best alternative. The research offers immediate value by improving container terminal productivity using existing facilities and resources. Simulating the yard truck double cycling strategy provides container terminal mangers and decision makers with a clear overview of their handling container operations. Optimizing fleet size is a key factor in minimizing container handling costs and time. The simulation model reveals a productivity improvement of about 19% per QC. A reasonable cost savings in terms of the cost index in unit cost was achieved using yard truck double cycling operation. The genetic algorithm corroborates the achievements thus gained and determines the optimal fleet size that will result in the maximum terminal productivity (quickest vessel turnaround time) with the minimal cost. A time reduction of more than 26% was achieved in most cases, compared to previous research efforts