Jasem M Alrajhi’s scientific contributions

Calculating the unit transportation costs for trucks and buses is very important for companies in logistics and transportation. This helps companies to manage operations efficiently, remain profitable, and set competitive prices. Transportation companies gain insights into each component of their expenses by analyzing costs per ton-kilometer (for trucks) or per passenger-kilometer (for buses), such as fuel, maintenance, and labor. Based on these calculations, the cost for a whole trip can be easily calculated. This enables them to control budgets and optimize resource allocation. This metric also supports efficient route planning, fleet management, and cost-effective scheduling. Additionally, lowering unit costs often aligns with sustainability goals, reducing emissions through practices like load consolidation and fuel-efficient routing. Overall, understanding unit transportation costs fosters financial stability, customer satisfaction, and environmental responsibility. In this paper, we illustrate the different parameters which affect the calculation of unit transportation cost and the step-by-step methodology to calculate the variable, fixed, total and unit transportation costs for both trucks and buses. Two case studies with result analysis and comparisons with the actual costs are also included in this paper.

The equipment replacement problem plays a crucial role in various engineering disciplines. It helps to determine the optimal economic lifespan of equipment. Optimizing the decision-making process for equipment replacement is essential for enhancing management effectiveness and supporting the achievement of business goals. Numerous models have been developed to identify the optimal replacement strategies. Some of these models are grounded in mathematical programming techniques such as linear programming, integer programming, goal programming, and dynamic programming. Additionally, some researchers have designed their heuristic models to determine the most suitable replacement policies. However, many of these heuristic models are limited by their focus on a single objective, often overlooking the complexity of multi-objective scenarios and uncertainties. A lot of the mathematical programming models concentrate on sensitivity analysis, parametric study, muti-objective and fuzzy parameters. In this paper, we present a comparative analysis of various replacement models, highlighting the strengths and weaknesses of both heuristic and mathematical programming models. The solution algorithm as well as an illustrative example using backward dynamic programming are included.