A driver’s field of view is an essential requirement for decreasing traffic crashes and increasing safety. This paper improves driver safety by analyzing factors that affect the invisibility angles formed by a vehicle’s A and B-pillars. An experiment was conducted with 117 participants. Two models were developed, each associated with one invisibility angle. In the A-pillar invisibility model, the age, weight, waist circumference, torso angle, and distance between eyes and windshield were significant. For the B-pillar model, the age, gender, stature, waist depth, waist breadth, torso angle, and distance between the steering wheel and abdomen were significant. Some of these factors increase the invisibility angle(s), including the age, stature, torso angle, distance between the windshield and driver eyes, and the distance between the steering wheel and abdomen. Other factors decrease the invisibility angle(s), including weight, waist circumference, waist depth, and waist breadth. In addition, gender affects significantly the invisibility angle.

Discrete Event Simulation (DES) is the main tool used in analyzing supply chains (SCs). In many simulation studies the effect of the modeling approach on SC performance is ignored. Many researches have overlooked the fact that in DES where events are the drivers of variable changes; the modeler has the option to change these variables pre or post delay event occurrence. In SCs this choice is critical and has a significant effect on the results obtained. Another modeling approach is System Dynamics (SD) which overcomes the difficulties of DES because variables do not change in discrete jumps but rather smoothly overtime. Both DES and SD approaches are combined in one model and are compared to two types of DES modeling approaches; Pre-delay and Post-delay DES. A case study is implemented in a local cement SC. Inventory levels (IL), holding costs (HC) and ordering costs (OC) for the Combined DES/SD model are shown to be between those of the other two models. Post-delay DES is shown to overestimate IL and HC but underestimate OC; while, Pre- delay DES is shown to underestimate IL and HC while overestimating OC.

A new optimization model and solution approach for the Electric Vehicle Routing Problem (EVRP) is presented in this research, where the electric vehicles are used to deliver the products from manufacturing plant to multiple customers through a split delivery system which allows vehicles to partially recharged and return to a depot to get more loads if needed. The proposed model is a Mixed Integer Linear Programming (MILP) model used to determine the optimal route achieving the desired objective function for each vehicle which ensures maximization of customer satisfaction by minimizing total amount deviations and minimization of total distribution time. CPLEX solver is used to solving the different sizes of data sets to test the performance of the model, while a new heuristic approach is constructed to solve the same data sets. Then the results of the heuristic are compared with those obtained by CPLEX. The results of the small size data sets show that the CPLEX and heuristic obtain a competitive solution in terms of both computation time and solution quality. For medium-size data sets, the proposed heuristic can find the same or better solution for some of them compared with the result produced by CPLEX.

A single machine scheduling problem with sequence-dependent setup times and setup costs has a critical role in manufacturing and service sectors to minimize orders delays and costs. This study provides multiple objectives model to solve the single machine total weighted tardiness and setup costs scheduling problem associated with sequence-dependent setup times and sequence-dependent setup costs (MOSTWTSCSD). The objectives are to minimize the total weighted tardiness and the total setup costs for all jobs without any certain relationship between the setup time and the setup cost, as happened in some machines. An alternative heuristic procedure based on discrete particle swarm optimization (DPSO) is suggested to find representative Pareto front (non-dominated) solutions. Many efficient methods have been used inside the DPSO to improve its performance. Results show that the ability of the suggested solution approach to provide a representative Pareto fronts in reasonable computational efforts compared with another heuristic.