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A mathematical model is formulated for the time-dependent vehicle routing problem (TDVRP) and a genetic algorithm (GA) is proposed for solving it. Formulation of the problem considers multiple vehicles with different capacities, pick-up or delivery demands with soft time windows, real-time service requests, and real-time variations in travel times between demand nodes. The objective is to minimize the total cost, which consists of routing cost, fixed cost for using the vehicles, and customer inconvenience costs. A mixed-integer linear programming formulation of the TDVRP is presented. Like other combinatorial problems, to solve the TDVRP exactly, a significant amount of processing time is required. A GA is proposed to solve the problem. The proposed GA was tested on the test problems, and GA results were compared with the exact solutions for small test problems. GA results were also compared with the lower bounds obtained for the solution of the larger problems. In the case of small problems, only 2 of 33 cases have gaps between the GA solutions and the exact solutions, and the maximum gap is less than 5 percent. For larger problems, the maximum gaps between GA solutions and lower bound solutions are less than 7 percent.

A mathematical model for a multivehicle pickup and delivery problem with time windows is presented, and a genetic algorithm (GA) for solving it is proposed. The mathematical model is formulated as a mixedinteger linear programming problem. The objective of the proposed model is to minimize the total cost, which consists of the fixed cost for the vehicles, the routing cost, and the customer inconvenience cost, which is modeled as a penalty cost for violation of the time windows of each customer. Like other combinatorial problems, solving this pickup and delivery problem is time-consuming, and sometimes it is impossible to find an exact solution. The problem is solved exactly for up to six demands (12 nodes), and GA is used for larger problems with more than six demands. The proposed GA can solve a pickup and delivery problem in an extremely short time compared with the exact solution procedure. It also produces excellent results for small problems. A GA used to solve a 30-demands problem (60 nodes) with 10 vehicles is illustrated.

Abstract-Device-to-Device (D2D) communications have been proposed as a means of realizing the potential advantage of the physical proximity of communicating devices, improving user experience and resource utilization. Discovery is one of the major design issues in the D2D communications, since they must discover each other and identify services provided by each other to directly communicate with one another. There are some requirements for discovery such as energy-efficiency (e.g. low duty cycle), scalability (e.g. support for high device density) and proximity-based autonomous detection in the D2D communications. In this paper, we propose a discovery scheme for D2D communications in synchronous distributed networks. In particular, we present a discovery scheme that each device advertises its presence and service and discovers other nearby devices autonomously and continuously, along with resource allocation in distributed manner. Using simulation, we evaluate the performances of our proposed scheme in terms of discovery latency and the number of discovered devices.

The effects of advanced traveler information system (ATIS) accuracy and the extent of ATIS roadway instrumentation on the on-time reliability benefits to routine users of ATISs were evaluated by using archived estimates of roadway travel times to re-create hypothetical, retrospective paired driving trials between travelers with and without ATISs. Previous research using this technique demonstrated that travelers who received notification of congestion before departure could realize time management benefits from improved on-time reliability and trip predictability. On the basis of millions of hypothetical trips over a 12-month period in Los Angeles, California, it was found that a net benefit to an average user of ATIS existed if the error in travel time estimation was in the range of 14% to 21% or better, a threshold that depended on regional day-to-day travel time variability. For less accurate ATIS services, only certain subsets of the driving population, such as those with relatively long or highly variable trips, may realize any benefit. Furthermore, it was observed that a nearly optimal geographical deployment of ATIS could garner as much as 56% of the benefit of full coverage from the first 30% of deployment. Yet, the best deployment strategy is not as simple as prioritizing links on the basis of day-to-day variability. In making cost-effective trade-off decisions about how to invest in improved ATISs, be it expanding geographic coverage or improving accuracy, the findings underscore the importance of understanding the accuracy required to generate ATIS user benefits on the basis of regional day-to-day roadway variability.