Journal of Heuristics最新文献

This paper considers the problem of scheduling a set of time- and energy-constrained preemptive tasks on a discrete time horizon. At each time period, the total energy required by the tasks that are in process can be provided by two energy sources: a reversible one and a non-reversible one. The non-reversible energy source can provide an unlimited amount of energy for a given period but at the expense of a time-dependent piecewise linear cost. The reversible energy source is a storage resource. The goal is to schedule each task preemptively inside its time window and to dispatch the required energy to the sources at each time period, while satisfying the reversible source capacity constraints and minimizing the total cost. We propose a mixed integer linear program of pseudo-polynomial size to solve this NP-hard problem. Acknowledging the limits of this model for problem instances of modest size, we propose an iterative decomposition matheuristic to compute an upper bound. The method relies on an efficient branch-and-price method or on a local search procedure to solve the scheduling problem without storage. The energy source allocation problem for a fixed schedule can in turn be solved efficiently by dynamic programming as a particular lot-sizing problem. We also propose a lower bound obtained by solving the linear programming relaxation of a new extended formulation by column generation. Experimental results show the quality of the bounds compared to the ones obtained using mixed integer linear program.

This paper addresses the vehicle routing and driver scheduling problem of finding a low cost route and stoppage schedule for long-haul point-to-point full-load trips with intermediate stops due to refueling needs and driver hours-of-service regulatory restrictions. This is an important problem for long-haul truck drivers because in practice regulatory driving limits often do not coincide with availability of stoppage alternatives for quick rest, for meal, for overnight, or for weekly downtime required stops. The paper presents a methodology and algorithm to pick routes that optimize stoppages within the HOS constraints, an important factor of both highway safety and driver productivity. A solution for this variant of the vehicle routing and truck driver scheduling problem (VRTDS-HOS) that is fast enough to potentially be used in real time is proposed by modeling possible stoppage configurations as nodes in an iteratively built multi-dimensional state-space graph and by using heuristics to decrease processing time when searching for the lowest-cost path in that graph. Individual nodes in the graph are characterized by spatial, temporal, and stoppage attributes, and are expanded sequentially to search for low-cost paths between the origin and the destination. Within this multi-dimensional state-space graph, the paper proposes two heuristics applied to a shortest-path algorithmic solution based on the (A^*) algorithm to increase processing speed enough to potentially permit real-time usage. An illustrative application to Brazilian regulations is provided. Results were successful and are reported together with sensitivity analyses comparing alternative routes and different heuristics processing speeds.

Roads intersections are one of the main causes of traffic jams since vehicles need to stop and wait for their time to go. Scenarios that only consider autonomous vehicles can minimize this problem using intelligent systems that manage the time when each vehicle will pass across the intersection. This paper proposes a mathematical model and a heuristic that optimize this management. The efficiency of this approach is demonstrated using traffic simulations, with scenarios of different complexities, and metrics representing the arrival time, (hbox {CO}_{{2}}) emission and fuel consumption. The results show that the present approach is scalable, maintaining its performance even in complex real scenarios. Moreover, its execution time is maintained in milliseconds, what suggests this approach as a candidate for dealing with real-time and dynamic scenarios.