Interval-Based Dynamic Discretization Discovery for Solving the Continuous-Time Service Network Design Problem

We introduce an effective and efficient iterative algorithm for solving the continuous-time service network design problem. The algorithm achieves its efficiency by carefully and dynamically refining partially time-expanded network models so that only a small number of small integer programs, defined over these networks, need to be solved. An extensive computational study shows that the algorithm performs well in practice, often using time-expanded network models with size much less than 1% (in terms of number of variables and constraints) of a full time-expanded network model. The algorithm is inspired by and has many similarities to the dynamic discretization discovery algorithm introduced in Boland et al. [Boland N, Hewitt M, Marshall L, Savelsbergh M (2017) The continuous-time service network design problem. Oper. Res. 65(5):1303–1321.], but generates smaller partially time-expanded models, produces high-quality solutions more quickly, and converges more quickly.

A Magnetic Field Gradiometer based on the Laser Helium Optically Pumped Magnetic Field Sensors

The theory of ows is one of the most important parts of Combinatorial Optimiza- tion and it has various applications. In this pa- per we study optimum (maximum or minimum) ows in directed bipartite dynamic network and is an extension of article [9]. In practical situa- tions, it is easy to see many time-varying opti- mum problems. In these instances, to account properly for the evolution of the underlying sys- tem overtime, we need to use dynamic network ow models. When the time is considered as a variable discrete values, these problems can be solved by constructing an equivalent, static time expanded network. This is a static approach

Optimum Flows in Directed Bipartite Dynamic Network. The Static Approach

The theory of flows is one of the most important parts of Combinatorial Optimization and it has various applications. In this paper we study optimum (maximum or minimum) flows in directed bipartite dynamic network and is an extension of article [9]. In practical situations, it is easy to see many time-varying optimum problems. In these instances, to account properly for the evolution of the underlying system overtime, we need to use dynamic network flow models. When the time is considered as a variable discrete values, these problems can be solved by constructing an equivalent, static time expanded network. This is a static approach.

Dynamic Discretization Discovery for Solving the Time-Dependent Traveling Salesman Problem with Time Windows

We present a new solution approach for the time-dependent traveling salesman problem with time windows. This problem considers a salesman who departs from his home, has to visit a number of cities within a predetermined period of time, and then, returns home. The problem allows for travel times that can depend on the time of departure. We consider two objectives for the problem: (1) a makespan objective that seeks to return the salesman to his home as early as possible and (2) a duration objective that seeks to minimize the amount of time that he is away from his home. The solution approach is based on an integer programming formulation of the problem on a time-expanded network, because doing so enables time dependencies to be embedded in the definition of the network. However, because such a time-expanded network (and thus, the integer programming formulation) can rapidly become prohibitively large, the solution approach uses a dynamic discretization discovery framework, which has been effective in other contexts. Our computational results indicate that the solution approach outperforms the best-known methods on benchmark instances and is robust with respect to instance parameters.

On three approaches to length-bounded maximum multicommodity flow with unit edge-lengths

The paper presents a comparison between three approaches to solving the length-bounded maximum multicommodity flow problem with unit edge-lengths. Following the first approach, Garg and K?nemann?s, we developed an improved fully polynomial time approximation scheme for this problem. As the second alternative, we considered the well-known greedy approach. The third approach is the one that yields exact solutions by means of a standard LP solver applied to an LP model on the time-expanded network. Computational experiments are carried out on benchmark graphs and the graphs that model software defined satellite networks, to compare the proposed algorithms with an exact linear programming solver. The results of the experiments demonstrate a trade-off between the computing time and the precision of algorithms under consideration.

FLOW OVER TIME PROBLEM WITH INFLOW-DEPENDENT TRANSIT TIMES

Network flow over time is an important area for the researcher relating to the traffic assignment problem. Transmission times of the vehicles directly depend on the number of vehicles entering the road. Flow over time with fixed transit times can be solved by using classical (static) flow algorithms in a corresponding time expanded network which is not exactly applicable for flow over time with inflow dependent transit times. In this paper we discuss the time expanded graph for inflow-dependent transit times and non-existence of earliest arrival flow on it. Flow over time with inflow-dependent transit times are turned to inflow-preserving flow by pushing the flow from slower arc to the fast flow carrying arc. We gave an example to show that time horizon of quickest flow in bow graph GB was strictly smaller than time horizon of any inflow-preserving flow over time in GB satisfying the same demand. The relaxation in the modified bow graph turns the problem into the linear programming problem.