This paper presents a solution methodology for the heterogeneous fleet vehicle routing problem with time windows. The objective is to minimize the total distribution costs, or similarly to determine the optimal fleet size and mix that minimizes both the total distance travelled by vehicles and the fixed vehicle costs, such that all problem's constraints are satisfied. The problem is solved using a two-phase solution framework based upon a hybridized Tabu Search, within a new Reactive Variable Neighborhood Search metaheuristic algorithm. Computational experiments on benchmark data sets yield high quality solutions, illustrating the effectiveness of the approach and its applicability to realistic routing problems.

In the service industry, it is crucial to efficiently allocate scarce resources to perform tasks and meet particular service requirements. What considerably complicates matters is when these resources, for example skilled technicians, nurses, and home carers have to visit different customer locations. This paper provides a comprehensive survey on resource constrained routing and scheduling that unveils the problem characteristics with respect to resource qualifications, service requirements and problem objectives. It also identifies the most effective exact and heuristic algorithms for this class of problems. The paper closes with several research prospects.

A common assumption in the shop scheduling literature is that the processing order of the operations of each job is sequential; however, in practice, there can be multiple connections and finish-to-start dependencies among the operations of each job. This paper studies flexible job shop scheduling problems with arbitrary precedence graphs. Rigorous mixed integer and constraint programming models are presented, as well as an evolutionary algorithm is proposed to solve large-scale problems. The proposed heuristic solution framework is equipped with efficient evolution and local search mechanisms as well as new feasibility detection and makespan estimation methods. To that end, new theorems are derived that extend previous theoretical contributions of the literature. Computational experiments on existing benchmark datasets show that the proposed solution methods outperform the current state-of-the-art. Overall, 59 new best solutions and 61 new lower bounds are produced for a total of 228 benchmark problem instances of the literature. To explore the impact of the arbitrary precedence graphs, lower bounds and heuristic solutions are generated for new large-scale problems. These experiments illustrate that the machine assignment flexibility and density of the precedence graphs, affect not only the makespan, but also the difficulty of producing good upper bounds.

No abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThis paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. The goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIPEmail addresses: prepouss@stevens.edu (Panagiotis P. Repoussis), d.paraskevopoulos@bath.ac.uk (Dimitris C. Paraskevopoulos), alkis@optimizationdirect.com (Alkiviadis Vazacopoulos), nah2005@med.cornell.edu (Nathaniel Hupert) Preprint submitted to European Journal of Operational ResearchMay 25, 2016 based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times.