The renowned nursing shortage has attracted much attention from national level law makers, state legislatures, commercial organizations, and researchers due to its direct impact on the quality of patient care. High workloads and undesirable schedules are two major issues that cause nurses’ job dissatisfaction. The focus of this paper is to find nondominated solutions to an integrated nurse staf...

This paper addresses two major issues related to the convergence of generalized Benders decomposition which is an algorithm for the solution of mixed integer linear and nonlinear programming problems. First, it is proved that a mixed integer nonlinear programming formulation with zero nonlinear programming relaxation gap requires only one Benders cut in order to converge, namely the cut corresp...

majority of models in location literature are based on assumptions such as point demand, absence of competitors, as well as monopoly in location, products, and services. however in real-world applications, these assumptions are not well-matched with reality. in this study, a new mixed integer nonlinear programming model based on weighted goal programming approach is proposed to maximize the cap...

We study an unrelated parallel machines scheduling problem with sequence and machine dependent setup times. A logicbased Benders decomposition approach is proposed to minimize the makespan. This approach is a hybrid model that makes use of a mixed integer programming master problem and a specialized solver for travelling salesman subproblems. The master problem is used to assign jobs to machine...

With the introduction of alternative fuel vehicles into the transportation system, the refueling station location research has been touted in the literature. The flow refueling location problem (FRLP) locates p stations in order to maximize the flow volume that can be accommodated in a road network respecting the range limitations of the vehicles. This paper introduces the hybrid charging stati...

We study a class of chance-constrained two-stage stochastic optimization problems where second-stage feasible recourse decisions incur additional cost. In addition, we propose a new model, where “recovery” decisions are made for the infeasible scenarios to obtain feasible solutions to a relaxed second-stage problem. We develop decomposition algorithms with specialized optimality and feasibility...

We present the mean value cross decomposition algorithm and its simple enhancement for the two-stage stochastic linear programming problem with complete recourse. The mean value cross decomposition algorithm employs the Benders (primal) subproblems as in the so-called “L-shaped” method but eliminates the Benders master problem for generating the next trial first-stage solution, relying instead ...

This paper describes an exact algorithm capable of solving large-scale instances of an important hub location problem called the Uncapacitated Hub Location Problem with Multiple Assignments. The algorithm applies Benders decomposition to a strong path-based formulation of the problem. The standard decomposition algorithm is enhanced through the inclusion of several features such as the use of a...

Decomposition has proved to be one of the more effective tools for the solution of large-scale problems, especially those arising in stochastic programming. A decomposition method with wide applicability is Benders’ decomposition, and has been applied to both stochastic programming, as well as integer programming problems. However, this method of decomposition relies on convexity of the value f...