Search

In this paper, we study a project assignment problem. Specifically, a set of projects, each of which needs to be finished over a project development cycle, are to be assigned to a group of identical engineers over a discrete planning horizon. The workload of the projects is different and fluctuates over their development cycles. In any period, an engineer has a maximum allowed workload. The objective of the problem is to assign the projects to engineers with the objective of balancing the total workload among the engineers; the load balance is measured by the difference between the maximum and the minimum total workload. Such a problem is new to the literature. The problem is strongly NP-hard. Therefore, we propose a two-stage heuristic approach to solve it. Extensive numerical experiments show that the proposed approach can achieve optimal or nearly optimal solutions for all test cases; such performance is much better than what can be obtained from an IP model solved with ILOG CPLEX11. [ABSTRACT FROM AUTHOR]

We consider a HAZMAT transportation problem, which is modeled as the length-balanced two arc-disjoint shortest paths problem (LB2SP). The objective function of LB2SP is expressed as a weighted sum of two terms, i.e., the sum of the path lengths and the positive length difference between the paths. We demonstrate that LB2SP is NP-Hard, and formulate it as an Integer Programming (IP) model. We develop a Lagrangian relaxation based heuristic (LRBH) for LB2SP. Computational experiments are conducted to compare the performance of LRBH with the CPLEX solver, showing that the LRBH is efficient for LB2SP. [ABSTRACT FROM AUTHOR]

In this work, we develop a multi-exchange heuristic based on an estimation improvement graph embedded in a simulated annealing to solve a problem arising in plant location planning where tariff exemptions apply. The method is shown to be effective in experiments since it provides good solutions for problems of realistic size. It is superior to CPLEX in terms of time, and is able to provide solutions for large test problems. Keywords: Search, heuristics, planning. [ABSTRACT FROM AUTHOR]