Absence Of Resource Constraints Research Articles

A multiobjective, maximal conditional covering location problem applied to the relocation of hierarchical emergency response facilities

In this research we analyze the effectiveness of the current and optimal locations of a set of existing regional assets maintained by the Department of Defense to respond to large-scale emergencies. These assets have been incrementally resourced, established, sited over the past 20 years without regard to the entire enterprise and, due to fiscal and political costs, modifications to the current structure must yield significant gains to garner approval. We formulate a multiobjective hierarchical extension of the maximal covering location problem that seeks to maximize coverage of the population within a rapid response window while minimizing modifications to the existing structure. Additionally, we prevent facilities from covering nodes located within close proximity using a modified conditional covering problem (CCP) constraint; this constraint accounts for the large impact radius that can occur in a worst-case scenario. To solve our multiobjective problem, we develop a set of non-inferior solutions using the ε-constraint method. These non-inferior solutions explicitly represent the trade-off between maximizing coverage and minimizing cost, and they offer a decision maker a set of Pareto optimal decisions to consider for implementation. Applying our model and methodology to the current set of assets, we demonstrate that, in the absence of resource constraints, we can improve coverage by more than 15%, approximately 49 million people. Furthermore, with only 23 unit relocations (less than a 30% modification of the entire structure) coverage can exceed 98%, an improvement of an additional 45 million people covered. Finally, we demonstrate additional advantages of implementing the modified CCP constraint.

Water degradation implications when whole-farm irrigation water is binding

Abstract This paper demonstrates that with a binding resource constraint, such as water rights in the western United States, policy instruments that are effective in reducing water degradation associated with irrigated agriculture are likely to differ from those policies found effective in the absence of resource constraints. Under plausible circumstances, modest increases in per unit water charges will have no effect upon hectares irrigated or per hectare water application rates. Higher annual irrigation setup costs such as higher irrigated land taxes will result in reduced irrigated hectares with increased water applied per irrigated hectare and more chemical application per irrigated hectare. Such adjustments have the potential to exacerbate water contamination.

Condition-Based Maintenance Decision-Making for Multiple Machine Systems

Maintenance decision-making has emerged as an important area of industrial research. Over the past two decades, maintenance policies have evolved from simple reactive maintenance to complex versions of condition-based maintenance (CBM). A quantitative description of a machine’s health, as found in CBM, is essential to plan maintenance effectively as it helps avoid excessive or insufficient maintenance. In spite of several advancements in the degradation monitoring techniques, most CBM decision-making methods still focus on a single machine system. Maintenance analysis of a single machine provides good insights, but lacks practical applications. In this paper, we develop a continuous time Markov chain degradation model and a cost model to quantify the effects of maintenance on a multiple machine system. An optimal maintenance policy for a multiple machine system in the absence of resource constraints is obtained. In the presence of resource constraints, two prioritization methods are proposed to obtain effective maintenance policies for a multiple machine system. A case study focusing on a section of an automotive assembly line is used to illustrate the effectiveness of the proposed method.

Evaluating project completion time in project networks with discrete random activity durations

Deterministic models for project scheduling suffer from the fact that they assume complete information and neglect random influences, that occur during project execution. A typical consequence is the underestimation of the project duration as frequently observed in practice. This phenomenon occurs even in the absence of resource constraints and has been the subject of extensive research in the scientific community. This paper presents a method for obtaining relevant information about the project makespan for scheduling models, with dependent random processing time available in the form of scenarios.

Moral hazard with a finite number of states

Pure moral hazard situations with a finite number of underlying states and multidimensional actions are considered. Conditions are derived under which the first-best can be achieved. It is shown that the absence of resource constraints is important for this result.