Pavement Maintenance Decision Making Based on Optimization Models

Pavement maintenance prioritization considering both quality and cost is an important decision-making problem. In this paper, the actual pavement condition index of city roads was calculated using municipal patrol data. A linear optimization model that maximized maintenance quality with limited maintenance costs and a multi-objective optimization model that maximized maintenance quality while minimizing maintenance costs were developed based on the pavement condition index. These models were subsequently employed in making decisions for actual pavement maintenance using sequential quadratic programming and a genetic algorithm. The results showed that the proposed decision-making models could effectively address actual pavement maintenance issues. Additionally, the results of the single-objective linear optimization model verified that the multiobjective optimization model was accurate. Thus, they could provide optimal pavement maintenance schemes for roads according to actual pavement conditions. The reliability of the models was investigated by analyzing their assumptions and validating their optimization results. Furthermore, their applicability in pavement operation-related decision making and preventive maintenance for roads of different grades was confirmed.

Integrated planning of grids and energy conversion units in municipal multi-energy carrier systems

The ongoing energy transition requires the planning of low-emission municipal energy supply systems. These systems comprise distribution grids for electricity, gas, and heat, as well as energy conversion units such as heating systems. This paper presents a linear optimization model considering these elements in order to identify the cost-minimizing system design while achieving a given CO2 emission limit. The model is applied to an exemplary test case comprising 900 buildings. In order to increase scalability of the model to larger system sizes, the effect of reducing the spatial resolution on the optimization results is analyzed. The results show that the effect is small and that spatial aggregation is indeed a valid approach to reduce problem complexity and to allow significant speedups, reaching a factor of 200 for the given case study.

Portfolio-Wide Optimization of Pharmaceutical R&D Activities Using Mathematical Programming

The research and development (R&D) management in any major research pharmaceutical company is constantly faced with the need to make complicated activity scheduling and resource allocation decisions, as they carry out scientific work to develop new therapeutic products. This paper describes how we develop a decision support tool that allows practitioners to determine portfolio-wide optimal schedules in a systematic, quantitative, and largely automated fashion. Our tool is based on a novel mixed-integer linear optimization model that extends archetypal multimode resource-constrained project scheduling models in order to accommodate multiple rich features that are pertinent to the Chemistry, Manufacturing, and Controls (CMC) activities carried out within the pharmaceutical R&D setting. The tool addresses this problem at the operational level, determining schedules that are optimal in light of chosen business objectives under activity sequencing, resource availability, and deadline constraints. Applying the tool on current workload data demonstrates its tractability for practical adoption. We further illustrate how, by utilizing the tool under different input instances, one may conduct various tactical analyses to assess the system’s ability to cope with sudden changes or react to shifting management priorities.

Linear model for coputation of equilibrium point in bimatrix game

The methods for finding Nash equilibrium in bimatrix game are introduced in this paper. Linear optimization model with binary variables for coputation of all equilibrium points in nongenerate bimatrix game is proposed.

Employment Of Temporary Workers And Use Of Overtime To Achieve Volume Flexibility Using Master Production Scheduling: Monetary And Social Implications

Flexibility and in particular volume flexibility is an important topic for industrial manufacturing companies. In this context, the harmonization of the available and required capacity is a central task, especially with increasing fluctuations in customer demand. In classical approaches, this is considered only by the use of additional capacities and there are only a few approaches that combine aspects of personnel planning with production planning. Therefore, this article presents a linear optimization model for master production scheduling that includes aspects of personnel requirements planning. It is used to investigate different strategies for the use of overtime and temporary workers in order to achieve different levels of volume flexibility. With regard to the monetary and social impacts, the results indicate that overtime has a stronger influence to achieve volume flexibility than the use of temporary workers. However, both are affected by substantial deficits in human working conditions. But the results also imply a promising potential for improving the social aspects without a significant increase in costs.

An Economic Analysis on the Transition to Dryland Production in Deficit-Irrigated Cropping Systems of the Texas High Plains

The objective of this analysis was to integrate hydrologic, agronomic, and economic methods to evaluate various management strategies by changing crop acreage to better manage the declining resources of the Ogallala aquifer. A non-linear optimization model was used to estimate the optimal water use, crop mix, crop yield, and net returns over a 50 year period under dryland and deficit irrigation scenarios in the Texas High Plains. Results indicated that growers could maintain profitability by switching from fully irrigated center pivots to irrigating ½ and ¼ pivots.

An Optimal Customized Pricing Strategy for Truckload Transportation

The impacts of optimal pricing are rarely explored when it comes to truckload transportation. In this study, the question of what price should be given to which customer and for what service characteristics are investigated for truckload transportation. Accordingly, customers' attitudes and responses to the bid price must be modeled, and their flexibility in regards to the price must be analyzed. Bid response function is developed, and logit model is considered. The bid response function is examined from two different perspectives: the first one is a general model by which all data is used, and the second one is the logit model by using partitioned data obtained by clustering customers. Logit model sensitivity analysis is applied. After developing bid response functions, non-linear optimization model is developed to determine the bid price. The developed model will contribute to the logistics companies' profit margins in the long term.