Fuzzy vulnerability mapping of urban groundwater systems to nitrate contamination

The aim of this study is to develop a new fuzzy optimization model to find the optimal factor weights of modified DRASTIC index for groundwater vulnerability mapping an urban aquifer to nitrate contamination. Eight factors including water table depth, recharge, aquifer media, soil media, topography, impact of vadose zone, hydraulic conductivity, and land use are considered and rated. A fuzzy linear regression is formulated between the values of eight factors and corresponding nitrate concentration in groundwater. An optimization model based on real code genetic algorithm with objective of minimizing the sum of the fuzzy spread of the regression coefficients is implemented. Aquifer of Mashhad metropolis (northeast of Iran) is chosen to evaluate the proposed model. The results show the proposed model is a promising tool for weighting the factors with avoiding the subjectivity and also ambiguities accompanied by parameters to produce an accurate specific vulnerability mapping of an urban aquifer.     

Maximizing the efficiency of use of nurses under uncertain surgery durations: A case study

This paper studies a nurse scheduling problem of assigning a set of nurses to surgeries scheduled on each workday in an operating room (OR) suite. This problem plays a decisive role in utilizing nurses efficiently, which is of paramount importance for OR suites to provide high-quality service at ever reduced cost. Due to significant uncertainty in surgery durations, designing schedules that achieve high nurse efficiency is complicated by the competing objective of ensuring on-time starts of surgeries. For trading off between the two performance criteria, we formulate the problem as a mixed integer programming (MIP) model with explicit probability modeling of uncertainty. We are concerned about improving nurse efficiency in terms of overtime and idle time of nurses while mastering the risk of delay of surgeries. The MIP model is applied in a large size Chinese hospital, and the results are compared with the actual performance of the OR suite. The comparisons reveal that through examining the trade-off between the performance criteria, important nurse efficiency improvements can be achieved with good on-time start performance. Moreover, the applicability of the MIP model in various problem settings is also investigated.     

Rule-based meta-heuristics for integrated scheduling of unrelated parallel machines,batches, and heterogeneous delivery trucks

In this article, we deal with an integrated scheduling for unrelated parallel machines, batches, and heterogeneous delivery trucks. In a manufacturing plant, jobs ordered by customers are manufactured by one of several unrelated parallel machines. Then, they are grouped and delivered to the respective customers by heterogeneous trucks with different capacities and travel times. The objective of the problem is to simultaneously determine the machine schedule, batching, and truck-delivery schedule to minimize the make span of the entire process. To solve this problem, we derive a mathematical model to obtain the optimal solution, and we propose rule-based meta-heuristics using single-stage GA framework. Through randomly generated instance examples, the performances of the proposed meta-heuristics are compared.     

Production planning in automotive powertrain plants: a case study

Based on a real case study from the automotive industry, this paper deals with production planning in powertrain plants. We present an overview of the production planning process and propose a mixed integer linear programme to determine the production quantities of each product over a planning horizon of several days. Then, using real data of an engine assembly line, we simulate the performance obtained through the proposed model within a rolling horizon planning process. We perform multiple tests in order to evaluate the impact of two parameters involved in this process: planning frequency and frozen horizon length. Furthermore, in order to illustrate the value of improving coordination between engine plants and their customers, we evaluate the impact of the quality of demand information (orders and forecasts). We analyse the simulation results and provide insights and recommendations in order to achieve a good trade-off between service level, inventory, and planning stability.     

Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing

The coordinated supplier selection and customer order scheduling in the presence of supply chain disruption risks is studied for single and multiple sourcing strategies. Given a set of customer orders for products, the decision maker needs to select a single supplier or a subset of suppliers for purchasing parts required to complete the customer orders, and schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The suppliers are located in different geographic regions and the supplies are subject to different types of disruptions: to random local disruptions of each supplier individually, to random regional disruptions of all suppliers in the same region simultaneously and to random global disruptions of all suppliers simultaneously. For any combination of suppliers hit by different types of disruptions, a formula for calculating the corresponding disruption probability is developed. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional value-at-risk as a risk measure. The problem objective is either to minimize expected worst-case cost or to maximize expected worst-case customer service level, i.e., the expected worst-case fraction of customer orders filled on or before their due dates. The risk-averse solutions that optimize worst-case performance of a supply chain under disruptions risks are compared for the two sourcing strategies and the two objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

Design and analysis of an aging‐aware energy management system for islanded grids using mixed‐integer quadratic programming

The rapid increase of renewable energy sources made coordinated control of the distributed and intermittent generation units a more demanded task. Matching demand and supply is particularly challenging in islanded microgrids. In this study, we have demonstrated a mixed‐integer quadratic programming (MIQP) method to achieve efficient use of sources within an islanded microgrid. A unique objective function involving fuel consumption of diesel generator, degradation in a lithium‐ion battery energy storage system, carbon emissions, load shifting, and curtailment of the renewable sources is constructed, and an optimal operating point is pursued using the MIQP approach. A systematic and extensive methodology for building the objective function is given in a sequential and explicit manner with an emphasis on a novel model‐based battery aging formulation. Performance of the designed system and a sensitivity analysis of resulting battery dispatch, diesel generator usage, and storage aging against a range of optimization parameters are presented by considering real‐world specifications of the Semakau Island, an island in the vicinity of Singapore.     

基于熵权法的河北省水资源脆弱性评价

鉴于研究水资源脆弱性对河北省水资源可持续利用具有重要意义,特利用熵权法评价了河北省水资源脆弱性。发现河北省2005～2016年水资源脆弱性以中等脆弱性为主,水资源脆弱性呈现下降趋势;2005～2016年河北省平原区浅层地下水位不断下降;2005～2016年河北省总体上水资源利用效率得到明显提高,农业依然是用水大户。