A unified heuristic and an annotated bibliography for a large class of earliness-tardiness scheduling problems

This work proposes a unified heuristic algorithm for a large class of earliness-tardiness (E-T) scheduling problems. We consider single/parallel machine E-T problems that may or may not consider some additional features such as idle time, setup times and release dates. In addition, we also consider those problems whose objective is to minimize either the total (average) weighted completion time or the total (average) weighted flow time, which arise as particular cases when the due dates of all jobs are either set to zero or to their associated release dates, respectively. The developed local search based metaheuristic framework is quite simple, but at the same time relies on sophisticated procedures for efficiently performing local search according to the characteristics of the problem. We present efficient move evaluation approaches for some parallel machine problems that generalize the existing ones for single machine problems. The algorithm was tested in hundreds of instances of several E-T problems and particular cases. The results obtained show that our unified heuristic is capable of producing high quality solutions when compared to the best ones available in the literature that were obtained by specific methods. Moreover, we provide an extensive annotated bibliography on the problems related to those considered in this work, where we not only indicate the approach(es) used in each publication, but we also point out the characteristics of the problem(s) considered. Beyond that, we classify the existing methods in different categories so as to have a better idea of the popularity of each type of solution procedure

Rapid Method for Classification of Cheddar Cheese Based on Flavor Quality using Infrared Spectroscopy

Flavor quality of Cheddar cheese significantly influences its consumer acceptance, price and food processing application. Cheese flavor is currently determined using trained human tasting panels. This approach is expensive and time consuming. Rapid and high-throughput instrumental methods for predicting the flavor quality of Cheddar cheese would save time and money for the cheese industry. The objective of this research was to develop a rapid and simple technique based on Fourier transform infrared spectroscopy to predict the flavor quality of Cheddar cheese. Fifteen Cheddar cheese samples were ground into powders using liquid nitrogen. The water-soluble compounds from the cheese powder, without interfering compounds such as complex fat and protein, were extracted using water and organic solvents. Aliquots (10 µL) of the extract were placed on the sampling accessory, dried and scanned in the spectrometer (4000 to 700 cm-1). The spectra were matched with the flavor quality to build statistical classification models. The models provided 3D plots in which all the 15 cheese samples formed well separated clusters, whose orientation correlated well with their cheese flavor characteristics (fermented, unclean, low flavor, sour, good cheddar, etc.). The discrimination of the samples was mainly due to organic and fatty acids and their esters (1500 to 900 cm-1), which are known to contribute significantly to cheese flavor. The total analysis time, including the sample preparation time, was less than 20 min per sample. This technique can be a rapid, inexpensive, high-throughput and simple tool to the cheese industry for predicting the flavor quality of cheese.DairiConcepts LP. (Springfield, MO) and Midwest Advanced Food Manufacturing Allicance (MAFMA)A five-year embargo was granted for this item

Hybrid Metaheuristics for the Clustered Vehicle Routing Problem

The Clustered Vehicle Routing Problem (CluVRP) is a variant of the Capacitated Vehicle Routing Problem in which customers are grouped into clusters. Each cluster has to be visited once, and a vehicle entering a cluster cannot leave it until all customers have been visited. This article presents two alternative hybrid metaheuristic algorithms for the CluVRP. The first algorithm is based on an Iterated Local Search algorithm, in which only feasible solutions are explored and problem-specific local search moves are utilized. The second algorithm is a Hybrid Genetic Search, for which the shortest Hamiltonian path between each pair of vertices within each cluster should be precomputed. Using this information, a sequence of clusters can be used as a solution representation and large neighborhoods can be efficiently explored by means of bi-directional dynamic programming, sequence concatenations, by using appropriate data structures. Extensive computational experiments are performed on benchmark instances from the literature, as well as new large scale ones. Recommendations on promising algorithm choices are provided relatively to average cluster size.Comment: Working Paper, MIT -- 22 page

Monitoring composition and flavor quality of Cheddar cheese during ripening using a rapid spectroscopic method

FAES and Human Ecology: 3rd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)About 9.13 billion pounds of cheese is produced in the US every year, of which 34% is cheddar cheese. Cheddar cheese composition and flavor quality, which influence the consumer acceptance, price and food processing application, develop during the ripening process. However, ripening is not well understood due to complexity of the process and heterogeneous nature of cheese. Rapid monitoring of composition and flavor quality of cheese and understanding cheese ripening hold many advantages for the cheese-maker. Cheese composition and quality are currently determined using chromatographic techniques and trained human expert sensory panels, which are expensive and time consuming. Rapid methods capable of simultaneous monitoring of multiple components can save time and money. Fourier transform Infrared (FT-IR) spectroscopy, which monitors the light absorbing properties of chemical compounds, can be used as a rapid, inexpensive, and sensitive method to analyze cheese quality. Unlike many chromatographic techniques, FT-IR spectroscopy provides unique overall chemical fingerprints of cheese samples that can be analyzed through multivariate statistical techniques to rapidly determine cheese composition and flavor quality. Hence the objective of this research was to develop a rapid method based on FT-IR spectroscopy to monitor composition and flavor quality of Cheddar cheese during ripening. Twelve different Cheddar cheese samples ripened for a period of 73 days were provided by a commercial cheese manufacturer, along with their final moisture, pH, salt, fat content and flavor quality data. Samples were collected on days 7, 15, 30, 45 and 73 during ripening and analyzed for organic acid content using chromatographic techniques (reference method). For FT-IR analysis the samples were treated using organic solvents and the extracts were dried on zinc selenide crystal and scanned in the spectrometer (4000-700 wavenumbers). Infrared profiles (spectra) of the samples were matched with their composition and quality data to develop multivariate statistical regression and classification models. The infrared spectra of the samples were well defined, highly consistent within each sample and distinct from other samples. The regression models showed excellent fit (r-value>0.95) and could determine moisture, pH, salt, fat, organic acid contents in less than 20 min, which is significantly less than the current methods. Furthermore, cheeses could also be classified based on their flavor quality (sour, whey taint, good cheddar, etc.). The discrimination of the samples was due to organic acids, amino acids and short chain fatty acids (1800 to 900 cm-1), which are known to contribute significantly to cheese flavor. FT-IR spectroscopy based method shows great promise as a rapid, simple and cost-effective analytical and quality control tool for the industry. It will enable monitoring and controlling cheese ripening process to produce cheese of desired quality. For the cheese industry, this can be an extremely valuable tool as it will enable identification of quality defects early in the ripening process. The identification of defects will assist in deciding whether the cheese is marketable prior to incurring storage and interest charges associated with aging as well as deciding the future application of the cheese.A five-year embargo was granted for this item

A matheuristic approach for the Pollution-Routing Problem

This paper deals with the Pollution-Routing Problem (PRP), a Vehicle Routing Problem (VRP) with environmental considerations, recently introduced in the literature by [Bektas and Laporte (2011), Transport. Res. B-Meth. 45 (8), 1232-1250]. The objective is to minimize operational and environmental costs while respecting capacity constraints and service time windows. Costs are based on driver wages and fuel consumption, which depends on many factors, such as travel distance and vehicle load. The vehicle speeds are considered as decision variables. They complement routing decisions, impacting the total cost, the travel time between locations, and thus the set of feasible routes. We propose a method which combines a local search-based metaheuristic with an integer programming approach over a set covering formulation and a recursive speed-optimization algorithm. This hybridization enables to integrate more tightly route and speed decisions. Moreover, two other "green" VRP variants, the Fuel Consumption VRP (FCVRP) and the Energy Minimizing VRP (EMVRP), are addressed. The proposed method compares very favorably with previous algorithms from the literature and many new improved solutions are reported.Comment: Working Paper -- UFPB, 26 page

Assessing the readiness to implement lean in healthcare institutions – a case study

We develop a lean readiness framework and an assessment methodology to quantify the readiness of healthcare institutions for implementing lean. We use stakeholder theory and work with a lean implementation team responsible for process improvement in a healthcare group to develop the framework. The framework uses fuzzy based input derived from the stakeholders of the healthcare institution to generate an overall ranking through ideal solution technique. The assessment method derives input from the readiness scores shared by various stakeholders. The ranking suggests future improvement areas to prepare the healthcare institution for a lean implementation project. We provide an alternative perspective of assessing the lean readiness of healthcare institutions before beginning a lean implementation project for both researchers and practitioners. Our research is the first to develop a lean readiness framework for healthcare institutions and demonstrate it using an assessment technique

A heuristic algorithm for a single vehicle static bike sharing rebalancing problem

The static bike rebalancing problem (SBRP) concerns the task of repositioning bikes among stations in self-service bike-sharing systems. This problem can be seen as a variant of the one-commodity pickup and delivery vehicle routing problem, where multiple visits are allowed to be performed at each station, i.e., the demand of a station is allowed to be split. Moreover, a vehicle may temporarily drop its load at a station, leaving it in excess or, alternatively, collect more bikes from a station (even all of them), thus leaving it in default. Both cases require further visits in order to meet the actual demands of such station. This paper deals with a particular case of the SBRP, in which only a single vehicle is available and the objective is to find a least-cost route that meets the demand of all stations and does not violate the minimum (zero) and maximum (vehicle capacity) load limits along the tour. Therefore, the number of bikes to be collected or delivered at each station must be appropriately determined in order to respect such constraints. We propose an iterated local search (ILS) based heuristic to solve the problem. The ILS algorithm was tested on 980 benchmark instances from the literature and the results obtained are competitive when compared to other existing methods. Moreover, our heuristic was capable of finding most of the known optimal solutions and also of improving the results on a number of open instances