Advanced decision-support methods for the design, control, and optimization of perishable products life cycle

In the last decades, global food supply chains had to deal with the increasing awareness of the stakeholders and consumers about safety, quality, and sustainability. In order to address these new challenges for food supply chain systems, an integrated approach to design, control, and optimize product life cycle is required. Therefore, it is essential to introduce new models, methods, and decision-support platforms tailored to perishable products. This thesis aims to provide novel practice-ready decision-support models and methods to optimize the logistics of food items with an integrated and interdisciplinary approach. It proposes a comprehensive review of the main peculiarities of perishable products and the environmental stresses accelerating their quality decay. Then, it focuses on top-down strategies to optimize the supply chain system from the strategical to the operational decision level. Based on the criticality of the environmental conditions, the dissertation evaluates the main long-term logistics investment strategies to preserve products quality. Several models and methods are proposed to optimize the logistics decisions to enhance the sustainability of the supply chain system while guaranteeing adequate food preservation. The models and methods proposed in this dissertation promote a climate-driven approach integrating climate conditions and their consequences on the quality decay of products in innovative models supporting the logistics decisions. Given the uncertain nature of the environmental stresses affecting the product life cycle, an original stochastic model and solving method are proposed to support practitioners in controlling and optimizing the supply chain systems when facing uncertain scenarios. The application of the proposed decision-support methods to real case studies proved their effectiveness in increasing the sustainability of the perishable product life cycle. The dissertation also presents an industry application of a global food supply chain system, further demonstrating how the proposed models and tools can be integrated to provide significant savings and sustainability improvements.

Control of Aedes vectors of dengue in three provinces of Vietnam by use of Mesocyclops (Copepoda) and community-based methods validated by entomologic, clinical, and serological surveillance

We describe remarkable success in controlling dengue vectors, Aedes aegypti (L.) and Aedes albopictus (Skuse), in 6 communes with 11,675 households and 49,647 people in the northern provinces of Haiphong, Hung Yen, and Nam Dinh in Vietnam. The communes were selected for high-frequency use of large outdoor concrete tanks and wells. These were found to be the source of 49.6-98.4% of Ae. aegypti larvae, which were amenable to treatment with local Mesocyclops, mainly M. woutersi Van der Velde, M. aspericornis (Daday) and M. thermocyclopoides Harada. Knowledge, attitude, and practice surveys were performed to determine whether the communities viewed dengue and dengue hemorrhagic fever as a serious health threat; to determine their knowledge of the etiology, attitudes, and practices regarding control methods including Mesocyclops; and to determine their receptivity to various information methods. On the basis of the knowledge, attitude, and practice data, the community-based dengue control program comprised a system of local leaders, health volunteer teachers, and schoolchildren, supported by health professionals. Recycling of discards for economic gain was enhanced, where appropriate, and this, plus 37 clean-up campaigns, removed small containers unsuitable for Mesocyclops treatment. A previously successful eradication at Phan Boi village (Hung Yen province) was extended to 7 other villages forming Di Su commune (1,750 households) in the current study. Complete control was also achieved in Nghia Hiep (Hung Yen province) and in Xuan Phong (Nam Dinh province); control efficacy was greater than or equal to 99.7% in the other 3 communes (Lac Vien in Haiphong, Nghia Dong, and Xuan Kien in Nam Dinh). Although tanks and wells were the key container types of Ae. aegypti productivity, discarded materials were the source of 51% of the standing crop of Ae. albopictus. Aedes albopictus larvae were eliminated from the 3 Nam Dinh communes, and 86-98% control was achieved in the other 3 communes. Variable dengue attack rates made the clinical and serological comparison of control and untreated communes problematic, but these data indicate that clinical surveillance by itself is inadequate to monitor dengue transmission.

Comparison between deterministic and meta-heuristic methods applied to ancillary services dispatch

This paper proposes two meta-heuristics (Genetic Algorithm and Evolutionary Particle Swarm Optimization) for solving a 15 bid-based case of Ancillary Services Dispatch in an Electricity Market. A Linear Programming approach is also included for comparison purposes. A test case based on the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is used to demonstrate that the use of meta-heuristics is suitable for solving this kind of optimization problem. Faster execution times and lower computational resources requirements are the most relevant advantages of the used meta-heuristics when compared with the Linear Programming approach.

Derivative-free optimization and filter methods to solve nonlinear constrained problems

In real optimization problems, usually the analytical expression of the objective function is not known, nor its derivatives, or they are complex. In these cases it becomes essential to use optimization methods where the calculation of the derivatives, or the verification of their existence, is not necessary: the Direct Search Methods or Derivative-free Methods are one solution. When the problem has constraints, penalty functions are often used. Unfortunately the choice of the penalty parameters is, frequently, very difficult, because most strategies for choosing it are heuristics strategies. As an alternative to penalty function appeared the filter methods. A filter algorithm introduces a function that aggregates the constrained violations and constructs a biobjective problem. In this problem the step is accepted if it either reduces the objective function or the constrained violation. This implies that the filter methods are less parameter dependent than a penalty function. In this work, we present a new direct search method, based on simplex methods, for general constrained optimization that combines the features of the simplex method and filter methods. This method does not compute or approximate any derivatives, penalty constants or Lagrange multipliers. The basic idea of simplex filter algorithm is to construct an initial simplex and use the simplex to drive the search. We illustrate the behavior of our algorithm through some examples. The proposed methods were implemented in Java.

Quality assurance and quality control in mammography: a review of available guidance worldwide

Objectives - Review available guidance for quality assurance (QA) in mammography and discuss its contribution to harmonise practices worldwide. Methods - Literature search was performed on different sources to identify guidance documents for QA in mammography available worldwide in international bodies, healthcare providers, professional/scientific associations. The guidance documents identified were reviewed and a selection was compared for type of guidance (clinical/technical), technology and proposed QA methodologies focusing on dose and image quality (IQ) performance assessment. Results - Fourteen protocols (targeted at conventional and digital mammography) were reviewed. All included recommendations for testing acquisition, processing and display systems associated with mammographic equipment. All guidance reviewed highlighted the importance of dose assessment and testing the Automatic Exposure Control (AEC) system. Recommended tests for assessment of IQ showed variations in the proposed methodologies. Recommended testing focused on assessment of low-contrast detection, spatial resolution and noise. QC of image display is recommended following the American Association of Physicists in Medicine guidelines. Conclusions - The existing QA guidance for mammography is derived from key documents (American College of Radiology and European Union guidelines) and proposes similar tests despite the variations in detail and methodologies. Studies reported on QA data should provide detail on experimental technique to allow robust data comparison. Countries aiming to implement a mammography/QA program may select/prioritise the tests depending on available technology and resources.

Classification of some penalty methods

Optimization problems arise in science, engineering, economy, etc. and we need to find the best solutions for each reality. The methods used to solve these problems depend on several factors, including the amount and type of accessible information, the available algorithms for solving them, and, obviously, the intrinsic characteristics of the problem. There are many kinds of optimization problems and, consequently, many kinds of methods to solve them. When the involved functions are nonlinear and their derivatives are not known or are very difficult to calculate, these methods are more rare. These kinds of functions are frequently called black box functions. To solve such problems without constraints (unconstrained optimization), we can use direct search methods. These methods do not require any derivatives or approximations of them. But when the problem has constraints (nonlinear programming problems) and, additionally, the constraint functions are black box functions, it is much more difficult to find the most appropriate method. Penalty methods can then be used. They transform the original problem into a sequence of other problems, derived from the initial, all without constraints. Then this sequence of problems (without constraints) can be solved using the methods available for unconstrained optimization. In this chapter, we present a classification of some of the existing penalty methods and describe some of their assumptions and limitations. These methods allow the solving of optimization problems with continuous, discrete, and mixing constraints, without requiring continuity, differentiability, or convexity. Thus, penalty methods can be used as the first step in the resolution of constrained problems, by means of methods that typically are used by unconstrained problems. We also discuss a new class of penalty methods for nonlinear optimization, which adjust the penalty parameter dynamically.

Consistency and Context Management in a Multi Agent Belief Revision Testbed

Multi-agent architectures are well suited for complex inherently distributed problem solving domains. From the many challenging aspects that arise within this framework, a crucial one emerges: how to incorporate dynamic and conflicting agent beliefs? While the belief revision activity in a single agent scenario is concentrated on incorporating new information while preserving consistency, in a multi-agent system it also has to deal with possible conflicts between the agents perspectives. To provide an adequate framework, each agent, built as a combination of an assumption based belief revision system and a cooperation layer, was enriched with additional features: a distributed search control mechanism allowing dynamic context management, and a set of different distributed consistency methodologies. As a result, a Distributed Belief Revision Testbed (DiBeRT) was developed. This paper is a preliminary report presenting some of DiBeRT contributions: a concise representation of external beliefs; a simple and innovative methodology to achieve distributed context management; and a reduced inter-agent data exchange format.

GLODS: Global and Local Optimization using Direct Search

Locating and identifying points as global minimizers is, in general, a hard and time-consuming task. Difficulties increase in the impossibility of using the derivatives of the functions defining the problem. In this work, we propose a new class of methods suited for global derivative-free constrained optimization. Using direct search of directional type, the algorithm alternates between a search step, where potentially good regions are located, and a poll step where the previously located promising regions are explored. This exploitation is made through the launching of several instances of directional direct searches, one in each of the regions of interest. Differently from a simple multistart strategy, direct searches will merge when sufficiently close. The goal is to end with as many direct searches as the number of local minimizers, which would easily allow locating the global extreme value. We describe the algorithmic structure considered, present the corresponding convergence analysis and report numerical results, showing that the proposed method is competitive with currently commonly used global derivative-free optimization solvers.

Adaptive Penalty and Barrier function based on Fuzzy Logic

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.

Epidemiology and control of malaria and other arthropod born diseases

Malaria and other arthropod born diseases remain a serious public health problem affecting the lives and health of certain social groups when the two basic strategies to control fail due to : (1) the lack of effective chemoprophylaxis/chemotherapy or the rapid development of drug resistance of the infectious agents and (2) the ineffectiveness of pesticides or the arthropod vectors develop resistance to them. These situations enhances the need for the design and implementation of other alternatives for sustainable health programmes. The application of the epidemiological methods is essential not only for analyzing the relevant data for the understanding of the biological characteristics of the infectious agents, their reservoirs and vectors and the methods for their control, but also for the assessment of the human behaviour, the environmental, social and economic factors involved in disease transmission and the capacity of the health systems to implement interventions for both changes in human behaviour and environmental management to purpose guaranteed prevention and control of malaria and other arthropod born diseases with efficiency, efficacy and equity. This paper discuss the evolution of the malaria arthropod diseases programmes in the American Region and the perspectives for their integration into health promotion programs and emphasis is made in the need to establish solid basis in the decision-making process for the selection of intervention strategies to remove the risk factors determining the probability to get sick or die from ABDs. The implications of the general planning and the polices to be adopted in an area should be analyzed in the light of programme feasibility at the local level, in the multisectoral context specific social groups and taking in consideration the principles of stratification and equity

Decision Support Methods for Global Optomization

Immobile location-allocation (LA) problems is a type of LA problem that consists in determining the service each facility should offer in order to optimize some criterion (like the global demand), given the positions of the facilities and the customers. Due to the complexity of the problem, i.e. it is a combinatorial problem (where is the number of possible services and the number of facilities) with a non-convex search space with several sub-optimums, traditional methods cannot be applied directly to optimize this problem. Thus we proposed the use of clustering analysis to convert the initial problem into several smaller sub-problems. By this way, we presented and analyzed the suitability of some clustering methods to partition the commented LA problem. Then we explored the use of some metaheuristic techniques such as genetic algorithms, simulated annealing or cuckoo search in order to solve the sub-problems after the clustering analysis

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Optimised search heuristic combining valid inequalities and tabu search

This paper presents an Optimised Search Heuristic that combines a tabu search method with the verification of violated valid inequalities. The solution delivered by the tabu search is partially destroyed by a randomised greedy procedure, and then the valid inequalities are used to guide the reconstruction of a complete solution. An application of the new method to the Job-Shop Scheduling problem is presented.

Optimal information transmission in organizations: Search and congestion

We propose a stylized model of a problem-solving organization whoseinternal communication structure is given by a fixed network. Problemsarrive randomly anywhere in this network and must find their way to theirrespective specialized solvers by relying on local information alone.The organization handles multiple problems simultaneously. For this reason,the process may be subject to congestion. We provide a characterization ofthe threshold of collapse of the network and of the stock of foatingproblems (or average delay) that prevails below that threshold. We buildupon this characterization to address a design problem: the determinationof what kind of network architecture optimizes performance for any givenproblem arrival rate. We conclude that, for low arrival rates, the optimalnetwork is very polarized (i.e. star-like or centralized ), whereas it islargely homogenous (or decentralized ) for high arrival rates. We also showthat, if an auxiliary assumption holds, the transition between these twoopposite structures is sharp and they are the only ones to ever qualify asoptimal.

Tree and Brush Control For County Road Right-of-Way, Ocotober 2002

This manual summarizes the roadside tree and brush control methods used by all of Iowa's 99 counties. It is based on interviews conducted in Spring 2002 with county engineers, roadside managers and others. The target audience of this manual is the novice county engineer or roadside manager. Iowa law is nearly silent on roadside tree and brush control, so individual counties have been left to decide on the level of control they want to achieve and maintain. Different solutions have been developed but the goal of every county remains the same: to provide safe roads for the traveling public. Counties in eastern and southern Iowa appear to face the greatest brush control challenge. Most control efforts can be divided into two categories: mechanical and chemical. Mechanical control includes cutting tools and supporting equipment. A chain saw is the most widely used cutting tool. Tractor mounted boom mowers and brush cutters are used to prune miles of brush but have significant safety and aesthetic limitations and boom mowers are easily broken by inexperienced operators. The advent of tree shears and hydraulic thumbs offer unprecedented versatility. Bulldozers are often considered a method of last resort since they reduce large areas to bare ground. Any chipper that violently grabs brush should not be used. Chemical control is the application of herbicide to different parts of a plant: foliar spray is applied to leaves; basal bark spray is applied to the tree trunk; a cut stump treatment is applied to the cambium ring of a cut surface. There is reluctance by many to apply herbicide into the air due to drift concerns. One-third of Iowa counties do not use foliar spray. By contrast, several accepted control methods are directed toward the ground. Freshly cut stumps should be treated to prevent resprouting. Basal bark spray is highly effective in sensitive areas such as near houses. Interest in chemical control is slowly increasing as herbicides and application methods are refined. Fall burning, a third, distinctly separate technique is underused as a brush control method and can be effective if timed correctly. In all, control methods tend to reflect agricultural patterns in a county. The use of chain saws and foliar sprays tends to increase in counties where row crops predominate, and boom mowing tends to increase in counties where grassland predominates. For counties with light to moderate roadside brush, rotational maintenance is the key to effective control. The most comprehensive approach to control is to implement an integrated roadside vegetation management (IRVM) program. An IRVM program is usually directed by a Roadside Manager whose duties may be shared with another position. Funding for control programs comes from the Rural Services Basic portion of a county's budget. The average annual county brush control budget is about $76,000. That figure is thought not to include shared expenses such as fuel and buildings. Start up costs for an IRVM program are less if an existing control program is converted. In addition, IRVM budgets from three different northeastern Iowa counties are offered for comparison in this manual. The manual also includes a chapter on temporary traffic control in rural work zones, a summary of the Iowa Code as it relates to brush control, and rules on avoiding seasonal disturbance of the endangered Indiana bat. Appendices summarize survey and forest cover data, an equipment inventory, sample forms for record keeping, a sample brush control policy, a few legal opinions, a literature search, and a glossary.