Min-max regret versus gross margin maximization in arable sector modeling

"A sector model presented in this article, uses about 200 representative French cereal-oriented farms to estimate policy impacts by means of mathematical modeling. Usually, such models suppose that farmers intend to maximize expected gross margin. This rationality hypothesis however seems hardly justifiable, especially these days, when gross margin variability due to European Common Agricultural Policy changes may become significant. Increasing uncertainty introduces bounded rationality to the decision problem so that crop gross margins may be better approximated by interval rather than by expected (precise) values. The initial LP problem is specified as an “Interval Linear Programming (ILP)”. We assume that farmers tend to decide upon their surface allocation prudently in order to get through with minimum loss, which is precisely the rationale underlying the minimization of maximum regret decision criterion. Recent advances in operations research, namely Mausser and Laguna algorithms, are exploited to implement the min-max regret criterion to arable agriculture ILP. The validation against observed crop mix proved that as uncertainty increases about 40% of the farmers adopt the min-max regret decision rule instead of the gross margin maximization."Interval Linear Programming, Min-Max Regret, Common Agricultural Policy, Arable cropping, France

Hybrid linear programming to estimate CAP impacts of flatter rates and environmental top-ups

This paper examines evolutions of the Common Agricultural Policy (CAP) decoupling regime and their impacts on Greek arable agriculture. Policy analysis is performed by using mathematical programming tools. Taking into account increasing uncertainty, we assume that farmers perceive gross margin in intervals rather than as expected crisp values. A bottom-up hybrid model accommodates both profit maximizing and risk prudent attitudes in order to accurately assess farmers’ response. Marginal changes to crop plans are expected so that flatter single payment rates cause significant changes in incomes and subsidies. Nitrogen reduction incentives result in moderate changes putting their effectiveness in question.Interval Linear Programming, Min-Max Regret, Common Agricultural Policy, Arable cropping, Greece

Tasks complexity and decision making under uncertainty

Ova studija imala je za cilj da proveri na koji način kompleksnost zadatka, u sadejstvu s drugim potencijalnim faktorima, utiče na odlučivanje u uslovima neizvesnosti. Kompleksnost zadatka je objektivno definisana preko dve varijable: broja potencijalnih događaja u budućnosti (dva, tri i pet događaja) i razlika u neto vrednostima ishoda (male i velike razlike). U oba slučaja, kompleksnost je zasnovana na neizvesnosti. Sprovedena su četiri eksperimenta: u dva eksperimenta zadaci su bili smešteni u ekonomski, a u dva u medicinski domen. Pored dve varijable kojima je operacionalizovana kompleksnost, u svakom od eksperimenata variran je po još jedan faktor, koji se odnosi na karakteristike zadatka: referentna tačka (zona gubitka i zona dobitka), stepen uključenosti u odlučivanje (donošenje odluke za sebe i donošenje odluke za drugog) i okvir (pozitivan i negativan). Zadaci su osmišljeni tako da se na osnovu donesene odluke može jednoznačno odrediti koji od tri modela maximax, maximin, minimax se nalaze u osnovi izbora. Ujedno, ovakva procedura je omogućila objektivno praćenje promene strategije odlučivanja u zavisnosti od eksperimentalnih uslova. Nalazi ukazuju da se prilikom odlučivanja ispitanici oslanjaju na sva tri modela odlučivanja, s tim da je većina odluka donesena tako da smanji rizik od gubitka, tj. na osnovu maximin i minimax modela. Utvrđene su interakcije kompleksnosti zadataka, s jedne strane, i domena, stepena uključenosti i referentne tačke, s druge strane. Kada se radi o kompleksnijim zadacima, tj. u slučaju većeg kognitivnog opterećenja, ispitanici pokušavaju da olakšaju proces odlučivanja, zbog čega dolazi do većeg ispoljavanja kognitivnih pristrasnosti, kao što su uticaj okvira i averzija prema gubitkuThe aim of this study is to explore how task complexity and other potential factors impact decision making under uncertainty. Task complexity is objectively defined through two variables: the number of potential events in the future (two, three and five) and the difference in the net value of the outcomes (small and big differences). In both cases the complexity is based on uncertainty. Four experiments were carried out. In two experiments the tasks were placed in an economic domain, whereas the other two tasks were placed in a medical domain. Beside the two variables through which complexity was conducted, in each of the experiment one more factor referring to task characteristics was varied, i.e. the reference point (domain of loss and domain of gain), the level of decision making involvement (either for yourself or for somebody else) and the frame (as either positive or negative). The tasks were designed in a way that, based on the decision made, one can unambiguously define which of the three models – maximax, maximin or minimax – are at the base of the choice. In addition, this procedure enabled objective tracking of the change strategy of uncertain decision making depending on the experimental conditions. The findings show that in the course of decision making process, the participants rely on all three models of decision making, provided that most decisions are made in order to reduce the risk of loss, i.e. based on maximin and minimax model. Interactions of the task complexity, on one hand, and the domain, the level of involvement and the reference point, on the other hand, were determined. In the case of more complex tasks, i.e. in the case of greater cognitive load, the participants try to make decision making easier, which leads to more cognitive biases, such as frame impact and loss aversio

Systems Analysis For Urban Water Infrastructure Expansion With Global Change Impact Under Uncertainties

Over the past decades, cost-effectiveness principle or cost-benefit analysis has been employed oftentimes as a typical assessment tool for the expansion of drinking water utility. With changing public awareness of the inherent linkages between climate change, population growth and economic development, the addition of global change impact in the assessment regime has altered the landscape of traditional evaluation matrix. Nowadays, urban drinking water infrastructure requires careful long-term expansion planning to reduce the risk from global change impact with respect to greenhouse gas (GHG) emissions, economic boom and recession, as well as water demand variation associated with population growth and migration. Meanwhile, accurate prediction of municipal water demand is critically important to water utility in a fast growing urban region for the purpose of drinking water system planning, design and water utility asset management. A system analysis under global change impact due to the population dynamics, water resources conservation, and environmental management policies should be carried out to search for sustainable solutions temporally and spatially with different scales under uncertainties. This study is aimed to develop an innovative, interdisciplinary, and insightful modeling framework to deal with global change issues as a whole based on a real-world drinking water infrastructure system expansion program in Manatee County, Florida. Four intertwined components within the drinking water infrastructure system planning were investigated and integrated, which consists of water demand analysis, GHG emission potential, system optimization for infrastructure expansion, and nested minimax-regret (NMMR) decision analysis under uncertainties. In the water demand analysis, a new system dynamics model was developed to reflect the intrinsic relationship between water demand and changing socioeconomic iv environment. This system dynamics model is based on a coupled modeling structure that takes the interactions among economic and social dimensions into account offering a satisfactory platform. In the evaluation of GHG emission potential, a life cycle assessment (LCA) is conducted to estimate the carbon footprint for all expansion alternatives for water supply. The result of this LCA study provides an extra dimension for decision makers to extract more effective adaptation strategies. Both water demand forecasting and GHG emission potential were deemed as the input information for system optimization when all alternatives are taken into account simultaneously. In the system optimization for infrastructure expansion, a multiobjective optimization model was formulated for providing the multitemporal optimal facility expansion strategies. With the aid of a multi-stage planning methodology over the partitioned time horizon, such a systems analysis has resulted in a full-scale screening and sequencing with respect to multiple competing objectives across a suite of management strategies. In the decision analysis under uncertainty, such a system optimization model was further developed as a unique NMMR programming model due to the uncertainties imposed by the real-world problem. The proposed NMMR algorithm was successfully applied for solving the real-world problem with a limited scale for the purpose of demonstration

Supply chain design and distribution planning under supply uncertainty (Application to bulk liquid gas distribution)

La distribution de liquide cryogénique en vrac , ou par camions citernes, est un cas particulier des problèmes d optimisation logistique. Ces problèmes d optimisation de chaines logistiques et/ou de transport sont habituellement traités sous l hypothèse que les données sont connues à l avance et certaines. Or, la majorité des problèmes d optimisation industriels se placent dans un contexte incertain. Mes travaux de recherche s intéressent aussi bien aux méthodes d optimisation robuste que stochastiques.Mes travaux portent sur deux problèmes distincts. Le premier est un problème de tournées de véhicules avec gestion des stocks. Je propose une méthodologie basée sur les méthodes d optimisation robuste, représentant les pannes par des scénarios. Je montre qu il est possible de trouver des solutions qui réduisent de manière significative l impact des pannes d usine sur la distribution. Je montre aussi comment la méthode proposée peut aussi être appliquée à la version déterministe du problème en utilisant la méthode GRASP, et ainsi améliorer significativement les résultats obtenu par l algorithme en place. Le deuxième problème étudié concerne la planification de la production et d affectation les clients. Je modélise ce problème à l aide de la technique d optimisation stochastique avec recours. Le problème maître prend les décisions avant qu une panne ce produise, tandis que les problèmes esclaves optimisent le retour à la normale après la panne. Le but est de minimiser le coût de la chaîne logistique. Les résultats présentés contiennent non seulement la solution optimale au problème stochastique, mais aussi des indicateurs clés de performance. Je montre qu il est possible de trouver des solutions ou les pannes n ont qu un impact mineur.The distribution of liquid gazes (or cryogenic liquids) using bulks and tractors is a particular aspect of a fret distribution supply chain. Traditionally, these optimisation problems are treated under certainty assumptions. However, a large part of real world optimisation problems are subject to significant uncertainties due to noisy, approximated or unknown objective functions, data and/or environment parameters. In this research we investigate both robust and stochastic solutions. We study both an inventory routing problem (IRP) and a production planning and customer allocation problem. Thus, we present a robust methodology with an advanced scenario generation methodology. We show that with minimal cost increase, we can significantly reduce the impact of the outage on the supply chain. We also show how the solution generation used in this method can also be applied to the deterministic version of the problem to create an efficient GRASP and significantly improve the results of the existing algorithm. The production planning and customer allocation problem aims at making tactical decisions over a longer time horizon. We propose a single-period, two-stage stochastic model, where the first stage decisions represent the initial decisions taken for the entire period, and the second stage representing the recovery decision taken after an outage. We aim at making a tool that can be used both for decision making and supply chain analysis. Therefore, we not only present the optimized solution, but also key performance indicators. We show on multiple real-life test cases that it isoften possible to find solutions where a plant outage has only a minimal impact.NANTES-ENS Mines (441092314) / SudocSudocFranceF

Planeamento de equipamentos educativos em Cabo Verde

Esta tese visa contribuir para o desenvolvimento de modelos de planeamento e gestão de redes de equipamentos educativos mais realistas e operacionais. Entretanto, apresenta modelos de optimização para o problema de localização de equipamentos educativos em Cabo Verde, tendo em consideração que estes modelos quando aplicados aos serviços públicos são importantes, pois racionalizam os recursos disponíveis, beneficiando directamente a população. Geralmente, a localização adequada de equipamentos educativos e a atribuição dos utentes, a este serviço, têm constituído um dos grandes problemas enfrentados pelo sector da educação. Contudo, o desenvolvimento de uma proposta, que maximize a acessibilidade, traduzida na minimização da distância média, que estes utentes percorrem para utilizar esse serviço, é de fundamental importância, pois são serviços de necessidade básica e os seus recursos são normalmente muito escassos. Cabo Verde é um pequeno país africano, considerado emergente, que acaba de ser incluído no grupo de Países do Rendimento Médio. A sua economia está orientada para os serviços e depende quase totalmente do exterior. Tem sido feita uma grande aposta no seu capital humano, investimento na educação como a chave para seu sucesso. Consequentemente, a optimização dos seus recursos educativos é actualmente de grande interesse. Os objectivos dos modelos são a maximização da acessibilidade e a minimização do investimento (garantindo uma boa cobertura). Os modelos do tipo p-mediana foram utilizados e implementados, através dos métodos exactos e do algoritmo de entropia cruzada. Os modelos foram aplicados em dois estudos de caso: para as escolas básicas do município de Santa Cruz e para as escolas secundárias da ilha de Santiago. Ainda, foi concebido um Sistema de Informação para o Planeamento da Educação com o objectivo de integrar num único sistema os recursos necessários à tomada de decisão sobre o planeamento da educação. Os modelos apresentaram soluções óptimas ou muito boas. Em qualquer dos casos, as soluções parecem realistas e podem ser aplicadas à realidade cabo-verdiana. A resolução dos modelos não suscitou problemas em termos de processamento e o tempo computacional foi bastante reduzido