Eco-efficient supply chain networks: Development of a design framework and application to a real case study

© 2015 Taylor & Francis. This paper presents a supply chain network design framework that is based on multi-objective mathematical programming and that can identify 'eco-efficient' configuration alternatives that are both efficient and ecologically sound. This work is original in that it encompasses the environmental impact of both transportation and warehousing activities. We apply the proposed framework to a real-life case study (i.e. Lindt & Sprüngli) for the distribution of chocolate products. The results show that cost-driven network optimisation may lead to beneficial effects for the environment and that a minor increase in distribution costs can be offset by a major improvement in environmental performance. This paper contributes to the body of knowledge on eco-efficient supply chain design and closes the missing link between model-based methods and empirical applied research. It also generates insights into the growing debate on the trade-off between the economic and environmental performance of supply chains, supporting organisations in the eco-efficient configuration of their supply chains

Multi-objective optimization of environmentally conscious chemical supply chains under demand uncertainty

In this work, we analyze the effect of demand uncertainty on the multi-objective optimization of chemical supply chains (SC) considering simultaneously their economic and environmental performance. To this end, we present a stochastic multi-scenario mixed-integer linear program (MILP) with the unique feature of incorporating explicitly the demand uncertainty using scenarios with given probability of occurrence. The environmental performance is quantified following life cycle assessment (LCA) principles, which are represented in the model formulation through standard algebraic equations. The capabilities of our approach are illustrated through a case study. We show that the stochastic solution improves the economic performance of the SC in comparison with the deterministic one at any level of the environmental impact.The authors wish to acknowledge support from the Spanish Ministry of Education and Science (ENE2011-28269-C03-03, ENE2011-22722, DPI2012-37154-C02-02, CTQ2009-14420-C02, CTQ2012-37039-C02) and Programa DRAC de la Xarxa Vives d’Universitats

Uso combinado de métodos de reducción de objetivos y “modelos sustitutos” para acelerar la optimización en el diseño de edificios

El sector de los edificios es responsable de aproximadamente el 40% del consumo total de energía a nivel mundial. Para hacer frente a este problema muchos países de la OECD Europa han dictado medidas destinadas a reducir el consumo de energía en el sector de la edificación. El aislamiento térmico de edificios se presenta como una estrategia prometedora, ya que disminuye la demanda de aire acondicionado y calefacción sin comprometer el confort. El presente estudio se basa en un modelo de optimización multi-objetivo para minimizar al mismo tiempo los costes y el impacto ambiental de un edificio. Sin pérdida de generalidad, en el presente estudio, los espesores de aislamiento de las superficies exteriores del edificio se toman como las variables a evaluar. El impacto ambiental se cuantificó siguiendo la metodología de análisis del ciclo de vida (ACV), teniendo en cuenta las 10 categorías de puntos intermedios del Eco-Indicador 99 (EI99). La ventaja de trabajar con indicadores intermedios es que cargan con menos incertidumbre que los indicadores agregados finales, sin embargo, los resultados son más complejos de analizar. Para superar el problema de trabajar con muchos objetivos se recurrió a un método riguroso de reducción de objetivos, simplificando el análisis sin cambiar la estructura original del problema. Los métodos como la optimización o el análisis de sensibilidad pueden resultar poco eficientes para la resolución de problemas que requieren miles o incluso millones de evaluaciones mediante simulación directa. La estrategia usada en el presente estudio para superar esta limitación y acelerar el proceso de optimización es la construcción de modelos de aproximación, conocidos como modelos sustitutos, que imitan el comportamiento del modelo de simulación tan estrechamente como sea posible mientras que los requerimientos computacionales para la optimización pasan a ser mucho menos costosos. Los resultados muestran que el número de objetivos ambientales se pueden reducir significativamente manteniendo intacta la estructura del problema. Además, se demuestra que la solución del problema, considerando el coste económico y el ampliamente utilizado Eco-Indicador 99 (para la evaluación de impacto ambiental) podría cambiar la estructura original del problema, con la consecuente potencial pérdida de soluciones de Pareto. Por otro lado, mediante los modelos sustitutos el tiempo empleado para el proceso de optimización ha sido drásticamente reducido.Los autores quieren agradecer el soporte económico del Ministerio de Economía y Competitividad del gobierno español (DPI2012-37154-C02-02, CTQ2012-37039-C02

Optimization models for biorefinery supply chain network design under uncertainty

Biofuel industry has attracted much attention due to its potential to reduce dependency on fossil fuels and contribute to the renewable energy. The high levels of uncertainty in feedstock yield, market prices, production costs, and many other parameters are among the major challenges in this industry. This challenge has created an ongoing interest on studies considering different aspects of uncertainty in investment decisions of the biofuel industry. This study aims to determine the optimal design of supply chain for biofuel refineries in order to maximize annual profit considering uncertainties in fuel market price, feedstock yield, and logistic costs. In order to deal with the stochastic nature of parameters in the biofuel supply chain, we develop two-stage stochastic programming models in which Conditional Value at Risk (CVaR) is utilized as a risk measure to control the amount of shortage in demand zones. Two different approaches including the expected value and CVaR of the profit are considered as the objective function. We apply these models and compare the results for a case study of the biomass supply chain network in the state of Iowa to demonstrate the applicability and efficiency of the presented models

Systematic Tools for the Conceptual Design of Inherently Safer Chemical Processes

Society is continuously facing challenges for safer chemical plants design, which is usually driven by economic criteria during the early steps of the design process, relegating safety concerns to the latest stages. This paper highlights the synergy of merging Process System Engineering tools with inherent safety principles. First, we design a superstructure that comprises several alternatives for streams, equipment, and process conditions, which exhibit different performance of economic and inherently safer indicators, the total annualized cost, and the Dow’s Fire and Explosion Index, respectively. The solution to this multiobjective problem is given by a Pareto set of solutions that indicates the existing trade-off between both objectives. The capabilities of the proposed framework are illustrated through two case studies, which solutions provide valuable insights into the design problem and are intended to guide decision-makers toward the adoption of inherently safer process alternatives.The authors acknowledge financial support from “Proyectos de l+D para grupos de investigación emergentes GV/2016/005” (Conselleria d’Educació, Investigació, Cultura i Esport, GENERALITAT VALENCIANA) and from the Spanish “Ministerio de Economía, Industria y Competitividad” (CTQ2016-77968-C3-02-P, AEI/FEDER, UE)

Un modelo MILP multiperíodo para el diseño de una cadena de suministro de bioetanol considerando sustentabilidad

En este trabajo se propone un modelo matemático mixto entero lineal para el diseño óptimo de una cadena de suministro para producir azúcar y etanol. La formulación considera varios períodos de producción-inventario-distribución y restricciones ambientales. El impacto ambiental es incorporado en este modelo mediante los principios del Análisis de Ciclo de Vida, derivando en un problema de programación matemática multi-objetivo. Mediante la resolución sucesiva de modelos, donde se optimiza el objetivo económico y se manejan las restricciones ambientales mediante el método “ε-constraint”, se obtienen diferentes soluciones del tipo Pareto. Este enfoque permite evaluar diferentes escenarios de integración y sirve como guía en la toma de decisiones al momento de diseñar una cadena de suministro sustentable.Sociedad Argentina de Informática e Investigación Operativ

Nonlinear Model-Based Process Operation under Uncertainty Using Exact Parametric Programming

In the present work, two new, (multi-)parametric programming (mp-P)-inspired algorithms for the solution of mixed-integer nonlinear programming (MINLP) problems are developed, with their main focus being on process synthesis problems. The algorithms are developed for the special case in which the nonlinearities arise because of logarithmic terms, with the first one being developed for the deterministic case, and the second for the parametric case (p-MINLP). The key idea is to formulate and solve the square system of the first-order Karush-Kuhn-Tucker (KKT) conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution techniques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, two process synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicit function of the uncertain parameters