A sustainable supply chain for organic, conventional agro-food products: The role of demand substitution, climate change and public health

Sazvar, Zeinab; Rahmani, Mina; Govindan, Kannan

doi:10.1016/j.jclepro.2018.04.118

Cited by 100 publications

(64 citation statements)

References 96 publications

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“…AUGMECON has been employed in various applications and systems, including supply chain management (Torabi et al 2013;Bootaki et al 2014Bootaki et al , 2016Canales-Bustos et al 2017;Musavi and Bozorgi-Amiri 2017;Ravat et al 2017;Vieira et al 2017;Sazvar et al 2018;Ehrenstein et al 2019;Oiu et al 2019;Shekarjan et al 2019;Xin et al 2019), energy planning (Hombach and Walther 2015;Tartibu et al 2015;Arancibia et al 2016;Mohammadkhani et al 2018;Rabbani et al 2018;Sedighizadeh et al 2018;Razm et al 2019), waste management Mavrotas et al 2015a, b;Inghels et al 2016), portfolio analysis (Xidonas et al 2010(Xidonas et al , 2011 Mavrotas and Florios (2013) further extended this algorithm in AUGMECON 2, by introducing a bypass coefficient as well as a type of lexicographic optimisation to all objective functions, the order of which was insignificant in AUGMECON:…”

Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented ε-constraint algorithm, for solving multiobjective linear programming problems, by drawing from the weaknesses of AUG-MECON 2, one of the most widely used improvements of the ε-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.

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Section: A Brief Overview Of the Augmented ε-Constraint Methodsmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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“…Por otra parte, cuándo se trata de animales o subproductos de origen animal se debe evaluar el bienestar animal como la ubicación de sitios de descanso para animales transportados hacia plantas de beneficio [8]. Adicionalmente, en el estudio de las agro-cadenas debe tenerse en cuenta políticas gubernamentales que promueven la participación de grandes, medianos y pequeños eslabones que la componen [9]. El estudio de las agro-cadenas (cadenas agrícolas) no obedece solamente a disminuir costos o incrementar utilidades, es necesario evaluar la logística necesaria para disminuir desperdicios, impactos ambientales y/o sociales durante el proceso [10], [11], [12] y [13].…”

Section: Introductionunclassified

Modelación matemática en estudio de agro-cadenas: una revisión de literatura

et al. 2020

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El sector agrícola es el eje fundamental que mueve la economía del mundo, permite la generación de productos agrícolas y pecuarios para el abastecimiento de pequeñas y grandes ciudades. En los países subdesarrollados es necesaria la participación de la industria y la academia para el fortalecimiento de los sistemas productivos, esto a partir de la inyección de tecnología, así como la transferencia y apropiación de conocimiento en el sector. Un enfoque usado para el fortalecimiento del sector, es el estudio de las cadenas de suministro agrícolas (agro-cadenas) a partir de la modelación matemática, la cual permite el tratamiento de datos y facilita la toma de decisiones de orden estratégico, táctico y/o operativo. En el presente trabajo se realizó una revisión de literatura sobre la aplicación de la modelación matemática en el estudio de las Agro-cadenas durante los últimos 20 años. Se concluye del estudio que, existe un interés bastante grande por la comunidad académico-científica por fortalecer el sector agrícola en diferentes países como Estados Unidos, Brasil, india y Holanda entre otros. En el 36% de los trabajos consultados se emplean modelos de simulación estocástica, permitiendo abordar problemas complejos que involucran incertidumbre en con comportamiento de los datos. Además, en el 70% de los trabajos consultados, se utilizan modelos heurísticos para resolver problemas de diseño y distribución en agrocadenas, y el 30% restante requiere el uso de meta-heurísticas porque requieren resolver problemas con múltiples respuestas dada la complejidad de los datos. La modelación matemática se ha convertido en una herramienta de gran utilidad para la solución de problemas latentes en la agro-cadenas, facilita el tratamiento de datos y la toma de decisiones complejas, principalmente durante el diseño de cadena, el abastecimiento de producto y control de costos, tiempos de entrega e impactos ambientales, entre otras variables importantes.

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“…Longo et al used life cycle assessment methodology to compare the influence of organic apple supply chain and conventional apple supply chain on energy and environment in the North of Italy [42]. Sazvar et al compared the effects of organic agricultural product supply chain and conventional agricultural product supply chain on the global warming, climate change, and social health by mathematical programming method [43]. None of the above-mentioned literatures have studied the optimal strategy of the OASC in an omni-channel environment.…”

Section: Introductionmentioning

confidence: 99%

Differential Game Analysis of Optimal Strategies and Cooperation in Omni-Channel Organic Agricultural Supply Chain

2019

Sustainability

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This paper aims to investigate the optimal strategies for both centralized and decentralized modes in a two-echelon omni-channel organic agricultural supply chain (OASC) which consists of one farmer cooperative and one online retailer. Furthermore, the contracts of cooperation between the members in OASC are discussed. Based on both theory differential game and bi-level programming, we utilize the Nerlove–Arrow model and Stackelberg model to examine five cases of decision modes for both agents in the OASC. Then, we achieve the optimal strategies where the specified sets of organic growing effort, organic traceability technology effort, propaganda input, and service input can guarantee the maximization of the related profits. As a result, we could obtain the values of the corresponding optimal profits. For the centralized decision mode, the farmer cooperative and the online retailer make decisions with the goal of maximizing the overall profits of the OASC. Meanwhile, for the decentralized decision mode with four different cases, each member will independently make a decision with the goal of maximizing his own profit respectively. In detail, as for the fully decentralized decision mode, no contracts exist in OASC; regarding the decentralized decision mode with an information traceability cost sharing contract, two members pay the information traceability cost together; and for the decentralized decision mode with a revenue sharing contract, two members share the revenue together; as to the decentralized decision mode with a comprehensive contract, there are two cooperative ways that information traceability cost sharing and revenue sharing can be achieved. In addition, we also considered factors such as the consumer preferences of organic products and the cross influence between channels in models. Finally, through sensitivity analysis and comparison of optimal strategies and profits, we found that: (1) high consumer preferences of organic products and high cross influence between channels are profitable; and (2) the choice of contract is influenced by the relative size of the offline marginal income ratio and the online marginal income ratio.

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A sustainable supply chain for organic, conventional agro-food products: The role of demand substitution, climate change and public health

Cited by 100 publications

References 96 publications

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

Modelación matemática en estudio de agro-cadenas: una revisión de literatura

Differential Game Analysis of Optimal Strategies and Cooperation in Omni-Channel Organic Agricultural Supply Chain

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