Coordination of perishable crop production using auction mechanisms

Mason, A. Nicholas; Villalobos, J. René

doi:10.1016/j.agsy.2015.04.008

Cited by 25 publications

(12 citation statements)

References 43 publications

(48 reference statements)

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“…82 Recently, new approaches to production control have been proposed, such as, for example, several studies that have optimized the harvest quantities and yields using scheduling operations 83,84 and increasing production volumes through cooperative work in the case of vegetables. 21 Papers on the agricultural planning, sowing, cultivation and harvesting of agricultural produce lack integration of the environmental and climatic factors, product quality, minimization of wastes and operational efficiency of agri-chains. 85 The topics reviewed exhibit a wide variety, and there are many differing opinions among researchers who are pursuing solutions in agriculture.…”

Section: Problems Addressed In Agriculture Planning Based In Operatiomentioning

confidence: 99%

“…All production systems, including that of food production, can be described as sets of inputs and relationships. Indeed, programming production in the context of certain financial risks or setting production volumes to compensate for with market behavior requires the use of decision‐making models intended for agricultural chains . In general, agricultural chains require structured analysis for the development of optimization at all stages.…”

Section: Introductionmentioning

confidence: 99%

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Towards the integration of lean principles and optimization for agricultural production systems: a conceptual review proposition

2019

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Operative planning in agricultural production has historically had the objective of improving yields and quality. Sowing, cropping, and harvesting are usually treated independently, and waste and the sustainability of operations are generally not integrated into operational planning methodologies for agricultural production. This study shows the need to have a clear and precise methodology to minimize waste in agricultural production systems to ensure sustainability. This need is addressed with a novel methodological guide to minimizing waste in agricultural operations, crop maintenance, and harvesting. The proposed methodology is founded on the use of lean manufacturing as a waste‐management tool. Lean manufacturing principles allow agricultural operations and the variables that represent wastes to be identified, mathematical models to be built, constraints to be defined, and the cost of waste to be illustrated, as well as its minimization through an objective function. To guide implementation, we propose a conceptual model to explain the construction of a mathematical model that represents the development of decision variables on agricultural operations with the elements to consider and the constraints and theoretical proposal of the necessary objective function. The proposed conceptual model and the constructed methodology constitute a novel development within agricultural production systems that could be used by decision makers and farmers. © 2019 Society of Chemical Industry

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Section: Problems Addressed In Agriculture Planning Based In Operatiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards the integration of lean principles and optimization for agricultural production systems: a conceptual review proposition

2019

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“…In their review, [14] also identify mathematical modelling as one methodology used in agri-food supply chain coordination. One application can be found in the work of [15] who propose a distributed mathematical model for the coordination of perishable crop production among small farmers and a consolidation facility using auction mechanisms. Another example is the research of [9] where a collaborative mathematical model is proposed to improve farmers' skill level by investments in an uncertain context.…”

Section: Collaborative Planning For Agriculture or Horticulturementioning

confidence: 99%

How to Support Group Decision Making in Horticulture: An Approach Based on the Combination of a Centralized Mathematical Model and a Group Decision Support System

Zaraté

Alemany

Pino

et al. 2019

Decision Support Systems IX: Main Developments and Future Trends

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: ZaratéAbstract. Decision making for farms is a complex task. Farmers have to fix the price of their production but several parameters have to be taken into account: harvesting, seeds, ground, season etc… This task is even more difficult when a group of farmers must make the decision. Generally, optimization models support the farmers to find no dominated solutions, but the problem remains difficult if they have to agree on one solution. In order to support the farmers for this complex decision we combine two approaches. We firstly generate a set of no dominated solutions thanks to a centralized optimization model. Based on this set of solution we then used a Group Decision Support System called GRUS for choosing the best solution for the group of farmers. The combined approach allows us to determine the best solution for the group in a consensual way. This combination of approaches is very innovative for the Agriculture domain.

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“…Planning of production associated with farmers is influenced by external factors related to demand forecasting as well as by internal factors that pertain to the distribution of demand among associated members. The cooperative structure is not effective because of asymmetric information and decentralized control, which are primarily attributed to farmers' reluctance to share cost structures and expected outputs (Mason and Villalobos, 2015).…”

Section: Cold Chain Storage Requirement and Coordination Concernsmentioning

confidence: 99%

“…An analysis of the literature provided the following insights into the unexplored area of the location-allocation problem in cold chain configuration, which requires development to preserve value by ensuring acceptable shelf life quality (Wang and Shih, 2016) and emphasizing specific storage requirements (Saif et al, 2016;Zanoni and Zavanella, 2012): First, the necessity to characterize cold chain service requirement on the basis of the product level (Fredriksson and Liljestrand, 2015) with emphasis on addition of touch points to avoid wastage and uninterrupted supply; second, the misinterpretation of information, because of different temperature requirements at various stages (Arduino et al, 2015), and asymmetric information and decentralized control (Mason and Villalobos, 2015) require coordination with an initiative originating from mature downstream members; finally, the necessity to monitor the temperaturetime relationship requires the sophisticated use of technology to ensure safety and quality of products. However, most studies (Wang et al, 2015;Thakur and Forås, 2015;Gogou et al, 2015;Grunow and Piramuthu, 2013;Derens-Bertheau et al, 2015) have been conducted at an operational level and not at a planning level.…”

Section: Summary Of Research Gapsmentioning

confidence: 99%

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

et al. 2016

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The study proposes a cold chain location-allocation configuration decision model for shippers and customers by considering value deterioration and coordination by using big data approximation.Value deterioration is assessed in terms of limited shelf life, opportunity cost, and units of product transportation. In this study, a customer can be defined as a member of any cold chain, such as cold warehouse stores, retailers, and last mile service providers. Each customer only manages products that are in a certain stage of the product life cycle, which is referred to as the expected shelf life. Because of the geographical dispersion of customers and their unpredictable demands as well as the varying shelf life of products, complexity is another challenge in a cold chain.Improved coordination between shippers and customers is expected to reduce this complexity, and this is introduced in the model as a longitudinal factor for service distance requirement. We use big data information that reflects geospatial attributes of location to derive the real feasible distance between shippers and customers. We formulate the cold chain location-allocation decision problem as a mixed integer linear programming problem, which is solved using the CPLEX solver.The proposed decision model increases efficiency, adequately equates supply and demand, and reduces wastage. Our study encourages managers to ship full truck load consignments, to be aware of uneven allocation based on proximity, and to supervise heterogeneous product allocation according to storage requirements.

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Coordination of perishable crop production using auction mechanisms

Cited by 25 publications

References 43 publications

Towards the integration of lean principles and optimization for agricultural production systems: a conceptual review proposition

Towards the integration of lean principles and optimization for agricultural production systems: a conceptual review proposition

How to Support Group Decision Making in Horticulture: An Approach Based on the Combination of a Centralized Mathematical Model and a Group Decision Support System

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

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