Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model

Silva, Aneirson Francisco da; Marins, Fernando Augusto Silva; Dias, Erica Ximenes

doi:10.1016/j.apm.2015.01.007

Cited by 27 publications

(13 citation statements)

References 32 publications

(77 reference statements)

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“…Delgado et al (1989) presented a general model for solving fuzzy linear programming problems in which constraints are involved with fuzzy inequality and the parameters of the constraints are fuzzy numbers. Recent applications of fuzzy goal programming include, but are not limited to, supply chain network design (Selim & Ozkarahan, 2006), job shop problems (González‐Rodríguez et al, 2008), material requirements planning (Díaz‐Madroñero et al, 2014), aggregate production planning (Silva et al, 2015), solid waste management (Biswas & De, 2016), and optimizing renewable energy portfolios (Hocine et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic preemptive goal programming to balance goal achievements under uncertainty

Ledwith

Hufstetler

Gallagher

2021

Multi Criteria Decision Anal

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Structured decision‐making in the presence of conflicting goals is difficult, and even more so when accounting for uncertainties in the goals or constraints. In this article, we propose a new approach to multi‐criteria decision‐making that extends the deterministic preemptive goal programming approach to account for such uncertainties. The uncertainties may be characterized in various ways including a Bayesian network or extensive Monte Carlo multi‐variate output. We contend the proposed stochastic preemptive goal programming approach is particularly applicable when better goal achievement increases uncertainty or induces risk to other goals. Resulting solutions tend to be a balance of the goals' achievements and robust to minor changes to the goals or constraints. We demonstrate the approach using three illustrative examples: a univariate example, a bivariate example, and a stock portfolio optimization example along with an application to determine military requests for absence. Our preliminary results suggest that a stochastic preemptive goal programming approach represents an effective means of analysing multi‐criteria decision‐making problems under uncertainty.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic preemptive goal programming to balance goal achievements under uncertainty

Ledwith

Hufstetler

Gallagher

2021

Multi Criteria Decision Anal

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show abstract

“…The remaining amount (3.4 million metric tons) was lost at distribution, retail, and consumption. 12 This waste is generated in agri-chains, which encounter particular challenges, such as perishing risk, freshness requirements, physical and aesthetic attributes, 13,14 optimal delivery times, external uncertainties (i.e., climatic factors and market conditions), [15][16][17] and crop characteristics, 18 all of which increase the complexity of managing these chains. Decision making in this uncertainty is a central issue to be resolved.…”

Section: Background and Positioning Of This Paper Key Aspects Of Agrimentioning

confidence: 99%

“…87 Cox and Chicksand found that the use of lean thinking uncovered opportunities for cost reduction in livestock and red meat supply chains. 87 17 2015 X X Y/N P X X N Mul Optimization model for uncertainty in sugarcane harvest planning González et al 77 2015 X X Y/Y P X X Y MIP Edwards et al 60 2015…”

Section: Issues Addressed Using Lean Manufacturing In Agricultural Prmentioning

confidence: 99%

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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“…In da Silva et al (2015), a goal programming model is proposed for sugarcane harvest planning which aims to simulate several scenarios that involve uncertain parameters and hence minimize agro-industrial costs. The authors in Paiva and Morabito (2008) present an optimization model to support decision making in the aggregated production planning of sugar and ethanol companies based on industrial process selection and production lot-sizing models.…”

Section: Introductionmentioning

confidence: 99%

A multiple objective methodology for sugarcane harvest management with varying maturation periods

et al. 2017

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This paper addresses the management of a sugarcane harvest over a multi-year planning period. A methodology to assist the harvest planning of the sugarcane is proposed in order to improve the production of POL (a measure of the amount of sucrose contained in a sugar solution) and the quality of the raw material, considering the constraints imposed by the mill such as the demand per period. An extended goal programming model is proposed for optimizing the harvest plan of the sugarcane so the harvesting point is as close as possible to the ideal, considering the constrained nature of the problem. A genetic algorithm (GA) is developed to tackle the problem in order to solve realistically large problems within an appropriate computational time. A comparative analysis between the GA and an exact method for small instances is also given in order to validate the performance of the developed model and methods. Computational results for medium and large farm instances using GA are also presented in order to demonstrate the capability of the developed method. The computational results illustrate the trade-off between satisfying the conflicting goals of harvesting as closely as possible to the ideal and making optimum use of harvesting equipment with a minimum of movement between farms. They also demonstrate that, whilst harvesting plans for small scale farms can be generated by the exact method, a meta-heuristic GA method is currently required in order to devise plans for medium and large farms.

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Addressing uncertainty in sugarcane harvest planning through a revised multi-choice goal programming model

Cited by 27 publications

References 32 publications

Stochastic preemptive goal programming to balance goal achievements under uncertainty

Stochastic preemptive goal programming to balance goal achievements under uncertainty

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

A multiple objective methodology for sugarcane harvest management with varying maturation periods

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