Sustainable planning in sugar-bioethanol supply chain under deep uncertainty: A case study of South African sugarcane industry

Shavazipour, Babooshka; Stray, Jonas; Stewart, Theodor J.

doi:10.1016/j.compchemeng.2020.107091

Cited by 26 publications

(16 citation statements)

References 44 publications

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“…On the one hand, the problem may become computationally expensive, and on the other hand, the performances of solutions may be very similar in an ample portion of the scenario space. Utilizing the DM's preferences in reducing the number of scenarios is in line with the philosophy of scenario planning (Ram et al, 2011;Shavazipour et al, 2020).…”

Section: Discussionmentioning

confidence: 97%

“…For any scenario t, we also introduce ideal and nadir vectors in the objective space that are composed of the corresponding values in this scenario, respectively: One way of deriving solutions to multiobjective optimization problems is to use a socalled scalarizing function to transform the multiobjective problem into a single-objective problem incorporating the DM's preferences (see, e.g., Miettinen (1999); Miettinen and Mäkelä (2002); Ruiz et al (2009)). An extended version of a so-called achievement scalarizing function (Wierzbicki, 1986) has been recently introduced to solve multi-scenario multiobjective optimization problems (Shavazipour et al, 2020(Shavazipour et al, , 2021a. In this paper, we use the multi-scenario version of an achievement scalarizing function formulated by Shavazipour et al (2021a)…”

Section: Multi-scenario Multiobjective Optimizationmentioning

confidence: 99%

“…In multi-scenario multiobjective optimization problems, each objective in each scenario is represented by an individual objective function. Thus, the DM is expected to provide aspiration levels for all combinations of objectives and scenarios (Shavazipour et al, 2020;Shavazipour and Stewart, 2021). This can impose a high cognitive load on the DM, even for problems of moderate sizes.…”

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confidence: 99%

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Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

2022

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Sustainable environmental management often involves long-term time horizons, multiple conflicting objectives, and by nature, is affected by different sources of uncertainty. Many sources of uncertainty, such as climate change or government policies, cannot be addressed using probabilistic models, and, therefore, they can be seen to contain deep uncertainty. In this setting, the variety of possible future states is represented as a set of scenarios lacking any information about the likelihood of occurring. Integrating deep uncertainty into multiobjective decision support increases complexity, calling for the elaboration of appropriate methods and tools. This paper proposes a novel interactive multi-scenario multiobjective approach to support decision-making and trade-off analysis in sustainable forest landscape planning under multiple sources of uncertainty. It includes new preference simulation models aimed at reducing the decision-maker's cognitive load and supporting the preference elicitation process. The proposed approach is applied in a case study of long-term forest landscape planning with four sustainability objectives in twelve scenarios and a forestry expert as the decision-maker. The approach is demonstrated to be efficient in exploring trade-offs in different scenarios, helping the expert gain deep insights into the problem, understand the consequences of alternative strategies, and find the most preferred robust strategy.

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Section: Discussionmentioning

confidence: 97%

Section: Multi-scenario Multiobjective Optimizationmentioning

confidence: 99%

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confidence: 99%

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Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

2022

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“…Their proposed model was implemented in the tire industry, and two robust optimization approaches, including scenario-based and Soyster modes, were used to deal with uncertainty. Shavazipour et al [67] performed strategic planning for a sugar-bioethanol supply chain under uncertainty using a multi-objective optimization method based on a two-stage scenario. In another study, Abdel-Basset et al [1] conducted a set of measurements to finance a sustainable supply chain in the gas industry under conditions of uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated dynamic model to locate a competitive closed-loop supply chain facility under conditions of uncertainty: A case study of the auto parts industry

Ardekani¹,

Naeini

2022

RAIRO-Oper. Res.

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With the expansion of competitive markets, supply chain management has become one of the critical issues facing businesses. One of the advantages of sustainable competition for companies is to make supply chain activities more efficient and effective. This paper aims at an integrated closed-loop supply chain (CLSC) problem which is multi-objective, multi-product, multi-period, and multi-level with limited capacities and uncertain conditions of demand and return products. The proposed supply chain network consists of five levels in the forward flow. There are five centers in the backward flow as well. The purpose of this network is to determine the optimal number and location of facilities required in each period and the optimal amount of the transfer flow of products or raw materials through different transportation modes between facilities. In this proposed model, three objective functions are taken into consideration. The first one minimizes all the costs. The second objective function maximizes the quality of products. The third objective function seeks to minimize the sum of deviations from the ideal score of the principal component of each supplier. The data of this research are taken from Pishro Diesel Company. To solve the proposed problem, several methods and algorithms have been used, including unscaled goal programming, boundary objectives, three single-objective meta-heuristic algorithms (PSO, RDA, and TGA), and multi-objective meta-heuristic algorithm (MOGA-II). As the results show, considering products and returned parts in products, a simultaneous practice of forward and reverse supply chains leads to better product quality, less damage to the environment, and lower costs for customers.

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“…In the present review, nine of the 10 papers in the sample discuss the marketing stage also taking into account elements of the other stages. Only Shavazipour et al (2020) treat the marketing stage alone. All but one paper employed a general-purpose solver as a solution approach with promising results.…”

Section: Marketingmentioning

confidence: 99%

A review of mathematical optimization models applied to the sugarcane supply chain

Teixeira¹,

Rangel²,

Florentino³

et al. 2021

Int Trans Operational Res

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Sugarcane is a product of great economic relevance and, as well as its use for the production of different types of sugar, it is also a renewable source for the production of bio‐fuels, other bio‐products, and electricity. In this context, efficient planning and technology changes are necessary to ensure high productivity, competitiveness and harmony with economic, social, and environmental issues. Consequently, this sector needs technical and scientific support. Mathematical optimization models can be useful to build efficient planning tools for the activities involved in the sugarcane supply chain, helping to address these challenges. This paper describes the sugarcane supply chain and presents a literature review of mathematical optimization models that have been proposed to represent its main stages: planting, harvesting, transporting, industrial processing, and marketing. The findings show that several models have been proposed to represent these five stages, however, most of them have not been thoroughly explored from the point of view of strength of the models and solution methods. Moreover, the harvesting stage is the one that has received more attention than the other stages. In the integration of two or more stages, there are many challenges yet to be addressed by mathematical optimization models. This review contributes to a better understanding of the sugar‐energy sector and opens up new perspectives for future investigations.

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Sustainable planning in sugar-bioethanol supply chain under deep uncertainty: A case study of South African sugarcane industry

Cited by 26 publications

References 44 publications

Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

An integrated dynamic model to locate a competitive closed-loop supply chain facility under conditions of uncertainty: A case study of the auto parts industry

A review of mathematical optimization models applied to the sugarcane supply chain

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