Biorenewable fuels at the intersection of product and process flexibility: A novel modeling approach and application

https://doi.org/10.1016/j.ijpe.2013.11.024Get rights and content

Highlights

  • Process flexible biorenewable technologies show lower logistical costs.

  • Process flexible biorenewable technologies enable higher supplier profitability.

  • Process flexible biorenewable technologies show lower environmental burden.

  • Product and process flexibilities interfacing across firms is modeled.

Abstract

In recent years, governments, industry and academia have all invested increasing amounts of time, effort and resources into the production of biorenewable fuels. This interest owes, among other reasons, to our planet's growing demand for energy, depletion of fossil fuel resources and the negative effect of drilling for and burning fossil fuels on the health of our eco-systems and atmospheric chemistry. However, research suggests that biorenewable fuels have the potential to cause environmental and social calamities of their own—especially when produced in the same ways and at the expense of conventional food production. This paper proposes novel supply chains and land use plans for advanced biorenewable fuels which are measured for cost and environmental impact. A two-stage Stackelberg leader-follower mathematical optimization model is proposed. The model uses a series of integrated and sequenced linear programs to optimize the benefits of leveraging biodiversity for the production of advanced biorenewable fuels. Numerical experiments with our model show statistically significant cost, land use and environmental improvements on the order of 10% to 25%. Because the model captures two types of flexibilities (product and process) interfacing across firms, implications are drawn for production systems in other industries where distinct flexibilities meet and environmental impacts are critical.

Introduction

In both energy and agriculture, several changes are occurring at once: (1) Global supplies of fossil fuel are rising in price, and plausibly scarcity, as worldwide demand continues to grow; (2) industry and governments are investing heavily in alternative energies, one of the more popular being “biofuels”; (3) meanwhile, agricultural production in the developed world has become highly centralized and homogenized, commanding much larger swaths of land, employing larger fleets of equipment and generating negative environmental externalities, all of which has lead scientists, journalists, and the public to; (4) increasingly cast critical eyes towards biofuels’ potential to offset fossil fuel use without causing environmental and social calamities of their own. This research sits itself at the confluence of these four troubling, and seemingly disparate, developments. This paper proposes a way that advanced biofuels can be produced more efficiently and more sustainably, with optimized supply chains that capitalize on biodiversity in order to reduce land usage, environmental degradation, and overall costs of biofuels production. Our approach entails a unique application of operations research (OR) techniques to uncover the benefits of leveraging natural biodiversity in production systems for alternative fuels.

The production system under consideration (the farmer-bioprocessor dyad) and our mathematical model of it bears broader research implications too. We frame the farmer as a supplier (in this case of plant feedstock), who is product flexible, meaning “The ability to changeover to produce a new (set of) product(s) very economically and quickly” (Beach et al., 2000, Browne et al., 1984). Herein, product flexibility denotes the ability of the farmer-supplier to produce different crop types from year to year. The buyer in this dyad is the bioprocessor, who purchases from farmer-suppliers feedstock for conversion into biorenewable fuels. We frame the bioprocessor-buyer as a process flexible, meaning “The ability to produce a given set of part types, each possibly using different materials, in several ways” (Beach et al., 2000, Browne et al., 1984). Herein, process flexibility denotes the bioprocessor's ability to convert any of the farmer-suppliers’ crop types into biofuels. This process flexibility is unique to emerging advanced biorenewable fuel technology.

Production researchers have been increasingly interested in flexible manufacturing problems since the 1970s, when computer-controlled process automation and Japanese-style production systems began to be implemented across a wide variety of industries (Fine and Freund, 1990, Karsak and Kuzgunkaya, 2002). Over the years, this journal has published several modeling approaches to flexible manufacturing problems, including: Kumar (1995), who proposed finance literature's ‘options theory’ as a better way evaluate investments in expansion flexibility than traditional Net Present Value calculations; Gertosio et al. (2000), who suggested multi-layered discrete event simulation as a decision making tool for analyzing how different control systems and physical production systems interact under manufacturing flexibility; Karsak and Kuzgunkaya (2002), who proposed fuzzy multiple objective programming as a fitting methodology for evaluating the worth of flexible systems, because it uniquely incorporates both strategic and economic benefits, whereas classical analytical modeling considers only the latter; Tseng (2004), who employed elements of game theory to investigate under what types of competitive environments investments in more expensive flexible systems pay off and found that increased competition reduces firms’ incentive to invest in expensive flexible technologies; and Francas et al. (2011), who optimized two types of flexibility, labor and machine, in a single-firm production system using a two-stage stochastic programming approach.

In the research literature reviewed above, each type of flexibility has traditionally been considered either in isolation, or as it interfaces with another type of flexibility in a single firm. Examples of the latter include: Chod and Rudi (2005), who used a Stackelberg model to consider resource flexibility and “responsive pricing” in a single production system; Iravani et al. (2012), who modeled one firm's tradeoffs between process flexibility and inventory flexibility; and (Francas et al., 2011). In their recent review of supply chain flexibility, Jayant and Ghagra (2013) noted that more attention should be paid to inter-organizational flexibility in order to realistically depict real-world supply chains. Proposing an approach to modeling real-world circumstances of different types of flexibilities intersecting across firm boundaries is this paper's broader contribution to research. For practitioners, this approach also has merit in the classic sense of game-theoretic models: it allows one player (supplier or buyer) with a distinct flexibility to predict the moves of their partners (who have different flexibilities) under a variety of scenarios. Over time, however, it is possible that cooperatives of biomass processors and farmers could jointly own and operate both biorefineries and their surrounding farms, and then use the model presented in this paper to find optimal management strategies. Similarly, third-party service providers working in-between growing biorefineries and farming operations could use the model presented herein to discover appropriate price incentives for lowering overall logistical costs and protecting the natural environment.

The paper continues as follows. Section 2 gives further background to the problems above. Section 3 presents our proposed solution to the issues presented in 1 Introduction, 2 Problem background. Section 4 presents our mathematical formulation of a biodiverse biofuel supply chain, modeled as a Stackelberg leader-follower optimization based on a sequenced series of two basic types of integrated linear programs. In Section 5 we analyze the results of simulation runs on our model. Section 6 presents implications for biofuels producers, as well manufacturing flexibility research, and limitations and suggestions for further work.

Section snippets

Fossil fuels

Today's world faces the potential for serious energy shortages in the near-term, owing in part to: (1) our own profligate consumption of available energy sources over the last 200 years, and (2) the mounting environmental costs associated with supplying raw material for different energy conversion technologies. During the advent of coal and steam power in the 19th century, energy use by humans increased 10-fold (McNeil, 2000). The development of oil and natural gas resources in the 20th century

Modeling product and process flexibility in bioeconomy landscapes

To frame the presentation of the mathematical model, we first describe a conceptual picture of the relationship between farms and biorefineries in our model and the systems that are optimized. This conceptual framing of the biorenewables industry was based on discussions with both farmers and bioprocessing industry representatives. That system is shown in Fig. 1.

In this model, farm businesses are managed by independent actors (farmers), who make their own decisions regarding which crops to grow

General mathematical model

Our model consists of an integrated sequence of two basic types of linear programs interacting over time. First, the biorefinery seeks to minimize the cost of collecting a requisite amount of biomass every year (“the Biorefinery model”). Second, farmers seek to maximize their profit each year by choosing which crops to grow, given the price incentives offered by the biorefinery (“the Farmers’ Model”). The price incentives that biorefineries offer are calculated annually by empirically deriving

Numerical experiment

In this section we conduct a numerical experiment to test the effectiveness of our approach. Specifically, we address the following questions: First, can an optimized mix of crops reduce supply costs for a biorefinery? Second, what will happen to farm profits under our proposed regime? Third, how would optimized biodiverse feedstock landscapes impact the amount of land required to supply a biorefinery, as well as the environmental footprint of that land use?

For model analysis, we designed a

Results

Table 2 shows comparisons of mean values for four key measures of the numerical experiment: (1) overall biorefinery cost of harvest and transport; (2) overall farm profit; (3) total acres harvested for the biorefinery; and (4) landscape soil erosion. Table 3 shows all measures normalized to each scenario's monoculture baseline model for convenient comparison. In Table 4, p-values and t-statistics for a t-test comparing the means of the optimized runs and the baseline scenarios show that the

User implementation

At this point, we pause to consider how practitioners in this industry (biorenewables) could use our results and our model. First, our results indicate that both dyad partners stand to realize cost of production and environmental benefits by engaging their own flexibilities (product or process) with their trading partner's corresponding flexibility. In this context, both farmers and bioprocessors stand to benefit from linking biodiversity (product flexibility) with omnivorous technology

Implications and limitations

We present a way that advanced biofuels could be produced more cost effectively and with improved environmentally sustainability, while also reducing the amount of land taken out of food production or conservation. Our two-stage approach respects that biorefiners and their farmer suppliers are separate profit maximizing actors and provides a framework for both parties to leverage the natural cost savings and environmental benefits of biodiversity interactively. For the broader research

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