We consider emergency preparedness for foreseen disasters such as hurricanes and flooding. Proper preparation and planning for timely relief supply in a cost-effective manner can be crucial determinants of how quickly recoveries occur and with the least suffering of the affected populations. Assuming a host of shelter locations aggregated locally, we are interested in determining relief supply locations (distribution centers [DCs]) and routing of supply from the capacitated DCs to the shelters on an underlying time-phased network for cost-effective timely delivery. We present a mixed integer optimization model to address this problem under the assumption of covering the worst case demand at the shelters. To solve our model, we develop an efficient Benders decomposition-based algorithm that handles the challenges of obtaining optimality cuts via master problem solution modifications and various surrogate constraints among other enhancement techniques. We test the performance of the enhancement techniques on an extensive randomly generated test data set to identify the most effective approach. We finally use our model and the solution algorithm on an actual case study in southern Texas data incorporated and managed by a geographical information system to examine the impact of various input parameters on the design and tactical operation of the relief networks as well as for further model verification and validation.

We consider a multiperiod biomass-biofuel supply chain network planning problem in which we determine biomass collection and biofuel production locations and capacities in conjunction with biomass pricing that affects biomass supply quantity and locations. We present a framework for profit-maximizing tactical decisions on biomass and biofuel shipment patterns and quantities, inventory levels, and price offered individually to farms that are potential suppliers for each period for which the conversion efficiency changes depending on the biomass degradation rate in inventory. To efficiently solve our model, a Benders Decomposition (BD) algorithm enhanced with surrogate constraints for improved upper bounds is suggested. The model is tested on both randomly generated instances to examine algorithmic performance and a real case study based on the Midwest region of the United States, using available data sources managed via a Geographical Information System (GIS). We present analysis results on the effects of different input parameters on the final network solutions, and we also use these results to validate the model and the solution approach.

A recent trend in health care is to give patients more flexibility by taking their preferences into account. While this patient-centered approach adds further complexity to the management of operations, it also generates new opportunities for potential improvements in the system. In this study, we show that such an improvement can be obtained via appointment scheduling (AS) systems which are the critical component of any health care delivery system as they can easily be a source of dissatisfaction for the patients as well as for the providers. Accordingly, we propose a novel patient-oriented AS strategy that utilizes patients’ appointment date preferences. The main idea of the strategy is to accumulate patients’ preferences for some amount of time before deciding on their appointments via mathematical optimization, rather than traditional first-call first-booked strategy in which patients are appointed at the time they call. By this way, we aim to exploit the advantage of giving patients preferences to improve the system performance. To examine the proposed AS system with different model settings and problem parameters, we perform a comprehensive simulation study that incorporates several realistic operational features as well as an optimization model for patient to timeslot assignments. Computational results show that using this system can improve not only clinic utility but also patients’ AS experience significantly since it allows more patients to be appointed to one of their convenient dates. This simulation study presents a proof-of-concept for the proposed strategy while providing valuable managerial insights for implementing and operating such an AS system.

The truckload industry faces a serious problem of high driver shortage and turnover rate, typically around 100%. Among the major causes of this problem are extended on-the-road times, where drivers handle several truckload pickup and deliveries successively, non-regular schedules and get-home rates, and low utilization of drivers dedicated time. These are by-and-large consequences of the driver-to-load dispatching method, which is based on point-to-point dispatching or direct shipment from origin to destination, commonly employed in the industry. Use of relay networks has been suggested previously to alleviate this problem, mainly due to its underlying causes. In this scheme, a truckload on its way to destination visits multiple relay nodes (each representing a service region) and its driver and/or tractor are exchanged with a new one serving the next relay so that drivers stay close to their home domiciles. In this study, we consider a generalized relay network design problem whose specific design characteristics include the possibility of both direct and relay-network shipments and multi-route assignments in addition to fixed relay costs and control of route circuity levels. We present a new MILP model capturing these characteristics effectively and solve it effectively by Benders decomposition. The solution approach helps us to further examine the performance of the relay network under generalizing characteristics and quantify the improvements in practice when direct shipments and multi-assignment are employed. Computational study demonstrates the performance of the algorithm and include further analysis of results. In general, relay networks also find applications in transportation with alternative fuels, e.g., charging locations in long distance electric vehicle transportation networks, and in communication networks, e.g., signal regeneration facilities in long-distance data networks.

This paper addresses an integrated biomass pricing and logistics network design problem. A bilevel design and pricing model is proposed to capture the dynamic decision process between a biofuel producer as a Stackelberg leader and farmers as Stackelberg followers. The bilevel optimization model is transformed into a tractable single-level formulation by using optimality constraints. Other unique characteristics of our problem at hand include the incorporation of the harvesting time and frequency decisions in the biomass supply chain network design problem for the first time and consideration of the uncertainty in switchgrass yield in a robust optimization setting to take into account the risk-averse behavior of the farmers (suppliers). To efficiently solve the model, we propose a Benders decomposition algorithm enhanced by surrogate constraints, strengthened Benders cuts, and in-out cut loop stabilization. The numerical experiments show that the proposed algorithm is significantly superior to the branch-and-cut approach of CPLEX in terms of run times and gaps. We conduct a case study with data from Texas to validate the capabilities of our mathematical model and solution approach. Based on extensive experiments, the benefits of modeling are analyzed, and significant insights are explored. Funding: This research was partially supported by the National Natural Science Foundation of China [Grants 71771135, 72171129]; and the scholarship from China Scholarship Council (CSC) [Grant CSC 201906210092]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0357 .

The advent of mobile channels have changed retail business models, the choice of retail mix, and shopper behavior. As consumers do not differentiate among the channels where they try, purchase and/or take delivery of their product, they also expect maximum flexibility in the product returns process. On average, retailers forecasted returns to reach about 16.6% of the total merchandise that customers purchased in 2021, according to the National Retail Federation, which is an increase from an average return rate of 10.6% in 2020. The resulting cost of returns amounted to $761 billion worth of merchandise in 2021 (Repko in A more than $761 billion dilemma: retailers’ returns jump as online sales grow. https://www.cnbc.com/2022/01/25/retailers-average-return-rate-jumps-to-16point6percent-as-online-sales-grow-.html . Accessed 17 June 2022, 2022). For retailers and manufacturers, integration of different reverse channels is extremely important to deliver the seamless experience demanded by today’s discerning consumer while ensuring the profitable handling of the returned products as well as ensuring the environmental sustainability of the retailing operations. Regardless of which channel receives a return, the reverse logistics network should have the flexibility and the capability to remarket or to recover the value in the returned product in a cost efficient and timely manner that maximizes firm profitability. To the best of our knowledge, this paper is one of the first studies that develops a linear programming model with profit maximization objective to help determine how to optimally decide the returned product touch point(s) in the reverse logistics network. Unlike the extant literature, our model explicitly incorporates the marginal value of time for returns, product characteristics as well as the underling reverse logistics network configuration in return channel selection strategy. We present a comprehensive analysis on how and to what extent the return channel selection is dependent on the product characteristics such as time-based value decay rate, defective rates, and disposal rates as well as the network structure. Using data from HP and Bosch Power tools operations as well as real geographical US data, we show that our decision model can effectively help determine the reverse logistics network and the type of facility where a product is returned as a function of product characteristics and economic parameters. Our work emphasizes that product returns and waste reduction, improved firm sustainability and profitability can co-exist through effective reverse logistics planning.

We consider a fixed-charge capacitated relay network design problem (CRNDP), which has widespread applications in the design of telecommunication and transportation networks, where relay points are presented as signal regenerators, truckload driver and/or tractor exchange stations or electrical vehicle recharging station. Our CRNDP integrates the key features of single allocation hub location problem (SAHLP) and capacitated network design problem (CNDP). Typical range restrictions in truckload transportation, electrical vehicles as well as for keeping signal strength in telecommunications, and further limitations of flow on the links of an underlying network in these mediums motivate the use of distance constraints and link capacity constraints in the design of a relay network for this diverse set of applications. To efficiently solve our model, we propose a Lagrangean relaxation algorithm, where the upper bounds are enhanced by a heuristic during the iterations and the lower bound is improved by the introduction of surrogate constraints. The extensive computational studies are conducted to examine the performance of the algorithm and the results show that the solution method we propose can efficiently solve CRNDP and obtain good-quality solutions, even for very large size instances.& COPY; 2023 Elsevier Ltd. All rights reserved.

For foreseen natural disasters (e.g., hurricanes or floods), the uncertainties faced in relief logistics primarily stem from evacuation activities. We present a strategic planning problem to supply relief items by considering uncertainties in disaster location, intensity, duration, and evacuee compliance. To ensure time- and cost-effectiveness in relief distribution, we develop a robust optimization model to determine centralized supply locations, and supply quantities for different transportation modes in a five-tier network. In doing so, we consider the interaction between evacuation and supply-side activities and capture the inherent uncertainties using a combination of event and box uncertainty representations. Our model provides a decision maker with the flexibility of including or excluding the time dependency of evacuation-related uncertainties. Accordingly, it suggests a threshold time window for relief distribution, beyond which either the system cost increases or the benefits of early distribution diminish. Although the model primarily aids a policymaker in strategic preparedness, its tactical variant can aid the efficient distribution. We devise an enhanced Benders decomposition-based efficient solution method to solve realistic-size problems. In a case study using geographic information system data, we highlight the complex dynamics among various system components and discuss the resulting time-cost trade-offs that also influence the network structure.

This article presents a framework for profit-maximizing strategic bio-energy supply chain design by taking into account variability in biomass as a response to price set as well as uncertainty in biomass yield. We present our model as a two-stage stochastic integer program for a multi-period integrated design of a network in which the here-and-now strategic decisions include biorefinery locations and size as well as base biomass price. To efficiently solve our model, we suggest an L-shaped-based algorithm along with a Sample Average Approximation approach. Finally, we demonstrate our results in a case study in Texas using realistic data. Within our framework, we present the relationship between biomass and biofuel price, as well as the optimal network design for the biofuel producer.

We address a network design problem arising in the deployment of wireless charging stations (WCSs) within an urban transportation network. It is widely acknowledged that, despite the availability of EV conventional charging facilities, the relatively short driving range of EVs (due to low energy density of the batteries) and the long battery charging times (collectively leading to a phenomenon known as "range anxiety") remain the major factors that hamper EV adoption. Thus, in this research, we study a cost-effective WCS deployment network design that facilitates EV adoption by alleviating these two major anti-adoption factors. We consider the problem from the perspective of a city as the decision maker whose aim is to satisfy the charging demands of all EVs in its urban traffic network at the minimum cost including installation and charging costs. For this purpose, we suggest a new mathematical model to strategically deploy WCSs in the network in such a way that no EV runs out of energy before reaching its destination. To solve the proposed model, we devise a combined combinatorial classical Benders Decomposition approach and further enhance its efficiency via employing surrogate constraints and an upper bound heuristic. We present computational results illustrating the algorithmic efficiency of our approach as well as an analysis of the effect of varying system and new technology related parameters (i.e., product design) on the resulting network design based on a case study with urban network data from Chicago, IL.

The first four papers of this August issue form a special section, which presents recent work in the area of sustainable bioenergy systems design, planning and operations. An open call for papers on this special topic was announced in October 2018. After multiple rounds of review, four articles were accepted to this special section, following the standard, rigorous review procedures of IISE Transactions. The studies presented in this special section focus on the use of biomass for generation of renewable fuel, renewable electricity, and consumable products, such as paper and sugar. These studies are motivated by the potential to use biomass to meet our needs for energy and other bio-based products. The growing interest in this research is a reflection of increased awareness about the impacts that our decisions and lifestyles have on the environment. Bioenergy and biobased products are environmentally friendly, thus, sustainable. Additionally, the development of a bio-based economy enables farmers to find new markets for their products, creates new jobs in rural areas, and enhances value for farmers and national economies. The selected group of papers published in this special section addresses a wide range of problems that are motivated by existing challenges and recent developments faced by this industry. In the last two decades, most of the literature has focused on optimizing the design and management of biomass supply chains, due to the logistical challenges faced in biomass collection, storage and transportation. Biomass – in the form of agricultural products and agricultural waste, forest products and forest waste, animal waste, and municipal waste – is widely spread geographically. Biomass loses dry matter with time, it is bulky, its production yield is uncertain and difficult to predict. As a result, its collection, storage, and transportation are expensive. The work by Nur et al. extends this research by considering the impact that product characteristics, such as moisture content and ash content, have on the supply chain costs. For example, a bale with high moisture content is heavier, thus more expensive to transport. Certain conversion processes, such as biochemical conversion, use biomass with low ash content. This requirement restricts supplier selection process and increases transportation costs. Nevertheless, well-designed plans help biofuel producers to reduce the impact of moisture and ash on supply chain costs. For example, scheduling the delivery of biomass before the rain season reduces moisture content. The work by Nur et al. assumes a hub-and-spoke supply chain structure, which considers that biomass is pre-processed at a depot located near farms. The processed biomass has higher density; thus, it is easier to transport. Such a supply chain structure facilitates the use of high-volume transportation modes. These strategies lead to lower supply chain costs. Biomass supply and price uncertainty is another challenge faced by bioenergy producers. Biomass supply is affected by weather conditions, insect population, and plant diseases. Additionally, changes of land use from one year to the next impact biomass supply and price. Shortages of biomass supply have a negative impact on utilization of resources and production amounts, and thus on revenues and profits. Developing long-term contractual agreements among biomass providers and biofuel producers mitigates price and supply volatility. Establishment of long-term contracts also incentivizes farmers to produce energy crops such as switchgrass. The work by Memişo glu and € Uster presents a strategic framework that is needed to assist biofuel producers in designing their bioenergy supply chain networks while simultaneously determining the policies that give incentives to farmers to stimulate biomass supply. The model’s objective is to maximize the expected profit, which is the main objective for biofuel producers who consider investing in the bioenergy industry. The opportunity to use biomass to meet our needs for energy and to reduce greenhouse gas emissions (from burning of fossil fuels), were main motivations for the U.S. Environmental Protection Agency and the U.S. Department of Energy to develop policies, such as the renewable fuel standards and Production Tax Credit (PTC), which stimulate production of bioenergy and its consumption for commercial and personal use. The work by Khademi and Ekşio glu focuses on PTC, a federal incentive that provides financial support to power plants that cofire biomass. This is a flat rate per megawatt-hour of renewable electricity generated and is provided for the first 10 years of a plant’s operations. Khademi and Ekşio glu show that the current structure of the PTC (flat rate) prioritizes large-scale plants, as they can displace more coal with biomass and take advantage of the economies of scale. The ethics of this disproportionate allocation of taxpayers’ money motivated their work, which identifies two designs of the PTC that focus on fair allocation of resources. The proposed flexible tax credit provides a plant-specific tax credit rate based on plant capacity, with small-size plants receiving a higher tax credit than large-size plants. Such an approach leads to increased renewable electricity generated since it motivates smaller plants to participate. The work by Zhu et al. expands the scope of these biomass supply chain models by investigating opportunities to exploit unutilized resources or by-products, and thereby increase recourse utilization in the supply chain and

In the long-haul trucking industry, the turnover rate for drivers has been consistently near or higher than 100% for many years (Fournier, Lamontagne, & Gagnon, 2012; LeMay, Williams, & Garver, 2009). There are many complexly-interacting contributors to this high rate, including competition among industry members for the short supply of qualified drivers, characteristics of driving assignments, family needs, and interpersonal relationships among coworkers. Several costs to trucking companies are associated with a driver quitting their current position, including lost profit, underutilization of equipment, training costs, insurance investments, and administration costs. The overall turnover cost to the industry is estimated to be around $2.8 billion per year (Morrow et al., 2005), which is eventually passed on to consumers. The current and expected increase in the shortage of drivers provides additional motivation to investigate the high turnover problem. Several past studies have looked into the possible causes of high driver turnover, although only within consideration of current trucking operations. In addition to only considering the problem within current operations, most of these studies focused only on a particular subset of the possible issues. A driver survey was created as part of this research effort to take into account a broader range of issues and with a more generalizable approach to allow for consideration of new operational paradigms. In addition to gathering survey results, a key activity in this research effort was to develop a model that can accurately predict the likelihood that a truck driver will quit or stay at their job. When we reach a point when a driver’s decision to stay or quit can be accurately predicted based on the explored issues, it will reveal which combination of issues most strongly influences this decision. Once these issues are discovered, they can inform the development of new operational paradigms that may lead to a decrease in the high turnover problem. A thorough literature review informed the creation of the content of the survey. This was followed by multiple revisions based on feedback from an in-person focus group with truckers, a test launch with a sample of students (non-drivers), and a second test launch with a sample of local drivers. The survey was then administered and 308 valid responses were obtained. The final survey consisted of 84 questions covering demographic information, training and education, job preferences, job nature, management practices, relationship with supervisor, job risks and benefits, work history, job seeking experience, and alternate job opportunities. One survey question which asked: “How do you feel today about the likelihood you'll stay with your current company for the foreseeable future?” resulted in the dependent variable (range of 0.00 to 1.00) used in the analysis. The data was analyzed using the free statistical software environment, R. The first attempt to create a prediction model involved performing multiple linear regression with most of the independent variables and the stated dependent variable. From this analysis, the variables deemed significant included the following: how routes are communicated to the driver (in-person, by phone/radio, by text/email, or other), how often pre-planned routes are given to the driver, what the driver thinks about their work amount, how the driver feels about being affiliated with their company, how well the driver feels their supervisor represents them, how satisfied the driver is with their pay, the driver’s flexibility in being able to take time off work, how interesting the driver considers their job to be, and how frequently the driver interacts with other drivers by radio. This resulting linear model had an adjusted R-squared value of 0.4658. Further data analysis involving other variable selection methods and the creation of complex models will be performed in hopes of obtaining a more accurate model. Future analysis may also include clustering the drivers into groups based on their characteristics and preferences and then seeing if the variables that predict the job plans of the drivers in these groups are different.

We consider an integrated supply pricing and biomass logistics network design problem in which yield rates are uncertain. The base supply amount is modeled via a farmers’ decision model that estimates the amount of land dedicated to biomass production under a given biomass wholesale price. We develop a two-stage stochastic integer program for integrated design of a network as well as biomass pricing decisions under yield uncertainty. To efficiently solve our model, we suggest a Benders decomposition–based algorithm in which the Benders cuts, one for each scenario, are aggregated by utilizing a scheme that takes into account yield rates as well as the geographical nature of the underlying problem. With further strengthening of the cuts, we present a significantly improved approach for solving relatively large instances and illustrate its performance via a computational study. We further present an extensive case study in Texas using realistic data to test our model’s capabilities and to demonstrate its eff...

This study considers an integrated closed-loop supply chain (CLSC) network design problem under uncertainty with regard to product demand and return quantities. To incorporate uncertainty in decision making, we formulate a two-stage stochastic mixed integer linear programming model to determine the optimal locations of (re)manufacturing and processing facilities along with their capacity levels and forward and reverse product flows in the CLSC network to minimize total design and expected operation costs. For the solution of the model and its analysis, we develop a Benders Decomposition approach enhanced for computational efficiency using induced constraints, strengthened Benders cuts, and multiple Benders cuts as well as mean-value scenario based lower-bounding inequalities obtained by dual subproblem disaggregation. Computational results illustrate that the enhancements provide substantial improvements in terms of solution times and quality. Using our model and the solution approach in a sample average approximation framework, we provide further analysis of network designs based on inspection location and recovery rates. Although product inspection at retailer or collection center locations generally reduce costs by avoiding unnecessary use of resources, our analysis also indicates that parameters such as product type and reason-for-return, expected recovery rates, inspection costs, and transportation costs can be instrumental in deciding where the return product inspection should take place and, in turn, dictating the overall cost as well as the structure of the CLSC network.

High driver turnover and driver shortage are costly problems in the truckload trucking industry. Extended on-the-road times and low quality of life with irregular schedules and low get-home rates for drivers are commonly attributed as the main culprits in both academic and industry literature. The use of a relay network on which the truckloads switch drivers during their transportation can potentially help reduce drivers’ away-from-home times and regularize their schedule without sacrificing the mileage accumulation on which their pay is determined. Relay network design involves the determination of the relay point (RP) locations, their interconnections, assignment of non-RP nodes to RPs, and the routes for truckloads. Recognizing the importance of considering operational realities such as empty mileage and driver availability along with limited resources, we introduce link capacity constraints and the concept of link imbalance in strategic relay network design. The use of link imbalance is motivated by the need to improve operational efficiency via increased ability to return drivers to their home bases and reduce empty backhauls. To solve our mixed-integer programming design model, we develop an efficient Lagrangean decomposition algorithm that can provide solutions to large-size problems with small optimality gaps within reasonable runtimes. We also present computational experiments on the algorithmic performance, trade-offs between imbalance and cost components, effects of capacity, and the relationship between link- and node-imbalance concepts. The online appendix is available at https://doi.org/10.1287/trsc.2016.0704 .

Wireless charging station (WCS) enables in-motion charging of the electric vehicles (EVs). This paper presents the short-term operation of WCS by capturing the interdependence among the electricity and transportation networks. In the transportation network, the total travel cost consists of the cost associated with the travel time and the cost of utilized electricity along each path. Each EV takes the path that minimizes its total travel cost. In the electricity network, the changes in WCS demand as a result of changes in the traffic flow pattern impacts the price of electricity. The changes in the price of electricity further affect the charging strategy of the EVs and the associated traffic flow pattern. The coordination between electricity and transportation networks would help mitigate congestion in the electricity network by routing the traffic flow in the transportation network. The presented formulation leverages decentralized optimization to address the economic dispatch in the electricity network as well as the traffic assignment in the transportation network. The presented case studies highlight the merit of the presented model and the developed algorithms for the coordinated operation of WCS in electricity and transportation networks.

In this study, we pose and analyze an evacuation network design problem to provide a planning tool to help with high-level design decisions involved in strategic preparedness for large scale evacuations. In doing so, while incorporating evacuation time considerations, we also take a cost perspective in designing an effective evacuation network. Both the network design and the associated cost considerations in evacuation planning are commonly ignored in the literature due to a focus on evacuation time and the associated flow routing objective. We propose a mathematical model for Strategic Evacuation Network Design (SEND) that prescribes shelter regions and capacities, intermediate locations that support/supply for en route evacuees as well as road segments and their capacities under evacuation time constraints. To solve our model, we devise an efficient Benders Decomposition based approach enhanced with surrogate constraints, strengthened Benders cuts, heuristics, and the use of multi-cuts. We apply our methodology to solve test instances developed based on real data from Central Texas. We demonstrate by our analysis that the resulting approach does not only provide us with a means to design evacuation networks but also serves as a tool to study the trade-offs involved in design and operational performance measures as it captures the essence of high-level interactions between them. (C) 2017 Elsevier Ltd. All rights reserved.

We study an emergency response network design problem that integrates relief (supply) and evacuation (demand) sides under disaster location and intensity uncertainties which, in turn, dictate uncertainty in terms of the location and amount of demand. Representing these uncertainties by discrete scenarios, we present a stochastic programming framework in which two second stage objectives, the average and worst case costs, are combined. In our model, we minimize, over all of the scenarios, the fixed costs of opening supply centers and shelters, and theweighted sum of average andworst case flow costs. Thus, the model gives the decision maker the flexibility to put relative emphasis on the worst case and average flow cost minimization and explore outcomes in terms of total costs and network configurations. To solve large scale instances with varying relative weights, we devise alternative Benders Decomposition approaches. We implement these by using an advanced callback feature of the solver while simultaneously incorporating several performance-enhancing steps that help to improve runtimes significantly. We conduct a detailed computational study to highlight the efficiency of our proposed solution methodology. Furthermore, we apply our approach in a realistic case study based on Geographical Information Systems data on coastal Texas and present interesting insights about the problem and the resulting network structures for varying weights assigned to objectives.

In the Hamiltonian p-median problem (HpMP), the target is to find p cycles that partition a given undirected graph with the objective of minimizing the total sum of the costs of these p cycles. Even though this problem has several applications, the current state-of-the-art algorithms are only able to solve instances with up to 100 nodes. In this paper, we devise a branch-and-price algorithm that is able to solve instances with up to 318 nodes. To achieve this, we modified the set partitioning formulation of HpMP-a minor modification yet with significant algorithmic and computational advantages. Furthermore, our computational results demonstrate that the practical complexity of HpMP and the performance of the algorithms to solve it strongly depend on the value of p. In addition, to solve the pricing problem, we make contributions on a couple of problems that are important on their own right: (1) we develop a new efficient algorithm to find the least-cost cycle in undirected graphs with arbitrary edge costs and no negative cycles; and (2) we develop an algorithm to find the most negative cycle in undirected graphs with arbitrary edge costs. Finally, we prove that for every value of p, HpMP is NP-hard even when restricted to Euclidean graphs.

AbstractWe consider planning and design of an extended supply chain for bioenergy networks i.e., networks for multibiomass as well as biofuel logistics in an integrated fashion while simultaneously addressing strategic and tactical decisions pertaining to location, production, inventory, and distribution in a multiperiod planning horizon setting. In our modeling, we also explicitly incorporate realistic operational parameters, including biomass deterioration rates and transportation economies of scale. For an efficient solution of our model, we suggest a Benders decomposition-based algorithm that can handle realistic size problems for design and analysis purposes. We implement the approach in a particular way using callback functions, in which the master problem is solved only once; we also develop surrogate constraints for enhanced lower bounds to obtain improved convergence especially for large instances. We provide computational results that demonstrate the efficiency of the solution approach on a wide-ranging set of problem instances. Furthermore, we develop a realistic case using data pertaining to the state of Texas and conduct an extensive analysis on the effects of varying input parameters on the design outcomes for a bioenergy supply chain network.