Supply Uncertainty Research Articles

Toward circular economy for pomegranate fruit supply chain under dynamic uncertainty: A case study

Food security is a fundamental prerequisite of human survival. This study focuses on providing a novel bi-objective model for the design of a green closed-loop pomegranate supply chain based on the value chain. To maximize the benefits of circular economy, we propose a thermochemical conversion process and develop a novel hybrid risk-neutral and risk-averse multi-stage stochastic programming (RNRAMSSP) approach to cope with supply uncertainty. We employ the expected value of perfect information (EVPI) and value of stochastic solution (VSS) to validate the proposed approach. For the VSS metric, we define a novel measure to compare the worst-case cost of the stochastic and deterministic model solutions. To demonstrate the applicability of the proposed model, we provide a real case study in Iran. Based on the VSS metric, the risk-neutral and risk-averse stochastic model solutions save up to 1% and 1.76% in the worst-case cost, respectively, compared to the deterministic model.

A Bilevel Model for Robust Network Design and Biomass Pricing Under Farmers’ Risk Attitudes and Supply Uncertainty

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 .

How immune is the connectedness of European natural gas markets to exceptional shocks?

Numerous studies underline strong ties between European natural gas markets but they do not account for the Russian invasion of Ukraine, a true test for market integration causing serious disruptions in natural gas supply and triggering elevated uncertainty. In this article we describe how the joint dynamics of natural gas prices quoted at four major European hubs evolved during the turbulent period of 2021–2022. We present various spillover measures applied to the TVP-VAR-SV model to find that the total connectedness declined markedly after the Russian invasion of Ukraine. We show that this drop was almost entirely driven by the price divergence on the UK market, whereas continental hubs remained tightly integrated. Finally, we provide new evidence that price transmission among European markets is driven predominantly by short-term frequencies, with connectedness measures for day- and month-ahead markets sharing common medium-term trends, but different short-term dynamics. These findings grant two policy implications. First, due to pipelines capacity constrains the UK market might decouple from the continental one in turbulent periods. Second, to facilitate full market integration further infrastructure development across Europe is essential.

An analysis of the role of order quantity in supply chains with uncertainties from demand and/or supply

We study the role of order quantity in supply chains when uncertainty exists in demand and/or supply. We consider a supply chain model consisting of one supplier and one buyer. It is widely known that order quantity is a tool used by the buyer to deal with the potential supply shortage or supply surplus due to the uncertainties in supply and/or demand. However, it is unclear how the decision makers use order quantity to hedge against different types of uncertainties. In addition, to comprehensively explore the role of order quantity, we analyze the impact of order quantity on coordinating contract design. In the model with demand uncertainty only, order quantity is a tool applied to hedge against the uncertainty in both of the decentralized system and the centralized system, and the buyer’s incentive for order quantity in the decentralized system is lower than that in the centralized system. To achieve coordination, the buyer’s ordering strategy has to be conservative in both demand and supply, i.e., order quantity is the same as the supply and less than the maximal demand. In the model with supply uncertainty only, the order quantity is used as a tool to hedge against the uncertainty but not in the centralized system. To achieve coordination, the buyer’s ordering strategy has to be conservative in supply but aggressive in demand, i.e., order quantity is less than the maximal supply but higher than the maximal demand. Furthermore, compared to the demand uncertainty, the supply uncertainty makes it more difficult to design a quantity-based coordinating contract. This can be seen from two facts. Finally, in the hybrid model with both uncertainties, the role of the order quantity and the design of the quantity-based coordinating contracts are similar to the model with supply uncertainty only. These results imply that the supply uncertainty seems to have a more significant impact on the application of order quantity than the demand uncertainty.

Learning to schedule heuristics for the simultaneous stochastic optimization of mining complexes

The simultaneous stochastic optimization of mining complexes is a large-scale stochastic combinatorial optimization problem that simultaneously manages the extraction of materials from multiple mines and their processing through interconnected facilities to generate a set of final products, while taking into account geological (material supply) uncertainty to manage the associated risk. Existing methods do not offer solutions to such a complex problem in a reasonable time. This work proposes a data-driven framework for heuristic scheduling in a fully self-managed hyper-heuristic to solve the simultaneous stochastic optimization of mining complexes. The proposed learn-to-perturb hyper-heuristic is a multi-neighborhood simulated annealing algorithm that selects the heuristic (perturbation) to be applied in a self-adaptive manner using reinforcement learning to efficiently explore the local search that is best suited to a particular search point. By learning from data that describes the performance of the heuristics, a problem-specific ordering of heuristics that collectively find better solutions faster is obtained. To the best of our knowledge, this research proposes the first data-driven heuristic search tree for mine planning. Results from several instances of two types of large-scale industrial mining complexes show a reduction of up to 80% in execution time and an order of magnitude reduction in primal suboptimality compared to state-of-the-art methods.

Robust Optimal Scheduling of Integrated Energy Systems Considering the Uncertainty of Power Supply and Load in the Power Market

The integrated energy system is a complex energy system that involves multi-stakeholder and multi-energy coordinated operations. The key to improving its scale and sustainable development is to construct a better-integrated energy system dispatching method which is suitable for the power market. However, the randomness of the supply side and load side of the integrated energy system brings further challenges to system planning and scheduling. Therefore, the optimal scheduling method of an integrated energy system considering the uncertainty of supply and demand in the market environment is studied in this paper. Firstly, the uncertainty models of the supply side and load side of the integrated energy system are established. Then, the optimal scheduling model based on robust chance constraint is established. The reserve capacity constraint is set as a chance constraint with a certain confidence level to maximize the system profit in the power market. Finally, simulations show that the proposed method not only guarantees the robustness of the system but also improves the economy of the system. The method provides ideas for exploring the development mechanism and strategy of integrated energy systems in the electricity market environment.

Robust sustainable canola oil-based biodiesel supply chain network design under supply and demand uncertainty.

The excessive consumption of fossil fuels has sparked debates and caused environmental damage, leading the global community to search for a suitable alternative. To achieve sustainable development goals and prevent harmful climate scenarios, the world needs to increase its use of renewable energy. Biodiesel, a clean and eco-friendly fuel with a high flash point and more lubrication than petroleum-based fuels, and without the emission of harmful environmental gases, has emerged as one of the fossil fuel alternatives. To promote the mass-level production of biodiesel, a sustainable supply chain (SC) that does not depend on laboratory production is necessary. For this purpose, this research proposes a multi-objective mixed-integer non-linear mathematical programming (MINLP) model to design a sustainable canola oil-based biodiesel supply chain network (CO-BSCND) under supply and demand uncertainty. This mathematical model aims to minimize the total cost (TC) and total carbon emission while maximizing the total number of job opportunities simultaneously. A scenario-based robust optimization (SBRO) approach is applied to deal with uncertainty. The proposed model is implemented in a real case study in Iran, and numerical experiments and sensitivity analysis are conducted to demonstrate its applicability. The results of this research demonstrate that designing a sustainable supply chain network for the production and distribution of biodiesel fuel is achievable. Moreover, this mathematical modeling makes mass-scale production of biodiesel fuel a possibility. In addition, the SBRO method adopted in this research enables managers and researchers to explore the design conditions of the supply chain network by controlling the uncertainties that affect it. This approach allows the chain's performance to be as close as possible to the actual conditions. As a result, the SBRO method enhances the efficiency of the supply chain network and boosts productivity toward achieving desired goals.

The Distributionally Robust Inventory Strategy of the Overconfident Retailer under Supply Uncertainty

To factor in the retailer’s overconfidence when dealing with the inventory problem with supply uncertainty, this paper develops a distributionally robust optimization model by only considering the mean and variance of the yield rate distribution. We first show that overconfidence would prompt the retailer to order more under low-profit conditions, whereas it reduces the order quantity under high-profit conditions. The analysis results imply that the pull-to-center effect still exists when only supply uncertainty applies, and the asymmetry that the deviation is higher under low-profit conditions is proved. The performance of overconfidence is also characterized in the expected profits of both retailer and supplier. Numerical studies show that even though the retailer may suffer losses, the supplier can benefit from the retailer’s overconfidence in the low-profit case, which would positively increase the joint expected profit of the supply chain. Two extensions to the base model are also considered, including the scenario with both demand and supply uncertainties and an overconfident multi-product problem with budget constraints. This research provides tractable results to predict how the decision-maker is biased, and such insights would help the applications of de-biasing techniques in practice.

Determining number of suppliers, duration of supply cycle and allocation to in-house production under supply uncertainty

Supply chain management has become an increasingly critical strategic capability to gain competitive advantage in the global business environment. The COVID-19 pandemic, coupled with geopolitical conflicts, has caused unprecedented disruptions to global supply chains. Suggested strategies to cope with the uncertain supply environment include better supplier selection, review of the number of demand periods in the supply cycle and the possibility of in-house production. This research proposes a novel approach that considers the aforementioned strategies by considering that both the suppliers and the manufacturer have the capability to increase supply and production, respectively. The proposed model is applicable in manufacturing environments characterized by mature products and a well-consolidated pool of preferred suppliers. The effectiveness of the proposed model is evaluated through a set of experiments, revealing significant trade-offs among decision variables and model parameters. These trade-offs provide valuable managerial insights, such as the optimal percent allocation of in-house manufacturing increasing when both supplier and in-house manufacturing flexibility increase up to a certain point, but then decreasing. Overall, this research presents a valuable contribution to supply chain management literature, highlighting important insights that can inform decision-making in complex manufacturing environments.

The contribution of cleaner production in the material industry to reducing embodied energy and emissions in China's building sector

Quantifying embodied energy and emissions (EEEs) can help clarify the consumption-based responsibilities of the building sector. Material consumption accounts for over 80% of the total EEEs, which is substantially affected by cleaner production measures in the material industry. Previous studies have not accurately calculated the EEEs by overlooking or oversimplifying these effects. This study aims to address this issue by making predictions about the building sector of Mainland China, which includes the floor areas of new buildings and the related material consumption between 2020 and 2035. In total, 29 cleaner production measures for steel, cement, brick, glass, and timber industries are collected. The embodied energy and CO2, SO2, NOx, and PM emissions reduction effects of the adopted measures are quantified and analysed using conservation supply curve and uncertainty analysis approaches. The results show the EEEs from material consumption will decrease by 45.9–60.7% from 2020 to 2035, to which the cleaner production measures contribute about half. The EEEs have significant spatial disparity. For example, Guangdong Province takes 2% of China's land area, but undertakes 7–9% of the domestic EEEs. The uneven distributions may cause heavy energy and environmental pressures in some provinces. The specific costs of the EEEs reductions differ by 2–4 orders of magnitude, while 30.4–34.5% and 65.5–67.8% of the embodied energy and CO2 emission reduction effects are cost-effective. This study reveals the vital role of cleaner production in EEEs reductions and provides critical insights into the green development of the building sector.

Optimizing sustainable biomass–coal co-firing power plant location problem under ambiguous supply

Promoting biomass–coal co-firing power generation technology in existing coal-fired power plants is a practical and relatively low-cost short-run choice for the coal-fired power industry to reduce carbon emissions and develop sustainably. However, the uncertainty of biomass supply complicates the location of co-firing power plants among existing coal-fired plants. To address the challenge that only limited historical data of uncertain supply are available, this paper proposes a new ambiguity distribution set to characterize the uncertain supply capacity, supply price, and supply quality (i.e., energy conversion coefficient). Accordingly, a novel bi-objective distributionally robust fuzzy optimization model is developed for the sustainable co-firing power plant location (SCPPL) problem to balance conflicting objectives of minimizing total cost and maximizing carbon emission reduction. Subsequently, the computationally tractable counterpart of the proposed model is derived against the worst-case distribution by reformulating the distributionally robust credibility objective and constraints as their solvable equivalent forms. Furthermore, the Pareto optimal solutions of the proposed bi-objective model are generated using the augmented ϵ-constraint method. Finally, a realistic case study of Heilongjiang Province in China is conducted to verify the feasibility and effectiveness of the proposed optimization method. The experimental results demonstrate that the proposed model can hedge against the distributional ambiguity of uncertain parameters and provides a robust co-firing power plant location strategy. By comparison with the nominal distribution model and analysis of the sensitivity of selected parameters, management insights are summarized for decision-makers.

Decision Support System for Safety Stock and Safety Time Buffers in Multi-Item Single-Stage Industrial Supply Chains

Safety stock and safety time are two well-known stock buffering solutions of material requirements planning (MRP) processes to mitigate against supply and demand uncertainty. While the significance of proper inventory in managing change has been extensively researched, supply chain management research on safety period has gotten less attention. Earlier operations quantitative studies, in particular, have often evaluated the utilization of such stock buffers separately, rather than combined. Considering dynamic demands and stochastic timescales, the authors present the decision support system (DSS) that handle the efforts to integrate appropriate safety stock and safety time judgments at the system level in multi-item single-stage multi -supplier industrial supply chains. The DSS concept is formulated as a mixed bi-objective approach that optimizes upstream storage costs and -service levels at the same time, presenting decision-makers with alternative not dominated feasible solutions.

How can pilot and demonstration plants drive market formation? Lessons from advanced biofuel development in Europe

This paper analyzes through what enabling mechanisms pilot and demonstration plants (PDPs) reduce supply and demand uncertainties, and thereby contributing to the market formation for novel sustainable technologies. The analysis builds on three case studies within the advanced biofuel development in Europe. For each case, we construct a narrative of the technology development and derive detailed insights into how technology actors use PDPs to drive market formation. We develop a comprehensive analytical framework, which highlights how PDPs contribute to supply uncertainty reduction through three main enabling mechanisms: building credibility for the technology, business ecosystem orchestration, and technology learning. The corresponding enabling mechanisms behind demand uncertainty reduction include technology standardization, constructing the narrative, and the creation of legitimacy for the technology. The paper also unfolds the composite activities of each mechanism, and outlines implications for technology developers, policymakers, as well as for the research community.

UKRAINE CONFLICT’S IMPACT ON PAKISTAN FOREIGN POLICY

The situation in Ukraine has significantly impacted Pakistan's foreign policy, affecting a variety of areas and posing difficulties and options for the nation's international relations. This essay seeks to offer a thorough study of the conflict's repercussions on domestic and international politics, as well as its ramifications for trade, energy, food security, and shifting alliances. Despite Ukraine's distance from Pakistan, the crisis there has had an impact on world politics, changing the balance of power and necessitating tactical changes. Understanding Pakistan's reaction in its foreign policy depends on knowing how the conflict will affect regional and global affairs. This study investigates the effects of the conflict and sheds light on Pakistan's participation in the changing geopolitical environment. The conflict may have an impact on trade, food security, and the energy sector, to name a few industries. Global supply chains and trade routes could be disrupted, which would change economic dynamics. Additionally, because the conflict may affect trade routes and access to resources, issues to food security may appear. Geopolitical changes may cause volatility and uncertainty in the energy sector's energy supply and pricing. Comparative analysis of Ukraine's exports of military equipment to Pakistan and India can shed light on how the regional defense partnerships are changing. Understanding the choices both nations made when purchasing military hardware from Ukraine aids in clarifying Pakistan's foreign policy philosophies and strategic considerations. Pakistan's foreign policy has been significantly impacted by the situation in Ukraine. A thorough comprehension of the dynamics at play can be attained by analyzing the conflict's effects on various sectors, looking at shifting partnerships, and comprehending the decisions and difficulties faced by Pakistan. Policymakers, academics, and analysts who want to understand Pakistan's foreign policy in the wake of the Ukraine conflict can benefit greatly from this research.

The extent, drivers and production loss of farmland abandonment in China: Evidence from a spatiotemporal analysis of farm households survey

The uncertainty of the global food supply has increased, and land abandonment has affected food security. Based on nationally representative farm households in 1995, 2002, 2008 and 2020, this paper systematically reveals the extent, drivers, and production loss of farmland abandonment in China, providing a scientific basis for the comprehensive management of land abandonment. The extent of farmland abandonment has been increasing since 1995; 1/5 of farmers have abandoned their farmland, and 1/10 of farmland has been abandoned. The number of counties recording farmland abandonment increased from 34 (11.15%) at the beginning of the study period to 224 (73.44%) by the end, and these counties were mainly distributed in the hilly and mountainous areas along the Yangtze River as well as in Southeast, Southwest, Northwest, and Northeast China. Farmland abandonment is the result of a combination of multiple factors, including basic farmland conditions and socio-economic and ecological factors, among which the low agricultural comparative income caused by agricultural labour shortages and poor-quality farmland conditions is the root cause. In 2020, for example, the ratio of farmland abandonment in major grain-producing and non-grain-producing regions was 7.38% and 16.94%, respectively, resulting in a total loss of 49.23 million tons of grain, which is 7.36% of the total national grain output. The production loss experienced by China could feed 123 million people based on the per capita annual food consumption estimates of 400 kg. Currently, farmland abandonment poses a threat to China's food security, and the government should gradually improve the quality of farmland through comprehensive land management, improve rural infrastructure and develop small-scale machinery suitable for hilly and mountainous areas.

Modeling Multi-Objective Optimization with Updating Information on Humanitarian Response to Flood Disasters

Unpredictable natural disasters brought by extreme climate change compound difficulties and cause a variety of systemic risks. It is thus critical to provide possibilistic scheduling schemes that simultaneously involve emergency evacuation and relief allocation. But the existing literature seldom takes emergency evacuation and relief supplies as a joint consideration, nor do they explore the impact of an unpredictable flood disaster on the scheduling scheme. A multi-stage stochastic programming model with updating information is constructed in this study, which considers the uncertainty of supply and demand, road network, and multiple types of emergency reliefs and vehicles. In addition, a fuzzy algorithm based on the objective weighting of two-dimensional Euclidean distance is introduced, through moderating an effect analysis of the fuzzy number. Computational results show that humanitarian equity for allocating medical supplies in the fourth period under the medium and heavy flood is about 100%, which has the same as the value of daily and medical supplies within the first and third period in the heavy scenarios. Based on verifying the applicability and rationality of the model and method, the result also presents that the severity of the flood and the fairness of resources is not a simple cause-and-effect relationship, and the consideration of survivor is not the only factor for humanitarian rescue with multi-period. Specifically, paying more attention to a trade-off analysis between the survival probability, the timeliness, and the fairness of humanitarian service is essential. The work provides a reasonable scheme for updating information and responding to sudden natural disasters flexibly and efficiently.

Optimization Methodologies for Reserve with Large-scale Renewables Integration

Renewable energy has unpredictable intermittency and variability, increasing the complexity of grid dispatch. Fuzziness theory is introduced under the uncertainty of large-scale intermittent power supply. Renewable power is characterized by fuzziness parameters. Meanwhile, the conventional deterministic unit commitment model is transformed into a chance-constraint unit commitment with fuzziness parameters. The explicit equivalence classes of chance constraints with triangular fuzziness parameters are presented to clarify chance constraints. Also, the effectiveness of the proposed model is validated in a standard IEEE test system.

A holistic, integrated supply-production–distribution problem in the dairy industry under uncertain supply and demand

Although the simultaneous planning of several relevant operations in the form of an integrated model has been gaining prestige in recent years on miscellaneous topics, the dairy industry, due to multifaceted and complex planning aspects of each level, has been less investigated. Hence, this study introduces an unprecedented, integrated supply-production–distribution problem under uncertain supply and demand in the dairy industry. In this regard, a novel mathematical model is developed to minimize the total cost of the supply chain, wherein a broad range of decisions, including assignment, routing, and scheduling, are reflected. More importantly, each stage encompasses a wide range of realistic concerns, to name a few, a comprehensive supply network, the blending process, various time limitations, and coordination among means of transportation, such as lorries and different multi-compartment vehicles, are considered in the supply phase. Additionally, a multi-period, multi-product, multi-stage production system with various production capacities and perishability requirements is considered in the production phase. The considerations of multi-compartment vehicles alongside the assignment of product-oriented time windows and products' early and late delivery are also reflected in the distribution phase. Furthermore, a set-induced robust optimization technique with the interval-ellipsoidal-polyhedral uncertainty set is applied to handle supply and demand uncertainties. Finally, a real case study is investigated to demonstrate the applicability and validation of the proposed model and the uncertainty approach. The obtained results revealed that considering external cooling units and the blending process can diminish the system’s total costs by approximately 16% and 11%, respectively.

Managing Operations of a Hog Farm Facing Volatile Markets: Inventory and Selling Strategies

Problem definition: We study a dynamic finishing-stage planning problem of a pork producer who at the beginning of each week gets to see how many market-ready hogs she has available for sale and the current market prices. Then, she must decide which hogs to sell to a meatpacker and on the open market and which hogs to hold until the following week. The farmer is contracted to deliver a fixed quantity of hogs to the meatpacker each week priced according to a contractually predetermined market index. If the farmer underdelivers to the meatpacker, she pays a contractually predetermined unit penalty also linked to a market index. Biosecurity protocols prevent the farmer from buying hogs on the open market and selling them to the meatpacker. The farmer can, however, use the open market to sell hogs for prevailing market prices. Methodology/Results: We treat the problem as a dynamic, multiitem, nonstationary inventory problem with multiple sources of uncertainty. The optimal policy is a threshold policy with multiple price-dependent thresholds. The computational complexity required to evaluate the thresholds is the biggest impediment to using the optimal policy as a decision-support tool. So, we utilize an approximate dynamic programming approach that exploits the optimal policy structure and produces a sharp heuristic that is easy to implement. Managerial implications: Numerical experiments calibrated to a pork producer’s data reveal that the optimal policy with the heuristically estimated thresholds substantially improves the existing practice (around 25% on average). The success of the proposed model is attributed to recognizing the value of holding underweight hogs and effectively hedging supply uncertainty and future prices—an insight missed in the planning actions of the current practice.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1216 .

Modularity of food-sharing networks minimises the risk for individual and group starvation in hunter-gatherer societies.

It has been argued that hunter-gatherers' food-sharing may have provided the basis for a whole range of social interactions, and hence its study may provide important insight into the evolutionary origin of human sociality. Motivated by this observation, we propose a simple network optimization model inspired by a food-sharing dynamic that can recover some empirical patterns found in social networks. We focus on two of the main food-sharing drivers discussed by the anthropological literature: the reduction of individual starvation risk and the care for the group welfare or egalitarian access to food shares, and show that networks optimizing both criteria may exhibit a community structure of highly-cohesive groups around special agents that we call hunters, those who inject food into the system. These communities appear under conditions of uncertainty and scarcity in the food supply, which suggests their adaptive value in this context. We have additionally obtained that optimal welfare networks resemble social networks found in lab experiments that promote more egalitarian income distribution, and also distinct distributions of reciprocity among hunters and non-hunters, which may be consistent with some empirical reports on how sharing is distributed in waves, first among hunters, and then hunters with their families. These model results are consistent with the view that social networks functionally adaptive for optimal resource use, may have created the environment in which prosocial behaviors evolved. Finally, our model also relies on an original formulation of starvation risk, and it may contribute to a formal framework to proceed in this discussion regarding the principles guiding food-sharing networks.