Supply Uncertainty Research Articles

Multi-objective simulation-optimization for integrated automated storage and retrieval systems planning considering energy consumption

An Automated Storage and Retrieval System (AS/RS) is one of modern technologies in warehouse operation. Despite many advantages offered by AS/RS such as improving accuracy, efficiency, and safety, AS/RS operation is very complex started from strategical, tactical, to operational level. Reaching optimal combination for all decisions become important. However, due to the dynamic and combinatorial complexity as well as uncertainty in supply – demand, it cannot be solved through the general mathematical optimization. Therefore, this study introduces simulation–optimization (SO) framework for integrated AS/RS planning considering 7 decisions at a time. Furthermore, a comprehensive mathematical model for measuring AS/RS energy consumption is formulated. The proposed framework is implemented in the China’s warehouse company for optimizing multi-objective namely energy consumption and travel time per unit. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed as metaheuristics algorithm and discrete-event simulation is modeled based on FlexSim. The results produce non-dominated solutions that are further summarized through clustering algorithm resulting in 4 different clusters with significantly different impacts. This study provides insightful analysis and managerial implications for reaching near-global optimum in AS/RS planning towards green operation.

Implications for control systems in highly volatile energy markets: Using a high purity distillation electrification case study

The transition to increasingly renewable electricity grids and the electrification of industrial processes are two recent developments promising to be potential solutions towards net zero in industrial production processes. Generally, grid electricity is perceived as a stable and reliable resource that can be accessed on demand. However, that is not always the case for large industrial users (100 MW+), and a key future challenge that must be overcome is the inherent uncertainty of the electricity supply by renewable sources, as well as in pricing. In methanol production, the concept of using electrolysis and CO2 capture is being explored as a way of replacing reforming to produce syngas for “green” methanol production. With the omission of the reforming process which supplies energy as steam for the process, there is now a need to electrify the affected downstream unit operations. Distillation is a key unit operation in chemical operations that consumes large amounts of energy for reboiler duty and holds the key to making on-specification AA-grade methanol, a precursor chemical. With the volatility of the electricity market, hence supply, these columns must be able to withstand a sudden large reboiler duty drop. This paper developed a novel process operations and control principle to allow for the flexible operation of a high-purity methanol distillation column in volatile energy markets. At its core is the concept of using a distillation column as a “process battery”. Results obtained from a validated process simulation show that the novel “process battery” operations principle can withstand a supply-side change as great as 75% within a time span of 5 min without breaking complex quality and performance specifications of the column. This control philosophy is a major departure from the traditional process control and operating regimes that are focused on maintaining steady operations under all conditions.

Supply chain performance analysis using the SCOR method

The current era of globalization requires companies to meet market demands in order to get the right tactics or strategy. This research was conducted at PT. Budi Starch & Sweetener has several obstacles in carrying out its supply chain activities such as delivery delays, supply uncertainty and problems in the production process. So, measuring Supply Chain performance is very important so that companies can know the extent of Supply Chain performance achieved by the company. Supply chain performance measurement is carried out using the Supply Chain Operations Reference (SCOR) method with the Key Performance Indicator (KPI) validation phase, calculation of actual KPI values, and weighting of metrics for each level using the Analytical Hierarchy Process (AHP) method. After processing the data, the result was that 16 performance indicators were selected from a total of 36 performance indicators. To determine the main criteria and priorities, performance indicators are weighted using the AHP method. The calculation result of the total performance value is 76,41, so the performance measurement at PT. Budi Starch & Sweetener Tbk is included in the good scale. However, the results of data processing of actual values which were then normalized to Snorm De Boer still contained 5 indicators which showed a value of less than 90 and required improvement, namely accuracy of estimates, raw material planning, on-time delivery performance by suppliers, compliance with production schedules, and number of problems. machine. It is hoped that companies can issue appropriate policies regarding of these five performance indicators, so that the level of achievement of Supply Chain Management (SCM) targets in companies can be further increased.

Strategy For Procurement Of Imported Sesame Seed Raw Materials At PD Linggar Sari

The resources of each country are different. These results in countries trading internationally. Sesame is a commodity that has potential in the culinary industry. Sesame is categorized as quality if the cultivation of the plant is carried out on dry land or places with low rainfall. Most fellow producers in Indonesia have poor quality standards, and some even fail to harvest due to the unfavorable climate for producing sesame commodities. PD Linggar Sari is a small and medium enterprise (UKM) that produces sesame-married cakes and one-time seeds as complementary raw materials. Initially, PD Linggar Sari used 500 kg of imported Indian sesame seeds weekly, which were obtained through local importers. However, due to supply uncertainty, Pd Linggar Sari switched to local sesame seeds. However, this transition has an impact, namely a decrease in income of approximately 15% to 20% per year due to a decrease in product quality. This research aims to provide information to PD Linggar Sari regarding analyzing imported sesame seed procurement strategies. The method used is the Analysis Hierarchy Process with the types of data used, namely secondary and primary data. There are three alternatives for procuring imported sesame seeds: local importers, global marketplaces, and direct imports. The results of the analysis of procuring imported sesame seeds show that direct importation is an alternative strategy for procuring imported seeds at PD Linggar Sari.

Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics

Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory policies must be determined for multiple locations in the network, and transportation of hydrogen from the production location to customers must be scheduled. At the same time, production patterns of hydrogen are intermittent, which affects the possibilities to realize the planned transportation and inventory levels. To provide policies for efficient transportation and storage of hydrogen, this paper proposes a parameterized cost function approximation approach to the stochastic cyclic inventory routing problem. Firstly, our approach includes a parameterized mixed integer programming (MIP) model which yields fixed and repetitive schedules for vehicle transportation of hydrogen. Secondly, buying and selling decisions in case of underproduction or overproduction are optimized further via a Markov decision process (MDP) model, taking into account the uncertainties in production and demand quantities. To jointly optimize the parameterized MIP and the MDP model, our approach includes an algorithm that searches the parameter space by iteratively solving the MIP and MDP models. We conduct computational experiments to validate our model in various problem settings and show that it provides near-optimal solutions. Moreover, we test our approach on an expert-reviewed case study at two hydrogen production locations in the Netherlands. We offer insights for the stakeholders in the region and analyze the impact of various problem elements in these case studies. Funding: This project received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under [Grant Agreement 875090]. The Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme, Hydrogen Europe and Hydrogen Europe Research. A. H. Schrotenboer received support from the Dutch Science Foundation (Nederlandse Organisatie voor Wetenschappelijk Onderzoek; NWO) through [Grant VI.Veni.211E.043].

The Effect of Supply Uncertainty on Dynamic Procurement and Pricing Strategies Under Lost Sales

The uncertainty in the supplier’s material flows has become a norm rather than an exception in supply chains. The supply uncertainty can result in unexpected inventory shortfall, amplifying lost sales. However, the design of inventory replenishment and product pricing policy to mitigate both supply uncertainty and demand loss remains unexplored. This is because the resulting dynamic planning problem is highly nonconcave and thus intractable. To address this challenge, we propose an approach that focuses on a class of intuitively appealing and practically plausible policies. Specifically, as the level of on-hand inventory increases, we expect an increased amount of demand fulfillment and a decreased product price. Applying the notions of stochastic functions, we show that, under general conditions of the stochastic supply and demand functions, the dynamic planning problem becomes a concave optimization problem over the restricted policy class. We further reduce the set of candidate policies to a refined class by excluding the dominated policies. A refined policy can be easily computed, and appropriately selected refined policies produce close-to-optimal profits. These developments allow us to evaluate the consequences of demand loss. In particular, demand retention through backordering can be beneficial when overstocking is costly in relation to understocking, but the benefit is insensitive to supply and demand uncertainties. Moreover, inventory-based dynamic pricing is more valuable in mitigating supply risk under lost sales than under backordering.

Calculation of Power Purchase Imbalance Costs for Grid Enterprises’ Agents Considering Uncertainty of Supply and Demand and Price Fluctuations in the Context of Beijing-Tianjin-Tangshan Unified Spot

Abstract The regional unified electricity spot market plays an important role in promoting the optimal allocation of power resources over a wider range and cross-regional consumption of renewable energy. This paper examines the supply and demand uncertainty and price volatility of power grid agency power purchases in the context of the Beijing-Tianjin-Tangshan unified spot purchase. It delves into the specific mechanism of unbalanced costs in agency power purchase decisions and investigates the specific channeling methods of unbalanced costs in the dual-track system. Based on the dual-track unbalanced cost under the unified spot market, a dual-track power balance model is proposed based on the typical daily load curve, and a time-based unbalanced cost calculation model is designed. Using the typical daily electricity load data from the power supply enterprise in X province as an example, the calculation and simulation of unbalanced costs are carried out. The spot electricity price fluctuates between 0.25 yuan/(kW•h) and 0.45 yuan/(kW•h) in different periods. The overall unbalanced power purchased by the power grid in the load trough stage is only 6.24% of the total monthly electricity consumption. Still, the unbalanced cost in the study period is of a deficit nature, and its average value reaches −268 million yuan. The calculation method of unbalanced costs caused by agency power purchases clearly reveals the unbalanced cost-sharing mechanism in the context of the unified spot.

1 A Markov Decision Processes Model for Stochastic Inventory Problem Under Uncertainty in Supply

1 A Markov Decision Processes Model for Stochastic Inventory Problem Under Uncertainty in Supply

Optimal consumer capacity investment in solar panel system under supply and demand uncertainty

Our paper analyzes the optimal capacity investment in solar panels system for consumers. During the daytime, solar panel system absorbs energy from the sun and supplies the energy generated to the building structure. Our model takes into consideration the variations in the daily consumption of electricity and the variations in the daily available sun energy. We model these variations in our paper as uncertainty in energy demand and uncertainty in energy supply. Excessive usage of electricity that is beyond the energy generation of the solar panels are bought at a higher price from the utility company than the payment received from the utility company for excess energy sold to the grid. We analytically solve for the optimal quantity of solar panels to purchase and install. Our paper fills the research gap in quantifying the impact changes in solar energy yield rate, consumption, and sell-back rate has on the consumer’s optimal investment decision in solar panels. We find that the optimal quantity of solar panels increases with the cost of energy during the daytime and payment for excess energy sold to the grid. Interestingly, when solar yield increases, the optimal solution only increases initially, and then it will eventually decrease. Furthermore, our analytical models provide consumers with a decision support system to determine the optimal number of solar panels to purchase and install while taking into consideration the random yield of solar panels, variations in energy consumption, and potential for sell back of excess unused energy generated under complex billing structures from the utility company. Our mathematical models are adaptable to policy changes such as rebates and purchase and sell back rates for electricity. Our numerical study validates the mathematical models and further analyzes the impact changes in solar energy yield rate, energy consumption, and sell-back coefficient have on the consumer’s capacity investment decision in solar panels. Our numerical studies find that the lower the reliability factor of solar yield, the more impact the daytime energy cost has on the optimal quantity. When the sell-back coefficient exceeds a certain threshold, the optimal quantity of solar panels increases exponentially.

Critical assessment of supply chain risk, vulnerability and uncertainties in Indian agriculture during pandemic

Critical assessment of supply chain risk, vulnerability and uncertainties in Indian agriculture during pandemic

Effect of logistics integration and demand and supply uncertainties on manufacturing performance: A mediating role of product characteristics

This study examines the influence of distribution convergence, delivery lead times, manufacturing difficulties, material management, and procurement on company performance. Primary and secondary data from small and large-scale manufacturing enterprises will be gathered and analyzed using structured and explorative methods, with different analyses applied for causal and cross-comparison outcomes. It will be used as a mathematical model for the following investigation. It assessed the variables' correlation and function as a mediator between them. Cronbach's alpha, a measure of internal consistency, was used to assess overall variable agreement. The study shows the role of interdependence in logistics convergence, pricing fluctuation, and demand and supply uncertainty. Demand and supply uncertainty is one of lead time and substantially impacts outcomes. In this research, procurement lead time significantly impacts the relationship between overall market volatility and supply and demand characteristics. Efficacious Manufacturing requires well-planned procurement, as well as extensive logistical integration.

Techno-economic analysis for design and management of international green hydrogen supply chain under uncertainty: An integrated temporal planning approach

The international green hydrogen supply chain (HSC) is anticipated to play a pivotal role in the ongoing energy transition. Our research endeavors to assess the implications of considering fast timescale volatility in renewable energy sources and uncertainties in demand on the design and evaluation of the international green hydrogen supply chain. To achieve this objective, we introduce a comprehensive bi-level optimization method for evaluating the economic viability of the HSC between nations. In the upper level of the optimization, capacity investments are determined using Bayesian optimization and Hyperband (BOHB) for multiple components, including turbines, PV panels, batteries, water electrolyzers, hydrogen liquefaction plants, hydrogen tanks, and liquid hydrogen (LH2) ships. The lower level focuses on the optimal scheduling of LH2 shipments and the operation of each facility, utilizing a mixed-integer linear programming (MILP) model that considers lead time and uncertainties in both supply and demand. We conduct case studies involving nine projects, encompassing three potential import and export countries, to demonstrate and analyze the effectiveness of the proposed framework. Furthermore, we delve into the impacts of various factors, such as economies of scale, lead time, and the minimum operating constraint of the electrolyzer, on the economic performance of the green HSC project. The study also explores the optimal balance between facility installation scales and their relationships with techno-economic parameters, providing valuable insights for future research, technological advancement and supply chain design.

A two-stage robust configuration optimization framework for integrated energy system considering multiple uncertainties

The optimal configuration of integrated energy system (IES) is the cornerstone of efficient investment and low-carbon operation, which is of high importance for realizing energy system sustainable transformation. Yet, the intermittency of renewable energy and uncertainty of multiple energy demands pose severe challenges to IES configuration and operation. To address the issue, this research presents a two-stage robust configuration optimization model incorporating the multiple uncertainties of supply and demand. The model takes economic benefits, environmental benefits and energy utilization efficiency as objective functions, and is executed by column and constraint generation algorithm. A case study justifies the proposed model. The findings indicate the uncertainty from renewable energy and demand raises the equipment capacity and results in an increase of investment cost. Contrasted with the deterministic scenario, the total costs in the uncertainty scenario increase from 7043795.8CNY to 7785296CNY, but the performance shows more robust and the loss of load penalty is saved up to 296888CNY. Sensitivity analysis reveals energy price fluctuations primarily impair the operating costs, whereas they cause moderate effects on the configuration results. Load fluctuations cause greater consequences on both configuration and operating costs. The research provides references for IES configuration under multiple uncertainties with renewable energy penetration.

Reactive power optimization control for multi-energy system considering source-load uncertainty

Reactive power control in power systems is an important way to ensure stable system operation. However, as the coupling between multi-energy systems increases, the uncertainty of supply and demand in the power system poses new challenges for reactive power control. Therefore, the main objective of this study is to perform reactive power balance control of the grid in a multi-energy system. Firstly, we studied the characteristics of reactive power demand during the energy transport of a multi-energy system. In addition, we studied the reactive power output characteristics of various power sources and their uncertainties. The reactive power balance of the multi-energy system and its uncertainty model are established. Then, we studied the adjustable pressure limits of the energy medium transmission pipelines of the heat supply network and the gas pipeline network. The reactive power demand capacity of the system is determined according to the system's energy condition and safe operation boundary. Finally, we established a reactive power optimization simulation model of the multi-energy system, considering the heat-gas network constraints. The study proposes that the optimal reactive power control method can improve the voltage stability of the multi-energy system.

Physical Separations for Rare-Earth Beneficiation of the Nechalacho Deposit

The rare-earth elements (REEs) are strategic metals which are indispensable to the development of modern defence systems, electronic applications, and green technologies. The growing economic and strategic importance of these sectors, coupled with uncertainty in the global supply, has led to the development of many new deposits around the world. Many of these deposits, such as the Nechalacho deposit, are complex and contain multiple rare-earth element-bearing minerals (REMs) for which there is limited processing knowledge. This study explores a physical-separations-based flowsheet to beneficiate the Nechalacho deposit, which employs a spiral concentrator to preconcentrate the ore at a relatively coarse particle size (d80 = 120 μm), before further size reduction (d100 = 53 μm) and separation using a Mozley laboratory shaking table and two stages (low- and high-intensity) of magnetic separation. QEMSCAN was used to understand the effectiveness of each stage of separation and provide recommendations to improve the process. Although optimisation would be required, the results demonstrate that the physical-separations-based flowsheet could be an effective method of beneficiation.

Energy storage as a service to achieve a required reliability level for renewable-rich standalone microgrids

Energy storage systems (ESSs) can help to reduce the intermittency and uncertainty of renewable energy supplies in power systems. ESSs are critical components of renewable-rich standalone microgrids (SMGs) to balance power generation and load demand, which is referred to as reliability. To achieve the same level of reliability as conventional power systems for renewable-based SMGs, significant investment in ESSs is required. However, due to the high investment costs of ESSs, the installation of large ESSs will not result in an affordable solution for achieving renewable SMG at the required reliability. As a result, this paper proposes a new sharing concept for ESS, namely energy storage as a service (ESaaS), to be implemented across microgrids as a low-cost alternative for improving reliability. In the proposed ESaaS concept, microgrids can use ESS from an ESS provider as required for different timeframes such as monthly, weekly, or daily, depending on the renewable resources and load profile characteristics. In this paper, the use of ESaaS is investigated over a range of timeframes for a 100 % renewable-based SMG with photovoltaic (PV), wind turbine (WT), and ESS. The SMG reliability is evaluated using Monte Carlo simulation both before and after the ESaaS strategy has been implemented. To evaluate the ESaaS affordability in improving the reliability of an SMG, this paper proposes the criteria of marginal cost of reliability, which indicates the rate of additional investment amount per percentage of reliability improvement. The marginal cost of reliability combines the economic and technical aspects of ESaaS in one simple criterion for effective decision-making among investment strategies such as different timeframes of ESaaS or permanent ESS. The simulation results show the ESaaS based on daily contract results in a lower marginal cost of reliability for the case study. To validate the effectiveness of the proposed ESaaS approach using marginal cost of reliability, the levelized cost of electricity (LCOE) is also calculated for different strategies of reliability improvement. The results confirm that the lowest LCOE is obtained using the strategy that provides the lowest marginal cost of reliability for the case study. In addition, a sensitivity analysis is performed to assess the difference in marginal cost of reliability under various uncertainties associated with the installed capacity of PV and WT, and the cost of utilising the ESaaS.

Analysis of the Potential of Watersheds as a Source of Raw Water in the Batang Integrated Industrial Area (KITB) in Cluster 1

The Batang Integrated Industrial Area (KITB) is located in Banyuputih District, Batang Regency, Central Java, with a total area of 4,300 Ha and currently what has been developed is cluster 1 covering an area of 3100 Ha. Water has a central role in human and industrial activities, with potential impacts on health, worker safety, and the environment. The main problem is the uncertainty of raw water supply which poses a risk to Environment Health Safety (EHS). Therefore, this research aims to identify and analyze water sources, by EHS objectives, because safe water supply is key to environmental and worker safety. The water balance method is used to analyze water needs and availability. Analyze water needs in industrial areas by taking into account data for each type of industry. Water availability analysis includes the identification of raw water sources, including groundwater and surface water, taking into account that excessive exploitation of groundwater can hurt EHS. In contrast, surface water and rainwater collection are sustainable alternatives that are relevant to environmental and safety concerns. The research involved calculations using rainfall data from the Subah Rain Station and climatology data from the Central Java Climatology Station during the 2021-2022 period, as well as discharge analysis using the Mock Method. The research results display the potential of the river basin and show that rainwater collection can effectively overcome EHS challenges related to water availability in the Batang Integrated Industrial Area.

Unit Commitment Considering Long-term Supply Risk of Natural Gas Pipeline Network

As the coupling degree of the natural gas pipeline network (NGPN) and the power system increase, unreliable gas supply risk related to random failures in NGPN has threatened the operation security of the power system. However, present studies seldom discuss the effect of natural gas supply reliability on power system operation. In this manner, a stochastic unit commitment model is presented with the consideration of long-term gas supply risk in NGPN. Firstly, with the combination of the Monte Carlo simulation (MCS) technique and the maximum-flow algorithm, the failure scenario set corresponding to gas supply risk can be obtained. Then, a two-stage model for unit commitment is established considering gas supply uncertainty based on the stochastic optimization model. Finally, case studies have shown that the power operation reliability has been improved with the increased reliability indices.

A New Ternary Co-Free Layered Cathode, Li[Ni1-x-yTixAly]O2, for High-Energy Lithium-Ion Batteries

For the sustainable development of Li[NixCoyMn1−x−y]O2 and Li[NixCoyAl1−x−y]O2 cathodes, reducing the reliance on cobalt, which is extremely expensive with a fluctuating price and supply uncertainty, is considered essential. In this study, we propose a highly stable Co-free Ni-rich layered cathode developed through a new doping strategy that incorporates heteroelements at different doping stages, including the introduction of Ti during Ni(OH)2 synthesis and doping excess amounts of Al during the lithiation step. The multi-stage engineering strategy guarantees structural durability and electrochemical cycling stability of the inherently unstable LNO to a commercially viable level. Combined with particle surface protection, the Li[Ni0.951Ti0.008Al0.041]O2 cathode retains 72.0% of its initial capacity after 3500 cycles, which is unprecedented among previously reported Co-free cathodes. The proposed Co-free cathodes can meet the energy density required for next-generation electric vehicles, as the cathode delivers 890 Wh kgcathode-1, and presents a clear breakthrough for the development of commercially viable LNO cathode.

Developing a national pandemic vaccination calendar under supply uncertainty

During the COVID-19 pandemic, many countries faced challenges in developing and maintaining a reliable national pandemic vaccination calendar due to vaccine supply uncertainty. Deviating from the initial calendar due to vaccine delivery delays eroded public trust in health authorities and the government, hindering vaccination efforts. Motivated by these challenges, we address the problem of developing a long-term national pandemic vaccination calendar under supply uncertainty. We propose a novel two-stage mixed integer programming model that considers critical factors such as multiple doses, varying dosing schemes, and uncertainties in vaccine delivery timing and quantity. We present an efficient aggregation-based algorithm to solve this complex problem. Additionally, we extend our model to allow for dynamic adjustments to the vaccine schedule in response to mandatory policy changes, such as modifications to dose intervals, during ongoing vaccinations. We validate our model based on a case study developed by using real COVID-19 vaccination data from Norway. Our results demonstrate the advantages of the proposed stochastic optimization approach and heuristic algorithm. We also present valuable managerial insights through extensive numerical analysis, which can aid public health authorities in preparing for future pandemics.