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Decentralized manufacturing has the benefits of fast fulfillment, reducing risks of distant delivery, and improving patient access to personalised regenerative medicine (PRM). Implementing the decentralised PRM manufacturing system successfully needs a capacity planning strategy involving inventory replenishment, capacity allocation, and demand sharing to mitigate the impacts of supplier disruption and satisfy demand with a high service level. However, existing methods for generating optimal capacity planning policies for such PRM systems require knowing the distributions of the supplier disruption and demand uncertainty, which is usually unknown for PRM supply chains. This study proposes a data-driven approach that can learn effective capacity planning policy under various manufacturing circumstances without knowing the exact distributions. The proposed approach utilises a production simulation model and a deep reinforcement learning method. Case study results demonstrate that the proposed method can outperform existing methods when ground-truth demand forecasts differ from priori estimations. The results also support that the proposed method not only can be applied in regenerative medicine but also in many other sectors. [ABSTRACT FROM AUTHOR]

This paper examines supplier encroachment in a scenario where the supplier procures an input in spot and processes it into a final product, while the retailer chooses between contingent and committed pricing schemes for the contract price. We find that when the correlation between the input spot price and final product demand is relatively small, it is optimal for the retailer to adopt the contingent pricing scheme regardless of the supplier's encroachment decision. This is because this scheme enables the supplier to share the input price risk, resulting in a lower contract price. Furthermore, the likelihood of the retailer adopting the contingent pricing scheme increases as demand variability decreases or spot price volatility increases. However, supplier encroachment reduces the retailer's inclination to use the contingent pricing scheme. We further demonstrate that the supplier should encroach when the correlation is sufficiently small, because encroachment alleviates double marginalisation in the retail channel and brings the additional responsiveness in the direct channel. By contrast, a large correlation compels the supplier to encroach even with a sufficiently small market size. In this case, encroachment improves the responsiveness of both retail and direct selling quantities and enables the quantities to match the demand better. [ABSTRACT FROM AUTHOR]

Compared to the traditional channel, where a retailer bears the inventory risk, the drop-shipping channel operates with a distinct approach: the retailer avoids stocking products while the manufacturer directly handles stocking and shipping to consumers. Due to the different inventory risk allocations, the two channels have different product qualities and profits for the manufacturer and retailer. In this study, we investigate product quality decision of the manufacturer and delve into the impact of product quality on the manufacturer's and retailer's profitability. First, we find that product quality can be weakly higher in the drop-shipping channel than that in the traditional channel. This is because the drop-shipping channel allows the manufacturer who undertakes demand uncertainty to charge a higher wholesale price, resulting in a higher product quality. Second, our study reveals that the traditional channel can concurrently yield higher profits for the manufacturer and retailer when product quality is considered, which is in contrast to the previous works. Third, in the traditional channel, as demand uncertainty rises, the retailer's production quantity increases, while the manufacturer's product quality decreases. Finally, our results indicate that the product quality is more sensitive to the shipping cost when demand uncertainty is low. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In highly uncertain times, firms are increasingly exhibiting risk aversion to uncertain demand and altruism to supply chain partners to reduce risk and maintain stability. To deal with high demand fluctuations, numerous firms are adopting information‐sharing strategies. We study how a retailer's risk aversion and altruism affect her demand information‐sharing decision by constructing game‐theoretic models. We first show that information sharing makes double marginalization (DM) stronger and hurts the retailer and generates an information‐sharing DM effect. The retailer's risk aversion strengthens this effect, while her altruism weakens the effect. Meanwhile, information sharing generates an uncertainty reduction effect on the risk‐averse retailer by reducing volatility and an altruism improvement effect on the altruistic retailer by increasing the manufacturer's profit. Whether information sharing benefits the retailer depends on her level of risk aversion and altruism. The retailer prefers voluntary sharing when both her risk aversion and altruism are high. [ABSTRACT FROM AUTHOR]

To investigate the effect of uncertain demand on various omnichannel distribution network strategies and analyze the impacts of various online order fulfillment policies, a two‐staged network optimization model is built. The two stages are a deterministic model to define the optimal distribution network and a simulation model to determine the optimal network flow to maximize the total profit for a given distribution network and online order fulfillment policy. This study shows the importance of taking demand uncertainty, requested delivery time, and customer service level (CSL) into account in designing an omnichannel distribution network. The results of the simulation model indicate that shipments from warehouse (SFW) and hybrid strategies more successfully cope with variation because there is a greater profit decline in shipments from store (SFS) strategies. The results also show that applying a dynamic assignment policy plays an important role in handling variation in demand. The study shows that, as demand uncertainty and CSL increase, having products available in central repositories becomes even more important to cope with demand variation. The study also indicates SFS strategies that seem more profitable in the assumed‐to‐be deterministic model might be misleading because SFS strategies become less profitable (compared to SFW strategies) when demand uncertainty is taken into account. [ABSTRACT FROM AUTHOR]

This research examines how strategic consumer behavior influences e-tailers' decisions on inventory storage locations, pricing strategies, and inventory levels. The e-tailer opts for either a single central warehouse, which has lower holding but higher shipping costs due to its distance from consumers, or a mix of central and proximal local warehouses, which reduces shipping costs but incurs higher holding costs. We find that local warehousing prompts consumers to delay purchases in hopes of discounts, compelling e-tailers to lower prices to encourage early buying. Consequently, the firm may not utilize the local warehouse, even when it comes at no cost. Intriguingly, we find that increased local storage expenses or diminished product durability could paradoxically elevate firm profits. Our numerical analysis highlights the benefits of strategically distributing inventory across both central and local warehouses, especially under conditions of reduced demand uncertainty. Moreover, we establish that our key insights persist in the case of multiple local warehouses. [ABSTRACT FROM AUTHOR]

Transporting precast modules via water is a vital component of multimodal transportation systems, increasingly utilized in large-scale Modular integrated Construction (MiC) projects where modules are prefabricated in remote factories. The effectiveness of module transportation planning significantly impacts the overall costs and productivity of MiC projects. However, existing studies on module transportation planning neglect the uncertainty inherent in MiC projects, thereby resulting in increased costs. This study proposes a two-stage stochastic programming model to optimize transportation planning through water, addressing this uncertainty. A real Hong Kong case study with 418 modules is employed to assess the effectiveness of the proposed model in comparison with three deterministic models. The optimal transportation plan of modules solved by the proposed model costs HKD 148,951, comprising 21% from temporary rentals and 79% from advance bookings. The results show that the three deterministic models, without considering the uncertainty in module demand, will incur additional transportation costs that are 25% higher on average than the results of the developed two-stage stochastic model. Additionally, this paper conducts a sensitivity analysis on the price ratio of pre-booked barges to on-demand barges to evaluate its impact on total transportation costs. The two-stage programming model developed in this paper can effectively help transport planners reduce the costs associated with module water transportation. [ABSTRACT FROM AUTHOR]

Supply chains are becoming more and more uncertain. It is more relevant now than ever to plan and model supply chains to handle such uncertainties. This paper designs a supply chain network for medical oxygen under uncertain demand. The paper tackles the complex logistical challenge of managing emergency medical supplies of medical-grade oxygen in the scenario of a pandemic. A facility location problem considering scenario-based uncertain demand is formulated using two-stage stochastic programming. An inventory distribution problem is next formulated to model the flow of medical oxygen in multiple periods to provide maximum service to medical facilities when the available transportation capacity is finite. The model includes various aspects that reflect the scenarios originating in a pandemic, such as limited vehicle availability, limited production capability, uncertain demand, etc. A scenario-based stochastic approach is considered to include the uncertainty aspect of a pandemic scenario. The proposed methodology was studied using two numerical analyses. The results show that, as the number of cryogenic vehicles available was finite, having buffer facilities such as cryogenic tanks to store liquid oxygen helps absorb demand variations in a pandemic scenario. A greater number of medical facilities can be serviced with fewer storage facilities, which can be very crucial in a pandemic scenario. Considering the need for swift planning required in emergency scenarios, the results will be useful for managers, practitioners, and academicians to make supply chains more resilient to risks and uncertainties. [ABSTRACT FROM AUTHOR]

This study addresses the capacitated lot-sizing problem in the poultry industry for egg production planning, aiming to minimize production, transportation, and inventory costs. This problem has already been investigated with data certainty and formulated as a mathematical model and a heuristic algorithm has been applied to solve it due to high complexity. In this study, we reformulate the same problem as a new mixed integer linear programming model to achieve optimal solution in a relatively short time without the need for heuristic algorithms. To evaluate the model performance, it is executed using the available data, and its efficiency is validated by comparing the obtained results. Subsequently, the uncertainty of weekly demand is considered, leading to potential shortage or surplus in storage. To address this uncertainty, the chance-constraints method is employed with various attitudes, and several production plans are proposed accordingly. The performance of these plans is compared using random data, and the most suitable programs are identified. The presented decision-making tool can provide production planning that meets customer demand with high reliability while also minimizing surplus inventory in the warehouse. [ABSTRACT FROM AUTHOR]

Abstract Under the backdrop of frequent emergencies, the rational layout of emergency service facilities (ESF) and the effective allocation of emergency supplies have emerged as crucial in determining the timeliness of post-disaster response. By adequately accounting for potential uncertainties and carrying out comprehensive pre-planning, the robustness of location-allocation decisions can be significantly improved. This paper delves into the ESF network design problem under demand uncertainty and formulates this problem as a two-stage robust optimization model. The presented model defines a generalized budget uncertainty set to capture victims’ uncertain demand and minimizes the sum of the costs involved in the two stages. The objective function integrates the input cost in the preparedness phase, the deprivation cost from the victims’ perspective and the environmental impact cost responding to sustainable development in the response phase, which respectively correspond to the comprehensive optimization of the deployment of ESF, the distribution of emergency supplies and the implementation of sustainable measures. Subsequently, we employ the column and constraint generation (C&CG) algorithm to solve the proposed model and take the COVID-19 epidemic in Wuhan as a case to verify the effectiveness of the model and algorithm. Finally, we examine the influence of demand uncertainty and environmental impact cost on the optimal solution, yielding valuable managerial insights.

Dynamic spectrum sharing (DSS) is a highly efficient approach for deploying new radio (NR) on lower frequency bands currently utilized by long-term evolution (LTE), to enhance the coverage. This paper addresses several critical issues in DSS. Firstly, we investigate resource block (RB) allocation in a multi-cell environment, considering inter-cell interference (ICI) and frequency reuse (FR) to optimize the combined LTE and NR data rates in a DSS-enabled network. We then apply maximum throughput (MT) and enhanced proportional fairness (ePF) schedulers for RB allocation within our proposed simulation framework. Additionally, we explore the impact of satisfying users’ quality of service (QoS) on data rate and fairness across various sharing ratio values for LTE and NR guaranteed bit rate (GBR) users. Our results show that while MT achieves higher data rates, ePF ensures better fairness and QoS among users, albeit with a potential data rate reduction of 25-30%. Moreover, under high data rate GBR scenarios, the network can maintain an appropriate fairness index (FI) based on the sharing ratio while guaranteeing GBR users. The ePF scheduler tends to drop more users compared to MT, yet a balance among LTE/NR spectrum sharing ratio, fairness, GBR satisfaction, and overall data rate maximization can be achieved in DSS networks. We also evaluate DSS performance across various realistic propagation models, identifying an optimal sharing ratio that maximizes total data rates for LTE and NR in each environment, with the MT scheduler delivering the highest data rates in rural macro areas. Lastly, we address the issue of demand uncertainty to develop a robust DSS network. Our findings indicate that robust DSS outperforms unrobust DSS by 7-25% and unrobust static spectrum sharing (SSS) by 19-40%.

The Production Routing Problem under demand uncertainty is an integrated problem containing production, inventory, and distribution decisions. At the planning level, the aim is to meet retailers demand, when only the demand distribution is known in advance, while minimizing the corresponding costs. In this study, a two-stage formulation is presented in which the routing can be adjusted at short notice. In the first stage, only production decisions are made, while delivery and inventory quantities and retailer visit schedules are determined in the second stage. To handle a large number of scenarios, two solution methods based on Sample Average Approximation are introduced. Furthermore, the impact of the routing quality is explored by applying a simple heuristic and an effective metaheuristic on the routing part. It is shown that, on average, the simple heuristic within an adjustable Sample Average Approximation approach provides better objective function values than the metaheuristic within a non-adjustable approach. Also all solution approaches outperform an expected value based approach in terms of runtime and objective function value. [ABSTRACT FROM AUTHOR]

Recently, an increasing number of firms sell product bundles with after‐sale services. It is challenging for firms to select the best product bundle of the product and services since the product and the services are intercorrelated. In this paper, we study a product service system where a manufacturer provides products to a retailer, who sells them bundled with additional after‐sales services to customers under a service‐level commitment. The market demand is uncertain and depends on the service‐level commitment and product quality. We derive the optimal product (production quantity and product quality) and service (service‐level commitment and service capacity) decisions for this supply system under demand uncertainty. Additionally, this paper designs a coordination mechanism with the service‐level commitment and cost sharing. The coordination mechanism can achieve a Pareto improvement (i.e., both manufacturer and retailer can be better off) and render a higher service‐level commitment and product quality, resulting in a win‒win situation for both the firms and customers. The analytical results demonstrate that service‐level commitment and product quality are substitutes for the firm's profit. Namely, the impact of the commitment of the service level on the firm's profitability increases as product quality decreases. Conversely, service capacity and product quality are complements to the firm's profit. Namely, the better the product quality is, the higher the marginal effect of larger service capacity on profitability. [ABSTRACT FROM AUTHOR]

Abstract This research investigates the implications of incorporating blockchain technology into the process of making decisions for green supply chains, particularly under conditions of demand uncertainty. A model was formulated to encompass both environmentally friendly products enabled by blockchain technology and those without such enabling technology. The study further explores the optimal method of introducing green input in a duopoly market using game theory. It also examines how consumer uncertainty about green products and acceptance of the technical parameters of blockchain influence this strategy. The findings suggest that increased consumer uncertainty can, in some instances, motivate manufacturers to enhance the eco-friendliness of their products and improve supply chain performance. However, the universal adoption of blockchain does not necessarily ensure better results; on the contrary, it may compromise product sustainability while enhancing supply chain profitability. Moreover, research has indicated that products enabled by blockchain typically have lower prices, thereby offering potential benefits to consumers when acceptance of sustainable energy solutions is high or uncertain. Additionally, this paper analyzes the impact of changes in green supply chain decision-making on system reliability. This involves exploring the relationship between decision parameters and consumer reluctance towards sustainable products and the adoption of blockchain technology. To ensure stable market competition in dynamic complex systems, research shows that feedback control technology can effectively regulate unpredictable behavior.

An increasing number of retail platforms are adopting blockchain technology to mitigate information asymmetries and share data with upstream suppliers, thereby reducing demand uncertainty. However, these platforms often engage with multiple upstream suppliers of varying product quality. This study constructs a game-theoretic model within a supply chain framework, featuring a risk-averse retail platform and two upstream suppliers of different quality levels. As the core leader of the game, the retail platform decides whether to share demand information with the two competing suppliers after implementing the technology. Using mean-variance theory, this study addresses a key question: Which types of suppliers should be included in information sharing on a retail platform? The results show that when the unit cost of information sharing is low, allowing both suppliers to share demand information is most beneficial. Conversely, when the unit cost is high, only high-quality suppliers should be included. Notably, as the unit cost of information sharing and the intensity of competition between high-quality and low-quality products increase, low-quality suppliers are excluded from information sharing. Additionally, the model indicates that the greatest social welfare can be achieved whether both high-quality and low-quality suppliers are authorized to join the information sharing network, or only high-quality suppliers are included. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research investigates the implications of incorporating blockchain technology into the process of making decisions for green supply chains, particularly under conditions of demand uncertainty. A model was formulated to encompass both environmentally friendly products enabled by blockchain technology and those without such enabling technology. The study further explores the optimal method of introducing green input in a duopoly market using game theory. It also examines how consumer uncertainty about green products and acceptance of the technical parameters of blockchain influence this strategy. The findings suggest that increased consumer uncertainty can, in some instances, motivate manufacturers to enhance the eco-friendliness of their products and improve supply chain performance. However, the universal adoption of blockchain does not necessarily ensure better results; on the contrary, it may compromise product sustainability while enhancing supply chain profitability. Moreover, research has indicated that products enabled by blockchain typically have lower prices, thereby offering potential benefits to consumers when acceptance of sustainable energy solutions is high or uncertain. Additionally, this paper analyzes the impact of changes in green supply chain decision-making on system reliability. This involves exploring the relationship between decision parameters and consumer reluctance towards sustainable products and the adoption of blockchain technology. To ensure stable market competition in dynamic complex systems, research shows that feedback control technology can effectively regulate unpredictable behavior. [ABSTRACT FROM AUTHOR]

In typical business situations, sellers usually offer discount schemes to buyers to increase overall profitability. This study aims to design a supply chain network under uncertainty of demand by integrating an all-unit quantity discount policy. The objective is to maximize the profit of the entire supply chain. The proposed model is formulated as a mixed integer nonlinear programming model, which is subsequently linearized into a mixed integer linear programming model and hence able to obtain a global solution. Numerical examples in the manufacturing supply chain where customer demand follows normal distributions are used to assess the effect of quantity discount policies. Key findings demonstrate that the integration of quantity discount policies significantly reduces total supply chain costs and improves inventory management under demand uncertainty, and decision makers need to decide a balance level between service levels and profits. [ABSTRACT FROM AUTHOR]

Annually, over 10 billion tons of construction and demolition waste is transported globally from sites to reception facilities. Optimal and effective planning of waste transportation holds the potential to mitigate cost and carbon emissions, and alleviate road congestion. A major challenge for developing an effective transportation plan is the uncertainty of the precise volume of waste at each site during the planning stage. However, the existing studies have assumed known demand in planning models but the assumption does not reflect real-world volatility. Taking advantage of the problem structure, this study adopts the stochastic programming methodology to approach the construction waste planning problem. An integer programming model is developed that adeptly addresses the uncertainty of the amount of waste in an elegant manner. The proposed stochastic programming model can efficiently handle practical scale problems. Our numerical experiments amass a comprehensive dataset comprising nearly 4300 records of the actual amount of construction waste generated in Hong Kong. The results demonstrate that incorporating demand uncertainty can reduce the transportation cost by 1% correlating with an increase in profit of 14% compared to those that do not consider the demand uncertainty. [ABSTRACT FROM AUTHOR]

The multi‐compartment vehicle routing problem is an extension of the vehicle routing problem and consists of designing a set of routes to perform the collection or delivery of different product types from customers with minimal costs. The product types are incompatible with each other and must be transported separately in multiple compartments. In practice, several uncertainties such as uncertainty in demands can arise, where the exact demand of customers is not known at the time of planning. To deal with these uncertainties, decision‐makers have to rely on robust solutions. A solution is considered robust when it can resist perturbations in every possible demand scenario as much as possible. In the day to day business of most logistic companies, historical data about each customer can be stored and used to make intelligent decisions regarding the expected demands. In this article, we propose an adaptive large neighborhood search for solving the robust multi‐compartment vehicle routing problem under demand uncertainty and present a robust solution approach for the problem in practical settings by employing machine learning. We show that by using our approach, the solutions obtained have lower recourse costs and have a lower gap between expected and actual costs, which is a favorable outcome to have in practice. [ABSTRACT FROM AUTHOR]

Purpose: Pre-positioning is a crucial choice in pre-disaster humanitarian logistics planning that consists of deciding in advance how much aid and where should it be located to enable effective and prompt operations in the case of an emergency. To support managers making such decisions, we propose four mathematical formulations that, considering the uncertainty on the demand to satisfy, seek to optimize aid prepositioning (before the event) and further distribution (after the event) in order to minimize unmet demand (MUD). The purpose of this paper is to evaluate and compare the performance of these formulations on a real case to discuss when and why should each approach be applied. Design/methodology/approach: The two first formulations adopt the cardinality-constrained (CC) approach to handle uncertainty. These formulations differ in their objective functions, the first formulation's objective seeks to MUD, whilst the second incorporates equity in the way that demand is satisfied. The two remaining formulations are scenario-based (SB) and as in the previous two formulations, seek to MUD with and without equity considerations, respectively. Findings: Applying our formulations to a case study, we compare the differences between the solutions produced by the proposed formulations and the solutions that would have been produced without uncertainty (perfect information) to have a better understanding of their performance and their behavior. A discussion of the strengths and weaknesses of each model is provided to help managers choose the model that best suits their needs. Originality/value: The formulations are applied to a case study where a food bank is faced with the arrival of a hurricane in Mexico. As far as our knowledge, it is the first work in literature to deal with humanitarian logistics under a cardinality-constrained approach. [ABSTRACT FROM AUTHOR]

Environmental policies such as carbon tax implementation significantly impact the technology choice and cost structure of the upstream manufacturer, affecting the downstream retailer's the procurement decision and risk management in a sustainable supply chain. This study constructs a single-manufacturer-single-retailer supply chain model constrained by carbon tax policies to analyze the influence of retailer's anticipated regret on the supply chain operation and environmental performance. Utilizing game theory, the research finds that the retailer's anticipated regret behavior suppresses manufacturer's willingness to invest in the green initiative, resulting in a non-monotonic effect on profits. When the retailer's regret level is low and her demand for eco-friendly products is also low, the manufacturer tends to reduce the green investment and increase the wholesale price to maximize the marginal return. Although such the anticipated regret behavior by the retailer may stimulate the manufacturer's profit growth, it reduces retailer's profit. Conversely, as the retailer's regret level increases, the manufacturer increases the green investment and reduces the wholesale price to induce higher procurement by the retailer, leading to a decline in the manufacturer's profit but an upward trend in the retailer's profit. This discovery suggests that the upstream supply chain manager, should closely the monitor retailer's potential anticipated regret and adjust the corresponding strategy accordingly. Furthermore, the study finds that the lower level of retailer's anticipated regret positively impact environmental behavior, offering the policymaker a new perspective on promoting the green supply chain practice. [ABSTRACT FROM AUTHOR]

SUMMARY: Economic theory suggests that demand uncertainty should influence producer behavior. In this study, we empirically examine the impact of demand uncertainty on the production of audit services. Auditors must make resource allocation decisions in advance of exact demand being known, and because a large portion of auditors' capacity-related commitments are fixed and therefore difficult to adjust in the short run, uncertainty in client demand outcomes likely imposes costs on auditors. Consistent with auditors being compensated for these costs, our results indicate that both audit price and audit production timing are affected by a client's uncertainty in demand for audit services. We also find that these compensation mechanisms act as substitutes used by auditors to alleviate the costs imposed by demand uncertainty. Our study contributes to the growing literature on the underlying economics of the audit market and answers recent calls for analysis of demand-side factors that influence the audit industry. Data Availability: Data are available from the sources cited in the text. JEL Classifications: M40; M41; M42. [ABSTRACT FROM AUTHOR]

The demand for home health care services is rapidly increasing due to the growing number of older people. The uncertainty surrounding this demand affects the network design processes and the performance of the home health care system in the long term. This study aims to address the issue of demand uncertainty in a home health care location problem. While decisions regarding the location of home health care facilities must be made immediately, the determination of distribution can be postponed until actual demand is observed. In such situations, minimax/maximin robust optimization methods are commonly employed to address uncertainty and facilitate informed decision-making, even in cases where there is limited information about future demand. However, these methods are often too conservative and may lead to suboptimal solutions. To tackle this issue, we propose a regret minimization method, which is reformulated as a robust model to overcome its intractability. Additionally, we propose a column-and-constraint generation algorithm to solve the robust optimization and regret minimization models. Finally, we conduct a comprehensive set of numerical experiments to compare the performance of the models in terms of solution quality and computational time. The results demonstrate that the regret minimization model enhances solution quality and consumes less computational time when reformulated. [ABSTRACT FROM AUTHOR]

Considering the restriction of supply risk on optimal profit realization in advance selling, we discuss three selling strategies of a seller who produces and sells a seasonal product to a consumer under uncertain supply and demand: all advance selling, partial advance selling, and non‐advance selling. The robust newsvendor model is designed to solve the problem. Our results show that implementing an advance selling strategy is always beneficial from the demand uncertainty perspective to the seller. However, sellers should choose advance selling carefully from the standpoint of supply uncertainty: sellers will non‐advance selling under certain conditions. This condition is contingent on the market, capacity level, selling price, supply–demand correlation, consumer characteristics, and seller's pricing power. Interestingly, pricing power is the key driver to stimulate advance selling under supply uncertainty. In addition, the impact of supply and demand uncertainty and supply–demand correlation on these strategies are related to price discounts. [ABSTRACT FROM AUTHOR]

This paper introduces a model to aid decision-makers in answering many of the important questions regarding how best to operate a cancer center. This study aims to allocate the available cancer center capacity to different cancer types to minimize the deviation in patient demand satisfied from desired supply targets across multiple cancer types. A stochastic chance-constrained model is proposed to consider uncertainties in new and returning patient demand. The proposed model determines the optimal specialization mix for oncologists based on the distribution of demand by cancer type, preventing potential mismatches. Additionally, it aims to balance workloads among oncologists and individual clinics and indirectly reduce support service costs by limiting their clinic days. Numerical results are presented using historical data collected from our collaborating cancer center to demonstrate the usefulness of the model. The results confirm that the ability to satisfy patient demand increases as oncologists become more flexible. In addition, the results show that even having a small number of highly flexible oncologists is sufficient to achieve strong patient demand satisfaction. Moreover, restricting the allowable workload difference among oncologists achieves an acceptable trade-off between workload balance and satisfying patient demand. [ABSTRACT FROM AUTHOR]

This paper deals with the issue of safety lead time (SLT) calculations in production-inventory systems in presence of both demand and replenishment lead time variability. We provide some formulations of the SLT and numerically show their performance as compared to a benchmark in the literature. Thus the main objective of this paper is to provide analytical formulations to calculate the SLT that contemplate demand variability. To this end, a literature review was done to analyze the approaches and justifications of the different revised research works to identify reference formulations according to the objectives of this work. A supply chain from the automotive sector was used as the study frame and to validate the proposed formulations. This supply chain involved two companies: a car manufacturer and a first-tier supplier. In order to compare the proposed formulations with one another, and with that currently used by the first-tier supplier and is the study object, three parameters were used: safety stock, the number of times stockout occurs and the mean stock. They allowed the final selection of the most suitable SLT formulation for each case study.

In this paper, a two-stage robust optimisation is presented for an uncapacitated hub location problem in which demand is uncertain and the level of conservatism is controlled by an uncertainty budget. In the first stage, locations for establishing hub facilities were determined, and allocation decisions were made in the second stage. An accelerated Benders decomposition algorithm was used to solve the problem. Computational experiments showed better results in terms of number of iterations and computation time for Benders decomposition with Pareto-optimal cuts in comparison with the classical Benders decomposition algorithm. According to numerical analysis, it was concluded that increasing the uncertainty budget also increased total costs for more established hubs. To determine the uncertainty budget in an appropriate manner, a new expected aggregate function was introduced. The numerical studies demonstrated the usefulness of the proposed method in defining the appropriate uncertainty budget in the presence of uncertainty. [ABSTRACT FROM AUTHOR]

Transporting precast modules via water is a vital component of multimodal transportation systems, increasingly utilized in large-scale Modular integrated Construction (MiC) projects where modules are prefabricated in remote factories. The effectiveness of module transportation planning significantly impacts the overall costs and productivity of MiC projects. However, existing studies on module transportation planning neglect the uncertainty inherent in MiC projects, thereby resulting in increased costs. This study proposes a two-stage stochastic programming model to optimize transportation planning through water, addressing this uncertainty. A real Hong Kong case study with 418 modules is employed to assess the effectiveness of the proposed model in comparison with three deterministic models. The optimal transportation plan of modules solved by the proposed model costs HKD 148,951, comprising 21% from temporary rentals and 79% from advance bookings. The results show that the three deterministic models, without considering the uncertainty in module demand, will incur additional transportation costs that are 25% higher on average than the results of the developed two-stage stochastic model. Additionally, this paper conducts a sensitivity analysis on the price ratio of pre-booked barges to on-demand barges to evaluate its impact on total transportation costs. The two-stage programming model developed in this paper can effectively help transport planners reduce the costs associated with module water transportation.