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This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.

This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.

The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.

The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.

India has the largest public distribution system (PDS) in the world, working through over five million fair price shops (FPS) to distribute food grains among its beneficiaries at a subsidized rate. In this paper, the authors study the inventory system of Indian FPS. The system involves a distributor, who is solely responsible for the replenishment of the FPS. In a real-world scenario, the distributor is subjected to random supply and transportation disruptions. The purpose of this paper is to investigate and minimize the impacts of such disruptions.

In this paper, the authors adopt a simulation-based technique to explore the impacts of various traits of disruptions like frequency and duration on the FPS inventory system. A simulation model for the Indian FPS is developed and the impacts of disruptions are investigated by a case study.

The authors use a simulation-based optimization technique to suggest a simple managerial change that can lead to a minimization of inventory shortage up to 60 per cent and system cost up to 21 per cent over the existing practice.

The present study addresses the FPS inventory system of Indian PDS, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

The purpose of this paper is to provide a decision-making tool achieving robust supply flow by incorporating strategic stock and contingent sourcing in mitigating minor and major disruptions.

The authors consider a firm with two suppliers where the main supplier is cost-effective but prone to disruptions and the back-up supplier is reliable but expensive due to built-in volume flexibility. In order to incorporate the randomness associated with disruptions and the available capacity during response time in the decision-making stage, the authors present a multi-stage robust optimization (RO) model. The design problem is to determine optimal strategic stock level and response speed of volume-flexible back-up supplier in order to achieve a robust supply flow.

The results show that the quality of optimal solution is improved by considering the randomness associated with available capacity. In addition, incorporating congestion effects allows identifying the appropriate level of supply chain responsiveness, thus improving the overall performance.

The novelty of the proposed model is the consideration of both strategic stock and volume flexibility in maintaining a robust supply performance while incorporating response capability and congestion effects.

The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps.

Papers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed.

Five main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper.

Procurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty.

The contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems.

The purpose of this paper is to investigate the benefit of pre-emptive disruption management measures for assembly systems towards the target dimension adherence to delivery times.

The research was conducted by creating simulation models for typical assembly systems and measuring its varying throughput times due to changes in their disruption profiles. Due to the variability of assembly systems, key influence factors were investigated and used as a foundation for the simulation setup. Additionally, a disruption profile for each simulated process was developed, using the established disruption categories material, information and capacity. The categories are described by statistical distributions, defining the interval between the disruptions and the disruption duration. By a statistical experiment plan, the effect of a reduced disruption potential onto the throughput time was investigated.

Pre-emptive disruption management is beneficial, but its benefit depends on the operated assembly system and its organisation form, such as line or group assembly. Measures have on average a higher beneficial impact on group assemblies than on line assemblies. Furthermore, it was proven that the benefit, in form of better adherence to delivery times, per reduced disruption potential has a declining character and approximates a distinct maximum.

Characterising the benefit of pre-emptive disruption management measures enables managers to use this concept in their daily production to minimise overall costs. Despite the hardly predictable influence of pre-emptive disruption measures, these research results can be implemented into a heuristic for efficiently choosing these measures.

Proactive planning strategies for “slow-onset” disruptions that affect humanitarian supply chains (SC) developed to address chronic pressing societal problems, can have a significant impact on boosting the operational and financial performance of these chains. The purpose of this paper is to develop a methodology that quantifies the impact of a risk mitigation strategy widely employed in commercial SCs, namely emergency sourcing (ES), on the performance of humanitarian SCs taking into account backorders’ clearance time, unsatisfied demand, and cost.

Discrete event simulation is employed in order to evaluate alternative ES strategies based on a total cost criterion, which incorporates inventory-related costs, as well as premium contract costs paid for emergency replenishment. Backorders’ clearance time and time-to-recovery are also employed as a design parameters.

The results document the significant impact of disruptions on expected total cost, and the beneficial role of ES in hedging against disruptions. To that end, the proposed methodology determines the optimal emergency contracted capacity for a given premium, or alternatively the maximum premium cost value that ensures the feasibility of the implemented ES strategy in the long-run, along with the associated cost and time savings, and reduction of the unsatisfied demand.

The fundamental objective is to provide a decision-making support methodology for deciding on whether to implement an ES strategy or not in humanitarian SCs, and the level of the optimal contracted reserved capacity. The results could be of great value to aid providers, policy-makers, and regulators.

The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).

The proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.

The study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.

The study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.

The purpose of this paper is to examine supply‐side disruptions in a supply chain, and to analyse the effectiveness of two inventory‐based policies for mitigating the impact of supply disruptions: maintaining strategic inventory reserves (the R‐policy), and using larger orders (the Q‐policy).

The paper assess the effectiveness of two inventory‐based mitigating policies implemented at a reseller when end customer demand is stable but supply can be disrupted. An analytical model is provided, and numerical experiments are conducted to evaluate the effectiveness of the policies for mitigating the impact of disruption under different disruption scenarios.

Results indicate that the R‐policy performs consistently better than the Q‐policy in terms of product availability measures, as tested under a wide range of frequency and duration of supply disruptions.

Supply chain trends of lean operations and global sourcing have exposed business organizations to a greater risk and have further raised the need to protect businesses against random supply disruptions.

The paper intends to contribute to the narrowing of the gap in the research of supply‐side disruptions. Further, the topic of inventory reserves has been discussed to date in only a very general sense; the paper proposes conditions for practical implementation and provides unique insights into the effectiveness of the use of strategic inventory reserves as a supply disruption mitigation policy.

A variety of buffer allocation methods exist to distribute an aggregated time buffer among project activities. However, these methods do not pay simultaneous attention to two key attributes of disruptive events that may occur during the construction phase: probability and impact. This paper fills this research gap by developing a buffer allocation method that takes into account the synergistic impact of these two attributes on project activities.

This paper develops a three-step method, calculating the probability that project activities are disrupted in the first step, followed by measuring the potential impact of disruption on project activities, and then proposing a risk-informed buffer allocation index by simultaneously integrating probability and impact outputs from the first two steps.

The proposed method provides more accurate results by sidestepping the shortcomings of conventional fuzzy-based and simulation-based methods that are purely based on expert judgments or historical precedence. Further, the paper provides decision-makers with a buffer allocation method that helps in developing cost-effective buffering and backup strategies by prioritizing project activities and their required resources.

This paper develops a risk-informed buffer allocation method that differs from those already available. The simultaneous pursuit of the probability and impact of disruptions distinguishes our method from conventional buffer allocation methods. Further, this paper intertwines the research domains of complexity science and construction management by performing centrality analysis and incorporating a key attribute of project complexity (i.e. the interconnectedness between project activities) into the process for buffer allocation.

Due to uncertainty in supply chains caused by the coronavirus disease 2019 (COVID-19), organizations are adjusting their supply chain design to address challenges faced during the pandemic. To safeguard their operations against disruption in order quantities, supply chain members have been looking for alternate suppliers. This paper considers a two-level supply chain consisting of a manufacturer and two suppliers of a certain type of components required for the production of a finished product. The primary supplier (supplier A) is unreliable, in the sense that the quantity delivered is usually less than the ordered quantity. The proportion of the ordered quantity delivered by supplier A is a random variable with a known probability distribution. The secondary supplier (supplier B) always delivers the order in its entirety at a higher cost and can respond instantaneously. In order for supplier B to respond instantaneously, the manufacturer is required to reserve a certain quantity at an additional cost. Once the quantity received from the main supplier is observed, the manufacturer may place an order not exceeding the reserved quantity.

A mathematical model describing the production/inventory situation of the supply chain is formulated. The model allows the determination of the manufacturer's optimal ordering policy.

An expression for the expected total cost per unit time function is derived. The optimal solution is determined by solving a system of nonlinear equations obtained by minimizing the expected total cost function.

The proposed model can be used by supply chain managers aiming at identifying various ways of handling the uncertainty in the flow of supplies across the chain.

This proposed model addresses a gap in the production/inventory literature.