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This paper aims to design an optimal supply chain network and to develop a suitable distribution planning under uncertainty for perishable product's supply chain. The ultimate goal is to help in making decisions under uncertain environments.

In this paper, stochastic programming is used under conditions of demand, supply and process uncertainties, and a non-linear mathematical model is developed for perishable product’s supply chain. Authors’ study considers disruptions in transportation routes and also within the facilities and investigates optimal facility location and shipment decisions while minimising the total supply chain cost. A scenario-based approach is used to model these disruptions. The retailer level uncertainty due to demand-supply mismatch is handled by incorporating the newsvendor model into the last echelon of supply chain network. In this paper, two policies are proposed for making decisions under uncertain environments. In the first one, the expected cost of the supply chain is minimised. To also consider the risk behaviour of the decision maker, authors propose the second policy through a conditional value-at-risk approach.

Authors discuss the model output through various examples that are provided via a case study from the milk industry. The supply chain design and planning of the disruption-free model are different from those of the resilient model.

Authors’ research benefits the perishable products industries which encounter the disruption problems in their transportation routes as well as in the facilities. Authors have demonstrated the research through a real-life case in a milk industry.

The major contribution of authors’ work is the design of the supply chain network under disruption risks by incorporating aspects of product perishability. This work provides insight into areas such as the simultaneous consideration of demand, supply and process uncertainties. The amalgamation of newsvendor model and the approximation of the non-linearity of retailer level cost function especially in the context of supply chain under uncertainty is the first of its kind. We provide a comprehensive statistical study of uncertainties that are present in the supply chain in a unique manner.

This study aims to minimize the expected arrival time of relief vehicles to the affected areas, considering the destruction of potential routes and disruptions due to disasters. In relief operations, required relief items in each affected area and disrupted routes are considered as uncertain parameters. Additionally, for a more realistic consideration of the situations, it is assumed that the demand of each affected area could be met by multiple vehicles and distribution centers (DCs) and vehicles have limited capacity.

The current study developed a two-stage stochastic programming model for the distribution of relief items from DCs to the affected areas. Locating the DCs was the first-stage decisions in the introduced model. The second-stage decisions consisted of routing and scheduling of the vehicles to reach the affected areas.

In this paper, 7th district of Tehran was selected as a case study to assess the applicability of the model, and related results and different sensitivity analyses were presented as well. By carrying out a simultaneous sensitivity analysis on the capacity of vehicles and the maximum number of DCs that can be opened, optimal values for these parameters were determined, that would help making optimal decisions upon the occurrence of a disaster to decrease total relief time and to maximize the exploitation of available facilities.

The contributions of this paper are as below: presenting an integrated model for the distribution of relief items among affected areas in the response phase of a disaster, using a two-stage stochastic programming approach to cope with route disruptions and uncertain demands for relief items, determining location of the DCs and routing and scheduling of vehicles to relief operations and considering a heterogeneous fleet of capacitated relief vehicles and DCs with limited capacity and fulfilling the demand of each affected area by more than one vehicle to represent more realistic situations.

In this study, we discuss three DTN routing protocols, these are epidemic, PRoPHET and spray and wait routing protocols. A special simulator will be used; that is opportunistic network environment (ONE) to create a network environment. Spray and wait has highest delivery rate and low latency in most of the cases. Hence, spray and wait have better performance than others. This analysis of the performance of DTN protocols helps the researcher to learn better of these protocols in the different environment.

Delay-Tolerant Network (DTN) is a network designed to operate effectively over extreme distances, such as those encountered in space communications or on an interplanetary scale. In such an environment, nodes are occasional communication and are available among hubs, and determinations of the next node communications are not confirmed. In such network environment, the packet can be transferred by searching current efficient route available for a particular node. Due to the uncertainty of packet transfer route, DTN is affected by a variety of factors such as packet size, communication cost, node activity, etc.

Spray and wait have highest delivery rate and low latency in most of the cases. Hence, spray and wait have better performance than others.

The primary goal of the paper is to extend these works in an attempt to offer a better understanding of the behavior of different DTN routing protocols with delivery probability, latency and overhead ratio that depend on various amounts of network parameters such as buffer size, number of nodes, movement ratio, time to live, movement range, transmission range and message generation rate. In this study, we discuss three DTN routing protocols: these are epidemic, PRoPHET and spray and wait routing protocols. A special simulator will be used; that is opportunistic network environment (ONE) to create a network environment. Spray and wait have highest delivery rate and low latency in most of the cases. Hence, spray and wait have better performance than others. This analysis of the performance of DTN protocols helps the researcher to learn better of these protocols in the different environment.

Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

Changes frequently made to material delivery schedules (MDSs) accumulate upstream in the supply chain (SC), causing a bullwhip effect. This article seeks to elucidate how dynamic complexity generates MDS instability at OEMs in the automotive industry.

An exploratory multiple-case study methodology involved in-depth semistructured interviews with informants at three automotive original equipment manufacturers (OEMs).

Dynamic complexity destabilizes MDSs primarily via internal horizontal interactions between product and process complexities and demand and SC complexities. A network of complexity interactions causes and moderates such instability through complexity absorption and generation and complexity importation and exportation.

The multiple-case study contributes to empirical knowledge about the dynamics of MDS instability. Deductive research to validate the identified relationships remains for Future research.

In revealing antecedents of complexity’s effect on MDS instability, the findings imply the need to develop strategies, programs, and policies dedicated to improving capacity scalability, supplier flexibility, and the flexibility of material order fulfillment.

Building on complexity literature, the authors operationalize complexity transfer and develop a framework for analyzing dynamic complexity in SCs, focusing on complexity interactions. The identification and categorization of interactions provide a granular view of the dynamic complexity that generates MDS instability. The identified and proposed importance of readiness of the SC to absorb complexity challenges the literature focus on external factors for explaining complexity outcomes. The results can be used to operationalize such dynamic interactions by introducing new variables and networks of relationships. Moreover, the work showcases how a complexity perspective could be used to discern the root causes of a complex phenomenon driven by non-linear relationships.

This paper focusses on the aftermath of disruptions and the importance of the two largest canals (Suez and Panama), commenting on how during the pandemic the canal fees were lowered. Considering the ongoing efforts to decarbonize shipping, some of the ongoing disruptions will help reach these objectives faster.

Following a literature review of route choice in shipping, and a presentation of significant disruptions in recent years, the author deploys a simplified fuel consumption model and conduct case study analyses to compare different routes environmentally and economically.

The results explain why at times of low fuel prices as in 2020, canals provided discounts to entice ship operators to keep transiting these, instead of opting for longer routes. Considering the ongoing repercussions of the pandemic in supply chains, as well as the potential introduction of market-based measures in shipping, the value of transiting canals will be much higher in the coming years.

The main limitation in this work is that the author used the publicly available information on canal tolls, for the different ship types examined.

The envisioned model is simple, and it can be readily used for any ship and route (port to port) combination available, if ship data are available to researchers.

It is possible that canal tolls will increase, to account for the additional environmental benefits brought to ship operators.

The methodology is simple and transferable, and the author proposes several interesting research questions for follow-up work.

This study aims to design a reliable multi-level, multi-product and multi-period location-inventory-routing three-echelon supply chain network, which considers disruption risks and uncertainty in the inventory system.

A robust optimization approach is used to deal with the effects of uncertainty, and a mixed-integer nonlinear programming multi-objective model is proposed. The first objective function seeks to minimize inventory costs, such as ordering costs, holding costs and carrying costs. It also helps to choose one of the two modes of bearing the expenses of shortage or using the excess capacity to produce at the expense of each. The second objective function seeks to minimize the risk of disruption in distribution centers and suppliers, thereby increasing supply chain reliability. As the proposed model is an non-deterministic polynomial-time-hard model, the Lagrangian relaxation algorithm is used to solve it.

The proposed model is applied to a real supply chain in the aftermarket automotive service industry. The results of the model and the current status of the company under study are compared, and suggestions are made to improve the supply chain performance. Using the proposed model, companies are expected to manage the risk of supply chain disruptions and pay the lowest possible costs in the event of a shortage. They can also use reverse logistics to minimize environmental damage and use recycled goods.

In this paper, the problem definition is based on a real case; it is about the deficiencies in the after-sale services in the automobile industry. It considers the disruption risk at the first level of the supply chain, selects the supplier considering the parameters of price and disruption risk and examines surplus capacity over distributors’ nominal capacity.

Supply chains need to balance competing objectives; in addition to efficiency, supply chains need to be resilient to adversarial and environmental interference and robust to uncertainties in long-term demand. Significant research has been conducted designing efficient supply chains and recent research has focused on resilient supply chain design. However, the integration of resilient and robust supply chain design is less well studied. The purpose of the paper is to include resilience and robustness into supply chain design.

The paper develops a method to include resilience and robustness into supply chain design. Using the region of West Africa, which is plagued with persisting logistical issues, the authors develop a regional risk assessment framework and then apply categorical risk to the countries of West Africa using publicly available data. A scenario reduction technique is used to focus on the highest risk scenarios for the model to be tractable. Next, the authors develop a mathematical model leveraging this framework to design a resilient supply network that minimizes cost while ensuring the network functions following a disruption. Finally, the authors examine the network's robustness to demand uncertainty via several plausible emergency scenarios.

The authors provide optimal sets of transshipment hubs with varying counts from 5 through 15 hubs. The authors determine there is no feasible solution that uses only five transshipment hubs. The authors' findings reinforce those seven transshipment hubs – the solution currently employed in West Africa – is the cheapest architecture to achieve resilience and robustness. Additionally, for each set of feasibility transshipment hubs, the authors provide connections between hubs and demand spokes.

While, at the time of this research, three other manuscripts incorporated both resilience and robustness of the authors' research unique solved the problem as a network flow instead of as a set covering problem. Additionally, the authors establish a novel risk framework to guide the required amount of redundancy, and finally the out research proposes a scenario reduction heuristic to allow tractable exploration of 512 possible demand scenarios.

The purpose of this paper is to provide the organization with a process for assessing risk associated with their supply chain and a framework from which they can build their strategy to manage risk.

The proposed process is based on a compilation of research and interactions with supply chain managers in various industries, and these sources provide a specific process to identify how critical the risk is, when to act upon it, and how to manage it. An adapted risk mitigation framework organizes strategies according to the likelihood of disruption and consequences. Included is an industry example used to demonstrate the framework.

The variability and uncertainty associated with supply chain risks make disruption difficult to predict. Furthermore, getting information from suppliers about the amount of risk associated with their operation in an attempt to scope one's own risk can be a challenge. Management must consider the amount of risk the organization is going to accept and how much to invest to mitigate it.

To manage the risk associated with supply chain disruption, an organization must deploy a strategy for assessing it. Once risk areas have been identified, the organization must design strategies which will mitigate the risk. The depth and degree to which risk is mitigated depends upon how risk-averse a company is and what they are willing to invest in this activity.

Attracting commuters from driving to light rail systems has a good potential for reducing carbon emissions. However, the light rail system is interrupted by disruptions frequently, which reduces its attraction to passengers. Therefore, how to provide a quick replacement service during disruptions is of vital importance to avoid passengers change to other higher emission vehicles. The purpose of this paper is to focus on the decision analysis of the replacement tool for disruption recovery service in urban public light rail systems from the perspective of environmental effect.

The traditional approach – bus replacement service – and the new approach – taxi replacement service – which has been recently adopted by several cities, are examined individually and compared. The benefit of the light rail company is formulated by balancing between carbon emission and financial cost. The involving parties’ decision functions taking the passengers’ behaviors as well as numerous other important factors into account are formulated.

Both theoretical and numerical sensitivity analyses are conducted to shed light for light rail systems to better coping with disruptions, increasing service level, and attracting more passengers to the environmental transit system to reduce carbon emission.

It is worth mentioning that this research is a successful application for disruption recovery in a public transit system considering the environmental effect. To the authors’ knowledge, this research is one of the first of such applications in this area and can be used not only in the public light rail systems, but also in other urban public transport network components such as the subway and rail systems.