Today's competitive and turbulent environment persuades every organisation to implement a business continuity management system (BCMS) for dealing with disruptive incidents such as earthquake, flood, and terrorist...
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Today's competitive and turbulent environment persuades every organisation to implement a business continuity management system (BCMS) for dealing with disruptive incidents such as earthquake, flood, and terrorist attacks. Within a BCMS, effective and efficient business continuity plans (BCPs) must be provided to ensure the continuity of organisation's key products. This study develops a novel approach to select the most appropriate BCPs which can meet the business continuity key measures. First, a risk assessment process is conducted to define the disruptive incidents for which the organisation should have suitable BCPs. Then, two different possibilistic programming models including hard and soft BCP selection models are developed to determine appropriate BCPs under epistemic uncertainty of input data. These models aim to maximise the resilience level of the organisation while minimising the establishment cost of selected BCPs.& Finally, a real case study is provided whose results demonstrate the applicability and usefulness of the proposed approach.
In this paper, a hybrid cellular manufacturing (HCM) system is presented in which the main sources of uncertainty, e.g. the demands of parts and unit costs are treated as fuzzy numbers in the form of possibilistic dis...
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In this paper, a hybrid cellular manufacturing (HCM) system is presented in which the main sources of uncertainty, e.g. the demands of parts and unit costs are treated as fuzzy numbers in the form of possibilistic distributions. The basic concept of HCM is that high variation in demand might disturb cell efficiency, so forming cells with only those parts that have stable demand, will profit. Thus, to design stable and robust manufacturing cells, a two-phase method is proposed in which a fuzzy adaptive ranking method is first applied to identify those parts with low and non-repetitive demands (i.e. the special parts) which will then be assigned to a functional cell. Afterwards, an interactive possibilistic programming model is applied to cell formation of remaining regular parts while considering both part sequences and multiple routes. To show the capability and usefulness of the proposed method, an illustrative example is also provided. Finally, concluding remarks are reported.
Agricultural activities are the main contributors of nonpoint source water pollution within agricultural systems. In this study, a possibilistic stochastic water management (PSWM) model is developed and applied to a c...
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Agricultural activities are the main contributors of nonpoint source water pollution within agricultural systems. In this study, a possibilistic stochastic water management (PSWM) model is developed and applied to a case study of water quality management within an agricultural system in China. This study is a first application of hybrid possibilistic chance-constrained programming approach to nonpoint source water quality management problems within an agricultural system. Hybrid uncertainties with the synergy of fuzzy and stochastic implications are effectively characterized by the PSWM model with the following advantages: (1) it improves upon the existing possibilistic and chance-constrained programming methods through direct incorporation of fuzziness and randomness within a general optimization framework;(2) it will not lead to more complicated intermediate models and thus have lower computational requirements;(3) its solutions offer flexibility in interpreting the results and reflect the interactional effects of uncertain parameters on system conditions variations;and (4) it can help examine the risk of violating system constraints and the associated consequences. The results of the case study show useful information for feasible decision schemes of agricultural activities, including the trade-offs between economic and environmental considerations. Moreover, a strong desire to acquire high agricultural income will run into the risk of potentially violating the related water quality standards, while willingness to accept low agricultural income will increase the risk of potential system failure (violating system constraints). The results suggest that the developed approach is also applicable to many practical problems where hybrid uncertainties exist.
Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programmin...
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Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage's objective function is considered by utilising the conditional value at risk (CVaR) measure. The validation and applicability of the proposed model are examined through several numerical experiments.
Nowadays supply chains are facing disruptive events, and their adverse effects on supply chain performance, more than ever before. Disruptions can impact supply chain operations considerably, therefore it is of paramo...
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Nowadays supply chains are facing disruptive events, and their adverse effects on supply chain performance, more than ever before. Disruptions can impact supply chain operations considerably, therefore it is of paramount importance to design resilient supply chains that are able to perform well under risks caused by such events. Also, supply chain management involves making decisions regarding material and financial flows. Usually, these decisions are taken independently. Doing so can result in infeasible plans. That is why designing comprehensive models that integrate decisions regarding material and financial flows is important. In this paper, we offer a resilient supply chain model capable of performing well under disruptions and consider material and financial decisions concurrently. To increase the resilience of the supply chain we investigated several resilient strategies including multiple sourcing, emergency inventory, and additional production capacity. possibilistic program-ming is used to model disruption risks. Two-stage stochastic programming is used to consider uncertain demand for products. Furthermore, to incorporate financial flows into the model, we considered financing strategies, including the use of trade-credit and bank-credit. Using Conditional-Value-at-risk, we formulated a robust optimization model capable of performing well under worst-case scenarios. Finally, based on the computational experiments performed on a real-world case study as well as several test problems, the applicability of the presented model is confirmed and important managerial insights are presented. Results show that all three resilient strategies can improve supply chain performance. Moreover, it is concluded that incorporating different financing sources to purchase raw materials can positively increase supply chain profitability and improve supply chain performance when disruptions happen.
Supply chain sustainability, with economic, environmental, and social values, has gained attention in both academia and industry. For deteriorating and seasonal products, like fresh produce, the issues of timely suppl...
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Supply chain sustainability, with economic, environmental, and social values, has gained attention in both academia and industry. For deteriorating and seasonal products, like fresh produce, the issues of timely supply and disposal of the deteriorated products are of great concern. This paper is to develop a possibilistic mathematical model, solved after linearizing the non-linear statements, and to propose a new replenishment policy for a centralized Sustainable Supply Chain (SSC) for deteriorating items. Different transportation vehicle options, which produce various pollution and greenhouse gas (GHG) levels, are considered. Several variables such as the end-customer demand, the partial backordered ratio, and the deterioration rate are uncertain. Deterioration occurs for in-stock inventories and during transportation. The solution provides the optimum transportation modes and routes, and the inventory policy by finding a balance between financial, environmental, and social criteria. (C) 2016 Sharif University of Technology. All rights reserved.
To improve the coordination of blood supply and demand, the efficient design of the blood supply chain network (BSCN) is a proper strategy. In this regard, this paper is the first-ever study to strive for a simultaneo...
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To improve the coordination of blood supply and demand, the efficient design of the blood supply chain network (BSCN) is a proper strategy. In this regard, this paper is the first-ever study to strive for a simultaneous investigation on three interdependent challenges of the BSCN: (1) donors motivation, (2) optimizing location and capacity decisions, and (3) controlling the reliability and robustness of the network under combinatorial risk. As in reality, blood donors play a critical role in the BSC;this study undertakes motivational initiatives to encourage blood donors for maintaining sufficient blood supply. Advertisement, education and medical credits are the directions incorporated to construct the motivational function. To observe the efficiency as the most critical factor while evaluating the pool of location alternatives for establishing facilities, an augmented version of data envelopment analysis (DEA) is utilized. In addition, a mixed integer programming model is proposed by which the simultaneous location and capacity decisions are supported. The model is also extended to handle the combinatorial risk of uncertainty as well as disruption. Thus, a novel mixed possibilistic-stochastic flexible robust programming (MPSFRP) is developed. Eventually, the proposed model is implemented in a real case study to assess its practicality and then provide applicable insights for administrators.
In this paper, we deal with the vehicle routing problem where vehicles have finite capacities and demands of customers are uncertain. We represent the uncertain demands by triangular fuzzy numbers and interpret them a...
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In this paper, we deal with the vehicle routing problem where vehicles have finite capacities and demands of customers are uncertain. We represent the uncertain demands by triangular fuzzy numbers and interpret them as possibility distributions. According to the same consideration as the stochastic programming with recourse, we treat the influence of the fuzziness of customers’ demands as recourse cost and formulate the problem as a two-stage possibilistic programming model. Defining the Fuzzy Mean of a fuzzy number as its the generalized mean value, the proposed model is equivalent to an ordinary programming problem and then a solution method based on Ant Colony System （ACS） can be proposed to give the best solution of the problem. Finally, some examples are given to illustrate the two-stage model and the solution algorithm.
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