The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

Integrated business continuity and disaster recovery planning: Towards organizational resilience

Businesses are increasingly subject to disruptions. It is almost impossible to predict their nature, time and extent. Therefore, organizations need a proactive approach equipped with a decision support framework to protect themselves against the outcomes of disruptive events. In this paper, a novel framework is proposed for Integrated Business Continuity and Disaster Recovery Planning for efficient and effective resuming and recovering of critical operations after being disrupted. The proposed model addresses decision problems at all strategic, tactical and operational levels. At the strategic level, the context of the organization is first explored and the main features of the organizational resiliency are recognized. Then, a new multi-objective mixed integer linear programming model is formulated to allocate internal and external resources to both resuming and recovery plans simultaneously. The model aims to control the loss of resiliency by maximizing recovery point and minimizing recovery time objectives. Finally, at the operational level, hypothetical disruptive events are examined to evaluate the applicability of the plans. We also develop a novel interactive augmented ε-constraint method to find the final preferred compromise solution. The proposed model and solution method are finally validated through a real case study

A hybrid decision support system for managing humanitarian relief chains

Decisions regarding location, allocation and distribution of relief items are among the main concerns of the Humanitarian Relief Chain (HRC) managers in response to no-notice large-scale disasters such as earthquakes. In this paper, a Hybrid Decision Support System (HDSS) consisting of a simulator, a rule-based inference engine, and a knowledge-based system (KBS) is developed to configure a three level HRC. Three main performance measures including the coverage, total cost, and response time are considered to make an explicit trade-off analysis between cost efficiency and responsiveness of the designed HRC. In the first step, the simulator calculates the performance measures of the different configurations of the HRC under generated number of disaster scenarios. Then, the rule-based inference engine attempts to build the best configuration of the HRC including facilities’ locations, relief items’ allocation and distribution plan of the scenario under investigation based on calculated performance measures. Finally, the best configuration for each scenario is stored in the KBS as the extracted knowledge from the above analyses. In this way, the HRC managers can retrieve the most appropriate HRC configuration in accordance with the realized post-disaster scenario in an effective and timely manner. The results of a real case study in Tehran demonstrate that the developed HDSS is an effective tool for fast configuration of HRCs using stochastic data

Business continuity-inspired resilient supply chain network design

Supply chains are prone to several operational and disruption risks. In order to design a resilient supply chain network capable of responding to such potential risks suitably, this paper proposes a novel framework for the business continuity-inspired resilient supply chain network design (BCRSCND) problem, which includes three steps. First, four resilience dimensions including Anticipation, Preparation, Robustness, and Recovery are considered to quantify the resilience score of each facility using a multi-criteria decision-making technique and considering a comprehensive set of resilience strategies. In the second step, the critical processes and their business continuity metrics (which are vital for supply chain continuity), are identified. The outputs of the first two steps provide the inputs of a novel two-stage mixed possibilistic-stochastic programing (TSMPSP) model. The model aims to design a multi-echelon, multi-product resilient supply chain network under both operational and disruption risks. The proposed TSMPSP model allows decision makers to incorporate their risk attitudes into the design process. After converting the original TSMPSP model into the crisp counterpart, several sensitivity analyses are conducted on different features of hypothetical disruptions (i.e. their severity, likelihood and location) and DM’s risk attitudes from which useful managerial insights are provided

Building organizational resilience in the face of multiple disruptions

The increasing number of natural and man-made hazards is forcing organizations to build resilience against numerous types of disruptions that threaten continuity of their business processes. This paper presents an integrated business continuity and disaster recovery planning (IBCDRP) model to build organizational resilience that can respond to multiple disruptive incidents, which may occur simultaneously or sequentially. This problem involves multiple objectives and accounts for inherent epistemic uncertainty in input data. A multi-objective mixed-integer robust possibilistic programming model is formulated, which accounts for sensitivity and feasibility robustness. The model aims to plan both internal and external resources with minimal resumption time, restoration time, and loss in the operating level of critical functions by making tradeoffs between required resources for continuity plans, recovery time, and the recovery point. A real case study in a furniture manufacturing company is conducted to demonstrate the performance and applicability of the proposed IBCDRP model. The results confirm the capability of the proposed model to improve organizational resilience. In addition, the proposed model demonstrates the interaction between the organizational resilience and required resources, particularly in respect to the total budget and external resources, which is necessary for developing continuity and recovery strategies

Enhancement of education in farm and food industry with adoption of computer-based information systems

This study describes an information system to enhance farm and food industry. The model involves using electronic technology to collect a large amount of data from distributed farm industries. Major issues in the implementation of this model include interpreting the huge amount of data collected with different quality attributes. In this study, we developed a structured profile for higher agriculture education to distinguish the quality profile of food industries based on agricultural product attributes. The producer currently measures process key parameter and performance to improve quality of production. This information system manipulates those data to explore the optimum quality profile. This model is being able to propose sound strategies for variability management in farm and food industries. © 2008 Asian Network for Scientific Information