Search results

The purpose of this paper is to improve the efficiency of loading and discharging operations in container terminals. Accounting for an increase in the size of ships, the yard truck (YT) routing and scheduling problem has become an important issue to terminal operators.

A (binary) integer programming model is developed using the time-space network technique to optimally move YTs between quay cranes (QC) and yard cranes (YC) in the time and space dimensions. The objective of the model is to minimize the total operating cost, and the model employs the M/M/S model in the queuing theory to determine the waiting time of YTs. The developed model can obtain the optimal number of YTs and their scheduling and routing plans simultaneously, as shown by the computational results.

The results also show that the model can be applied to practical operations. In this research, an experimental design of the QC and YC operation networks was considered with the import and export containers carried by YTs. The model can be used to tackle a real world problem in an international port, and the analysis results could be useful references for port operators in actual practice.

The purpose of this research only focusses on YTs routing and scheduling problem, however, the container terminal operation problems are interrelated with berth allocation and yard stacking plan. The managerial application of this study is to analyze the trade-off between truck numbers and truck waiting time can be used for terminal operators to adjust the truck assignment. This research can assist an operator to determine the optimal fleet size and schedule in advance to avoid wasted costs and congestion in the quayside and yard block.

This research solves the YT scheduling and routing problem for container discharging and loading processes with a time-space network model, which has not been previously reported, through an empirical research.

The purpose of this paper is twofold: first to carry out a comprehensive literature review for state of the art regarding airline schedule planning and second to identify some new research directions that might help academic researchers and practitioners.

The authors mainly focus on the research work appeared in the last three decades. The search process was conducted in database searches using four keywords: “Flight scheduling,” “Fleet assignment,” “Aircraft maintenance routing” (AMR), and “Crew scheduling”. Moreover, the combination of the keywords was used to find the integrated models. Any duplications due to database variety and the articles that were written in non-English language were discarded.

The authors studied 106 research papers and categorized them into five categories. In addition, according to the model features, subcategories were further identified. Moreover, after discussing up-to-date research work, the authors suggested some future directions in order to contribute to the existing literature.

The presented categories and subcategories were based on the model characteristics rather than the model formulation and solution methodology that are commonly used in the literature. One advantage of this classification is that it might help scholars to deeply understand the main variation between the models. On the other hand, identifying future research opportunities should help academic researchers and practitioners to develop new models and improve the performance of the existing models.

This study proposed some considerations in order to enhance the efficiency of the schedule planning process practically, for example, using the dynamic Stackelberg game strategy for market competition in flight scheduling, considering re-fleeting mechanism under heterogeneous fleet for fleet assignment, and considering the stochastic departure and arrival times for AMR.

In the literature, all the review papers focused only on one category of the five categories. Then, this category was classified according to the model formulation and solution methodology. However, in this work, the authors attempted to propose a comprehensive review for all categories for the first time and develop new classifications for each category. The proposed classifications are hence novel and significant.

This paper aims to address how to systematically address vulnerability in a maritime transportation system using a formal vulnerability assessment approach, create quantitative measures of disruption risk and test the effect of mitigating measures. These quantitative data are prerequisites for cost efficiency calculations, and may be obtained without requiring excessive resources.

Supply chain simulation using heuristics‐based planning tools offers an approach to quantify the impact of disruption scenarios and mitigating measures. This is used to enrich a risk‐based approach to maritime supply chain vulnerability assessment. Monte Carlo simulation is used to simulate a stochastic nature of disruptions.

The exemplary assessment of a maritime liquefied natural gas (LNG) transportation system illustrates the potential for providing quantitative data about the cost of disruptions and the effects of mitigating measures, which are foundations for more precise cost efficiency estimates.

This simulation was done on a simplified version of a real transportation system. For resource reasons, several simplifications were made, both with regards to modeling the transportation system and with the implementation of the formal vulnerability assessment framework. Nevertheless, the authors believe the paper serves to illustrate the approach and potential outcome.

Practitioners are provided with an approach to get more precise quantitative data on disruption costs and cost/efficiency of mitigating measures, providing background data for decisions on investing in reduction of supply chain vulnerability.

The combination of risk assessment methods and inventory routing simulation of maritime supply chain problems is a novelty. Quantifying vulnerability, effects of disruptions and effects of mitigating measures in maritime transportation systems contributes to a little‐researched area.

Delivery control is a key requirement in all distribution operations. It is, however, a highly complex undertaking since it embraces a variety of activities (e.g., order processing, consignment tracing and vehicle round planning — (see Figure 1)). Moreover, delivery control invariably requires information channels to be established between the distribution function and other parts of the manufacturing/retailing organisation. The potential for misunderstandings and disagreements over, say, the design of a delivery control system is therefore great.

The purpose of this study is to explore what design principles need to be considered in Enterprise Resource Planning (ERP) systems for humanitarian organizations (HOs) to enable agile, adaptive and aligned (Triple-A) humanitarian supply chain capabilities and digitize humanitarian operations.

This study follows an embedded case study approach with a humanitarian medical relief organization, Médecins Sans Frontières (MSF), which engaged in a multiyear ERP design at its humanitarian field missions.

This research shows that ERP systems for humanitarian organizations should be designed as unique systems addressing humanitarian organizations' challenges and unique missions, their value generation processes, and resource base in an effort to improve organizational performance. This study presents 12 general design principles that are unique for humanitarian organizations. These design principles provide a high-level structure of guidance under which specific requirements can be further defined and engineered to achieve success.

The results of this study are based on a single case study limiting generalizability. However, the case study was analyzed and presented as an embedded case study with five autonomous subunits using different business processes and following different adoption and implementation approaches. Therefore, the findings are derived based on considerable variance reflective of humanitarian organizations beyond MSF.

This study recognizes that HOs have unique routines that standard commercial ERP packages do not address easily at the field level. The primary contribution of this research is a set of design principles that consider these unique routines and guide ERP development in practice. National and international HOs that are planning to implement information systems, private companies that are trading partners of HOs as well as vendors of ERP systems that are looking for new opportunities would all benefit from this research.

This study fills the gap in the humanitarian literature regarding the design of ERP systems for humanitarian organizations that enable Triple–A supply chain capabilities and it advances the knowledge of the challenges of ERP design by HOs in the context of humanitarian operations.

Relief item distribution to victims is a key activity during disaster response. Currently many humanitarian organizations follow simple guidelines based on experience to assess need and distribute relief supplies. However, the interviews with practitioners suggest a problem in efficiency in relief distribution efforts. The purpose of this paper is to develop a model and solution methodology that can estimate relief center (RC) performance, measured by waiting time for victims and throughput, for any RC design and analyze the impact of key design decisions on these performance measures.

Interviews with practitioners and current practice guidelines are used to understand relief distribution and a queuing network model is used to represent the relief distribution. Finally, the model is applied to data from the 2015 Nepal earthquake.

The findings identify that dissipating congestion created by crowds, varying item assignment decisions to points of distribution, limiting the physical RC capacity to control congestion and using triage queue to balance distribution times, are effective strategies that can improve RC performance.

This research bases the RC designs on Federal Emergency Management Agency guidelines and assumes a certain area and volunteer availability.

This paper contributes to humanitarian logistics by discussing useful insights that can impact how relief agencies set up and operate RCs. It also contributes to the queuing literature by deriving analytic solutions for the steady state probabilities of finite capacity, state dependent queues with blocking.

The case study highlights two strategic angles – that of the business unit (business strategy, profitability, market leadership. organizational culture, operational turnaround, industry structure and competitive dynamics) and the owner (returns, repositioning strategy and funding plan). By the end of this case study, students would be able to understand the changing competitive forces of a dynamic industry; analyse the circumstances leading to a change in the control of a firm from the state to the private sector; understand the logic of acquiring a perennially loss-making firm operating in a volatile environment without a unique strategy; identify a firm’s strategic and operational choices for financial turnaround, return to profitability and regaining market leadership; and learn about the actual strategic realities and choices confronting a troubled business organization in a difficult industry.

When the Tata Group took over Air India on 27 January 2022 from the state that had ownership for 68 years, Air India was under a long spell of poor performance, bleeding losses and unmanageable levels of debt. Unsatisfactory customer service, management issues and competition were the key reasons. Therefore, a crucial question facing the group’s Chairman N. Chandrasekaran was what workable strategy he could use to reposition Air India and make it profitable again so as to recover the $7.5bn of estimated investment involved in the acquisition and turnaround.

This case study is intended for undergraduate and graduate executive education levels in business administration and management and allied subjects, particularly for courses in strategic management, marketing, financial management, turnaround and transformation, mergers and acquisitions and organizational change.

Teaching notes are available for educators only.

CSS 11: Strategy.

The objective of this work is to provide a novel aircraft allocation model for fractional business aviation. This model may provide decision-makers with alternative routing solutions that take into consideration preventive maintenance and failure prognostics information. The expected results are more efficient routing solutions when compared to conventional planning models, to help decision-makers improve operations and maintenance planning.

The model is a mixed integer linear problem formulation addressing and considering preventive maintenance and failure prognostics for optimal operations. Numerical experiments were performed using both field and synthetic data to validate the proposed method. All instances are solved using branch, price and cut algorithms from open-source software.

The results obtained in this study show that the use of failure prognostics information in aircraft routing can provide improvements in overall planning. By choosing slightly longer flight legs, the flight cost will increase, but putting an aircraft with a higher risk of failure on a leg inbound to a maintenance base can reduce maintenance and overall operating cost.

The model and method provide decision-makers with routing solutions that consider new aspects of planning, not used in previous works, such as failure. Most of the literature focuses on solving routing problems for large commercial airlines. Considering that, few solutions are found in literature for fractional business operators, which have their own operational particularities, such as a company managing a fleet of aircraft belonging to multiple shareowners. In such operation, clients may not always fly in the aircraft that they are shareowners, but an aircraft from the fractional fleet of the same category. Here, the company managing the aircraft guarantees that an aircraft will be ready to attend client demands in minimum time. One of the major differences from other models of operation is the dynamic nature of its flight demands, thus requiring flexible and agile planning limiting the available time to find a routing solution.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.