Hierarchical Passenger Hub Location Problem in a Megaregion Area Considering Service Availability

The rapid growth of the intercity travel demand has resulted in enormous pressure on the passenger transportation network in a megaregion area. Optimally locating hubs and allocating demands to hubs influence the effectiveness of a passenger transportation network. This study develops a hierarchical passenger hub location model considering the service availability of hierarchical hubs. A mixed integer linear programming formulation was developed to minimize the total cost of hub operation and transportation for multiple travel demands and determine the proportion of passengers that access hubs at each level. This model was implemented for the Wuhan metropolitan area in four different scenarios to illustrate the applicability of the model. Then, a sensitivity analysis was performed to assess the impact of changing key parameters on the model results. The results are compared to those of traditional models, and the findings demonstrate the importance of considering hub choice behavior in demand allocation

Network Flexibility for Recourse Considerations in Bi-Criteria Facility Location

What is the best set of facility location decisions for the establishment of a logistics network when it is uncertain how a company’s distribution strategy will evolve? What is the best configuration of a distribution network that will most likely have to be altered in the future? Today’s business environment is turbulent, and operating conditions for firms can take a turn for the worse at any moment. This fact can and often does influence companies to occasionally expand or contract their distribution networks. For most companies operating in this chaotic business environment, there is a continuous struggle between staying cost efficient and supplying adequate service. Establishing a distribution network which is flexible or easily adaptable is the key to survival under these conditions. This research begins to address the problem of locating facilities in a logistics network in the face of an evolving strategic focus through the implicit consideration of the uncertainty of parameters. The trade-off of cost and customer service is thoroughly examined in a series of multi-criteria location problems. Modeling techniques for incorporating service restrictions for facility location in strategic network design are investigated. A flexibility metric is derived for the purposes of quantifying the similarity of a set of non-dominated solutions in strategic network design. Finally, a multi-objective greedy random adaptive search (MOG) metaheuristic is applied to solve a series of bi-criteria, multi-level facility location problems

A Multicriteria Optimization for Flight Route Networks in Large-Scale Airlines Using Intelligent Spatial Information

Air route network optimization, one of the airspace planning challenges, effectively manages airspace resources toward increasing airspace capacity and reducing air traffic congestion. In this paper, the structure of the flight network in air transport is analyzed with a multi-objective genetic algorithm regarding Geographic Information System (GIS) which is used to optimize this Iran airlines topology to reduce the number of airways and the aggregation of passengers in aviation industries organization and also to reduce changes in airways and the travel time for travelers. The proposed model of this study is based on the combination of two topologies – point-to-point and Hub-and-spoke – with multiple goals for causing a decrease in airways and travel length per passenger and also to reach the minimum number of air stops per passenger. The proposed Multi-objective Genetic Algorithm (MOGA) is tested and assessed in data of the Iran airlines industry in 2018, as an example to real-world applications, to design Iran airline topology. MOGA is proven to be effective in general to solve a network-wide flight trajectory planning. Using the combination of point-to-point and Hub-and-spoke topologies can improve the performance of the MOGA algorithm. Based on Iran airline traffic patterns in 2018, the proposed model successfully decreased 50.8% of air routes (184 air routes) compared to the current situations while the average travel length and the average changes in routes were increased up to 13.8% (about 100 kilometers) and up to 18%, respectively. The proposed algorithm also suggests that the current air routes of Iran can be decreased up to 24.7% (89 airways) if the travel length and the number of changes increase up to 4.5% (32 kilometers) and 5%, respectively. Two intermediate airports were supposed for these experiments. The computational results show the potential benefits of the proposed model and the advantage of the algorithm. The structure of the flight network in air transport can significantly reduce operational cost while ensuring the operation safety. According to the results, this intelligent multi-object optimization model would be able to be successfully used for a precise design and efficient optimization of existing and new airline topologies

Predictive Irrigation Scheduling Modeling in Nurseries

Water requirement allocation plays an important role in modern farming management. Evapotranspiration-based irrigation controllers can ideally provide irrigation according to the water requirements of the plant. This chapter describes predictive irrigation scheduling in nurseries with multiple crop species and high-frequency water requirements under limited resources. Based on historical data, time-series analysis is used to forecast evapotranspiration, an essential element in water balance equation. An algorithm based on a hierarchical research including dispatching priority rules and taking into account crop characteristics, available water, and constraints of the hydraulic network is proposed to predict irrigation schedules, with the objective of minimizing crop’s water stress periods and optimizing resource materials. Simulation results with different climatic conditions show on the one hand the ability of the time-series model to forecast potential evapotranspiration, and on the other hand that, given a typical nursery, the proposed predictive approach of irrigation scheduling compared to the non-predictive approach makes it possible to prevent crop’s water stress

Classification of the Existing Knowledge Base of OR/MS Research and Practice (1990-2019) using a Proposed Classification Scheme

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordOperations Research/Management Science (OR/MS) has traditionally been defined as the discipline that applies advanced analytical methods to help make better and more informed decisions. The purpose of this paper is to present an analysis of the existing knowledge base of OR/MS research and practice using a proposed keywords-based approach. A conceptual structure is necessary in order to place in context the findings of our keyword analysis. Towards this we first present a classification scheme that relies on keywords that appeared in articles published in important OR/MS journals from 1990-2019 (over 82,000 articles). Our classification scheme applies a methodological approach towards keyword selection and its systematic classification, wherein approximately 1300 most frequently used keywords (in terms of cumulative percentage, these keywords and their derivations account for more than 45% of the approx. 290,000 keyword occurrences used by the authors to represent the content of their articles) were selected and organised in a classification scheme with seven top-level categories and multiple levels of sub-categories. The scheme identified the most commonly used keywords relating to OR/MS problems, modeling techniques and applications. Next, we use this proposed scheme to present an analysis of the last 30 years, in three distinct time periods, to show the changes in OR/MS literature. The contribution of the paper is thus twofold, (a) the development of a proposed discipline-based classification of keywords (like the ACM Computer Classification System and the AMS Mathematics Subject Classification), and (b) an analysis of OR/MS research and practice using the proposed classification

A Multi-Objective Decision-Making Model for Resources Allocation in Humanitarian Relief

This thesis addresses the critical resource allocation in the initial days of a disaster relief operation. One of the most important and essential components of relief operations is the allocation of scarce resources to accomplish the relief efforts. Every operation for disaster relief needs various critical resources whether they are personnel, equipment, supplies, or simply finances. Several research efforts for disaster relief have suggested methods to allocate scarce resources across a variety of competing objectives and programs in a disaster relief operation. Many of those efforts focused on optimizing a mathematical programming model subject to budget constraints. However, capturing the values of the decision-maker(s) in such a model is relatively under explored. The lack of clear organizational values contributes to the inconsistency in practice and hinders effective resources allocation across the disaster relief system. The purpose of this study is to develop a multi-objective decision-making (MODM) model to incorporate the decision-maker(s) value trade-offs in the disaster relief resources allocation problem. The notional model is based on a hurricane and flood scenario and the decision window for the resource allocation is the critical first 72 hours after the initial damage assessment has been made. The value focused thinking (VFT) process is used to capture the value trade-offs and the resulting value hierarchy is optimized via a mathematical programming model to solve the multi-objective resource allocation problem