The runway configuration management (RCM) problem governs what combinations of airport runways are in use at a given time, and to what capacity. Runway configurations (groupings of runways) operate under runway configuration capacity envelopes (RCCEs) which limit arrival and departure capacities. The RCCE identifies unique capacity constraints based on which tarmacs are used for arrivals, departures, or both, and their direction of travel. When switching between RCCEs, some decrement in arrival and departure capacities is incurred by the transition. A previous RCM model (Frankovich et al., 2009) accounted for this cost through a required period of inactivity. In this paper, we instead focus on the introduction and assessment of a model capable of marginally decreasing RCCE capacities during configuration transitions. A transition penalty matrix is introduced, specifying the relative costs (in terms of accepted arrival and departure capacities) for switching between RCCEs. The new model benefits from customizable transition penalties which more closely represent real-world conditions, at a reasonable computational cost.

Christopher Weld

Michael Duarte

Rex Kincaid

A Runway Configuration Management Model with Marginally Decreasing Transition Capacities

Demand for a seasonal product persists for a fixed period of time. Normally
the “finite time horizon inventory control problems” are formulated for this type
of demands. In reality, it is difficult to predict the end of a season precisely. It is
thus represented as an uncertain variable and known as random planning horizon.
In this paper, we present a production-inventory model for deteriorating items in
an imprecise environment characterised by inflation and timed value of money and
considering a constant demand. It is assumed that the
time horizon of the business period is random in nature and follows exponential
distribution with a known mean. Here, we considered the resultant effect of inflation
and time value of money as both crisp and fuzzy. For crisp inflation effect, the
total expected profit from the planning horizon is maximized using genetic algorithm
(GA) to derive optimal decisions. This GA is developed using Roulette wheel
selection, arithmetic crossover, and random mutation. On the other hand when the
inflation effect is fuzzy, we can expect the profit to be fuzzy, too! As for the fuzzy
objective, the optimistic or pessimistic return of the expected total profit is obtained
using, respectively, a necessity or possibility measure of the fuzzy event. The GA we
have developed uses fuzzy simulation to maximize the optimistic/pessimistic return
in getting an optimal decision. We have provided some numerical examples and
some sensitivity analyses to illustrate the model.

Debasis Das

Arindam Roy

Samarjit Kar

A Production-Inventory Model for a Deteriorating Item Incorporating Learning Effect Using Genetic Algorithm

As classical methods are intractable for solving Markov decision processes
(MDPs) requiring a large state space, decomposition and aggregation techniques are very useful to cope with large problems. These techniques are
in general a special case of the classic Divide-and-Conquer framework to
split a large, unwieldy problem into smaller components and solving
the parts in order to construct the global solution. This paper reviews
most of decomposition approaches encountered in the associated literature
over the past two decades, weighing their pros and cons. We consider several categories of MDPs (average, discounted, and weighted MDPs), and
we present briefly a variety of methodologies to find or approximate optimal strategies.

Cherki Daoui

Mohamed Abbad

Mohamed Tkiouat

Exact Decomposition Approaches for Markov Decision Processes: A Survey

Paknejad et al.&#39;s model is considered in this paper. Itemwise multiobjective models for both exponential and uniform lead-time demand are taken and the results are compared numerically both in fuzzy optimization and intuitionistic fuzzy optimization techniques. Objective of this paper is to establish that intuitionistic fuzzy optimizaion method is better than usual fuzzy optimization technique as expected annual cost of this inventory model is more minimized in case of intuitionistic fuzzy optimization method. As a single objective stochastic inventory model where the lead-time demand follows normal distribution and with varying defective rate, expected annual cost is also measured. Finally the model considers for fuzzy cost components, which make the model more realistic, and numerical values for uniform, exponential, and normal lead-time demand are compared. Necessary graphical presentations are also given besides numerical illustrations.

S. Banerjee

T. K. Roy

Solution of Single and Multiobjective Stochastic Inventory Models with Fuzzy Cost Components by Intuitionistic Fuzzy Optimization Technique

This paper proposes a cluster partitioning technique to calculate improved upper bounds to the optimal solution of maximal covering location problems. Given a covering distance, a graph is built considering as vertices the potential facility locations, and with an edge connecting each pair of facilities that attend a same client. Coupling constraints, corresponding to some edges of this graph, are identified and relaxed in the Lagrangean way, resulting in disconnected subgraphs representing smaller subproblems that are computationally easier to solve by exact methods. The proposed technique is compared to the classical approach, using real data and instances from the available literature.

Edson Luiz França Senne

Marcos Antonio Pereira

Luiz Antonio Nogueira Lorena

A Decomposition Heuristic for the Maximal Covering Location Problem

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Advances in Operations Research - Table of contents 2010

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Advances in Operations Research publishes original research and review articles contributing to the theory and methodology of operational research.

Advances in Operations Research