Güvenç Şahin

A review of hierarchical facility location models

In this study, we review the hierarchical facility location models. Although there have been a number of review papers on hierarchical facility problems, a comprehensive treatment of models has not been provided since the mid-80s. This review fills the gap in the literature. We first classify the hierarchical facility problems according to the features of systems studied, which are based on flow pattern, service availability at each level of the hierarchy, and spatial configuration of services in addition to the objectives to locate facilities. We then investigate the applications, mixed integer programming models, and solution methods presented for the problem. With an overview of the selected works, we consolidate the main results in the literature. Scope and purpose: Hierarchical systems have to decide about the locations of their interacting facilities within a multiple layer configuration. Although literature on hierarchical location decisions has grown, a review of hierarchical location models has not been published since 1986-1987. In this review, we consolidate the selected material in the literature, including more than 70 studies dated 1986 or later.

Locational analysis for regionalization of Turkish Red Crescent blood services

Abstract After a series of earthquakes in 1999, Turkish Red Crescent (TRC) has engaged in a restructuring for all of its activities, including the blood services. Our study on the blood management system had been started as part of this initiative to restructure the blood services and improve both their effectiveness and efficiency. In the current system of TRC, not much consideration has been given to how the locational decisions affect the performance of blood centers, stations and mobile units. In recent years, however, there has been much discussion regarding the regionalization of the blood management system in Turkey. In this study, we develop several mathematical models to solve the location–allocation decision problems in regionalization of blood services. We report our computational results, obtained by using real data, for TRC blood services. Statement of scope and purpose Regionalization of blood services has been implemented in many countries and found to be successful in resolving the management problems. In this study we formulate several mathematical problems to address the location–allocation aspects of regionalization of blood services of the Turkish Red Crescent Society.

Hybrid simulation-analytical modeling approaches for the reverse logistics network design of a third-party logistics provider

In this study, we consider a manufacturer that has strategically decided to outsource the company specific reverse logistics (RL) activities to a third-party logistics (3PL) service provider. Given the locations of the collection centers and reprocessing facilities, the RL network design of the 3PL involves finding the number and places of the test centers under supply uncertainty associated with the quantity of the returns. Hybrid simulation-analytical modeling, which iteratively uses mixed integer programming models and simulation, is a suitable framework for handling the uncertainties in the stochastic RL network design problem. We present two hybrid simulation-analytical modeling approaches for the RL network design of the 3PL. The first one is an adaptation of a problem-specific approach proposed in the literature for the design of a distribution network design of a 3PL. The second one involves the development of a generic approach based on a recently proposed novel solution methodology. In the generic approach instead of exchanging problem-specific parameters between the analytical and simulation model, the interaction is governed by reflecting the impact of uncertainty obtained via simulation to the objective function of the analytical model. The results obtained from the two approaches under different scenario and parameter settings are discussed.

Solving a robust airline crew pairing problem with column generation

In this study, we solve a robust version of the airline crew pairing problem. Our concept of robustness was partially shaped during our discussions with small local airlines in Turkey which may have to add a set of extra flights into their schedule at short notice during operation. Thus, robustness in this case is related to the ability of accommodating these extra flights at the time of operation by disrupting the original plans as minimally as possible. We focus on the crew pairing aspect of robustness and prescribe that the planned crew pairings incorporate a number of predefined recovery solutions for each potential extra flight. These solutions are implemented only if necessary for recovery purposes and involve either inserting an extra flight into an existing pairing or partially swapping the flights in two existing pairings in order to cover an extra flight. The resulting mathematical programming model follows the conventional set covering formulation of the airline crew pairing problem typically solved by column generation with an additional complication. The model includes constraints that depend on the columns due to the robustness consideration and grows not only column-wise but also row-wise as new columns are generated. To solve this difficult model, we propose a row and column generation approach. This approach requires a set of modifications to the multi-label shortest path problem for pricing out new columns (pairings) and various mechanisms to handle the simultaneous increase in the number of rows and columns in the restricted master problem during column generation. We conduct computational experiments on a set of real instances compiled from local airlines in Turkey.

Combination of Metaheuristic and Exact Algorithms for Solving Set Covering-Type Optimization Problems

We propose a new generic framework for solving combinatorial optimization problems that can be modeled as a set covering problem. The proposed algorithmic framework combines metaheuristics with exact algorithms through a guiding mechanism based on diversification and intensification decisions. After presenting this generic framework, we extensively demonstrate its application to the vehicle routing problem with time windows. We then conduct a thorough computational study on a set of well-known test problems, where we show that the proposed approach not only finds solutions that are very close to the best-known solutions reported in the literature, but also improves them. We finally set up an experimental design to analyze the effects of different parameters used in the proposed algorithm.

A Decomposition-Based Approach for the Multiperiod Multiproduct Distribution Planning Problem

We address the most general case of multiperiod, multiproduct network planning problems, where we allow spoilage on arcs and storage at nodes. In our models, all network parameters change over time and products. The minimum-cost flow problem in the discrete-time model with varying network parameters is investigated when we allow storage and/or spoilage, and some reformulation techniques employing polyhedrals are developed to obtain optimal solutions for a predefined horizon. Our methods rely on appropriate definitions of polyhedrals and matrices that lead to LP problems comprising a set of sparse subproblems with special structures. Knowing that computational expenses of solving such a large-scale planning problem can be decreased by using decomposition techniques, the special structure of polyhedrals is utilized to develop algorithmic approaches based on decomposition techniques to handle the global problem aiming to save computational resources.

A note on “A LP-based heuristic for a time-constrained routing problem”

In their paper, Avella et al. (2006) investigate a time-constrained routing problem. The core of the proposed solution approach is a large-scale linear program that grows both row- and column-wise when new variables are introduced. Thus, a column-and-row generation algorithm is proposed to solve this linear program optimally, and an optimality condition is presented to terminate the column-and-row generation algorithm. We demonstrate by using Lagrangian duality that this optimality condition is incorrect and may lead to a suboptimal solution at termination.

A penalty-based scaling algorithm for the multi-period multi-product distribution planning problem

ABSTRACT Multi-period multi-product distribution planning problems are depicted as multi-commodity network flow problems where parameters may change over time. The corresponding mathematical formulation is presented for a discrete time setting, and it can also be used as an approximation for a continuous time setting. A penalty-based method which employs a cost-scaling approach is developed to solve some auxiliary penalty problems aiming to obtain an optimal solution for the original problem. The experiments on both random instances and case study problems show that the algorithm finds good-quality solutions with reasonable computational effort.

Workload Balancing in Transportation Crew Rostering

In crew rostering, balanced workload allocation is a critical issue and an important planning phenomenon that affects both the quality of crew schedules and personnel satisfaction. We focus on workload balancing in transportation systems where deadheading of crew is possible. A network flow formulation of the problem is developed, and an optimal solution method is proposed. We compare the computational performance of the optimal solution method with the solution of the problem with a commercial solver only. We present the results of our computational experiments with well-known problem instances from the crew scheduling literature.

A Column-and-Row Generation Algorithm for a Crew Planning Problem in Railways

We develop a set-covering type formulation for a crew planning problem that determines the minimum sufficient crew size for a region over a finite planning horizon where the periodic repeatability of crew schedules is considered as well. The resulting problem formulation cannot be solved with a traditional column generation algorithm. We propose a column-and-row generation algorithm and present preliminary computational results.