nep-cmp New Economics Papers
on Computational Economics
Issue of 2006‒09‒11
three papers chosen by
Stan Miles
York University

  1. Hybrid Meta-Heuristics for Robust Scheduling By Surico, M.; Kaymak, U.; Naso, D.; Dekker, R.
  2. Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact On Optimizing Shelf Arrangements By Nierop, J.E.M. van; Fok, D.; Franses, Ph.H.B.F.
  3. Risk Transfer with CDOs and Systemic Risk in Banking By Jan Pieter Krahnen; Berc Rustem; Volker Wieland; Stan Zakovic

  1. By: Surico, M.; Kaymak, U.; Naso, D.; Dekker, R. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    Abstract: The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimization problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronization, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel metaheuristic approach for robust scheduling. Our approach integrates mathematical programming, multi-objective evolutionary computation, and problem-specific constructive heuristics. The optimization algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.
    Keywords: Supply Networks;Robust Scheduling;Meta-Heuristics;Multi-Objective Genetic Optimization;
    Date: 2006–03–30
  2. By: Nierop, J.E.M. van; Fok, D.; Franses, Ph.H.B.F. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    Abstract: Allocating the proper amount of shelf space to stock keeping units [SKUs] is an increasingly relevant and difficult topic for managers. Shelf space is a scarce resource and it has to be distributed across a larger and larger number of items. It is in particular important because the amount of space allocated to a specific item has a substantial impact on the sales level of that item. This relation between shelf space and sales has been widely documented in the literature. However, besides the amount of space, the exact location of the SKU on the shelf is also an important moderator of sales. At the same time, the effectiveness of marketing instruments of an SKU may also depend on the shelf layout. In practice, retailers recognize that these dependencies exist. However, they often revert to rules of thumb to actually arrange their shelf layout. We propose a new model to optimize shelf arrangements in which we use a complete set of shelf descriptors. The goal of the paper is twofold. First of all, we aim to gain insight into the dependencies of SKU sales and SKU marketing effectiveness on the shelf layout. Second, we use these insights to improve the shelf layout in a practical setting. The basis of our model is a standard sales equation that explains sales from item-specific marketing-effect parameters and intercepts. In a Hierarchical Bayes fashion, we augment this model with a second equation that relates the effect parameters to shelf and SKU descriptors. We estimate the parameters of the two-level model using Bayesian methodology, in particular Gibbs sampling. Next, we optimize the total profit over the shelf arrangement. Using the posterior draws from our Gibbs sampling algorithm, we can generate the probability distribution of sales and profit in the optimization period for any feasible shelf arrangement. To find the optimal shelf arrangement, we use simulated annealing. This heuristic approach has proven to be able to effectively search an enormous solution space. Our results indicate that our model is able to fit and forecast the sales levels quite accurately. Next, when applying the simulated annealing algorithm to the shelf layout, we appear to be able to increase profits for all the stores analyzed. We compare our approach to commonly used shelf optimization rules of thumb. Most sensible rules of thumb also increase expected profits (although not as much as our optimization algorithm). In particular, it is beneficial to put high-margin items close to the beginning of the aisle (or the “racetrack"). Finally, we provide managerial implications and directions for further research.
    Keywords: Shelf Management;Sales Models;Hierarchical Bayes;Markov Chain Monte Carlo;Simulated Annealing;
    Date: 2006–03–27
  3. By: Jan Pieter Krahnen (Imperial College, London); Berc Rustem (Imperial College, London); Volker Wieland (University of Frankfurt); Stan Zakovic (Imperial College London)
    Abstract: Large banks often sell part of their loan portfolio in the form of collateralized debt obligations (CDO) to investors. In this paper we raise the question whether credit asset securitization affects the cyclicality (or commonality) of bank equity values. The commonality of bank equity values reflects a major component of systemic risks in the banking market, caused by correlated defaults of loans in the banks’ loan books. Our simulations take into account the major stylized fact of CDO transactions, the nonproportional nature of risk sharing that goes along with tranching. We provide a theoretical framework for the risk transfer through securitization that builds on a macro risk factor and an idiosyncratic risk factor, allowing an identification of the types of risk that the individual tranche holders bear. This allows conclusions about the risk positions of issuing banks after risk transfer. Building on the strict subordination of tranches, we first evaluate the correlation properties both within and across risk classes. We then determine the effect of securitization on the systematic risk of all tranches, and derive its effect on the issuing bank’s equity beta. The simulation results show that under plausible assumptions concerning bank reinvestment behaviour and capital structure choice, the issuing intermediary’s systematic risk tends to rise. We discuss the implications of our findings for financial stability supervision.
    JEL: G28
    Date: 2006–03–01

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