Laoucine Kerbache

Vehicle routing with dynamic travel times: A queueing approach

Transportation is an important component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routes is a crucial management problem. In this paper, a vehicle routing problem with dynamic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture travel times. The queueing approach is compared with other approaches and its potential benefits are described and quantified. Moreover, the optimization of the starting times of a route at the distribution center is evaluated. Finally, the trade-off between solution quality and calculation time is discussed. Numerous test instances are used, both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment, which is usually the case on European road networks.

Asymptotic behavior of the expansion method for open finite queueing networks

In previous papers, we have reported on the use of the expansion method for estimating sojourn times in finite network topologies. In this paper, we focus on comparing the expansion method with P. C. Bells consistency conditions where subject to unbalanced service rates at tandem queues, other decomposition approaches yield impossible throughput results. We compare numerical results of the expansion method with the other approaches in light of these conditions.

Optimal Internet Media Selection

In this study we develop a method that optimally selects online media vehicles and determines the number of advertising impressions that should be purchased and then served from each chosen website. As a starting point, we apply Danahers [Danaher, P. J. 2007. Modeling page views across multiple websites with an application to Internet reach and frequency prediction. Marketing Sci. 26(3) 422-437] multivariate negative binomial distribution (MNBD) for predicting online media exposure distributions. The MNBD is used as a component in the broader task of media selection. Rather than simply adapting previous selection methods used in traditional media, we show that the Internet poses some unique challenges. Specifically, online banner ads and other forms of online advertising are sold by methods that differ substantially from the way other media advertising is sold. We use a nonlinear optimization algorithm to solve the optimization problem and derive the optimum online media schedule. Data from an online audience measurement firm and an advertising agency are used to illustrate the speed and accuracy of our method, which is substantially quicker than using complete enumeration.

Thoughts on kaizen and its evolution: Three different perspectives and guiding principles

Purpose – Since Masaaki Imai coined the term Kaizen in the mid 1980s it has been regarded as a key element in the competitiveness of Japanese companies. However, even though Kaizen was defined by the author who created the term, writings by scholars and practitioners in the field exhibit a certain degree of ambiguity and inconsistency. Finally, there is a clear need to develop this theory in the field of operations management. The purpose of this paper is to analyse Kaizen in the academic and practitioner literature, in order to better understand it and further explore and contribute to its potential theoretical profile.Design/methodology/approach – A literature review was carried out using Kaizen as a search term. Various databases were used for this purpose and books written by both scholars and by practitioners on the subject were also consulted. The literature concerning Kaizen was methodically analysed and categorised.Findings – The findings of the study indicate that Kaizen is presently displayed un...

A Queueing Framework for Routing Problems with Time-dependent Travel Times

Assigning and scheduling vehicle routes in a dynamic environment is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic and dynamic nature of travel times. In this paper, a vehicle routing problem with time-dependent travel times due to potential traffic congestion is considered. The approach developed introduces the traffic congestion component based on queueing theory. This is an innovative modelling scheme to capture the stochastic behavior of travel times as it generates an analytical expression for the expected travel times as well as for the variance of the travel times. Routing solutions that perform well in the face of the extra complications due to congestion are developed. These more realistic solutions have the potential to reduce real operating costs for a broad range of industries which daily face routing problems. A number of datasets are used to illustrate the appropriateness of the novel approach. Moreover it is shown that static (or time-independent) solutions are often infeasible within a congested traffic environment which is generally the case on European road networks. Finally, the effect of travel time variability (obtained via the queueing approach) is quantified for the different datasets.

Buffer and Server Allocation in General Multi-Server Queueing Networks

This paper deals with the joint optimization of the number of buffers and servers, an important issue since buffers and servers represent a significant amount of investment for many companies. The joint buffer and server optimization problem (BCAP) is a non-linear optimization problem with integer decision variables. The performance of the BCAP is evaluated by a combination of a two-moment approximation (developed for the performance analysis of finite general-service queues) and the generalized expansion method (a well-known method for performance analysis of acyclic networks of finite queues). A standard non-linear optimization package is used to optimize the BCAP for a large number of experiments. A comprehensive set of numerical results is presented. The results show that the methodology is capable of handling the trade-off between the number of servers and buffers, yielding better throughputs than previously published studies. Also, the importance of the squared coefficient of variation of the service time is stressed, since it strongly influences the approximate optimal allocation.

Upper Bounds on Performance Measures of Heterogeneous M/M/c Queues

In many real-life queueing systems, the servers are often heterogeneous, namely they work at different rates. This paper provides a simple method to compute tight upper bounds on two important performance measures of single-class heterogeneous multi-server Markovian queueing systems, namely the average number in queue and the average waiting time in queue. This method is based on an expansion of the state space that is followed by an approximate reduction of the state space, only considering the most probable states. In most cases tested, we were able to approximate the actual behavior of the system with smaller errors than those obtained from traditional homogeneous multiserver Markovian queues, as shown by GPSS simulations. In addition, we have correlated the quality of the approximation with the degree of heterogeneity of the system, which was evaluated using its Gini index. Finally, we have shown that the bounds are robust and still useful, even considering quite different allocation strategies. A large number of simulation results show the accuracy of the proposed method that is better than that of classical homogeneous multiserver Markovian formulae in many situations.

Impacts of adaptive collaboration on demand forecasting accuracy of different product categories throughout the product life cycle

Purpose – This paper aims to empirically analyze how adaptive collaboration in supply chain management impacts demand forecast accuracy in short life-cycle products, depending on collaboration intensity, product life-cycle stage, retailer type and product category. Design/methodology/approach – The authors assembled a data set of forecasts and sales of 169 still-camera models, made by the same manufacturer and sold by three different retailers in France over five years. Collaboration intensity, coded by collaborative planning forecasting and replenishment level, was used to analyze the main effects and specific interaction effects of all variables using ANOVA and ordered feature evaluation analysis (OFEA). Findings – The findings lend empirical support to the long-standing assumption that supply chain collaboration intensity increases demand forecast accuracy and that product maturation also increases forecast accuracy even in short life-cycle products. Furthermore, the findings show that it is particular...

State Dependent Models of Material Handling Systems in Closed Queueing Networks

A comprehensive algorithmic analysis of finite state-dependent queueing models and exponentially distributed workstations is formulated and presented. The material handling system is modeled with finite state-dependent queueing network M/G/c/c models and the individual workstations are modeled with exponentially distributed single and multi-server M/M/c queueing models. The coupling of these queueing models is unique via the material handling structure. The performance modeling of the systems for series, merge, and split and other complex network topologies are included so as to demonstrate the type of topological network design that is possible with these incorporated material handling systems. Of some importance, it is shown that these integrated M/M/c and M/G/c/c networks have a product form when the population arriving at the M/G/c/c queues is controlled. Numerous experimental results demonstrate the efficacy of our approach for a variety of contexts and situations.

Performance Evaluation and Dimensioning of /// Systems through Kernel Estimation

We extend the analysis of queueing systems for real-life situations, where the arrival pattern of customers is unknown. In real systems, we must understand how the choice of a method of estimation influences the configuration of the system. Using kernel smoothing, we evaluate algorithms to estimate performance measures of a 𝐺𝐼𝑋/𝑀/𝑐/𝑁 system, including the invariant probability distribution of the number of customers in the system, the blocking probability, the average queue size, and the average client queue time. We successfully apply the method to the arrivals to a call center to plan and improve the performance of these important queueing systems.