Atidel B. Hadj-Alouane

A Near-Optimal Solution Approach for the Multi-hop Traffic Grooming Problem

In this paper we consider a capacity sizing and routing problem in synchronous digital hierarchy/wavelength division multiplexing networks with nodes having switching capabilities. This problem is well known in the literature as the multi-hop traffic grooming problem and is generally formulated as an integer linear program, which is especially hard to solve when the demand channels are unsplittable and nonuniform. To overcome this difficulty we develop a branching strategy, using a lower bound that is close to the optimal solution. We also devise a reformulation which accelerates branch-and-bound-based solvers. The computational results clearly show that our method is effective in reducing execution time as well as memory consumption, in comparison to traditional methods based on branch-and-bound.

Traffic-aware virtual machine placement in geographically distributed Clouds

In this work, we focus on the problem of virtual machines (VMs) placement in geographically distributed data centers, where tenants may require a set of networking VMs. The aim of the present work is to plan and optimize the placement of tenants VMs in a distributed Cloud environment while considering location and system performance constraints. Thus, we propose ILP formulations which have as objective the minimization of traffic generated by networking VMs and circulating on the backbone network. The different experiments conducted on the proposed formulations show the effectiveness of our model for large-scale Cloud systems in terms of running time and computational resources.

A scenario approach for a capacity planning problem with stochastic demands

A scenario-based approach to solving a multiproduct and stochastic capacity-planning problem, is presented. This approach is a flexible management tool for strategic decision-makers to optimise, over a given planning horizon, and under uncertain demand, capacity-planning strategies. It is applicable in situations where different products can be produced, stored, and distributed to different sites having flexible capacities, and with multiple technological choices. It works by attempting to strike a compromise between profit and risk, and by analysing a set of feasible strategies, derived under specific demand scenarios, using the notion of confidence intervals.

NP-hard and polynomial cases for the single-item lot sizing problem with batch ordering under capacity reservation contract

In this paper, we study the single-item lot sizing problem under a capacity reservation contract. A manufacturer is replenished by an external supplier with batch deliveries and a certain capacity is reserved at the supplier level with an advantageous cost. In addition to the classical ordering and inventory holding costs, for each batch ordered under the reserved capacity a fixed cost per batch is incurred; and for batches exceeding this capacity a higher fixed cost per batch is paid, typically through the purchase from the spot market. We identify various NP-hard cases, propose a pseudo-polynomial time dynamic programming algorithm under arbitrary parameters, show that the problem admits an FPTAS and give polynomial time algorithms for special cases. We finally state a list of open problems for further research.

A branch-and-bound method for the single-machine scheduling problem under a non-availability constraint for maximum delivery time minimization

We consider the single machine scheduling problem with release dates and tails, provided that the machine is unavailable during a fixed interval. We aim to minimize the maximum delivery time under the nonresumable senario. This problem is strongly NP-hard. The proposed algorithm is based on a branch-and-bound method. We use Jacksons preemptive algorithm with precedence constraints to compute the lower bound and Schrages sequence as an upper bound. Numerical experiments show that the algorithm can solve large-size instances with up to 1000 jobs.

A MIXED INTEGER DYNAMIC PROGRAMMING APPROACH TO A CLASS OF OPTIMAL CONTROL PROBLEMS IN HYBRID SYSTEMS

An optimal control problem for hybrid systems is formulated based on hybrid machine models. A practical approach to solving the problem, suitable for a class of hybrid systems, is presented. This approach consists of transforming the hybrid machine model into a dynamic programming model. Transition costs, in this latter model, are computed using mixed integer programs formulated based on the structure of the original hybrid machine. It is shown that the optimal solution for this dynamic program corresponds to the optimal control decision sequence in the hybrid system. Practical examples, inspired from process-oriented industry applications, are provided to illustrate the solution approach.

An iterative lower bound algorithm for the single-machine scheduling problem under a non-availability constraint for maximum delivery time minimization

We consider the single machine scheduling problem with release dates and tails, provided that the machine is unavailable during a fixed interval. This problem is strongly NP-hard. We use the Jacksons preemptive algorithm with precedence constraints to compute a lower bound and the Schrages sequence as an upper bound. Then, we propose an improved lower bound which is based on an iterative procedure. Numerical experiments show the effectiveness of the bounds.

A heuristic for the Network Migration Scheduling Problem

In the present paper, we consider the Network Migration Scheduling Problem which consists of finding an order of migration of the nodes from an outdated network to a new one. It has been shown that this problem is very difficult to solve exactly even for small instances. We give a simple heuristic based on sorting the demands according to the decreasing order of their size. Through computational experiments, we show that this heuristic is computationally rapid and provides near optimal solutions.

On The consideration of carbon emissions in modelling-based supply chain literature: the state of the art, relevant features and research gaps

This review paper provides the operations management (OM) community with an exhaustive analysis of the mathematical models developed for the problem of low-carbon supply chain management (LCSCM). Our paper belongs to the green supply chain management (GSCM) reviews but is distinguished by its specific interest in analysing research works on supply chain (SC) management regarding the reduction of carbon emissions and its related constraints. To facilitate our benchmarking of the 83 selected papers, we adopt a literature classification based on the logistic decisions studied within the developed models. We distinguish three categories of logistic decisions: operational management, technology investment and SC design coordination. Companies are currently facing great external pressures from governments and their conscientious customers to reduce their overall emissions. We analyse how these environmental constraints, which we believe are key drivers for low-carbon emissions management, have been incorporated into mathematical models. Analysing these external pressures in terms of concern about carbon emissions constitutes our main contribution through this literature review. In addition, companies are facing a challenge to reduce their carbon emissions, which are mainly generated from production, transport and storage activities. Consequently, the modelling of carbon emissions remains a crucial task when addressing the LCSCM problem. We suggest analysing the techniques used thus far to approximate those carbon emissions. Furthermore, to illustrate our literature classification and the features of the LCSCM problem, we provide the framework on which we based our analysis of the selected literature. We discuss the modelling aspects of this problem to highlight the limits of the existing literature and consequently suggest recommendations for future research. We believe that this issue will continue to be one of the top concerns of the OM community within the GSCM field as it continues to gain importance among business leaders, and political and social actors.

Optimization of integrated employee timetabling and Hybrid Job Shop Scheduling under time lag constraints

This paper studies two important problems, often encountered in the manufacturing industry and particularly in the tanneries: human resources planning and production scheduling. In this work, we focus on the problem that integrates Employee Timetabling [ETP] by considering their skills under various constraints, with a Hybrid Job-Shop Scheduling [HJS] under time lag constraints. After presenting and formulating our problem as a Mixed Linear Program, we propose a resolution method based on decomposition and cut generation by combining Mixed Linear Programming and Constraint Programming. Using numerical experimentations, we show the advantage of decomposition and cut generation, in terms of resolution time.