Nabil Absi

Lot Sizing with Carbon Emission Constraints

This paper introduces new environmental constraints, namely carbon emission constraints, in multi-sourcing lot-sizing problems. These constraints aim at limiting the carbon emission per unit of product supplied with different modes. A mode corresponds to the combination of a production facility and a transportation mode and is characterized by its economical costs and its unitary carbon emission. Four types of constraints are proposed and analyzed in the single-item uncapacitated lot-sizing problem. The periodic case is shown to be polynomially solvable, while the cumulative, global and rolling cases are NP-hard. Perspectives to extend this work are discussed.

A memetic algorithm for the Multi Trip Vehicle Routing Problem

We consider the Multi Trip Vehicle Routing Problem, in which a set of geographically scattered customers have to be served by a fleet of vehicles. Each vehicle can perform several trips during the working day. The objective is to minimize the total travel time while respecting temporal and capacity constraints.

The multi-item capacitated lot-sizing problem with setup times and shortage costs

We address a multi-item capacitated lot-sizing problem with setup times and shortage costs that arises in real-world production planning problems. Demand cannot be backlogged, but can be totally or partially lost. The problem is NP-hard. A mixed integer mathematical formulation is presented. Our approach in this paper is to propose some classes of valid inequalities based on a generalization of Miller et al. [A.J. Miller, G.L. Nemhauser, M.W.P. Savelsbergh, On the polyhedral structure of a multi-item production planning model with setup times, Mathematical Programming 94 (2003) 375–405] and Marchand and Wolsey [H. Marchand, L.A. Wolsey, The 0–1 knapsack problem with a single continuous variable, Mathematical Programming 85 (1999) 15–33] results. We also describe fast combinatorial separation algorithms for these new inequalities. We use them in a branch-and-cut framework to solve the problem. Some experimental results showing the effectiveness of the approach are reported.

Vehicle routing problems for city logistics

This paper surveys the vehicle routing problems met in cities for good distribution. It applies the following methodology. First, it gives an overview of the literature devoted to vehicle route optimization in cities. Then, it classifies and analyses urban logistic flows. As a result, it identifies the principal scientific challenges that need to be addressed: time-dependency, multi-level and multi-trip organization of the distribution, dynamic information. Finally, it focuses on each one of these challenges, analyses the main difficulties they imply and how they are treated in the literature.

MIP-BASED HEURISTICS FOR MULTI-ITEM CAPACITATED LOT-SIZING PROBLEM WITH SETUP TIMES AND SHORTAGE COSTS*

We address a multi-item capacitated lot-sizing problem with setup times that arises in real-world production planning contexts. Demand cannot be backlogged, but can be totally or partially lost. Safety stock is an objective to reach rather than an industrial constraint to respect. The problem is NP-hard. We propose mixed integer programming heuristics based on a planning horizon decomposition strategy to find a feasible solution. The planning horizon is partitioned into several sub-horizons over which a freezing or a relaxation strategy is applied. Some experimental results showing the effectiveness of the approach on real-world instances are presented. A sensitivity analysis on the parameters of the heuristics is reported.

A Two-Phase Iterative Heuristic Approach for the Production Routing Problem

This paper investigates the integrated optimization of production, distribution, and inventory decisions related to supplying multiple retailers from a central production facility. A single-item capacitated lot-sizing problem is defined for optimizing production decisions and inventory management. The optimization of daily distribution is modeled as a traveling salesman problem or a vehicle routing problem depending on the number of vehicles. A two-phase iterative method, from which several heuristics are derived, is proposed that iteratively focuses on lot-sizing and distribution decisions. Computational results show that our best heuristic outperforms existing methods.

An iterated local search for the multi-commodity multi-trip vehicle routing problem with time windows

The Multi-Commodity Multi-Trip Vehicle Routing Problem with Time Windows calls for the determination of a routing planning to serve a set of customers that require products belonging to incompatible commodities. Two commodities are incompatible if they cannot be transported together into the same vehicle. Vehicles are allowed to perform several trips during the working day. The objective is to minimize the number of used vehicles. We propose an Iterated Local Search that outperforms the previous algorithm designed for the problem. Moreover, we conduct an analysis on the benefit that can be obtained introducing the multi-trip aspect at the fleet dimensioning level. Results on classical VRPTW instances show that, in some cases, the fleet can be halved.

Uncapacitated lot-sizing problem with production time windows, early productions, backlogs and lost sales

We consider the single item uncapacitated lot-sizing problem with production time windows, lost sales, early productions and backlogs over a planning horizon of T periods. In this context, a demand not processed within its time window can either be lost (lost sale), satisfied from a production that is processed before the release period of the demand (early production) or satisfied from a production that occurs after the demand due period (backlog). We present several properties of the optimal solution for different variants of the problem when production time windows are non-customer specific. We propose dynamic programming algorithms to solve the examined problems in O(T 2).

The multi-item capacitated lot-sizing problem with safety stocks and demand shortage costs

We address a multi-item capacitated lot-sizing problem with setup times, safety stock and demand shortages. Demand cannot be backlogged, but can be totally or partially lost. Safety stock is an objective to reach rather than an industrial constraint to respect. The problem is np-hard. We propose a Lagrangian relaxation of the resource capacity constraints. We develop a dynamic programming algorithm to solve the induced sub-problems. An upper bound is also proposed using a Lagrangian heuristic with several smoothing algorithms. Some experimental results showing the effectiveness of the approach are reported.

A simulation approach to evaluate the impact of introducing RFID technologies in a three-level supply chain

The aim of this paper is to analyze the impacts of RFID technologies on supply chain performances, in particular to evaluate their economical impacts and to conduct ROI (return-on-investment) analyses. We simulate a three-level supply chain in which thefts, misplacements and unavailable items for sale cause inventory inaccuracies that decrease the supply chain performance. We compare the effects of different RFID technologies and with different tagging levels for different product types. The main originality of our research is that we are considering that there are various possible RFID systems of different costs and potential profits. The results indicate that different technologies can improve the supply chain performance at different ratios. The economical impacts depend on the chosen technology, the tagging level and the product. Our analyses thus show that the ROI of RFID applications strongly depends on the settings.