Besoa Rabenasolo

Multiple criteria decision-making for environmental impacts optimisation

Life cycle assessment (LCA) is increasingly used as a decision support system that enables the modelling, the evaluation and the comparison of different alternatives of products, processes or supply chains as regards their environmental and sustainable impacts. In the textile-garment domain, the complexity of the supply chain adds to the difficulty of the interpretation of the LCA results. The authors purpose is to use multiple criteria decision-making (MCDM) method in order to analyse the results of the life cycle assessment of textile products to help the different actors in the supply chain. Within this framework, the paper studies the choice of the most suitable multicriteria analysis method from the literature and shows its application in the textile supply chain.

Multicriteria Decision Aid Tool for Third-Party Logistics Providers' Selection

The outsourcing of logistics activities has become a common practice by many companies, which implies an efficient choice of the third-party logistics (3PLs) providers. A literature review on the 3PLs providers selection problem shows that this selection is a very complex process which depends on several factors, which are often in conflict with one another, such as price, quality, service, location, technology, etc. This paper proposes a software tool using ELECTRE method for selecting the 3PLs providers. This tool incorporates various selection criteria and allows the user to introduce other criteria according to his needs. The application of our tool is demonstrated through an illustrative example

Sales partition for forecasting into textile distribution network

This paper presents a new method for the management of buffer-stocks for textile items by a sales partition approach. The proposed algorithm enables to partition the set of items into different optimal classes, so that items belonging to each class follow the same forecasting sales graph. This method uses a sequence of merging of classes. In this sequence, an internal index measuring the compactness inside each class and the separability between different classes enables to determine the best partition. Then, for each class created, the compactness is compared to the initial compactness criterion given by the industrial manager and, the class which doesnt respect the classification accuracy is rejected. A faster searching of the forecasted sales graph for an item is thus achieved by the determination of a mean graph corresponding to its family sale.

Benchmarking of the textile garment Supply Chain using the SCOR model

In order to achieve modeling of the supply chain of textile and garment, we collected information from the industry through an external benchmarking project, involving 29 textile garment supply chains. Then, using these performance data we identified important variables and investigated the endogenous and exogenous relationships amongst variables. The SCOR model had to be adapted to this study. We obtain as a result of the study a classification of the interactions between the performance measures on the factory level and on the supply chain level

Analysis of the temporal decomposition procedure for scheduling with release and due dates

This paper first presents an improved method of temporal decomposition for minimising the searching space of scheduling problems. Next, the effects of the temporal decomposition procedure in scheduling problems are analysed. It is theoretically shown that the complexity of a scheduling algorithm using decomposed subsets varies inversely with that of the decomposition procedure. Therefore, the efficiency of the overall scheduling algorithm is strongly related to the decomposability of the set of operations to be processed on each machine. This decomposability is evaluated using a probabilistic approach where the probability distributions of the scheduling parameters are obtained from historical workshop data. The average number of decomposed subsets and the average size of these subsets are estimated. Both theoretical analysis and simulation results have revealed that the decomposition procedure leads to optimal effects when some conditions on scheduling parameters are met.

Modelling and Simulation of the Textile Channel by HyperNets

To achieve better communication between those involved in the various aspects of textile-apparel distribution (TAD), a general methodology for the analysis and for the management simulation of the logistic flows must be implemented by the different participants. The model derived in this paper is based on a high-level Petri-net graphical representation, and several simulations are realised among the TAD participants.

Using the Intermeans Parameter to Measure the Dispersion when Solving the Newsboy Problem

What is the best quantity of a given good that we can buy today for selling tomorrow? Obviously, the answer depends on the knowledge we have about the future trends of the market. When expressing this knowledge by a probability distribution, many assumptions are often introduced that are not founded on actual knowledge, but only on computational ease. In a former paper we have introduced the intermeans parameter as a measure of the dispersion of the demand and we have shown that the robust solution of the newsboy problem when this parameter and the mean are known is not the usual Scarfs rule that gives the robust solution when variance and mean are known. In this paper, the statistical properties of the intermeans parameter are investigated. It will be especially discussed which properties depend on the families of distributions that are addressed and which ones are distribution free. Our assertions are illustrated by numerical examples, and the information value of the various assumptions are observed

Resolution approach for multiobjective scheduling problems with uncertain demands

The job-shop scheduling problem (JSP) is one of the hardest problems (NP-complete problem). In a lot of cases, the combination of goals and resource exponentially increases search space. The objective of resolution of such a problem is generally, to maximize the production with a lower cost and makespan. In this paper, we explain how to modify the objective function of genetic algorithms to treat the multi-objective problem and to generate a set of diversified optimal solutions in order to help decision maker. We are interested in one of the problems occurring in the production workshops where the list of demands is split into firm (certain) jobs and predicted jobs. One wishes to maximize the produced quantity, while minimizing as well as possible the makespan and the production costs. Genetic algorithms are used to find the scheduling solution of the firm jobs because they are well adapted to the treatment of the multi-objective optimization problems. The predicted jobs was inserted in the real solutions (given by genetic algorithms). The solutions proposed by our approach are compared to the lower bound of the cost and makespan in order to prove the quality and robustness of our proposed approach

Optimization of Supply Chain Planning Using Stochastic Sales Forecasts

Abstract The supply chain management is a strategic tool for enterprises which want to improve their performance and competitiveness. This project needs a strong partnership between a company, its customers and its suppliers. Beside the organizational and information system aspects, mathematical tools are also needed for the analysis and optimization of the decision at each stage of the supply chain. This paper deals with models of dynamic optimization of the requirement planning, which use some knowledge of the stochastic properties of the sales process. Some recent results are recalled and extended. Then, the effect of transportation delay is analyzed in the case of a supply chain which is coherent with respect to the planning parameters. A comparison between theoretical results and simulation is given in the last section.

Sales Forecasting Under Uncertain Environment Fuzzy Classification in Textile Distribution

Abstract This paper presents a methodology for the management of buffer-stocks for textile items by a sales partition approach. The proposed approach uses a fuzzy clustering method to find the optimal classes, where items belonging to each class have similar sales graph, so as to design the same forecasting method. The compactness of the generated classes are constrained to be lower than a compactness index given by the industrial manager, which characterizes the required classification accuracy. An application for Textile/Gannent/Distribution industry is treated.