Faouzi Masmoudi

A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2)

This paper presents a new extension of SALBP-2, so called assembly line resource assignment and balancing problem of type 2 (ALRABP-2). Two main differences from the existing literature are revealed in this work. The first is on the objective function which is a multiple one. It is aimed here to minimize both the cycle time and the cost per time unit (hour) of a line for a fixed number of stations to satisfy the constraints of precedence between tasks and compatibility between resources. The second difference lies in the proposed method to solve this problem. A new version of multi-objective genetic algorithm (MOGA) called hybrid MOGA (HMOGA) is elaborated. Full experiment design is used to obtain a better MOGA parameters combination. The effectiveness of the HMOGA was assessed through a set of literature problems. The performance of HMOGA shows a good quality of the fronts generated and a better problem-solving capacity for two optimisations.

Conceptual Model for Management of Digital Factory Simulation Information

Digital Factory (DF) aims at proposing simulation tools to design a product and its production system in parallel. Nevertheless, DF is marked by the multiplicity and heterogeneity of simulation models that are used, that slows down its usage in industry. We propose in this paper a conceptual model to manage the different simulation information created and manipulated through a DF project. This model is based on an analysis of the current design strategies and the used simulation tools. Finally, an industrial application has been developed to validate the completeness of this model.

Optimization of a supply portfolio in the context of supply chain risk management: literature review

The aim of this paper is to review the literature in the field of supplier selection under supply chain risk management. Collected papers from 2003 to 2014 are analyzed and classified, first, according to the characteristics of the problem they deal with, secondly, according to the approach they propose, and thirdly, according to the techniques they use. The papers have been grouped into five categories: the first group relates to quantitative approaches to supplier selection, the second concerns qualitative approaches, the third consists of hybrid approaches that blend two or more different approaches together, the fourth relates to simulation approaches and the last group to artificial intelligence. The techniques used in each category are outlined. The different approaches and their associated techniques are analyzed and some recommendations are made on improving their efficiency and performance. This paper is thus a systematic scope review of journal articles and conference papers issued during this period. It brings together a collection of 124 papers on the topic of supplier selection under supply chain risk management.

A decision support system assessing the trust level in supply chains based on information sharing dimensions

Assessing customer trust in suppliers with regards to its influencing factors is an important open issue in supply chain management literature. In this paper, a customer trust index is designed as the trust level arising from the information sharing degree and quality, related to the information shared by a supplier with his customer. The customer trust level is evaluated using a fuzzy decision support system integrating information sharing dimensions. The core is a rule-based system designed using the results of questionnaires and interviews with supply chain experts. Several tests were generated in order to analyze the impact of the different information sharing attributes on the customer trust index. The developed approach is then applied to a real supply chain from the textile industry. Results show large differences of weight and impact between the different information-related factors that build the customer trust index. It is also shown that the proposed system has an important role in ensuring the objectivity of the trust assessment process and in helping decision makers evaluate their business partners.

Multi-objective stochastic multi-site supply chain planning under demand uncertainty considering downside risk

A multi-site supply chain planning problem under demand uncertainty is considered.A multi-objective stochastic model is developed for the considered problem.The proposed solution approach yields to a front of Pareto optimal robust solutions.A fuzzy decision making approach is applied to select the most preferred Pareto solution. In this paper, a multi-period, multi-product, multi-site, multi-stage supply chain planning problem under demand uncertainty is considered. The problem is formulated as a two-stage stochastic linear programming model. In order to generate a robust supply chain planning solution, the downside risk is incorporated into the objective functions of the stochastic programming model as a risk measure. So, the proposed multi-objective stochastic model aims to simultaneously minimize the expected total cost, to minimize the lost customer demand level and to minimize the downside risk. The proposed solution approach yields to a front of Pareto optimal robust solutions. A fuzzy decision making approach is applied to select the most preferred solution among the Pareto optimal robust solutions. A numerical example from a real textile and apparel industry is addressed in order to illustrate the robustness of the supply chain network planning solutions and the effectiveness of the solution approach.

Multi-objective Optimization of a Multi-site Manufacturing Network

This chapter considers multi-site manufacturing network where multi plants are considered in order to satisfy customer demand. A multi-objective, multi-stage, multi-product, and multi-period model for production and transportation planning in a multi-site manufacturing network is formulated. Two measure criteria, total cost and products’ quality level, are simultaneously considered as objective functions to be optimized. The solution of this problem is a set of Pareto fronts that can be used for decision-making. Three optimization method- weighted sum method, epsilon constraint method and goal attainment method- are adapted to solve the considered problem and corresponding results are compared based on an illustrative example. The results show that the epsilon constraint method outperforms the other technique for the considered case.

A fuzzy-based negotiation approach for collaborative planning in manufacturing supply chains

The coordination of the planning operations across the manufacturing supply chains (MSC) is considered as a major component of supply chain management. As centralized coordination requires relevant information sharing, alternative approaches are needed to synchronize production plans between partners of MSC characterized by decentralized decision making systems with limited information sharing. In this paper, a bi-level fuzzy-based negotiation approach is proposed in order to model collaborative planning between MSC partners. During negotiation, each manufacturer is optimizing a bi-objective planning model. In order to generate optimal production plans, a genetic algorithm is used. To evaluate the exchanged proposals and the satisfaction degree of each partner, the fuzzy logic approach is adopted in the both negotiation levels. The main result of the developed approach consists in a collaborative decision making mechanism allowing the MSC partners to define their optimal production plans while considering the whole negotiating process with the pre-negotiation and post-negotiation stages. Computational tests done for different MSC structures show the effectiveness of the proposed mechanism, which ensures the satisfaction of the manufacturers and the optimality of the final solution. By comparing the results with the ones obtained considering a centralized planning approach, it is shown that the developed negotiation mechanism yields to near optimal solutions with insignificant gaps from the global optimal solutions.

A decision-making approach for a multi-objective multisite supply network planning problem

The current manufacturing environment has changed from traditional single plant to multisite supply network where multiple plants are serving customer demands. In this paper, a multi-objective, multistage, multi-product and multi-period production and transportation planning problem is considered in the context of a multisite supply network. The developed optimisation model aims simultaneously to minimise the total cost and to maximise products quality level. The main purpose of this paper is to provide the planner with a front of Pareto solutions and to help him to select a fair optimal solution that satisfies equitably the two considered objectives. A modified version of the epsilon-constraint method (AUGMECON) is applied in this paper to generate an efficient set of Pareto solutions. Then, the lexicographic minimax method is used in order to find the fair solution. A numerical example from a real textile and apparel industry is presented to illustrate the planning model and the solution approach.

A Multi-objective Optimization for Multi-period Planning in Multi-item Cooperative Manufacturing Supply Chain

Consumer goods are mainly manufactured in multiple steps often done by separate, independent production nodes, related to each others to form manufacturing supply chains (MSC). Mostly, each member of a supply chain optimizes his own local objective and accordingly, plans his operations (e.g., production, inventory, capacity planning). The purpose of this work is to improve the efficiency of production networks as a whole by developing a multi-objective optimization model for cooperative planning which aims at minimizing simultaneously the total production cost and the average inventory levels in a multi-period, multi-item environment. To solve this problem, we adopt an elitist non-dominated Sorting Genetic Algorithm (NSGA-II) to find optimal solutions. Several tests are developed to show the performance of the model.

Supplier selection under risk of delivery failure: a decision-support model considering managers’ risk sensitivity

This paper studies the problem of supplier selection and order allocation in a retail supply chain (comprising suppliers, a central purchasing unit and outlets) under disruption risk. The final demand is deterministic. Suppliers are located in different geographic areas, and supplies are subject to a positive probability of disruption. Different capacity and failure probabilities for each supplier are considered. Our analysis focuses on the insurance versus profitability trade-off faced by a supply manager who buys from suppliers for the outlets. Instead of determining optimal decisions given an objective function and the risk sensitivity of the decision-maker, we use a mixed integer linear programming approach to provide decision-making support that shows a supply manager the ‘elasticity of (expected) losses versus (expected) profits’. Under this model, and depending on the profit-and-loss targets, a supply manager of known risk sensitivity (i.e. risk aversion and loss aversion) can make better decisions when choosing suppliers. Moreover, taking into account, the impact of the share of fixed costs that must be covered by the operation, we consider the net values of expected profit and loss. We discuss the potential influence of the level of the firm’s fixed costs on the supply strategy. In particular, we show how the minimum value of the gross margin needed for the strategy’s profitability affects that strategy. A numerical application is conducted to illustrate the contribution of our decision-making support mechanism, and several managerial insights are obtained.