Omar Ayadi

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.

An Extensive Group Decision Methodology for Alliance Partner Selection Problem in Collaborative Networked Organisations

More and more companies address collaborations and cooperation as strategic topics to build competitive networks and broaden the global competences provided by the community thus created. These companies can cooperate with partners to share resources, competences, risks, or costs. Besides, penetrating new markets can be easier when associating new partners. The selection process of an adequate partner considered for a specific objective is a key success factor. This paper proposes an evaluation methodology for selecting an alliance partner in the case of a network of enterprises, manufacturing high precision mechanical components. An extensive group decision methodology is developed using both quantitative data and qualitative judgments in the evaluation of criteria. Since some performances cannot be represented with crisp numbers, the proposed methodology allows experts to use linguistic variables to express their judgments for the assessment of qualitative criteria. Two main phases are integrated in this methodology. In the first phase, criteria and experts weightings are calculated to determine the criteria importance using fuzzy the analytical hierarchy process. In the second phase, a technique for order preference by similarity to ideal solution is proposed to rank the different alternatives, corresponding to the alternative partnerships.

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.

Pareto Optimal Solution Selection for a Multi-Site Supply Chain Planning Problem Using the VIKOR and TOPSIS Methods

In this paper, a multi-objective, multi-product, multi-period production and transportation planning problem in the context of a multi-site supply chain is proposed. The developed model attempts simultaneously to maximize the profit and to maximize the product quality level. The objective of this paper is to provide the decision maker with a front of Pareto optimal solutions and to help him to select the best Pareto solution. To do so, the epsilon-constraint method is adopted to generate the set of Pareto optimal solutions. Then, the technique for order preference by similarity to ideal solution (TOSIS) is used to choose the best compromise solution. The multi-criteria optimization and compromise solution (VIKOR), a commonly used method in multiple criteria analysis, is applied in order to evaluate the selected solutions using TOPSIS method. This paper offers a numerical example to illustrate the solution approach and to compare the obtained results using TOSIS and VIKOR methods.

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

ABSTRACT The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

A Fuzzy Collaborative Assessment Methodology for Partner Trust Evaluation

Trust level assessment within collaborative networks is an interesting issue in the partner evaluation and partner selection literature. This paper proposes a fuzzy collaborative assessment methodology for partner trust evaluation within horizontal collaborative networks. The proposed approach concerns a group evaluation context where a decision‐making comity associated with a manufacturer needs to evaluate its companys partners for their ranking purposes. Different expertise levels are attributed to the comity members. In this paper, trust level is evaluated based on information‐sharing attributes considered in the literature as critical influencing factors. Different weights are associated with these attributes with respect to their corresponding influence on trust. The semantic fuzzy partitioning method is considered for the collaborative trust assessment based on unbalanced linguistic term sets representing information‐sharing attributes. The developed approach is applied to a real case showing its effectiveness and its objectivity.