Mansour Rached

Decentralised decision-making with information sharing vs. centralised decision-making in supply chains

In recent years, implementing coordination mechanisms in decentralised supply chains to reduce the well-known negative effects of decentralisation, such as the ‘bullwhip effect’, has become a considerable challenge. Furthermore, with the dramatic developments in information and communication technologies, real-time information sharing has become increasingly easier to implement. In this work, we study a mono-product divergent supply chain composed of a supplier, a warehouse, retailers and customers in the context of decentralised and centralised decisions. The main objective of this study is to compare a decentralised supply chain combined with different scenarios of simultaneous upstream and downstream information sharing vs. a centralised supply chain. A mathematical model is developed to compare the logistics costs in the two decision contexts. The experimental results clearly show that the simultaneous sharing of customer demand and supplier-warehouse lead time information in a decentralised supply chain yields nearly equivalent logistics costs as the centralised supply chain context. However, the main beneficiary of the sharing is the warehouse, which receives approximately two-thirds of the benefit. Thus, incentives and revenue sharing contracts should be implemented to motivate and balance the benefits between supply chain partners.

Assessing the value of information sharing and its impact on the performance of the various partners in supply chains

We study the influence of sharing perturbed information on a serial supply chain.We evaluate the gains of sharing different types of information for each partner.We estimate the cumulative impact of simultaneously sharing different information.The gains are not cumulative when we simultaneously share different information.Incentive cooperation mechanisms should be established between partners. With major developments in information and communication technologies, real-time information sharing becomes a significant challenge and has a considerable impact on the overall performance of supply chains. Here, we study the influence of information sharing for a monoproduct serial supply chain consisting of a supplier, warehouse, retailer and customers in the context of a decentralized decision. The objectives of this study are twofold: (1) to estimate the gains from sharing different types of information on each elementary cost and for each partner of the supply chain in detail and (2) to determine the cumulative impact of simultaneously sharing different types of information.A mathematical model is developed to assess the value of information sharing in terms of logistic costs and for different combinations related to the sharing or non-sharing of three types of upstream and downstream information: the customer demand and the supplier-warehouse and warehouse-retailer lead times. A perturbation is also injected to consider the intended or unintended distortion in the communicated information.Our study clearly showed that the gains are not cumulative when we simultaneously share different types of information. The results also highlighted the necessity to establish incentive cooperation mechanisms between the different links in the supply chain in many scenarios where the gains are not balanced. A distortion in the communicated information can also have a significant effect on the gains from sharing.

A method to evaluate downstream and upstream information sharing using the genetic algorithm

This paper deals with four scenarios of sharing upstream and downstream information simultaneously in supply chains. Replenishment leadtime is the upstream information studied in this work and demand information is the downstream one. We propose a system cost formulation for two-echelon (a depot and several retailers) and multi-products supply chain. We focused our study on a centralized system case. In our formulation, we consider holding, ordering, penalty and transportation costs. Then, we use a Genetic Algorithm in order to approximate the optimal echelon inventory position at depot which minimizes the system cost. Our approach is illustrated by some numerical experiments.

Mono-objective and multi-objective models for the pickup and delivery problem with time windows

In this paper, we introduce models for the optimization of the door-to-door freight transportation. The main thrust of these models stand in the allowance to forecast of freight amounts that will be transported daily considering capacity as well as time constraints (e.g. time availability of retailers and customers). We also have integrated real-world constraints to meet practical difficulties that may actually face transportation (e.g., fixed number of working hours of drivers, availability of vehicles). The transportation scheme is characterized by a consolidation center. We assumed, also, that freight transportation is mutualized. Thus, we have split the FDPTW (Pickup and Delivery Problem with Time Windows) into two problems: (i) is a Vehicle Routing Problem with Time Windows (VRPTW) with Pickup; (ii) is a VRPTW with Delivery. The proposed models are solved by LINGO. The output results are the optimal trucks in each model. Lastly, one of the elaborated models (VRPTW with delivery) is tested with Solomons benchmark and for the other models we propose different numerical experiments to validate our contributions.

A bi-objective model for collaborative planning in dyadic supply chain

The collaborative planning and the management of production and storage processes are important components in supply chain management. The goal of this paper is topresent the reliability of genetic algorithms on solving bi-objective models compared to mono-objective models. To do this we will be based initially on the mono-objective Dudeks model and then we propose a division of the objective function in two objective functions. Finally we compare the results given by the genetic algorithms with the optimality result obtained using the LINGO solver on the mono-objective Dudeks model. This model aims at simultaneously minimizing the total production cost and the total holding cost. To solve the proposed model, we use a genetic algorithm NSGA-II. The proposed several test provide results that demonstrate and validate the effectiveness of the multi-objective approach and elitists genetic algorithms in solving this type of problem, compared to the literature in the proposed test.The validation of our approach will allow us later to use this algorithm in solving complex multi-objective models approaching the real context.

An Approach to Study the Value of Information Sharing in a Serial Supply Chain with Decentralised Decision

Abstract In this paper, we study two information shared simultaneously, the first one coming from the upstream and the second coming from the downstream of the supply chain. We treat the case of a multiechelons multi-products serial supply chain. This chain is made up of one supplier, one warehouse, one retailer and several customers. Thus, we develop a model to study various scenarios of information sharing. We focus our approach on the decentralised decision, in which, the goal of each link is to minimise its own logistic cost. The difference between these costs shows the value of the studied information sharing and represents the contribution of a scenario compared to another. The proposed model is solved by ILOG CPLEX integrated in JAVA program. The output results are the optimal cost in each studied situation. The numerical experimentation proves to validate contribution of our approach.