Logistics pooling presents a very important solution for companies to improve the different aspects of sustainability. The objective of this study is to propose a mathematical approach to the pooling of sustainable freight transport. In this work, the pooling performance is evaluated at different levels of sustainability. The proposed MILP (Mixed Integer Linear Programming) consists of designing a shared logistics chain, taking into account economic and environmental objectives, namely the different logistics cost and the different CO₂ emissions. The social/societal level, namely the accident rates and the noise level are assessed after model resolving. Also, the transportation fill rate is calculated. Finally, we demonstrated the pooling performance with an illustrative example of freight transport, which is made up of four suppliers who collaborate to deliver six common distribution centers. Firstly, we solve the mathematical model by the mono-objective functions (economic or environmental). Secondly, we solved the model with a multi-objective function, which combines the two objectives (economic and environmental) with the Ɛ-Constraint method.

Horizontal collaboration is one of the most effective strategies applied to address issues related to sustainability and the adverse effects of commodity distribution. Although it has received increased attention in recent years, no synthesis has been conducted to present the main practices and sustainability indicators used to assess the performance of horizontal collaboration. In addition, the factors that can further improve the performance of horizontal collaboration in terms of sustainability have not been presented in any literature review. Thus, we expose, in this paper, a review of the existing studies dealing with horizontal collaboration, and we define the key indicators used to measure its performance. The most commonly used practices of horizontal collaboration are also illustrated. In fact, the main objectives of the present study are to highlight the impact of certain factors on the success of collaboration and to identify several guidelines for researchers and companies wishing to implement horizontal collaboration.

In this paper, we propose robust optimisation models for the distribution network design problem (DNDP) to deal with uncertainty cases in a collaborative context. The studied network consists of collaborative suppliers who satisfy their customers’ needs by delivering their products through common platforms. Several parameters—namely, demands, unit transportation costs, the maximum number of vehicles in use, etc.—are subject to interval uncertainty. Mixed-integer linear programming formulations are presented for each of these cases, in which the economic and environmental dimensions of the sustainability are studied and applied to minimise the logistical costs and the CO₂ emissions, respectively. These formulations are solved using CPLEX. In this study, we propose a case study of a distribution network in France to validate our models. The obtained results show the impacts of considering uncertainty by comparing the robust model to the deterministic one. We also address the impacts of the uncertainty level and uncertainty budget on logistical costs and CO₂ emissions.

By creating new job opportunities and developing the regional economy, the transport of goods generates significant costs, environmental and sanitary nuisances, and high greenhouse gas (GHG) emissions. In this context, collaboration is an interesting solution that can be used to enable companies to overcome some problems such as globalization, economic crisis, health crisis, issues related to sustainability, etc. This study deals with the design of a multiperiod multiproduct three-echelon collaborative distribution network with a heterogeneous fleet. By applying the mixed integer linear problem (MILP) formulations, it was possible to study the three dimensions of sustainability (economic, environmental, and social/societal). Since the examined problem was NP-hard, it was solved using four metaheuristic approaches to minimize the different logistics costs or CO₂ emissions. The social/societal aspect evaluated the accident rate and the noise level generated by the freight transport. Four algorithms were developed to achieve our objectives: a genetic algorithm, a simulated annealing, a particle swarm algorithm, and a vibration damping optimization algorithm. Considering a French distribution network, these algorithms overcame the limits of the exact solution method by obtaining optimal solutions with reasonable execution time.

La mutualisation logistique est considérée comme l’une des solutions innovantes pour relever efficacement les défis logistiques croissants et améliorer le développement durable. Elle a déjà montré sa performance en terme économique par la diminution des coûts et en terme environnemental par la minimisation des émissions des gaz à effet de serre. Malgré ses avantages en durabilité, il existe des facteurs qui bloquent la réussite de la mutualisation, à savoir le problème de coordination et d’interaction entre les différents partenaires, etc. La supply chain 4.0 (SC 4.0) mutualisée représente une meilleure solution pour remédier à ces problèmes et améliorer l’interaction entre les différents partenaires. Grâce à la combinaison entre la SC 4.0 et la mutualisation, le résultat du développement durable peut être amélioré par rapport à celui des chaînes logistiques traditionnelles. Cette étude vise à proposer un modèle de simulation générique pour une SC 4.0 mutualisée. Afin de démontrer sa performance, un exemple d’illustration sera traité tout en évaluant les indicateurs économiques (coût de transport, coût de chargement, coût de déchargement et taux de remplissage des véhicules) et les indicateurs environnementaux sous forme d’émissions de CO₂. Des meilleurs résultats sont obtenus lors de l’évaluation de l’efficacité de cette solution par rapport à celle de la chaîne logistique traditionnelle.