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• The model allocates safety stocks in supply chains at minimum cost. • An MIQCP reformulation with piecewise approximation greatly improves computational efficiency. • The piecewise function yields an improved estimation for the fill rates. • The GSM is extended to handle non-normally distributed demands. • Real-world case studies solved to optimality within few seconds of computational time. This paper presents a Multi-Echelon Inventory Optimization (MEIO) framework, based on the Guaranteed-Service Model (GSM), to allocate safety stocks across a supply chain with several locations and products, minimizing costs while meeting service level objectives. Extending previous work by Achkar et al. (2023), this paper enhances the Mixed-Integer Quadratically Constrained Program (MIQCP) with a highly efficient solution approach. The model introduces a piecewise linear approximation, significantly improving computational efficiency and the accuracy of the approximation for the fill rate function. It also introduces a different and more efficient approach to account for stochastic lead times using a discrete function. Moreover, an extension of the approach to account for non-normally distributed demands is proposed. The model is applied to several instances of a real-world case study from a pharmaceutical company, with more than 7300 product-location combinations, showing that optimal solutions can be obtained within few seconds of computational time. [ABSTRACT FROM AUTHOR]

• We develop a mathematical programming model for scheduling turnaround maintenance activities for a network of plants in a supply chain. • We propose a heuristic algorithm for solving the model when the network has large number of plants. • A case study petrochemical supply chain is used to demonstrate the utilization of the model and the effectiveness of the heuristic. Turnaround (TAM) is a periodic maintenance in which plants are shutdown to conduct inspections, repairs, preventive maintenance and overhauls. The shutdown periods range from three to four weeks and prolonging this period is expensive and therefore requires effective planning. The primary purpose of this paper is to develop a mathematical programming model to coordinate and plan TAM for several plants in an integrated supply chain network. A secondary objective is to develop a heuristic algorithm for solving the model when the network has large number of plants. The model and the algorithm are developed, tested and applied on a case study petrochemical supply chain. The exact algorithm takes 8.3 times the time of the heuristic. In addition the heuristic algorithm provides near optimal solution in much shorter time. The developed model provides a tool to plan TAM for several plants in an integrated supply in an optimal fashion. [ABSTRACT FROM AUTHOR]

In today's business world, oil companies cannot be productive and competitive, and thus, will not survive without taking the supply chain management concepts into account. Consequently, the management of a crude oil supply chain (COSC) is increasingly receiving substantial importance. The growing number of papers and books on this topic is a further witness of this fact. To foster insight into issues intertwined with COSC problems, this paper is devoted to an extensive review of mathematical programming models in this context. The classification approach for this review is based on a taxonomy framework. In this framework, ongoing and emerging challenges surrounding the strategic and tactical decisions of COSC problems are investigated. As a main goal, the gaps of literature are analyzed to recommend possible research directions. [ABSTRACT FROM AUTHOR]

This paper shows a multi-objectives mixed-integer non-linear programming (MINLP) model for a petrochemical supply chain under uncertainty environments, namely disruption risks and less knowledge of parameters. In this model, two efficient queuing systems are applied in nylon plastic manufacturing and recycling centers, in which a Jackson network is also used. The aims are to minimize the average tardiness to deliver products, total cost and transportation cost. In addition the developed model specifies the optimal locations for a new distribution center (DC), collection center and disposal center as well as the optimal allocation of customer zones to each DC. This model is solved in three stages: (1) a Jackson network determines the queuing parameters, (2) an Lp -metric approach makes the multi-objectives into a single objective, and (3) an efficient Lagrangian relaxation based on a sub-gradient approach solves the presented model. Additionally, a real case study is shown in details to depict the application of the presented model. At the end, the sensitivity analyses are carried out to check the optimal objectives by changing the important parameters. [ABSTRACT FROM AUTHOR]

• A multi scenario and multi objective optimization strategy is presented. • The effect of biogas demand and biomass availability in a supply chain are studied. • Uncertainty in the model is considered. • Economic and environmental balance for the solutions are obtained. This paper explores the impact of biomass availability and biogas demand on the economic and environmental performance of a biogas supply chain. A mixed-integer nonlinear programming model is proposed in a multi-objective form, then the problem is reformulated as a single objective problem with a multi-scenario strategy solution and a CVaR approach. The uncertainty associated with biomass availability and biogas demand is obtained from historical data of the central-western region of Mexico. Two case studies are presented to show the applicability of the proposed mathematical model to minimize the criteria gap in economic and environmental functions and to obtain cumulative probability graphs. The results showed that it is possible to minimize the gap between the objective functions, aiding the decision-making process. The average annual profit is at least 1,740,600 € with annual emissions of 292,440 tons of CO 2. Image, graphical abstract [ABSTRACT FROM AUTHOR]