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Published in : IFAC-PapersOnLine 55(10):1764-1768. doi : 10.1016/j.ifacol.2022.09.653; International audience; This paper deals with the simulation and optimisation of disassembly-reconditioningassembly system (DRAS) taken into account random machines failures and repairs. The proposed system is composed of one disassembly machine, two parallel structures, one assembly machine and stocks to store the used products, components and finished product. In this study a complete disassembly and assembly process in a supply chain is considered, and which allows to acquire used products, disassemble the used products, storing reconditioning components, assemble the components and selling the finished product to reply customers demand. The objective is to determine the optimal acquisition quantity of used products that maximises the total profit. A mathematical model is formulated on the basis of discrete flow model that allows to express the behaviour of the system. The proposed model is then simulated and the optimal acquisition quantity is determined. Numerical results are presented to discuss the optimal acquisition quantity and the lost profit risk.

This paper study a manufacturing system composed of a machine, a purchase warehouse, a vehicle and customers that demand a random quantity of products. For transporting the products from the warehouse to the customer in urban area, two cases are considered: in the first case the transport is performed by an electric vehicle and the second case by a petrol vehicle. The objective of this paper is to provide a decision aid to the manufacturer to choose the suitable transport vehicle type according to different costs of the manufacturing system. To describe both cases, a discrete flow model is adopted that takes into account the machine and vehicle failures, carbon penalty and maintenances actions. Both cases of the transport are simulated and numerical results are presented to compare the totals costs and then to provide an aid decision for the manufacturer.

This work considers a manufacturing-remanufacturing system composed of two parallel machines, a manufacturing inventory, a purchase warehouse, a recovery inventory and customers who demand a constant quantity of product. Stochastic fluid model is adopted to describe the system and to take into account machine failure, returned products, remanufacturing products and delivery activity. The objective of this paper is to establish an optimal production control which allows minimizing the sum of inventory, transportation and lost sales costs. Perturbation analysis is applied to the stochastic fluid model to determine the optimal manufacturing inventory level that describes the optimal production control. The trajectories of the manufacturing inventory level are studied and the perturbation analysis estimates are evaluated. The unbiasedness of these estimates is proved and then they are implemented in an optimization algorithm which determines the optimal manufacturing inventory in the presence of returned products and delivery time.

This paper deals with a manufacturing remanufacturing system composed by two parallel machines, a serviceable inventory, a remanufacturing inventory and customers that demand a constant quantity of product. To describe the system, a stochastic fluid model is adopted that takes into account the return of the used products and the machines degradation. The objective of this paper is to evaluate the optimal serviceable inventory level which allows minimizing the sum of inventory, lost sales costs and the machines degradation cost. For the performance evaluation of the system, the stochastic fluid model is simulated in order to study the impact of the unit degradation costs on the optimal serviceable inventory.

In this article, we propose a new management strategy combining optimal production plan with maintenance strategy as well as a management strategy of spare parts environmentally conscious. We consider the spare parts features (used or new) which will be used during maintenance operations (corrective or preventive) in order to reduce management costs and particularly the carbon footprint. The manufacturing process considered in this study makes one unique type of product so as to satisfy a random demand. Our objective with this work is first, to provide an optimal production plan minimizing the total inventories and production costs over a finite horizon. Afterwards, we consider the influence of this optimal production plan on the system level degradation so as to simultaneously determine the optimal maintenance plan and the optimal order quantity of spare parts (used and new). Thereby, we obtain a minimization of the global cost (maintenance and production) and carbon footprint. Finally, we provide numerical examples in order to illustrate ours policies.

In this paper, we considered a failure-prone manufacturing system composed by a single-product machine, a stock and a customer who demands a stochastic quantity of product. To describe the proposed manufacturing system, a discrete flow model is adopted and which takes into account machine failure, lost demands and machine degradation. The goal of this paper is to determine the optimal production planning taken into account service level by minimizing the sum of production, inventory, lost sales and degradation costs. Perturbation analysis method is applied to the discrete flow model for optimizing the proposed system. Then the trajectories of production rate, stock level, degradation rate, and lost demands are studied and the perturbation analysis estimators are determined. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm which determines the optimal production planning in the presence of service level.

The exponential growth in air traffic and unplanned events on the day of flight schedules such as adverse weather conditions led to initial flight disruptions. In this paper, we propose a decision support framework for flight rescheduling in air traffic management based on ground delay, changing the initial flight and flight canceling. This approach is based on using Time Petri Nets (TPN) to model air traffic networks. We introduce a Binary Decision Diagram based tool to represent the state space of a class of Time Petri Nets. This tool, called Time Reduced Ordered Binary Decision Diagram (T-ROBDD) is able to represent a large state space of a TPN with a small data structure and enable the efficient manipulation of this set. In what follows, we developed a technique to generate all the rescheduling flights plans taking into account the temporal and spatial constraints.

In this paper, a manufacturing system composed by a single-product machine, a buffer and a stochastic demand is considered. A discrete flow model is adopted to describe the system and to take into account the lost demands. The objective of this paper is to determine the optimal production planning taken into account service level. This optimal production planning minimizing the sum of production, inventory, and lost sales costs. Perturbation analysis method is used for optimizing the proposed system. Using the discrete flow model, the trajectories of production rate, buffer level and lost demands are studied and the perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implanted in an optimization algorithm which determines the optimal production planning in the presence of service level.

In this paper, we deal with the problem of preventive maintenance combined with an efficient spare parts strategy. In fact, we established simultaneously the optimal preventive maintenance period and the optimal quantity of spare parts to order for non-self-announcing systems. We considered a manufacturing system of one type of product. The preventive and corrective maintenance actions consist both in replacing the defective part by a new identical one. A mathematical model is developed based on cost criteria and a numerical procedure is used to find the optimal maintenance actions and spare parts quantity to order minimizing operating costs over finite horizon.

Motivated by the importance of congestion in air traffic problem and its bad effects on airlines companies and passenger's security, we propose a decision support framework for flight rescheduling in air traffic management based on ground delay, rerouting and flight canceling. This approach is based on using Time Petri Nets (TPN) to model air traffic networks. We introduce a Binary Decision Diagram based tool to represent the state space of Time Petri Nets. This tool, called Time Ordered Binary Decision Diagrams (TOBDDs) is able to represent a large state space of a TPN with a small data structure and enable the efficient manipulation of this set. In what follows, we developed a technique to generate all the rescheduling flights plans taking into account the temporal and spatial constraints.

In this paper, we deal with the problem of a joint maintenance and production policy under a subcontracting constraint. In fact, this paper is the continuity of our previous work. The manufacturing system under consideration consists of a machine M that produces a single product. To satisfy a random demand d under a given service level constraint, the system called upon another machine M 2 (the subcontractor) which recycles and remanufactures the products rejected by the customer. We first establish an optimal production plan which minimizes the total inventory and production cost taking into consideration the subcontractor constraint. Secondly, using this optimal production plan and based on the operational age concept, we derive an optimal maintenance schedule which minimizes the total maintenance cost. Finally, a numerical example is studied in order to apply the developed approach.