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This study introduces new probabilistic constraints and objective functions to manage the uncertain nature of the renewable energy sources in single-item capacitated lot sizing problem for flow-shop configurations by integrating the capacity contract selection problem with multiple energy sources. The aim of the probabilistic models built by considering the different probabilistic constraints and objective functions is to provide a decision-making tool and to promote the use of renewable energy sources in manufacturing industry despite of their stochastic nature. Mixed Integer Non-Linear Programming models are proposed by integrating the uncertainty of the renewable energy sources based on different features. The developed models are tested on a small-size instance and the results of the models are compared in terms of economical, ecological and reliability aspects.

This paper addresses the problem of scheduling different non-preemptive jobs on a single machine under time of use electricity tariffs consideration. The considered machine has three main states (OFF, ON, Idle) and two transition states (Turn-on and Turn-off). Each of these machine’s states as well as the processing jobs, consume a specific amount of energy. Moreover, a speed-scalable case of the problem is considered, in which jobs can be processed at an arbitrary speed with a trade-off between speed and energy consumption. First, a mixed-integer linear programming model with the objective of minimizing total energy consumption costs formulates this scheduling problem. Then, since this problem is strongly NP-hard, different approximate optimization methods are investigated to provide near-optimal solutions. Finally, an extensive computational study is carried out to establish the efficiency of the proposed algorithms. The obtained results show, how a well-tuned genetic algorithm combined with an adequate local search procedure constitutes an efficient method for solving this energy-aware scheduling problem.

International audience; The problem addressed in this paper was motivated by a real optimization problem of supply chain of the hospital center of Troyes (HCT). The HCT is currently seeking to review and improve its logistics processes. The implementation of techniques and methods of operational research must provide solutions to improve the efficiency of logistics activities. In this work, the studied problem focuses on the catering component of the hospital logistics. A novel mathematical model for the production scheduling of multi-products and multi-stages food processes in hospital catering is proposed. This mathematical model has been implemented in commercial solver CPLEX and it has been tested on real instances of HCT and from the literature. The implementation results of the mathematical model proposed have proved its efficiency for the scheduling of the food production process.

Part VI - Meta-Heuristics and Optimization Techniques for Energy-Oriented Manufacturing Systems; International audience; This work presents an Order Acceptance Scheduling (OAS) problem with the introduction of energy consumption limits at each period of the planning horizon. The objective is to maximize total profit of accepted orders while considering energy usage over time, in accordance with imposed deadlines. A time-indexed formulation is presented to tackle the investigated problem.

Part II - Hybrid Approaches for Production Planning and Scheduling; International audience; This study tackles an unrelated parallel machines rescheduling problem. Sequence and machine dependent setup times and limited resources are taken into consideration. The study focuses on the objective of proposing an efficient and stable rescheduling solution. The resolution approach is explained and illustrated. Different indicators to optimize the rescheduling planning are tested and results are analyzed. The problem is inspired from a concrete case of textile industry.

Order Acceptance Scheduling (OAS) is a two-fold decision problem that consists in the sequencing of a subset of selected orders with the objective of maximizing the total profit. In recent years, emphasis focused on the reduction of energy consumption and carbon emissions. Optimizing production schedules is a promising lever for this purpose. In this context, we provide a mathematical formulation for the OAS problem under energy aspects. To assess the performance of our model, a comparative analysis is conducted. Our results clearly point out that the arc-time-indexed model is better than the disjunctive one.

International audience; Around the world, the hospital world comes up against scaling and organization challenges in order to satisfy patient needs. These needs may often be urgent and require high quality and safety. This chapter discusses hospital logistics and the links it has with operational research. It finds complex systems, which are increasingly being studied by operational research, with the aim of bringing in optimized solutions while respecting a pre-established set of rules. The chapter takes into account the journeys made by good drivers and also consider the welfare of the drivers on these journeys. It also presents some optimization methods and techniques that are proposed and tested on real-life instances, as well as those taken from literature. The Champagne Sud hospitals are currently carrying out a review to improve the management of processes and procedures within their logistics network.

International audience

International audience; This paper aims to study a real case of an optimization problem derived from a hospital supply chain. The present work focuses on developing operational decision support models and algorithms for production process scheduling in hospital catering. The addressed production system is considered as a flexible job shop system. The objective is to minimize the total flow time. A novel mathematical model and two metaheuristics for the production scheduling of multi-product and multi-stage food processes are developed. These methods have proven their effectiveness for the scheduling of operations of the food production processes and allowed significant improvements in the performance of the studied production system.