In response to the ongoing global energy crisis, particularly prevalent in Europe, there is a critical need to identify alternative energy sources and reduce energy consumption. This study presents an integrated inventory model that incorporates energy consumption from a supplier’s production and storage operations. The model focuses on a manufacturing system consisting of a single machine for production and a warehouse responsible for storing a single product. Energy consumption in the production process is attributed to the machine’s operation, which can be either in a production or non-production phase. Additionally, energy is utilized to maintain goods at the required temperature, accounting for ambient temperature fluctuations and varying levels of warehouse occupancy. The objective is to determine optimal decision variables, including production rate, cycle time, and machine status in the non-production phase, to minimize the overall system cost, encompassing energy costs. The proposed optimisation procedure simplifies solution identification for the nonlinear programming (NLP) problem. A realistic numerical example illustrates the model’s applicability and reveals that optimal machine status during non-production phases significantly reduces both energy costs and total costs compared to the remaining status. Sensitivity analysis explores the effects of different parameters on the model, emphasizing the considerable impact of factors like energy consumption per unit, energy cost, and demand rate on the total cost.

International audience

This research is motivated by the growing demand for electric transportation in all sectors. Our aim through this study is to investigate the use of electric vehicles for simultaneous pickup and delivery request, which has application in the last mile logistics or in a system (industrial, warehousing, hospitals, etc.) using Automated Intelligent Vehicle (AIV). The main contribution are as follows: a new mathematical model for the Vehicle Routing Problem is proposed to minimize the total cost, considering the impact of the goods load on the energy consumption, simultaneous pickup and delivery request, times windows and the use of a heterogeneous fleet. Then, an experimental study on small instances is conducted to assess the proposed model.

This paper proposes a planning approach that integrates two levels of decision-making (strategic/tactical), using a mixed integer non-linear programming (MINLP) model. This model is designed to minimize costs in a supply chain process. It takes into account the management of several actors, the integration of several sources of energy, fuel consumption and the control of travel speeds during transport operations. This model could be used as a decision support tool to assess and control the effects of carbon emissions in a global supply chain. The model is an extension of the model proposed in [1], with a new contribution to the integration of energy aspects and vehicle speed control. The approach is tested on a real case from the literature taken from the textile and clothing industry. The results show that, considering the control of vehicle speed, the type of fuel, the energy used, and the carbon tax would significantly influence the economic and environmental activities of an industry.

As companies strive to reduce energy consumption and minimize their carbon footprint, there is a growing trend among customers to choose eco-friendly products. In order to address these concerns, the present study puts forth a new economic production quantity (EPQ) model that takes into account various factors such as energy usage, carbon emissions, and market demand in relation to product price and environmental impact. This is achieved through an analysis of the working phases of the manufacturing machine. By maximizing the overall profitability of the system, optimal decisions can be made regarding cycle time, production rate, demand rate, and machine states during non-production phases (standby or powered off). A Mixed integer nonlinear programming (MINLP) problem is proposed and analyzed. A case study demonstrates that meeting customer expectations for sustainable products can lead to lower profits for businesses, but it also results in reduced energy consumption and environmental emissions. The sensitivity analysis demonstrates that market demand, price sensitivity coefficient, and unit production cost have a more substantial impact on profit compared to the other parameters.

This paper deals with an application of constraint programming (CP) for a hybrid flexible flow shop scheduling problem (HFFS) with realistic features, such as job-dependent and sequence-dependent setup times, due dates, and waiting policies, with the aim of minimizing the total tardiness, so that the workshop can fulfill the delivery deadline. After describing all the characteristics of the proposed problem, we formulate it using CP model. Numerical simulations are prformed to evaluate its performance. The results show that the proposed CP model is highly effective in finding good quality solutions for both small-sized and large-sized instances. The conducted experiments, using case study instances, show that our model performs well at exploring optimal solutions in a short computational time and hence allows us to find good quality solutions. The proposed method can be easily adjusted to a huge number of real-life environements for scheduling problems with similar features.

International audience

In mobile sensor networks, it is important to manage the mobility of the nodes in order to improve the performances of the network. This paper addresses the problem of single target tracking in controlled mobility sensor networks. The proposed method consists of estimating the current position of a single target. Estimated positions are then used to predict the following location of the target. Once an area of interest is defined, the proposed approach consists of moving the mobile nodes in order to cover it in an optimal way. It thus defines a strategy for choosing the set of new sensors locations. Each node is then assigned one position within the set in the way to minimize the total traveled distance by the nodes. While the estimation and the prediction phases are performed using the interval theory, relocating nodes employs the ant colony optimization algorithm. Simulations results corroborate the efficiency of the proposed method compared to the target tracking methods considered for networks with static nodes.

This paper presents our work on modeling and performance analysis of inventory systems using batch deterministic and stochastic Petri nets (BDSPNs). It addresses issues frequently raised by industrial companies, but did not receive enough attention by the Petri nets (PNs) community in spite of its important role in the study of discrete event systems. The BDSPN is a new class of PNs capable of describing the synchronization of discrete and batch token flows in discrete batch processes. Such processes appear in inventory systems or more general supply chains where materials are purchased in finite discrete quantities (batches of different sizes), and many operations such as inventory replenishment and customer order fulfillment are usually performed in a batch way because of the batch nature of customer orders and/or in order to take advantages of the economies of scale. In this paper, the BDSPN model is formally introduced, and its conflict resolutions of transitions and batch firing indexes are addressed. The model is then applied to the modeling and performance evaluation of various inventory systems. Analytic performance evaluation techniques are developed for the model with illustrative applications to the inventory systems. Our study shows that the model is powerful for both modeling and performance evaluation of the systems.

Batch deterministic and stochastic Petri nets are introduced as a tool for modeling and performance evaluation of supply chains. The new model is developed by enhancing deterministic and stochastic Petri nets (DSPNs) with batch places and batch tokens. By incorporating stochastic Petri nets (SPNs) with the batch features, inhibitor arcs, and marking-dependent weights, operational policies of supply chains such as inventory policies can be easily described in the model. Methods for structural and performance analysis of the model are developed by extending existing ones for DSPNs. As applications, an inventory system and an industrial supply chain are modeled and their performances are evaluated analytically and by simulation, respectively, using this BSPN model. The applications demonstrate that our model and associated methods can solve some important supply chain modeling and analysis issues. Note to Practitioners-This paper was motivated by the problem of performance analysis and optimization of supply chains but it also applies to other discrete event systems where materials are processed in finite discrete quantities (batches) and operations are performed in a batch way because of batch inputs and/or in order to take advantages of the economies of scale. Existing Petri net modeling and analysis tools for such systems ignore their batch features, making their modeling complicated. This paper suggests a new model called batch deterministic and stochastic Petri nets (BDSPNs) by enhancing deterministic and stochastic Petri nets with batch places and batch tokens. Methods for structural and performance analysis of the model are developed. We then show how an inventory system and a real-life supply chain can be modeled and their performances can be evaluated analytically and by simulation respectively based on the model. The model and associated analysis methods therefore provide a promising tool for modeling and performance evaluation of supply chains.

Batch deterministic and stochastic Petri nets are developed as a new tool for modeling and performance evaluation of supply chain. It is derived by enhancing deterministic and stochastic Petri nets. with batch places and batch tokens. Batch tokens, which have sizes and reside in batch places, are used to describe the information flow of a supply chain, while discrete tokens residing in discrete places are used to describe the material flow and the financial flow. By incorporating stochastic Petri nets with the batch features, inhibitor arcs, and marking-dependent weights, operational policies of a supply chain can be easily described in the model. A real-life supply chain is modeled by applying this tool and its performance is evaluated and optimized by simulation.