Improving the Performance of Urban Waste Management Systems in the Context of a Closed-Loop Supply Chain

The saving of non-renewable energies, as well as the reduction of emissions into the environment, are two crucial objectives of industrial production. The recovery of post-consumer products associated with the use of end-of-life products is part of a context of optimization of these objectives. This recovery maximizes the use of resources from end-of-life products in a circular logic while recording the environmental footprint. This study considers a recycling strategy adapted to the need and urgency to reduce greenhouse gas emissions caused by global warming. The proposed model aims to optimize the profits of the circular manufacturing strategies while minimizing operational costs (collection, sorting, recycling), transport, GHG emissions and recycling. In this paper, a compromise between the gains of CM and the costs associated with it was studied. The robustness of the designed model was tested using a case study based on real-world scenarios. A sensitivity analysis was carried out to study the impact of the emission cost on the overall objective, considering the two options currently offered to industries. The obtained results support companies to take the ecological aspect into account and integrate sustainable development into their strategic axes for their logistics supply chains.

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources;we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)’s historical bus failure data as well as weather data from Environment Canada. We thank Facebook’s Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values.

Production Planning of a Failure-Prone Manufacturing/Remanufacturing System with Production-Dependent Failure Rates

This paper deals with the production-dependent failure rates for a hybrid manufacturing/remanufacturing system subject to random failures and repairs. The failure rate of the manufacturing machine depends on its production rate, while the failure rate of the remanufacturing machine is constant. In the proposed model, the manufacturing machine is characterized by a higher production rate. The machines produce one type of final product and unmet demand is backlogged. At the expected end of their usage, products are collected from the market and kept in recoverable inventory for future remanufacturing, or disposed of. The objective of the system is to find the production rates of the manufacturing and the remanufacturing machines that would minimize a discounted overall cost consisting of serviceable inventory cost, backlog cost and holding cost for returns. A computational algorithm, based on numerical methods, is used for solving the optimality conditions obtained from the application of the stochastic dynamic programming approach. Finally, a numerical example and sensitivity analyses are presented to illustrate the usefulness of the proposed approach. Our results clearly show that the optimal control policy of the system is obtained when the failure rates of the machine depend on its production rate.

Optimal Production Control of Hybrid Manufacturing/Remanufacturing Failure-Prone Systems under Diffusion-Type Demand

The problem of production control for a hybrid manufacturing/remanufacturing system under uncertainty is analyzed. Two sources of uncertainty are considered: machines are subject to random breakdowns and repairs, and demand level is modeled as a diffusion type stochastic process. Contrary to most of studies where the demand level is considered constant and fewer results where the demand is modeled as a Poisson process with few discrete levels and exponentially distributed switching time, the demand is modeled here as a diffusion type process. In particular Wiener and Ornstein-Uhlenbeck processes for cumulative demands are analyzed. We formulate the stochastic control problem and develop optimality conditions for it in the form of Hamilton-Jacobi-Bellman (HJB) partial differential equations (PDEs). We demonstrate that HJB equations are of the second order contrary to the case of constant demand rate (corresponding to the average demand in our case), where HJB equations are linear PDEs. We apply the Kushner-type finite difference scheme and the policy improvement procedure to solve HJB equations numerically and show that the optimal production policy is of hedging-point type for both demand models we have introduced, similarly to the known case of a constant demand. Obtained results allow to compute numerically the optimal production policy in hybrid manufacturing/ remanufacturing systems taking into account the demand variability, and also show that Kushner-type discrete scheme can be successfully applied for solving underlying second order HJB equations.

Effect of the Variable B-Field on the Dynamic of a Central Electron Spin Coupled to an Anti-Ferromagnetic Qubit Bath

This present issue is an extension of the work of Y. Xiao-Zhong et al. who investigated the influence of constant external magnetic field on the decoherence of a central electron spin of atom coupled to an anti-ferromagnetic environment. We have shown in this work that the character variability of the field induces oscillations amongst the eigen modes of the environment. This observation is made via the derivation of the transition probability density of state, a manner by which critical parameters (parameters where transition occur) of the system could be obtained as it shows resonance peak. We equally observed that the two different magnons modes resulting from the frequency splitting via the application of the time-varying external B-Field, exhibit each a resonant peak of similar amplitude at different temperature ranges. This additional information shows that the probability for the central spin system to remain in its initially prepared diabatic state is enhanced for some temperature ranges for the corresponding two magnon modes. Hence, these temperature ranges where the probability density is maximum could save as decoherence free environment;an important requirement for the implementation of quantum computation and information processing in solid state circuitry. The theoretical and numerical results presented for the decoherence time and the probability density are that of a decohered central electron spin coupled to an anti-ferromagnetic spin bath. The theory is based on a spin wave approximation and on the density matrix using both transformations of Bloch, Primakov and Bogoliobuv in the adiabatic limit.

Development of a Tool Cost Optimization Model for Stochastic Demand of Machined Products

Cutting tool management in manufacturing firms constitutes an essential element in production cost optimization. In order to optimize the cutting tool stock level while concurrently minimizing production costs, a cost optimization model which considers machining parameters is required. This inclusive modeling consideration is a major step towards achieving effectiveness of cutting tool management policy in manufacturing systems with stochastic driven policies for tool demand. This paper presents a cost optimization model for cutting tools whose utilization level is assumed to be optimized in respect of the machining parameters. The proposed cost model in this research incorporated the effects of diversified machining costs ranging from operational through machining, shortage, holding, material and ordering costs. The machining of parts was assumed to be a single cutting operation. Holt-Winters forecasting technique was used to create a stochastic demand dataset for a test scenario in the production of a high-end automotive part. Some numerical examples used to validate the developed model were implemented to illustrate the optimal machining and tool inventory conditions. Furthermore, a sensitivity analysis was carried out to study the influence of varying production parameters such as: machine uptime, demand and cutting parameters on the overall production cost. The results showed that a desired low level of tool storage and holding costs were obtained at the optimal stock levels. The machining uptime had a significant influence on the total cost while tool life and cutting feed rate were both identified as the most influential cutting variables on the total cost. Furthermore, the cutting speed rate had a marginal effect on both costs and tool life. Other cost variables such as shortage and tool costs had significantly low effect on the overall cost. The output trend showed that the feed rate is the most significant cutting parameter in the machining operation, hence influencing the cost the most. Also, machine uptime and demand significantly influenced the total production cost.