Adnène Hajji

Production rate control of an unreliable manufacturing cell with adjustable capacity

This article addresses the production control problem of an adjustable capacity unreliable manufacturing cell responding to a single product type demand. The manufacturing cell is composed of an unreliable machine, called the ‘central machine’. Due to availability fluctuations, the central machine may fall short of meeting the long-term demand rate. In order to quickly adjust the production capacity and thus meet the demand, a reserve machine is called upon in support if the finished product inventory level drops below a specific threshold. Such a machine involves higher production costs compared with the central one. This article aims to determine the optimal production control policy for the involved machines in order to minimise production, inventory and backlog costs over an infinite horizon. This article proposes a continuous dynamic programming formulation of the problem and adopted a numerical scheme to solve the optimality conditions equations. The optimal production policy is shown to be described by a state dependent hedging point policy (SDHPP). To determine the optimal control policy parameters, an experimental approach based on design of experiments, simulation modelling, and response surface methodology is proposed. Several sensitivity analyses have been carried out and have shown the robust behaviour of the developed policy facing expected variations of the system parameters. The results also show that the proposed SDHPP policy outperforms classical stand-by and parallel machines based control policies. The usefulness of the proposed approach is outlined for more complex situations where the system must deal with non-exponential failure and repair time distributions.

Joint replenishment and manufacturing activities control in a two stage unreliable supply chain

This paper deals with dynamic stochastic situations faced by supply chains. In this context, various interactions, disparate decisions and random phenomena must be considered. These issues are considered in this paper through a two stage supply chain control problem. The supplier and the transformation stage are both subject to random events such as periods of unavailability due to internal difficulties or market constraints. Our objective is to find information sharing control policies for the supply and production activities that minimises the expected discounted cost of ordering, inventories/backlog and transformation over an infinite horizon. This is an optimal control problem with state constraints and hybrid dynamics of the production and replenishment activities. It is shown that, from a mathematical point of view, the considered problem is difficult to tackle and it calls upon optimal and impulsive control theory notions. A dynamic stochastic model is thus proposed. The existence of an optimal control policy and Hamilton-Jacobi-Bellman optimality condition in terms of the value function of the problem are derived and discussed. A numerical schema is then proposed to solve the obtained optimality conditions equations. A complete control policy is finally developed. The confirmation of such a policy structure is illustrated through sensitivity analysis. Some particular cases are also presented and discussed.

An Environmental Hedging Point Policy to control production rate and emissions in unreliable manufacturing systems

This paper proposes a new Hedging Point Policy (HPP) which integrates environmental concerns into the optimal control of unreliable manufacturing systems. The considered system is composed of a production facility subjects to random failures and producing a product family intended for a given market with stable demand. The manufacturing facility’s operations cause harmful emissions to the environment, and may incur sanctions in the form of an environmental tax imposed by the relevant authorities. Given the significant compromise that must take place between inventory, backlog and taxes costs, the main objective of this paper is to propose a feedback adaptive control policy which provides a better control of the production rate and the emissions generated. Under the HPP category, a new structure called the Environmental Hedging Point Policy (EHPP) is proposed. To illustrate the effectiveness of the proposal, an experimental approach based on simulation modelling, variance analysis and response surface methodology (RSM) is applied. The results show a significant gain in terms of incurred costs compared to those incurred when the system is governed by a classical HPP. An improved version of EHPP is also proposed for systems with high emission rates. Several sensitivity analyses are conducted to illustrate the robustness and effectiveness of the proposed policies.

Joint production, setup and preventive maintenance policies of unreliable two-product manufacturing systems

This article addresses the problem of joint optimisation of production, setup and maintenance activities of unreliable manufacturing system producing two products. Given the complexity of the problem in a dynamic and stochastic environment, the literature has treated the problem separately by considering each axis individually (setup, production and maintenance) or by combining two axes simultaneously (production-setup, production-maintenance). Following the trend of scientific research advances that supports the fact that an integrated control leads to best performances, the main objective of this paper is to provide a control policy that will simultaneously combine the production, the setup and the preventive maintenance activities. To tackle the problem, an experimental resolution approach using combined continuous/discrete event simulation models is considered. The aim is to accurately imitate the production system behaviour, and to optimise the control policy parameters which minimise the total cost incurred. An in-depth study of the effects of the system parameter variation on the performance of the studied policies is performed in order to draw meaningful conclusions and to illustrate the robustness of the proposed resolution approach.

Optimal production control policy in unreliable batch processing manufacturing systems with transportation delay

This paper considers the problem of production planning of unreliable batch processing manufacturing systems. The finished goods are produced in lots, and are then transported to a storage area in order to continuously meet a constant demand rate. The main objective of this work is to jointly determine the optimal lot sizing and optimal production control policy that minimise the total expected cost of inventory/backlog and transportation, over an infinite time horizon. The decision variables are the lot sizing and the production rate. The problem is formulated with a stochastic dynamic programming model and the impulse control theory is applied to establish the Hamilton–Jacobi–Bellman (HJB) equations. Based on a numerical resolution of the HJB equations, it is shown that the optimal control policy is governed by a base stock policy for production rate control and economic lot size for batch processing. A thorough analysis and practical issues are addressed with a simulation-based approach. Thus, a combined discrete–continuous simulation model is developed to determine the optimal parameters of the proposed policy when the failure and repair times follow general distributions. The results are illustrated with numerical examples and confirmed through sensitivity analysis.

Joint production control and product quality decision making in a failure prone multiple-product manufacturing system

This paper considers joint production control and product quality specifications decision making in unreliable multiple-product manufacturing system. This is with the knowledge that an optimum compromise should guide the decision making process. In fact, tight process specifications will generally lead to products with good quality and higher market values, but at the same time associated with a higher rate of non-conforming parts rejection leading to higher non quality costs and lower plant productivity. Moreover, in unreliable manufacturing context the decision maker should adopt an adequate production policy to hedge against future capacity shortages caused by machine failures in order to meet customer demand. This paper intends to extend previous findings to tackle this problem and study the overall decision making process aiming to guide the production and quality specification decisions in multiple-product context. The overall optimal decision policy is defined here as one that maximises the long term average per unit time profit of a combined measure of quality and quantity dependent sales revenue, minus inventory and backlog costs, in the presence of random plant failures and random repair durations.

Impact of random delay on Replenishment and production control strategies

This paper considers a stochastic optimal control problem of unreliable three stages manufacturing systems. The supplier and the transformation stage are both subject to random events. Moreover, due to the periods of unavailability of the supplier, a random delay could postpone the reception of the order. Our objective is to find a control policy for the supply and production activities that minimizes the incurred cost and to propose a practical approach aiming to evaluate and quantify the control policy. Stochastic dynamic programming and numerical methods combined to a simulation based approach are thus proposed to achieve a close approximation of the production and supply policy. To illustrate the usefulness of the combined approach extensions to cover more complex systems, were optimal control theory may not be easily used, are developed and analyzed. To illustrate the practical usefulness of the approach, an application aiming to develop a quantitative tool to help establishing and negotiating order costs is presented.

Analysing a Segmentation Pricing Strategy for ERP Systems under Diffusion Patterns

In this paper, we evaluate the options of developing a price discrimination policy for Enterprise Resource Planning (ERP) systems in a business sector governed by a diffusion pattern influenced by the network position of each firm. Based on a real data from the automotive industry, the proposed strategy is operationalized through discounts and optimized through a simulation-based model coupled with design of experiment and response surface techniques. Our results suggest that a segmentation pricing strategy is likely to increase total revenues in network with low initial adoption rates and that price discounts should be adapted according to adoption rates in order to maximize total revenues.

Transforming city initiative into a smart city initiative: the case of "winter maintenance operations"

A smart city has the objective to improve the quality of life of citizens by the extensive use of Information and Communication Technologies. In this project, the focus will be made on winter maintenance operations (WMO). Based on an integrative framework for smart cities initiatives, we will try to understand the links that can be established between smart city theoretical concepts and winter maintenance smart initiatives in practice. This proposal is a first step to bridge the gap between theory and practice by analysing smart city initiatives related to snow collecting. A qualitative analysis, based on structured observations and interviews with decision-makers of WMO in snowy cities, will be made in order to provide relevant knowledge that would serve as a basis for decision-makers to better plan their smart city initiatives.

Dynamic production planning in unreliable Reconfigurable Manufacturing Systems

This paper is intended to deal with a dynamic production planning problem for unreliable Reconfigurable Manufacturing Systems (RMS). The considered system is composed of an industrial facility subject to random failures and producing a product family intended to a given market with dynamic demand. The market demand varies in term of product type and demand rate. To face the three sources of uncertainty (failures, product type and demand rate) the manufacturing facility is designed to be reconfigurable. Within the class of RMS the considered system is designed to change its structure quickly in order to adjust its production capability in term of capacity and product type to produce. Given the significant compromise between reconfiguration, production, inventory and backlog costs, this paper main objective aims to propose a feedback adaptive strategy which provides a better control of the reconfiguration sequence and the production rate of the system and minimizes a cost function. A dynamic programming formulation of the problem is presented. Then, a numerical schema is adopted to solve the obtained optimality conditions. Under the hedging point policies (HPP) class, a configuration dependent HPP is proposed. The results show a significant gain in term of incurred costs compared to those incurred when the reconfiguration decision are developed independently of the production planning decisions. Several sensitivity analysis are conducted to illustrate the robustness and effectiveness of the proposed policies.