Faicel Hnaien

Optimal supply planning in MRP environments for assembly systems with random component procurement times

This paper examines supply planning in an MRP environment for assembly systems under lead time uncertainties. Indeed, inventory control in a supply chain is crucial for companies who wish to satisfy their customer demands on time as well as controlling costs. A common approach is to use the MRP techniques. However, these techniques are based on the supposition that lead times are known. In an actual supply chain the lead times are often random variables. Therefore, we develop an efficient exact model to aid in MRP parameterization under lead time uncertainties, more precisely to calculate planned lead times when the component procurement times are random. The aim is to find the values of planned lead times which minimize the sum of the average component holding cost and the average backlogging cost. The developed approach is based on a mathematical model of this problem with discrete decision variables and a branch and bound algorithm.

Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks

In this study, we aim to cover a sensing area by deploying a minimum number of wireless sensors while maintaining the connectivity between the deployed sensors. The problem may be reduced to a two-dimensional critical coverage problem which is an NP-Complete problem. We develop an integer linear programming model to solve the problem optimally. We also propose a local search (LS) algorithm and a genetic algorithm (GA) as approximate methods. We verify by computational experiments that the integer linear model, using Cplex, is able to provide an optimal solution of all our small and medium size problems. We also show that the proposed methods outperform some regular sensor deployment patterns.

Genetic algorithm for supply planning in two-level assembly systems with random lead times

This paper examines supply planning for two-level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated.

Multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times

Supply planning for two-level assembly systems under lead time uncertainties is considered. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. A holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the component lead time is a random discrete variable. The objective is to find the release dates for the components at level 2 in order to minimize the expected component holding costs and to maximize the customer service level for the finished product. For this new problem, we consider two multi-objective approaches, which are both based on genetic algorithms. They are evaluated with a variety of supply chain settings, and their respective performance is reported and commented. These two heuristics permitted to obtain interesting results within a reasonable computational time.

Handling uncertainty in agricultural supply chain management: A state of the art

Given the evolution in the agricultural sector and the new challenges it faces, managing agricultural supply chains efficiently has become an attractive topic for researchers and practitioners. Against this background, the integration of uncertain aspects has continuously gained importance for managerial decision making since it can lead to an increase in efficiency, responsiveness, business integration, and ultimately in market competitiveness. In order to capture appropriately the uncertain conjuncture of most agricultural real-life applications, an increasing amount of research effort is especially dedicated to treating uncertainty. In particular, quantitative modeling approaches have found extensive use in agricultural supply chain management. This paper provides an overview of the latest advances and developments in the application of operations research methodologies to handling uncertainty occurring in the agricultural supply chain management problems. It seeks to: (i) offer a representative overview of the predominant research topics, (ii) highlight the most pertinent and widely used frameworks, and (iii) discuss the emergence of new operations research advances in the agricultural sector. The broad spectrum of reviewed contributions is classified and presented with respect to three most relevant discerned features: uncertainty modeling types, programming approaches, and functional application areas. Ultimately, main review findings are pointed out and future research directions which emerge are suggested.

Multi-objective optimization in wireless sensors networks

In most wireless sensor network (WSN), energy is a limited resource. Indeed, a sensor has a limited power source (like a battery). When this power source is empty, the sensor stops working. In this paper, we will present the modeling of a multi-objective problem. The first objective is the maximization of the coverage of the area under time, the second objective is the maximization of the lifetime of the network depending on coverage, and finally the minimization of the financial cost (i.e. the number of sensors). The resolution of this problem will be done by multi-objective algorithm NSGA-II [6], SPEA-II [5] and multi-objective ant colony optimization (MOACO) [8].

Single-period inventory model for one-level assembly system with stochastic lead times and demand

Replenishment planning of an assembly system with one type of finished product assembled from different types of components is considered. The components are procured from diverse external suppliers to satisfy finished product demand. It is supposed that the component lead times and finished product demand are random discrete variables. The assembly company must determine what are the best quantities of components and when is the right time to order. The objective is to minimise the total cost which is composed of holding component costs, tardiness penalties, lost sales and surplus item costs for finished products. A single-period analytical model is proposed. Several properties of the objective function are proven. They are used to develop a Branch and Bound algorithm. Numerical tests for the algorithm are presented. Five heuristics based on Newsvendor model for lead time and demand are proposed and compared with the Branch and Bound algorithm. These tests show that the suggested Branch and Bound algorithm can solve large size problems within a short time. The proposed heuristics but one are not competitive with the Branch and Bound algorithm. The truncated version of Branch and Bound gives better results. The model suggested is better adapted to actual contract assembler environments, more realistic and can better approximate real-life industrial situations. The proposed exact algorithm provides optimal solutions for all discrete distributions of probabilities of lead times and demand. A new general approach to design such discrete optimisation algorithms is presented.

A quality risk management problem: case of annual crop harvest scheduling

This paper presents a stochastic optimisation model for the annual harvest scheduling problem of the farmers’ entire cereal crop production at optimum maturity. Gathering the harvest represents an important stage for both agricultural cooperatives and individual farmers due to its high cost and considerable impact on seed quality and yield. The meteorological conditions represent the deciding factor that affects the harvest scheduling and progress. Using chance-constrained programming, a mixed-integer probabilistically constrained model is proposed, with a view to minimising the risk of crop quality degradation under climate uncertainty with a safe confidence level. The chance-constrained optimisation problem is tackled and solved via an equivalent linear mixed-integer reformulation jointly with scenario-based approaches. Moreover, a new concept of -scenario pertinence is introduced in order to defy efficiently the probabilistically constrained problem complexity and time limitations. From the practical standpoint, this study is aimed at helping an agricultural cooperative in decision-making on crop quality risk management and harvest scheduling over a medium time horizon (10–15 time periods).

Linear models for the total coverage problem in wireless sensor networks

In this paper, we consider the total grid coverage problem in wireless sensor networks. Our proposal aims to determine the optimal number of sensors and their positions in a sensing area represented by a grid. The deployed sensors should achieve the total grid point coverage. The problem is proved NP-complete in [15]. We propose two mathematical linear models to solve optimally two problem cases. In the first case, the connectivity between deployed sensors is not required. However, in the second case, the sensors should communicate with each other. Computational experiments are generated on different grid sizes and multiple sensor ranges. The results show that the proposed linear models can produce appropriate solutions for the two problem cases.

A Branch and Bound Algorithm for the Critical Grid Coverage Problem in Wireless Sensor Networks

We aim to cover a grid fully by deploying the necessary wireless sensors while maintaining connectivity between the deployed sensors and a base station (the sink). The problem is NP-Complete as it can be reduced to a 2-dimensional critical coverage problem, which is an NP-Complete problem. We develop a branch and bound (B&B) algorithm to solve the problem optimally. We verify by computational experiments that the proposed B&B algorithm is more efficient, in terms of computation time, than the integer linear programming model developed by Rebai et al. (2013), for the same problem.