David Lemoine

Dynamic cluster in particle swarm optimization algorithm

Particle swarm optimization is an optimization method based on a simulated social behavior displayed by artificial particles in a swarm, inspired from bird flocks and fish schools. An underlying component that influences the exchange of information between particles in a swarm, is its topological structure. Therefore, this property has a great influence on the comportment of the optimization method. In this study, we propose DCluster: a dynamic topology, based on a combination of two well-known topologies viz. Four-cluster and Fitness. The proposed topology is analyzed, and compared to six other topologies used in the standard PSO algorithm using a set of benchmark test functions and several well-known constrained and unconstrained engineering design problems. Our comparisons demonstrate that DCluster outperforms the other tested topologies and leads to satisfactory performance while avoiding the problem of premature convergence.

Discrete Particle Swarm Optimization for the Multi-Level Lot-Sizing Problem

This paper presents a Discrete Particle Swarm Optimization (DPSO) approach for the Multi-Level Lot-Sizing Problem (MLLP), which is an uncapacitated lot sizing problem dedicated to materials requirements planning (MRP) systems. The proposed DPSO approach is based on cost modification and uses PSO in its original form with continuous velocity equations. Each particle of the swarm is represented by a matrix of logistic costs. A sequential approach heuristic, using Wagner-Whitin algorithm, is used to determine the associated production planning. The authors demonstrate that any solution of the MLLP can be reached by particles. The sequential heuristic is a subjective function from the particles space to the set of the production plans, which meet the customers demand. The authors test the DPSO Scheme on benchmarks found in literature, more specifically the unique DPSO that has been developed to solve the MLLP.

Integration of Maintenance in the Tactical Production Planning Process under Feasibility Constraint

This paper deals with the problem of the joint optimization of the master production schedule and maintenance strategy for a manufacturing system. An efficient production planning and maintenance policy will allow to minimize the impacts of the potential random failures and will let the plan to be feasible. We present a modelisation where we take into account a feasibility constraint; the optimization problem is formulated as a linear program. We propose a heuristic algorithm to solve it and we show the impact of the feasibility constraint on different criteria.

Integrated dynamic single item lot-sizing and quality inspection planning

This paper proposes an integrated model for single item dynamic lot-sizing problem and Quality Inspection Planning (QIP). The objective is to provide a model of production planning that takes into account a targeted level of outgoing quality or an Acceptable quality level (AQL) when the manufacturing system inherently generates a proportion of defectives that increases significantly when the system switches from the in-control state to the out-of-control state. The average outgoing quality of each period of time of the planning horizon is bounded as a function of the inspection capacity. The effects of integrating QIP are analysed and discussed through several experiments representing different quality control system’s parameters, i.e. inspection capacity, inspection cost and AQL. The simulation results show that it is very important to take into account the inspection process into production planning decisions. This study will help the decision-makers to negotiate service levels or react properly to given customer quality requirements based on cost and lead time parameters in addition to their process characteristics in terms of capability and stability.

Surgical case scheduling with sterilising activity constraints

In this paper, we study a real scheduling problem which consists in scheduling a set of elective surgical cases requiring surgical instruments and tools in several operating rooms. The objectives are to minimise the overtime of the surgical unit staff, the number of operating rooms used and the number of instruments processed in emergency in the sterilising unit while respecting the current level of service represented by the total number of patients operated per month at the orthopaedic surgery unit. This research was performed in collaboration with the University Hospital of Angers in France (CHU Angers), which has also provided historical data for the experiments. We propose a mixed integer linear programming model for the problem which is solved in a lexicographic fashion. We also propose a robust formulation to deal with the uncertain surgery durations. Both the deterministic and robust formulations are then compared over a set of instances provided by the CHU. The solutions obtained are competitive in terms of number of operating rooms and significantly improve those implemented operationally at the CHU in terms of overtime and emergencies at the sterilising unit.

Dynamic lot-sizing-based Working Capital Requirement minimization model with infinite capacity

Tactical planning consists of developing production plans to fulfill client demands with a minimal logistic cost. However, plans generated by classical models for tactical planning do not consider a minimum financial need in terms of Working Capital Requirements (WCR) to maintain the activities related to the operating cycle. In this paper, we introduce a first link between tactical planning and the financial aspects of WCR. The concept of WCR is widely used in practice to assess the financial situation at any time. We propose a new generic WCR model which allows us to evaluate the companys financial situation during the planning horizon. In addition, we develop a dynamic lot-sizing-based model with WCR modeling for singlesite, single-level, single-product and infinite capacity cases. An exact algorithm is also presented with numerical tests in order to compare our approach with the traditional dynamic lot-sizing model.

Integration approaches of forecasting methods selection with inventory management indicators in the case of spare parts supply chain

The main scope of this paper is to improve management policies for spare parts, within the context of centralized management and more particularly with regard to forecast methods. The specificity of low and erratic demand does not allow the use of conventional approaches of forecasting. Moreover, the associated performance measurements, based on purely statistical indicators, are not adapted to the context of supply chain management. Here we propose two new performance analysis approaches seeking to combine the statistical performance of forecasting methods and inventory management performance. A comparative analysis of forecasting methods on real data enable the definition of a strategy for selecting the appropriate method when integrated with spare parts inventory management model using a continuous review (s, S).