Lionel Amodeo

Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling

This paper presents an algorithm based on Ant Colony Optimization paradigm to solve the joint production and maintenance scheduling problem. This approach is developed to deal with the model previously proposed in [3] for the parallel machine case. This model is formulated according to a bi-objective approach to find trade-off solutions between both objectives of production and maintenance. Reliability models are used to take into account the maintenance aspect. To improve the quality of solutions found in our previous study, an algorithm based on Multi-Objective Ant Colony Optimization (MOACO) approach is developed. The goal is to simultaneously determine the best assignment of production tasks to machines as well as preventive maintenance (PM) periods of the production system, satisfying at best both objectives of production and maintenance. The experimental results show that the proposed method outperforms two well-known Multi-Objective Genetic Algorithms (MOGAs): SPEA 2 and NSGA II.

New multi-objective method to solve reentrant hybrid flow shop scheduling problem

This paper focuses on the multi-objective resolution of a reentrant hybrid flow shop scheduling problem (RHFS). In our case the two objectives are: the maximization of the utilization rate of the bottleneck and the minimization of the maximum completion time. This problem is solved with a new multi-objective genetic algorithm called L-NSGA which uses the Lorenz dominance relationship. The results of L-NSGA are compared with NSGA2, SPEA2 and an exact method. A stochastic model of the system is proposed and used with a discrete event simulation module. A test protocol is applied to compare the four methods on various configurations of the problem. The comparison is established using two standard multi-objective metrics. The Lorenz dominance relationship provides a stronger selection than the Pareto dominance and gives better results than the latter. The computational tests show that L-NSGA provides better solutions than NSGA2 and SPEA2; moreover, its solutions are closer to the optimal front. The efficiency of our method is verified in an industrial field-experiment.

Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem

This paper deals with the joint production and maintenance scheduling problem according to a new bi-objective approach. This method allows the decision maker to find compromise solutions between the production objectives and maintenance ones. Reliability models are used to take into account the maintenance aspect of the problem. The aim is to simultaneously optimize two criteria: the minimization of the makespan for the production part and the minimization of the system unavailability for the maintenance side. Two decisions are taken at the same time: finding the best assignment of n jobs to m machines in order to minimize the makespan and deciding when to carry out the preventive maintenance actions in order to minimize the system unavailability. The maintenance actions numbers as well as the maintenance intervals are not fixed in advance. Two evolutionary genetic algorithms are compared to find an approximation of the Pareto-optimal front in the parallel machine case. On a large number of test instances (more than 5000), the obtained results are promising.

Ant colony optimization for solving an industrial layout problem

This paper presents ACO_GLS, a hybrid ant colony optimization approach coupled with a guided local search, applied to a layout problem. ACO_GLS is applied to an industrial case, in a train maintenance facility of the French railway system (SNCF). Results show that an improvement of near 20% is achieved with respect to the actual layout. Since the problem is modeled as a quadratic assignment problem (QAP), we compared our approach with some of the best heuristics available for this problem. Experimental results show that ACO_GLS performs better for small instances, while its performance is still satisfactory for large instances. 2006 Elsevier B.V. All rights reserved.

Modeling and performance evaluation of supply chains using batch deterministic and stochastic Petri nets

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.

Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows

Today, companies need to collect and to deliver goods from and to their depots and their customers. This problem is described as a Vehicle Routing Problem with Mixed Linehaul and Backhaul customers (VRPMB). The goods delivered from the depot to the customers can be alternated with the goods picked up. Other variants of VRP added to VRPMB are Heterogeneous fleet and Time Windows. This paper studies a complex VRP called HVRPMBTW which concerns a logistic/transport society, a problem rarely studied in literature. In this paper, we propose a Particle Swarm Optimization (PSO) with a local search. This approach has shown its effectiveness on several combinatorial problems. The adaptation of this approach to the problem studied is explained and tested on the benchmarks. The results are compared with our previous methods and they show that in several cases PSO improves the results.

Controlled Mobility Sensor Networks for Target Tracking Using Ant Colony Optimization

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.

Multi-start iterated local search for the periodic vehicle routing problem with time windows and time spread constraints on services

In the field of high-value shipment transportation, companies are faced to the malevolence problem. The risk of ambush increases with the predictability of vehicle routes. This paper addresses a very hard periodic vehicle routing problem with time windows, submitted by a software company specialized in transportation problems with security constraints. The hours of visits to each customer over the planning horizon must be spread in the customers time window. As the aim is to solve real instances, the running time must be reasonable. A mixed integer linear model and a multi-start iterated local search are proposed. Results are reported on instances derived from classical benchmarks for the vehicle routing problem with time windows, and on two practical instances. Experiments are also conducted on a particular case with a single period, the vehicle routing problem with soft time windows: the new metaheuristic competes with two published algorithms and improves six best known solutions.

Multi-objective Supply Chain Optimization: An Industrial Case Study

Supply chain optimization usually involves multiple objectives. In this paper, supply chains are optimized with a multi-objective optimization approach based on genetic algorithm and simulation model. The supply chains are first modeled as batch deterministic and stochastic Petri nets, and a simulation-based optimization method is developed for inventory policies of the supply chains with a multi-objective optimization approach as its search engine. In this method, the performance of a supply chain is evaluated by simulating its Petri net model, and a Non dominated Sorting Genetic Algorithm (NSGA2) is used to guide the optimization search process towards global optima. An application to a real-life supply chain demonstrates that our approach can obtain inventory policies better than ones currently used in practice in terms of two objectives: inventory cost and service level.

Efficient combined immune-decomposition algorithm for optimal buffer allocation in production lines for throughput and profit maximization

Adequate allocation of buffers in transfer lines is crucial to the optimization of line throughput and work in process (WIP) inventory. Their optimal allocation is subject to specific constraints, associated costs, and revenue projections. In this paper, we implement a combined artificial immune system optimization algorithm in conjunction with a decomposition method to optimally allocate buffers in transfer lines. The aim of the buffer allocation problem (BAP) is to achieve optimal system performance under buffers space constraints. Maximizing line throughput does not necessarily achieve maximum profit. In this study the immune decomposition algorithm (IDA) is used to determine optimal buffer allocation for maximum line throughput and maximum line economic profit. Results of extensive series of tests carried out to compare, in production lines with different characteristics, the performances of the proposed method and those of other algorithms are presented.