Nikolay Tchernev

An MILP for scheduling problems in an FMS with one vehicle

This paper concerns the mathematical formulation and optimal solutions for the Flexible Manufacturing Systems Scheduling Problem (FMSSP) with one vehicle. This linear formulation differs from the previously published ones as it takes into account the maximum number of jobs allowed in the system, limited input/output buffer capacities, empty vehicle trips and no-move-ahead trips simultaneously. Our objective is to propose optimal solutions for small and medium-sized instances and to examine a number of commonly used assumptions and heuristics. Computational experiments are carried out on instances adapted from Bilge and Ulusoy [Bilge, U., Ulusoy, G., 1995. A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Operations Research 43, 1058-1070] and the following heuristics are evaluated: FIFO (First In First Out) rules for input/output buffer management; and FIFO, SPT (Shortest Processing Time), STT (Shortest Travel Time) and MOQS (Maximum Outgoing Queue Size) rules concerning the vehicle. The consequences of classical assumptions are also studied: ignoring empty trips, ignoring no-move-ahead constraints, and ignoring vehicle-disjunction constraints. The numerical experiments provide a set of optimal solutions and allow to evaluate the performances of heuristic search schemes.

A simple metaheuristic approach to the simultaneous scheduling of machines and automated guided vehicles

In this paper we address the problem of simultaneous scheduling of machines and vehicles in flexible manufacturing systems. The studied problem is a job shop where the jobs have to be transported between the machines by automatic guided vehicles. In addition to the processing of jobs, we consider the transportation aspect as an integral part of the optimization process. To deal with this problem, we propose a new solution representation based on vehicles rather than machines. Each solution can thus be evaluated using a discrete event approach. An efficient neighbouring system is then described and implemented into three different metaheuristics: iterated local search, simulated annealing and their hybridisation. Computational results are presented for a benchmark of 40 literature instances. New upper bounds are found for 11 of them, showing the effectiveness of the presented approach.

Simultaneous job input sequencing and vehicle dispatching in a single-vehicle automated guided vehicle system: a heuristic branch-and-bound approach coupled with a discrete events simulation model

This paper addresses the scheduling problem in automated manufacturing environments, whose problem encompasses all the decisions related to the allocation of resources over the time horizon in order to best satisfy a set of objectives. It concentrates in particular on the job-input sequencing and vehicle-dispatching problems in a manufacturing environment using a single-vehicle automated guided vehicle system. The problem is solved using a branch-and-bound coupled with a discrete event simulation model. The branch-and-bound focuses on the job-input sequencing problem to determine the order in which the jobs enter the manufacturing system. The discrete event simulation model evaluates this job sequence under given vehicle and machine dispatching rules. The discrete event simulation model permits one to take into account all the working constraints: the maximal number of jobs simultaneously allowed in the system, the input/output buffers with finite capacities, the dynamic behaviour of the system under study and, thus, the impact of vehicle blocking and congestion as well as the impact of the machine blocking. A benchmark test is performed to investigate the system performances and the makespan depending on the job input sequencing, the vehicle and machine dispatching. The framework is benchmarked on 20 instances under different vehicle dispatching rules.

Model for flexibility evaluation in manufacturing network strategic planning

In response to market pressures, manufacturers have adopted different approaches to provide flexibility regarding several aspects. In this paper, we suggest a model for the evaluation of the flexibility of the manufacturing supply chain, based on graph theory techniques. This model defines maximum excess demand that may be met using flexibility. Recourse to flexibility enablers is determined based on cost minimisation. Such enablers are volume flexibility, mix flexibility and safety stocks. The proposed model is solved using a two-step Mix Integer Linear Programme; the first step consists in defining maximum demand that may be met while the second step concerns minimising cost. The main benefit of our model is to deal with realistic problems in a rather short time. Therefore, it can be used in a wide ‘what-if’ design process. It means evaluating various contemplated flexibility configurations in multiple demand scenarios in order to choose the best option. It can be also used during operational supply chain planning in order to face to an unbalanced situation. This paper ends with a numerical example illustrating our model’s efficiency.

A GRASP×ELS approach for the job-shop with a web service paradigm packaging

The Job-Shop Scheduling Problem (JSSP) is well known for its complexity as an NP-hard disjunctive scheduling problem. The problem addressed in this paper is JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. An efficient GRASPxELS approach is introduced for solving this problem. The efficiency is evaluated using the widely known 40 Laurences instances which encompass medium and large scale instances. The computational results prove that the proposed method competes with the best published methods in both quality of results and computational time. Recently, Web services have generated great interest in researchers. Such application architecture is based on the client-server model using existing Internet protocols and open standards. It provides new approaches to optimization methods. The proposed GRASPxELS is packaged into a Web Service (WS), i.e., it offers for the research community an open access to our optimization approach. Moreover, the proposed web service can be even included in research future works with a very small programming effort. To favor utilization of the web service and to prove the facility in which the service could be used, we provide an example in Java proving that it is possible to obtain in less than 10min a client application using the different methods exposed by this web service. Such usage extends to classical library inclusion in program with the difference that a method is called in the client side and represents an execution on the server. The Web Service paradigm is a new approach in spreading algorithms and therefore this paper stands at the crossroads of optimization research community and the web service community expectations. The GRASPxELS provided in the web service, is a state of the art method which competes with previously published ones and which has the advantage of being available for free, in any languages, everywhere contributing in spreading operational research contribution.

Un environnement de modélisation pour le système d'information de la Supply Chain : Application au Nouvel Hôpital Estaing

This paper objective is to propose an approach and a modelling environment for the design of Supply Chain Information System and decisional tools. This software environment is an integrated set of tools and methods organized in order to model and evaluate Supply Chain performance. The interactions between Supply Chain entities and the problems complexity for Supply Chain Manager and Data Processing Specialist show that it is necessary to conceive and implement a modelling environment for Supply Chain. The method is applied on a real case study, the New Hospital Estaing.

Combining Optimization Methods and Discrete Event Simulation: A Case Study in Flexible Manufacturing Systems

The studied problem is an extension of the well known job shop scheduling problem, in which the material handling system is considered as an overall part of the scheduling. This problem has many applications in flexible manufacturing systems. In these systems, jobs are transported from any machine to any other machine by automated guided vehicles. This problem has both an algorithmic and a structural complexity. We propose to take into account this double complexity by combining metaheuristic (for the optimization process), and discrete event simulation (for the evaluation of a solution). The results obtained show the efficiency of the presented approach, 11 new upper bounds are found on a benchmark of 40 instances of the literature. Realistic constraints like traffic congestion or collision could be included by a more sophisticated evaluation model

An effective multi-start multi-level evolutionary local search for the flexible job-shop problem☆

Abstract In this paper, an improved greedy randomized adaptive search procedure (GRASP) with a multi-level evolutionary local search (mELS) paradigm is proposed to solve the Flexible Job-shop Problem (FJSP). The FJSP is a generalisation of the well-known Job-Shop Problem with the specificity of allowing an operation to be processed by any machine from a given set. The GRASP metaheuristic is used for diversification and the mELS is used for intensification. Four different neighbourhood structures are formalised. A procedure for fast estimation of the neighbourhood quality is also proposed to accelerate local search phases. The metaheuristic has been tested on several datasets from the literature. The experimental results demonstrate that the proposed GRASP-mELS has achieved significant improvements for solving FJSP from the viewpoint of both quality of solutions and computation time. A comparison among the proposed GRASP-mELS and other state-of-the-art algorithms is also provided in order to show the effectiveness and efficiency of the proposed metaheuristic.

A GRASPxELS for Scheduling of Job-Shop Like Manufacturing Systems and CO2 Emission Reduction

The issue of reducing CO2 emission and associated carbon footprint consumption for manufacturing scheduling is addressed. We focus our attention on a job-shop environment where machines can work at different speeds and therefore different energies consumed, i.e. CO2 emissions. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds, problem which has been introduced by [1]. Energy-efficient scheduling of such type of manufacturing systems demands an optimization approach whose dual objectives are to minimize both the CO2 emissions and the makespan. To solve this new problem, a GRASPxELS is developed. New instances benchmark based on well know Laurence’s instances are introduced and numerical experiments are proposed trying to evaluate the method convergence. The performance is evaluated using the optimal solutions found after a strongly time consuming resolution based on a linear formulation of the problem.

Heuristic-based approach for an industrial three-dimensional layout problem

In this paper, we consider a particular three-dimensional layout problem in a steel sheet manufacturing workshop. The products are laid in a machine according to packs and stacks. Constraints determine if a product or a pack can be laid above another product or another pack. For this NP-complete problem, we propose two kinds of method: heuristic method (based on enumeration) and combination between heuristic and metaheuristic. The proposed methods are validated and compared on real instances. The results are of good quality.