An improved tabu search approach for solving the job shop scheduling problem with tooling constraints

AbstractFlexible manufacturing systems (FMSs) are nowadays installed in the mechanical industry. In such systems, many different part types are produced simultaneously and it is necessary to take tooling constraints into account for finding an optimal schedule.A heuristic method is presented for solving the m-machine, n-job shop scheduling problem with tooling constraints. This method, named TOMATO, is based on an adaptation of tabu search techniques and is an improvement on the JEST algorithm proposed by Widmer in 1991

A SURVEY ON MACHINE SCHEDULING TECHNIQUES

ABSTRACT In this paper the study about the different methodologies and techniques implemented for different types of scheduling problems in single machine, job shop and flow shop scheduling. Every author tells about the different scenario and approach to minimize the Make span, Tardiness and different parameters in scheduling. Every author implements their own algorithms and the strategies to find out the result, it may be positive or negative. This paper gives the clear idea for the future research work

A new innovative cooling law for simulated annealing algorithms

The present paper proposes an original and innovative cooling law in the field of Simulated Annealing (SA) algorithms. Particularly, such a law is based on the evolution of different initial seeds on which the algorithm works in parallel. The efficiency control of the new proposal, executed on problems of different kind, shows that the convergence quickness by using such a new cooling law is considerably greater than that obtained by traditional laws. Furthermore, it is shown that the effectiveness of the SA algorithm arising from the proposed cooling law is independent of the problem type. This last feature reduces the number of parameters to be initially fixed, so simplifying the preliminary calibration process necessary to optimize the algorithm efficiency

Implementación de un híbrido entre Grasp y Tabú search para la solución del problema de programación de la producción en un ambiente Job Shop para la minimización de la tardanza total ponderada

El presente trabajo resuelve un Job shop para la minimización de la tardanza total ponderada ya que ésta es una medida de desempeño que tiene en cuenta no solo el nivel de cumplimiento de los clientes sino la importancia de los mismos. Como método de solución se propone un algoritmo híbrido entre la metodología GRASP la cual no ha sido muy estudiada para la solución de éste problema (y es de gran ayuda para la construcción inicial de una solución), y la búsqueda tabú (con la cual se han obtenido muy buenos resultados para Job Shop) para la fase de búsqueda local del algoritmo. Los resultados obtenidos se comparan con el algoritmo de búsqueda local genética propuesto por (Essafi, Mati, - Dauzère-Pérès, 2008). Éste documento presenta inicialmente el planteamiento de problema y la justificación del mismo, seguido por una explicación del problema, la meta heurística realizada, y los antecedentes relacionados con investigación del problema y métodos de solución propuestos para éste. Posteriormente se plantean los objetivos y alcance del documento, junto con el desarrollo, análisis de resultados del mismo, y finalmente algunas recomendaciones para futuros trabajos.This paper addresses a job shop problem minimizing the total weighted tardiness as this is a performance measure that takes into account, not only the level of compliance with the customers but the importance of them. The paper proposes a hybrid solution method algorithm between the GRASP methodology which has not been studied for the solution of this problem (and it is helpful for the initial construction of a solution), and Tabu search (which have obtained very good results for Job Shop) for the local search phase of the algorithm. The results obtained are compared with the local search algorithm proposed by genetic (Essafi Mati, - DauzèrePeres, 2008). This paper first presents the problem approach and its justification, followed by an explanation of the problem, metaheuristics used, relating literature to research the problem and proposed methods of solution for this. Then the objectives and scope of the document, along with the development, analysis of results, and finally some recommendations for future work are suggested.Ingeniero (a) IndustrialPregrad

Solutions to decision-making problems in management engineering using molecular computational algorithms and experimentations

制度:新 ; 報告番号:甲3368号 ; 学位の種類:博士(工学) ; 授与年月日:2011/5/23 ; 早大学位記番号:新568

A Unified Framework for Solving Multiagent Task Assignment Problems

Multiagent task assignment problem descriptors do not fully represent the complex interactions in a multiagent domain, and algorithmic solutions vary widely depending on how the domain is represented. This issue is compounded as related research fields contain descriptors that similarly describe multiagent task assignment problems, including complex domain interactions, but generally do not provide the mechanisms needed to solve the multiagent aspect of task assignment. This research presents a unified approach to representing and solving the multiagent task assignment problem for complex problem domains. Ideas central to multiagent task allocation, project scheduling, constraint satisfaction, and coalition formation are combined to form the basis of the constrained multiagent task scheduling (CMTS) problem. Basic analysis reveals the exponential size of the solution space for a CMTS problem, approximated by O(2n(m+n)) based on the number of agents and tasks involved in a problem. The shape of the solution space is shown to contain numerous discontinuous regions due to the complexities involved in relational constraints defined between agents and tasks. The CMTS descriptor represents a wide range of classical and modern problems, such as job shop scheduling, the traveling salesman problem, vehicle routing, and cooperative multi-object tracking. Problems using the CMTS representation are solvable by a suite of algorithms, with varying degrees of suitability. Solution generating methods range from simple random scheduling to state-of-the-art biologically inspired approaches. Techniques from classical task assignment solvers are extended to handle multiagent task problems where agents can also multitask. Additional ideas are incorporated from constraint satisfaction, project scheduling, evolutionary algorithms, dynamic coalition formation, auctioning, and behavior-based robotics to highlight how different solution generation strategies apply to the complex problem space

The dynamic, resource-constrained shortest path problem on an acyclic graph with application in column generation and literature review on sequence-dependent scheduling

This dissertation discusses two independent topics: a resource-constrained shortest-path problem (RCSP) and a literature review on scheduling problems involving sequence-dependent setup (SDS) times (costs). RCSP is often used as a subproblem in column generation because it can be used to solve many practical problems. This dissertation studies RCSP with multiple resource constraints on an acyclic graph, because many applications involve this configuration, especially in column genetation formulations. In particular, this research focuses on a dynamic RCSP since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-time âÂÂpreliminaryâ phase to transform RCSP into an unconstrained shortest path problem (SPP) and then solves the resulting SPP after new values of dual variables are used to update objective function coefficients (i.e., reduced costs) at each iteration. Network reduction techniques are considered to remove some nodes and/or arcs permanently in the preliminary phase. Specified techniques are explored to reoptimize when only several coefficients change and for dealing with forbidden and prescribed arcs in the context of a column generation/branch-and-bound approach. As a benchmark method, a label-setting algorithm is also proposed. Computational tests are designed to show the effectiveness of the proposed algorithms and procedures. This dissertation also gives a literature review related to the class of scheduling problems that involve SDS times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations - single machine, parallel machine, flow shop, and job shop - reviewing both optimizing and heuristic solution methods in each category. Since lot-sizing is so intimately related to scheduling, this dissertation reviews work that integrates these issues in relationship to each configuration. This dissertation provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems. since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-tim