Fuzzy Scheduling Problem Research Articles

Techniques to Restrict an Interval of a Lower Bound in Fuzzy Scheduling Problems

Techniques to Restrict an Interval of a Lower Bound in Fuzzy Scheduling Problems

Techniques to Restrict an Interval of a Lower Bound in Fuzzy Scheduling Problems

Techniques to Restrict an Interval of a Lower Bound in Fuzzy Scheduling Problems

An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II

Improving the performance of manufacturing systems is a vital issue in today’s rival market. For this purpose, during the last decade, scientists have considered more than one objective function while scheduling a production line. This paper develops a 4-phased fuzzy framework to identify effective factors, determine their weights on multi-objective functions, and, accordingly, schedule manufacturing systems in a fuzzy environment. The aim is to optimize product completion time and operational and product defect costs in a job-shop-based multi-objective fuzzy scheduling problem. In the first and second phases of the proposed framework, it was shown that the existing uncertainty of the internal factors for the studied cases causes the weights of factors to change up to 44.5%. Then, a fuzzy-weighted NSGA-II is proposed (FW-NSGA-II) to address the developed Non-linear Fuzzy Multi-objective Dual resource-constrained scheduling problem. Comparing the outcomes of the proposed method with other solving algorithms, such as the Sine Cosine Algorithm, Simulated Annealing, Tabu Search, and TLBO heuristic, using seven series of comprehensive computational experiments, indicates the superiority of the proposed framework in scheduling manufacturing systems. The outcomes indicated that using the proposed method for the studied cases saved up to 5% in the objective function for small-scale, 11.2% for medium-scale, and 3.8% for large-scale manufacturing systems. The outcomes of this study can help production planning managers to provide more realistic schedules by considering fuzzy factors in their manufacturing systems. Further investigating the proposed method for dynamic product conditions is another direction for future research.

Fuzzy Scheduling Problem of Vessels in One-Way Waterway

Effective use of port waterways is conducive to enhancing port competitiveness. To minimize the waiting time of ships, improve traffic efficiency, and enhance the applicability of the model to the presence of uncertain factors, a fuzzy scheduling optimization method for ships suitable for one-way waterways is proposed based on fuzzy theory. Considering the ambiguity of the speed of ships entering and exiting the port or the time it takes to cross the channel, the previous research on vessel scheduling on one-way waterways has been extended by introducing a triangular fuzzy number and a method for determining the feasible navigable time window of a ship subject to the tide height constraint was proposed. In this study, the genetic algorithm is used to construct the mathematical model for solving fuzzy vessel scheduling problems based on time optimization, and the minimum delay strategy is used to determine the service sequence. Then, the parameters setting are discussed in detail to find the optimal settings. Finally, an experimental comparative analysis of the randomly generated cases was conducted based on the simulated data. The results show that the designed fuzzy vessel scheduling algorithm reduces the dependence on the port environment, is versatile, and can effectively improve the efficiency of ship schedules and traffic safety compared to other methods. Moreover, it can avoid the problem of the illegal solution occurring in the manual scheduling method.

On Pareto Set for a Bi-criterion Scheduling Problem Under Fuzziness

In this paper a bi-criterion fuzzy scheduling problem was presented and the problem under consideration is total fuzzy completion time and maximum earliness, where the processing times and the due dates are triangular fuzzy numbers. Each of jobs is to be processed without interruption on a single machine and becomes available for processing at time zero. A new definition of fuzzy numbers was given namely m-strongly positive fuzzy numbers, through this definition we found an interval which restricts the range of the fuzzy lower bound and presented a relation between the fuzzy lower bound and the fuzzy optimal solution with number of efficient solutions. Also we found the exact solution of the problem through finding the Pareto set.

Fuzzy Scheduling Problem on Unrelated Parallel Machine in JIT Production System

This paper deals with unrelated parallel machines scheduling problem with sequence dependent setup times under fully fuzzy environment to minimize total weighted fuzzy earliness and tardiness penalties, which belongs to NP-hard class. Due to inherent uncertainty in Processing times, setup times and due dates of jobs, they are considered here with triangular and trapezoidal fuzzy numbers in order to take into account the unpredictability of parameters in practical settings. Although this study is not the first one to study on fuzzy parallel machines scheduling problem, it advances this area of research in three fields: (1) it selects a fuzzy environment to cover the whole area of the considered problem not just part of it, and also, it chooses an appropriate fuzzy method based on an in-depth investigation of the effect of spread of fuzziness on the variables; (2) It introduces a mathematical programming model for the addressed problem as an exact method; and (3) due to NP-hardness of the problem, it develops an existing algorithm in the literature for the considered problem through extensive simulated experiments and statistical tests on the same benchmark problem test by proposing a genetic algorithm (GA) and a modified simulated annealing (SA) methods to solve this hard combinatorial optimization problem. The result shows the superiority of our modified SA.

A Multi-Objective Memetic Algorithm for a Fuzzy Parallel Blocking Flow Shop Scheduling Problem of Panel Block Assembly in Shipbuilding

Scheduling plays an important role in improving the efficiency of panel block assembly. Because of the characteristics of the assembly lines and the imprecise and vague temporal parameters in real-world production, the scheduling of panel block assembly on parallel lines is formulated as a fuzzy parallel blocking flow shop scheduling problem with fuzzy processing time and fuzzy due date to minimize the fuzzy makespan and maximize the average agreement index. To solve this combinational optimization problem, a multiobjective memetic algorithm (MOMA) is proposed. In the MOMA, two novel heuristics are designed to generate several promising initial solutions, and a local search method is embedded to improve the exploitation capability. The performance of the MOMA is tested on the production instances of panel block assembly in shipbuilding. Computational comparisons of the MOMA with two other well-known multi-objective evolutionary algorithms demonstrate its feasibility and effectiveness in generating optimal solutions to the bi-criterion fuzzy scheduling problem of panel block assembly. 1. Introduction The development of large ships leads to an increased demand for panel blocks. Most large shipyards have established panel block assembly lines. The scheduling of panel block assembly belongs to a class of blocking flow shop scheduling problem (BFSP) because there is no buffer between consecutive stations (machines) in an assembly line. The BFSP has received increasing attention in the literature, and most studies consider minimizing the makespan. For more than a decade, various heuristics have been proposed for the scheduling problem with a makespan criterion, including the Nawaz-Enscore-Ham (NEH) heuristic (Nawaz et al. 1983), Min-Max enumeration heuristic (Ronconi 2004), and NEH-Wang-Pan-Tasgetiren (NEH-WPT) heuristic (Wang et al. 2011). Moreover, a growing number of meta-heuristics have also been developed for this type of problem, such as the genetic algorithm (GA) (Caraffa et al. 2001), tabu search approaches (TS and TS+M) (Grabowski & Pempera 2007), iterated greedy algorithm (Ribas et al. 2011), discrete particle swarm optimization algorithm (Wang & Tang 2012), memetic algorithm (MA) (Pan et al. 2013), and modified fruit fly optimization algorithm (Han et al. 2016).

Optimal fuzzy scheduling and sequencing of multiple-period operating room

AbstractLittle research has considered fuzzy scheduling and sequencing problem in operating rooms. Multiple-period fuzzy scheduling and sequencing of patients in operating rooms optimization models are proposed in this research taking into consideration patient‘s preference. The objective of the scheduling optimization model is obtaining minimal undertime and overtime and maximum patients' satisfaction about the assigned date. The objective of sequencing the optimization model is both to minimize overtime and to maximize patients' satisfaction about the assigned time. A real-life case study from a hospital that offers comprehensive surgical procedures for all surgical specialties is considered for illustration. Research results showed that the proposed models efficiently scheduled and sequenced patients while considering their preferences and hospitals operating costs. In conclusion, the proposed optimization models may result in improving patient satisfaction, utilizing hospital's resources efficiently, and providing assistance to decision makers and planners in solving effectively fuzzy scheduling and sequencing problems of operating rooms.

English

English

Evaluation of optimality in the fuzzy single machine scheduling problem including discounted costs

The single machine scheduling problem has been often regarded as a simplified representation that contains many polynomial solvable cases. However, in real-world applications, the imprecision of data at the level of each job can be critical for the implementation of scheduling strategies. Therefore, the single machine scheduling problem with the weighted discounted sum of completion times is treated in this paper, where we assume that the processing times, weighting coefficients and discount factor are all described using trapezoidal fuzzy numbers. Our aim in this study is to elaborate adequate measures in the context of possibility theory for the assessment of the optimality of a fixed schedule. Two optimization approaches namely genetic algorithm and pattern search are proposed as computational tools for the validation of the obtained properties and results. The proposed approaches are experimented on the benchmark problem instances and a sensitivity analysis with respect to some configuration parameters is conducted. Modeling and resolution frameworks considered in this research offer promise to deal with optimality in the wide class of fuzzy scheduling problems, which is recognized to be a difficult task by both researchers and practitioners.

Fuzzy distances and their applications on fuzzy scheduling

The aim of this paper is to develop a fuzzy scheduling problem for solving a multi-objective functions on single machine scheduling problems when processing time and due date are a triangular fuzzy numbers. We are used fuzzy distance function concepts which introduced by Lam and Cai. The objective is to minimize the maximum fuzzy lateness and maximum fuzzy completion time. In this paper we compare and test different local search methods (Threshold accepted (TA), Tabu search (TS), and Memetic algorithm (MA)) computational experience 1000 jobs with reasonable time. For comprise results we use probability and cost considerations in project scheduling

A New Algorithm for a Fuzzy Vehicle Routing and Scheduling Problem: Imperialist Competitive Algorithm

A New Algorithm for a Fuzzy Vehicle Routing and Scheduling Problem: Imperialist Competitive Algorithm

Fuzzy job shop scheduling problem with availability constraints

This paper presents the fuzzy job shop scheduling problem with availability constraints. The objective is to find a schedule that maximizes the minimum agreement index subject to periodic maintenance, non-resumable jobs and fuzzy due-date. A random key genetic algorithm (RKGA) is proposed for the problem, in which a novel random key representation, a new decoding strategy incorporating maintenance operation and discrete crossover ( DX) are used. RKGA is applied to some fuzzy scheduling problem with availability constraints and compared with other algorithms. Computational results show that RKGA performs better than other algorithms.

Research on Fuzzy Scheduling Problem of Single-Stage Multi-Parallel Machines

This paper considers single-stage multi-parallel machines scheduling problems. A fuzzy scheduling model is developed with objective to minimize the makespan. In the scheduling model, the uncertainty of processing time is represented by triangular fuzzy number, and the credibility theory is used to assess fuzzy number. In the optimal scheduling searching process, we introduce matching theory to model the scheduling problem consider in this paper, and two heuristic rules are used to improve searching efficiency. At last, genetic algorithm is developed for solving the scheduling problem, and numerical example shows good result.

Structured intelligence for self-organizing manufacturing systems

This paper deals with fuzzy scheduling and path planning problems by genetic algorithms. We have proposed a self-organizing manufacturing system (SOMS) that is composed of a number of autonomous modules. Each module decides output through interaction with other modules, but the module does not share complete information concerning other modules in the SOMS. Therefore, we require structured intelligence as a whole system. In this paper, we consider a manufacturing line composed of machining centres and conveyor units. The manufacturing procedure can be divided into a sequence of three modules: (a) tool locating module, (b) scheduling module, and (c) path planning module. The tool locating problems have been already solved. In this paper, we first solve the scheduling problem as global preplanning. Here we assume that the processing time is not constant, because some delay may occur in the machining centres. We therefore apply fuzzy theory to represent incomplete information abou t the machining time. We solve the fuzzy scheduling problem with a genetic algorithm. After global preplanning, the path planning module transports materials and products. Next, the scheduling module acquires the actual processing time of each machining centre. Based on the processing time, the schedule module generates a fuzzy number for the processing time. We discuss the effectiveness of the proposed method through the computer simulation results.

重要度を考慮した多目的ファジィスケジューリング問題の定式化

本研究では, まず, OWAオペレータを用いた多目的ファジィスケジューリング問題の拡張を提案する.総満足度最大化問題と最小満足度最大化問題が既に提案されているが, これら二つの問題の間に位置する多目的ファジィスケジューリング問題をOWAオペレータを用いて定式化する.我々の多目的スケジューリング問題の特徴は, 各ジョブがそれぞれの目的にしたがってスケジューリングされることである.各ジョブの目的は, 対応するジョブの加工終了時間に対する意思決定者の満足度を表すメンバーシップ関数により定義される.次に, 各ジョブの重要度を考慮したスケジューリング問題の定式化を行う.従来の定式化では, スケジューリングの対象となる全てのジョブは, 同じ重要度で取り扱われていた.しかし, 現実には, ジョブによって重要度が異なることが生じる.すなわち, あるジョブは他のジョブより重要であるような状況が考えられる.そこで, ジョブの重要度を考慮するため, 目標満足度を導入した定式化と重要度ごとに異なる報酬を導入した定式化を提案する.最後に, 数値実験により, 各ジョブの目的を考慮した妥当なスケジュールが得られることを示す.

TWO-MACHINES SCHEDULING PROBLEM WITH FUZZY ALLOWABLE TIME CONSTRAINT

We have already introduced many fuzzy concepts to scheduling problem and discussed so called fuzzy scheduling problems. In this paper, we introduce fuzzy allowable time concept newly to two identical machine problem, which is a fuzzy version of M. R. Garey and D. S. Johnson. That is, there are two identical machines and fuzzy ready times and deadlines are associated to each job, i.e., membership function representing a satisfaction degree of start times and completion times are considered to each job and minimal one of them is to be maximized. Further among jobs, there exists fuzzy precedence relation, which is a fuzzy relation with membership function representing satisfaction degree of precedence order of each job pair and again minimal one of them is to be maximized. The aim is to maximize both minimal degrees at a time if possible, but usually there exists no schedule maximizing both of them, and so we seek nondominated schedules.

Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems

Abstract This paper examines two fuzzy flowshop scheduling problems with fuzzy due dates. The membership function of a fuzzy due date assigned to each job represents the grade of satisfaction of a decision maker for the completion time of that job. One fuzzy flowshop scheduling problem is to maximize the minimum grade of satisfaction over given jobs, and the other is to maximize the total grade of satisfaction. First, we investigate the relations between the fuzzy scheduling problems and conventional scheduling problems. We next apply a genetic algorithm and neighborhood search algorithms (multi-start descent, taboo search and simulated annealing) to the fuzzy flowshop scheduling problems in order to examine the ability of these algorithms to find near optimal solutions. By computer simulations, it is shown that the maximization problem of the total satisfaction grade is even more tractable by these algorithms than that of the minimum satisfaction grade. Then, we show how the performance of these algorithms can be improved for the latter problem by utilizing problem domain knowledge. Finally, high performance of a hybrid genetic algorithm with neighborhood search is demonstrated.

Fuzzy project scheduling system for software development

This paper presents an FPS (Fuzzy Project Scheduling) decision support system applied to software project scheduling. The purpose of the FPS system is to allocate resources (software engineers) among dependent activities (system design, user interface design, modules implementation, modules integration and tests), taking into account one of two criteria: project completion time and maximum lateness, under uncertain time parameters of activities. By time parameters we understand durations, ready times and due dates of particular activities. Uncertainty of these parameters is modelled by means of L-R fuzzy numbers. A general procedure for transforming the fuzzy scheduling problem into a set of associate deterministic problems is based on the use of α-cuts. Optimistic and pessimistic schedules are heuristically generated for given α-levels. Aggregation of optimistic and pessimitic values of a minimized criterion for all α-levels gives a fuzzy result. Comparison of fuzzy result is based on a compensation of areas determined by the membership functions. The FPS system is described and its application to software project management is presented on a real example.

A DSS for Ressource—Constrained Project Scheduling under Uncertainty

ABSTRACT This paper presents a decision support system, called FPS (Fuzzy Project Scheduling), for solving a problem of an allocation of renewable resources among nonpreemtable and dependent activities, talking into account three criteria: project completion time, maximum lateness and mean flow time, under uncertain time parameters. By time parameters we understand: durations, ready times and due dates of particular activities. Uncertainty of these parameters is modelled by means of L—R fuzzy numbers. A general procedure for transforming the fuzzy scheduling problem into a set of associate are generated for given α-cuts. Optimistic and pessimistic schedules are generated for given α-levels. The comparison of obtained fuzzy result is based on the compensation of areas determined by membership function. The FPS system is described and illustrated using a real, agricultural example.