Solving Flow Shop Scheduling Problem Research Articles

Mixed integer linear programming models for Flow Shop Scheduling with a demand plan of job types

This paper presents two mixed integer linear programming (MILP) models that extend two basic Flow Shop Scheduling problems: $$ {\text{Fm}} $$/$$ {\text{prmu}} $$/$$ {\text{C}}_{ \hbox{max} } $$ and $$ {\text{Fm}} $$/$$ {\text{block}} $$/$$ {\text{C}}_{ \hbox{max} } $$. This extension incorporates the concept of an overall demand plan for types of jobs or products. After using an example to illustrate the new problems under study, we evaluated the new models and analyzed their behaviors when applied to instances found in the literature and industrial instances of a case study from Nissan’s plant in Barcelona. CPLEX solver was used as a solution tool and obtained acceptable results, allowing us to conclude that MILP can be used as a method for solving Flow Shop Scheduling problems with an overall demand plan.

An effective new hybrid optimization algorithm for solving flow shop scheduling problems

An effective new hybrid optimization algorithm for solving flow shop scheduling problems

A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems

Cuckoo search (CS) is a recently developed meta-heuristic algorithm, which has shown good performance on many continuous optimization problems. In this paper, we present a new CS algorithm, called NCS, for solving flow shop scheduling problems (FSSP). The NCS hybridizes four strategies: (1) The FSSP is a typical NP-hard problem with discrete characteristics. To deal with the discrete variables, the smallest position value (SPV) rule is employed to convert continuous solutions into discrete job permutations; (2) To generate high quality initial solutions, a new method based on the Nawaz-Enscore-Ham (NEH) heuristic is used for population initialization; (3) A modified generalized opposition-based learning (GOBL) is utilized to accelerate the convergence speed; and (4) To enhance the exploitation, a local search strategy is proposed. Experimental study is conducted on a set of Taillard's benchmark instances. Results show that NCS obtains better performance than the standard CS and some other meta-heuristic algorithms.

A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time

In this paper, we consider m-machine flow shop scheduling problems (FSSPs). The objective is to schedule the available n jobs to minimise the total flow time. The flow shop scheduling problems have been proved to be strongly non-deterministic polynomial-time hard (NP-hard). Hence, exact methods cannot be used to solve even small size problems. Researchers addressed many heuristics and meta-heuristics to solve the problems. Firefly algorithm (FA) is one of the recently developed meta-heuristic algorithms. We propose a hybrid discrete firefly algorithm (HDFA) to solve the FSSPs to minimise the total flow time. To validate the performances of the HDFA, computational experiments are conducted on a number of randomly generated test problems with different parameters and the results proved the effectiveness of the proposed algorithm.

A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time

In this paper, we consider m-machine flow shop scheduling problems (FSSPs). The objective is to schedule the available n jobs to minimise the total flow time. The flow shop scheduling problems have been proved to be strongly non-deterministic polynomial-time hard (NP-hard). Hence, exact methods cannot be used to solve even small size problems. Researchers addressed many heuristics and meta-heuristics to solve the problems. Firefly algorithm (FA) is one of the recently developed meta-heuristic algorithms. We propose a hybrid discrete firefly algorithm (HDFA) to solve the FSSPs to minimise the total flow time. To validate the performances of the HDFA, computational experiments are conducted on a number of randomly generated test problems with different parameters and the results proved the effectiveness of the proposed algorithm.

Heuristics for solving flow shop scheduling problem under resources constraints

Most of traditional scheduling problems deal with machine as the only resource, however, other resources such as raw materials is often disregards. Considering the second resource makes scheduling problems more realistic and practical to implement in manufacturing industries. Due to the applicability of flow shop environment in different manufacturing, scheduling of these types of shops are extensively studied by several authors. However, introducing an additional resource in this environment is not well studied. The present work deals with makespan minimization in flow shop scheduling problems where no renewable resources constraints are considered. The paper illustrates the importance of Johnson (1954) algorithm for the two machine flow shop under resources constraints. A mathematical model is also presented. Then a well-know heuristic is adapted to propose fast solution for the m machines flow shop problem subject to resource constraint.

Basic Concept of Implementing Artificial Bee Colony (ABC) System in Flow Shop Scheduling

Flow shop scheduling is a common operational problem in a production system. Effective flow shop scheduling can help the company to improve the management system, hence increase income. Artificial Bee Colony (ABC) is a system that is widely used for scheduling optimization in a production system since 2005. However, the fundamental ABC system uses a heuristic approach to obtain an optimum solution which may not be the optimum solution at all. The ABC system is tested on the speed to obtain the optimum solution for a flowshop scheduling problem and measures the applicability of the schedule in terms of makespan. A simple model of ABC algorithm was developed to identify the effectiveness of the ABC for solving flow shop scheduling problem compared to other established methods. Result shows the ABC model is capable of producing best makespan in flow shop problem tested.

Optimization of Flow Shop Scheduling Problem Using Differential Evolution and Variable Neighborhood Search

This paper proposed a new differential evolution algorithm based on variable neighbourhood search which is called as VNSDE. In VNSDE, the operation of variable neighbourhood search is performed after three basic operations of differential evolution, which can enhance the global search and improve the convergence. VNSDE is applied for solving flow shop scheduling problem with the makespan criterion. Computational experiment is performed over a typical FSSP benchmark using VNSDE, GA and DE, and result shows that VNSDE has higher performance than GA and DE.

Main aspects concerning PCB manufacturing optimization

PurposeThe purpose of this paper is to outline the main features concerning the optimization of printed circuit board (PCB) fabrication by improving the manufacturing process productivity.Design/methodology/approachThe author explored two different approaches to increase the manufacturing process productivity of PCBs. The first approach involved optimization of the PCB manufacturing process as a whole. The second approach was based on increasing the process productivity at the operational level.FindingsTo reduce the total manufacturing time, two heuristic algorithms for solving flowshop scheduling problems were designed. These algorithms were used for the computation of an optimal PCB manufacturing schedule. The case study shows both mono‐ and bi‐criteria optimization of the PCBs manufacturing.Research limitations/implicationsWhile the input data used in the case study were based on random numbers, the mathematical considerations drew only the main directions for manufacturing process optimization.Originality/valueThe paper shows two original heuristic algorithms for solving the flowshop scheduling problem, with high performance according to the best heuristics in the field. Besides their performances, these algorithms have the advantage of simplicity and ease of implementation on a computer. Using these algorithms, the optimal schedule for the PCB manufacturing process was calculated. For the case of the bi‐criteria optimization, the study of points which belong to the Pareto‐optimal set are presented.

Solving flow shop scheduling problem using a parallel genetic algorithm

The effort of searching an optimal solution for scheduling problems is important for real-world industrial applications especially for mission-time critical systems. In this paper, a parallel GA is employed to solve flow shop scheduling problems to minimize the makespan.According to our experimental results, the proposed parallel genetic algorithm (PPGA) considerably decreases the CPU time without adversely affecting the makespan.

HYBRID FUZZY LOGIC-BASED PARTICLE SWARM OPTIMIZATION FOR FLOW SHOP SCHEDULING PROBLEM

This paper, proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly.

Solving flow shop scheduling problems by quantum differential evolutionary algorithm

This paper proposed a novel quantum differential evolutionary algorithm (QDEA) based on the basic quantum-inspired evolutionary algorithm (QEA) for permutation flow shop scheduling problem (PFSP). In this QDEA, the quantum chromosomes are encoded and decoded by using the quantum rotating angle and a simple strategy named largest rotating angle value rule to determine job sequence based on job’s quantum information is proposed for the representation of PFSP, firstly. Then, we merge the advantages of differential evolution strategy, variable neighborhood search and QEA by adopting the differential evolution to perform the updating of quantum gate and variable neighborhood search to raise the performance of the local search. We adopted QDEA to minimize the makespan, total flowtime and the maximum lateness of jobs and make the simulations. The results and comparisons with other algorithms based on famous benchmarks demonstrated the effectiveness of the proposed QDEA. Another contribution of this paper is to report new absolute values of total flowtime and maximum lateness for various benchmark problem sets.

A Proposed Genetic Algorithm Coding for Flow-Shop Scheduling Problems

A new genetic algorithm coding is proposed in this paper to solve flowshop scheduling problems. To show the efficiency of the considered approach, two examples, in pharmaceutical and agro-food industries are considered with minimization of different costs related to each problem as a scope. Multi-objective optimization is thus, used and its performances proved.

A new multiobjective genetic algorithm with heterogeneous population for solving flowshop scheduling problems

The present paper discusses the application of a new genetic algorithm (GA) featuring heterogeneous population to solve multiobjective flowshop scheduling problems. Many GAs have been developed to solve multiobjective scheduling problems, but they used a non-heterogeneous population approach, which could lead to premature convergence and local Pareto-optimum solutions. Our experiments with a 20-job and 20-machine benchmark problem given in Taillard (1993) show that the heterogeneous multiobjective genetic algorithm (hMGA) developed in this research outperforms NSGA-II (Deb 2001) one of the widely used algorithms with non-heterogeneous population. Moreover, in this paper we also present the comparison of hMGA with another meta-heuristic method, i.e. multi-objective simulated annealing (MOSA), proposed by Varadharajan and Rajendran (2005). This research concludes that hMGA developed in this work is promising as it can produce a new set of Pareto-optimum solutions that have not been found by MOSA before.

A Beam Search Based Algorithm to Solve Flowshop Scheduling Problems with Constraints on Shared Resources

In this paper it is presented a beam search based algorithm to solve tlowshop scheduling problems in Chemical Process Industries (CPI) under Unlimited Intermediate Storage (VIS) policy. Flowshops are typical in chemical process industries where the order in which operations are executed is the same for all products because these products belong to a common family and because links between units cannot be changed easily. Other important characteristic of CPIs is the intensive use of shared consumption resources, e.g. steam, water and electricity, representing capacity constraints on the problem formulation. These characteristics make the CPI tlowshop problem more difficulty to solve than traditional flowshops. The main algorithm implemented is based on the A* algorithm combined with the beam search strategy.

An effective method for solving flow shop scheduling problems with fuzzy processing times

This paper show that fuzzy set theory can be useful in modelling and solving flow shop scheduling problems with uncertain processing times and illustrates a methodology for solving job sequencing problem which the opinions of experts greatly disagree in each processing time. Triangular fuzzy numbers (TFNs) are used to represent the processing times of experts. And the comparison methods based on the dominance property is sued to determine the ranking of the fuzzy numbers. By the dominance criteria, for each job, a major TFN and a minor TFN are selected and a pessimistic sequence with major TFNs and an optimistic sequence with minor TFNs are computer. Branch and bound algorithm for makespan in three-machine flow shop is utilized to illustrate the proposed methodology.

Flow Shop Scheduling with Controllable Operation Processing Times

This paper presents a fast branch and bound algorithm for solving flow shop scheduling problem when operation processing times are themselves decision variables. Models of operations are assumed to be duration versus resource convex functions. The problem under consideration is a generalization of the classical flow shop problem. Due to the presented elimination properties, the search tree of the branch and bound procedure is strongly restricted. To obtain quickly a good upper bound some special expressions evaluating all the descendants, which can be received from a current solution, are employed. Computational results are also included.