Narrow Results

Scheduling is considered to be a major task to improve the shop-floor productivity. The job shop problem is under this category and is combinatorial in nature. Research on optimization of job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the Ant Colony Optimization meta heuristic to job shop problem. The main characteristics of this model are positive feedback and distributed computation. The settings of parameter values have more influence in solving instances of job shop problem. An algorithm is introduced to improve the basic ant colony system by using a pheromone updating strategy and also to analyze the quality of the solution for different values of the parameters. In this paper, we present statistical analysis for parameter tuning and we compare the quality of obtained solutions by the proposed method with the competing algorithms given in the literature for well known benchmark problems in job shop scheduling.

This paper discusses about the flow shop scheduling problems using shortest processing time, earliest due date (EDD), Nawaz, Enscore, and Ham (NEH), NEH-EDD, and modified-NEH methods. The objective of this research is to determine the performance of these methods in minimizing makespan and total tardiness. Processing times and due dates were randomly generated, and computational studies were performed in Microsoft Visual Basic 6.0. The experiments are performed for small and medium data sets. Efficiency index, relative error, and run time measure the performance of each method. Experimental results showed that NEH has the best performance in minimizing the makespan in both data sets; these are 53.35 time unit for small data sets and 83.803 time unit for medium data sets. NEH-EDD has the best performance in minimizing total tardiness with 9.37 time unit for small data sets and 231.02 time unit for medium data sets. Modified-NEH, as the proposed method for minimizing makespan and total tardiness at the same time, has good enough result. For minimizing the makespan, modified-NEH results in 57.15 time unit for small data sets and 88.107 time unit for medium data sets. For minimizing total tardiness, the modified-NEH results in 14.21 time unit for small data sets and 246.57 time unit for medium sets.

We address a generalization of the classical job-shop problem which is called a hybrid job-shop problem. The criteria under consideration are the minimization of the makespan and mean flow time. In the hybrid job-shop, machines of type $k$ are available for processing the specific subset ${{𝒪}}^k$ of the given operations. Each set ${{𝒪}}^k$ may be partitioned into subsets for their processing on the machines of type $k$. Solving the hybrid job-shop problem implies the solution of two subproblems: an assignment of all operations from the set ${{𝒪}}^k$ to the machines of type $k$ and finding optimal sequences of the operations for their processing on each machine. In this paper, a genetic algorithm is developed to solve these two subproblems simultaneously. For solving the subproblems, a special chromosome is used in the genetic algorithm based on a mixed graph model. We compare our genetic algorithms with a branch-and-bound algorithm and three other recent heuristic algorithms from the literature. Computational results for benchmark instances with 10 jobs and up to 50 machines show that the proposed genetic algorithm is rather efficient for both criteria. Compared with the other heuristics, the new algorithm gives most often an optimal solution and the average percentage deviation from the optimal function value is about 4%.

This paper develops a new harmony search (HS) algorithm for the hybrid flow shop scheduling problem with multiprocessor tasks, which is known to be NP-hard in the strong sense. The goal is to minimize the makespan. Thus, the proposed HS starts with the application of an opposition based learning (OBL) method to increase the diversity level of the initial harmony memory (HM). Furthermore, we use novel improvisation rules of producing new harmonies based on crossover and mutation operators. Also, neighborhood techniques are employed to enhance the searching and improve the solution quality. The proposed HS is compared with three algorithms from the literature. The computational experiments demonstrate the effectiveness of the proposed HS algorithm in terms of makespan.

Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in under utilization of capital intensive machines and a high level of machine idle time. Therefore, efficient scheduling of jobs and tools enables a manufacturing system to increase machines’ utilization and decrease their idle times. This paper addresses machines’ and tools’ joint scheduling with alternate machines in a multimachine flexible manufacturing system (FMS) to minimize makespan (MSN). Only one copy of each type of tool is made available in FMS where tools are expensive. The tools are stored in the central tool magazine (CTM), which shares and serves them to several machines in order to reduce the cost of duplicating the tools in every machine. The problem is to select machines from alternate machines for job-operations, allocation of tools to the job-operations and job-operations’ sequencing on machines for MSN minimization. This paper presents nonlinear mixed integer programming (MIP) formulation to model this simultaneous scheduling problem and crow search algorithm (CSA) built on the crows’ intelligent behavior for solving this problem. The results show that CSA is providing better solutions than Jaya algorithm and the usage of alternate machines for the operations can reduce MSN.

Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in the underutilization of capital-intensive machines and a high machine idle time. Tools are transferred by a tool transporter (TT) between machines. Therefore, efficient scheduling of jobs, TT, and the tools with alternate machines enables a manufacturing system to increase machines’ utilization and decrease their idle times. This paper addresses machines, TT, and tools concurrent scheduling with alternate machines and only one copy of every tool variety is made available where the tools are costly, in a multi-machine flexible manufacturing system, taking into account tool transfer times for makespan (MS) minimization. The tools are placed in a central tool magazine, which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. The problem is to assign machines from alternate machines, tools, and corresponding TT trips, including the deadheading trip and loaded trip, to job operations for MS minimization. This paper uses a nonlinear mixed-integer programming framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. The results show that FPA outperforms the Jaya algorithm, and the usage of alternate machines for the operations reduces the MS. Reduction in MS indicates an improvement in utilization of resources.

This paper addresses machines and automated guided vehicles (AGVs) simultaneous scheduling with alternative machines in a multi-machine flexible manufacturing system (FMS) to produce the best optimal sequences for the minimization of makespan (MKSN). This problem is highly complex to solve because it involves the selection of machines for job operations (jb-ons), the sequencing of jb-ons on the machines, the assignment of AGVs, and associated trips such as AGVs’ deadheaded trip and loaded trip times to jb-ons. This paper offers a nonlinear mixed integer programming (MIP) formulation for modeling the problem and the symbiotic organisms search algorithm (SOSA) for solving the problem. For verification, a manufacturing company’s industrial problem is employed. The results show that SOSA outperforms the existing methods and the Jaya algorithm, and using alternate machines for the operations can reduce the MKSN.

In this paper, a multi-objective permutation flow shop scheduling problem with sequence independent setup time is studied. The bi-objective function that we consider is a linear combination of the makespan and total flow time with a weighted factor for each criterion. We propose a set of exact and approximate methods to minimize the makespan and total flow time in our flow shop optimization problem case. Hence, our main goal is to find the sequence of jobs that minimizes the two criteria of makespan and total flow time. The purpose is to solve this problem, with a mixed integer linear programming model (MILP) and a collection of efficient metaheuristics for different sizes of instances. Moreover, three metaheuristics are used: the Genetic Algorithm (GA), the Iterative Local Search (ILS) algorithm and the Iterated Greedy (IG) algorithm. The three last algorithms GA, ILS and IG are suggested in two ways for exploring the neighborhood. In order to test the efficacy of our resolution approach, different series of instances containing n jobs and m machines are generated randomly ranging from small to relatively large instances. The examination of the suggested simulations allowed us to remark that, for large and medium-scale instances, IG based on the exploration of the neighborhood records the best performances in terms of comparison with the other metaheuristics.

Non-permutation flow shop scheduling (NPFS) is an extension of the traditional permutation flow shop scheduling, with a broader solution space. The effectiveness of reinforcement learning in solving flow shop scheduling problems has been demonstrated through its powerful combinatorial optimization capabilities. However, the design and training of the end-to-end policy network is complex, leading to long online training time and limited adaptability of offline training. To overcome these problems, we introduced a NPFS dynamic decision-making process and then proposed a novel NPFS method that combines the Q-learning algorithm with the priority dispatching rule (PDR) set. The NPFS dynamic decision-making process involves decomposing the entire process into multiple sub-job queues for scheduling. The PDR demonstrates better scheduling performance when applied to smaller job queues. By utilizing the Q-learning algorithm, PDRs with superior performance are assigned to sub-scheduling queues based on the generation process of sub-job sequences, resulting in an optimized NPFS strategy. The limited number of PDRs in the PDR set and the small number of sub-job queues in the NPFS process contribute to the efficiency of Q-learning in solving NPFS problem. Finally, we demonstrate the superiority of the proposed NPFS method by a series of numerical experiments using the machine-based assignment PDR method and NSGA-II algorithms as performance benchmarks.