Advanced scheduling problem using constraint programming techniques in SCM environment

The job shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem, as it is classified as NP-hard problem and therefore no deterministic algorithms can solve them in a reasonable amount of time. The main objective of solving this problem is to find suitable job sequences on machines to optimize the performance criteria. In this paper, a meta-heuristic approach for solving the job-shop scheduling problem (JSSP) is presented. This approach uses a hybrid genetic algorithm that is suggested by previous stuody, to generate the best solutions and then a simulated annealing algorithm to improve the quality and performance of the best solutions to produce the optimal/near-optimal solution. Ten benchmark problems adopted from the previous study are used to evaluate the performance of the proposed algorithm. The computational results validate the quality of the proposed algorithm; this is done by calculating the completion time Cmax

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.

The wafer probing scheduling problem (WPSP) is a variation of the parallel-machine problem, which carries with the objective to minimize the total workload to enhance the utilization rate of machine capacity. However, to minimize the maximum completion time for the wafer probing scheduling problem (WPSP) is very important for equally utilizing the capacity of parallel-machine, while satisfying the requirements of product types, product family, sequence dependent setup time, product-type dependent processing time, due dates of jobs, and capacities of machines. To solve such a complicated problem efficiently and effectively, we adopt the hybrid genetic algorithm approach, which generates initial population with the insertion and savings algorithms for WPSP and provide a new crossover operator to generate better solutions. Considering the disturbance of due dates of jobs in scheduling, we design a new crossover which makes each machine keep the critical sub-schedule in the generation of offspring. The performance of the proposed hybrid genetic algorithm is evaluated by two real-world problems and the solution quality of the proposed approach is analyzed under five levels of mutation rates and four levels of number of generations. Computational results reveal that the hybrid genetic algorithm can efficiently solve the considered problem and the reduction of makespan is related to the mutation rates and the number of generations.

Production scheduling is a critical requirement in today’s production systems. The production scheduling problem focuses on the rational allocation of production resources, determining the processing order of production tasks and operations, satisfying production constraints arising from the production environment such as the number of machines and task duration, and achieving optimal production goals, such as completing all tasks in the fastest way possible. The Flexible Job Shop Scheduling (FJSP) problem is a classical production scheduling issue, an extension of the Job Shop Scheduling (JSP) problem. The JSP problem involves allocating a set of jobs to a set of machines, where each job consists of multiple operations that must adhere to a precedence order. Each machine can process only one operation at a time for each job, and the time required for each operation on the corresponding machine is predetermined. The challenge in the JSP problem is to determine an appropriate order for processing operations or assignments, based on the production objective. FJSP represents a more complex and flexible variant of this problem, where at least one operation of a task can be performed on a set of alternative machines. Given that FJSP problems are NP-hard, solutions tend to be complex, varied, and often have limited applicability. Many FJSP scenarios require targeted modeling. In this paper, I propose a modeling approach for the static scheduling problem of job shops, considering low flexibility and demonstrating its usability by comparing it with other scheduling approaches, such as those based on genetic algorithm modeling. The paper is organized as follows:The first part of this paper offers a brief introduction to the FJSP problem, including background, research motivation, and main contributions, the second part provides a literature review,including a brief review and introduction of recent solutions to the FJSP problem, the third part describes a specific modeling approach, the fourth part details the test experiments and results of the modeling approach and includes comparisons with some other approaches, the fifth part analyzes the results from the fourth part and discusses the advantages and disadvantages of the modeling approach relative to others, and the sixth part concludes with a summary of findings and recommendations.

A hybrid genetic local search algorithm for the permutation flowshop scheduling problem

In this study, a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, is derived for nonlinear channel blind equalization using RBF networks. The proposed hybrid genetic algorithm is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. From these estimated output states, the desired channel states of the nonlinear channel are derived and placed at the center of a RBF equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm (GA) and a simplex GA. It is shown that the relatively high accuracy and fast convergence speed have been achieved.KeywordsHybrid Genetic AlgorithmTransmitted SymbolNormalize Root Mean Square ErrorVolterra KernelDecision Feedback EqualizerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this paper, we propose a hybrid algorithm combining two different metaheuristic methods, “Genetic Algorithms (GA)” and “Sperm Swarm Optimization (SSO)”, for the global optimization of multimodal benchmarks functions. The proposed Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) operates based on incorporates concepts from GA and SSO in which generates individuals in a new iteration not only by crossover and mutation operations as proposed in GA, but also by techniques of local search of SSO. The main idea behind this hybridization is to reduce the probability of trapping in local optimum of multi modal problem. Our algorithm is compared against GA, and SSO metaheuristic optimization algorithms. The experimental results using a suite of multimodal benchmarks functions taken from the literature have evinced the superiority of the proposed HGASSO approach over the other approaches in terms of quality of results and convergence rates in which obtained good results in solving the multimodal benchmarks functions that include cosine, sine, and exponent in their formulation.

To solve a class of complex job shop scheduling problem with characteristics of re-entrant and flexible, which is a NP-hard problem, an improved genetic algorithm (GA) is proposed in this paper. In order to effectively overcome the contradiction between “convergence rate” and “convergence accuracy” of GA, a comprehensive search mechanism is presented. In this search mechanism, a strategy with excluding close relative and an elitist selection have been applied into the select operator. Meanwhile, a partition crossover operator with competition between parents and children has also been used in GA. In addition, considering diversity of population, random perturbation is added into the evolution process of GA. Finally, the improved algorithm is applied into the optimization scheduling problem of automatic fluorescence analyzer which has characteristics of re-entrant and flexible. And its experimental results compared with other GA indicate the improved GA with comprehensive search mechanism can effectively overcome the contradiction between “convergence rate” and “convergence accuracy”.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

There are many uncertain factors in job shop scheduling problems. However, those uncertainties are critical for the scheduling procedures. The imprecise processing times are modeled as triangular fuzzy numbers (TFNs) and the due dates are modeled as trapezium fuzzy numbers in this paper. A multi-objective genetic algorithm is proposed to solve fuzzy job shop scheduling problems, in which the objective functions are conflicting. Agreement index (AI) is used to show the satisfaction of client which is defined as value of the area of processing time membership function intersection divided by the area of the due date membership function. The multi-objective function is composed of maximize both the minimum agreement and maximize the average agreement index. Two benchmark problems were used to show the effectiveness of the proposed approach. Experimental results demonstrate that the multi-objective genetic algorithm does not get stuck at a local optimum easily, and it can solve job-shop scheduling problems with fuzzy processing time and fuzzy due date effectively.

In many industries including metallurgy, petrochemicals, paper, aerospace, pharmaceutical, electronics, ceramic and automotive manufacturing, the production system can contain a machine bottleneck which processes, in some cases, all the jobs. The management of this machine is crucial for the enterprise because it is responsible for delays in customers' orders delivery. The jobs scheduling on this machine is a way of approaching the problem and treats the whole production system. The single machine model provides a basis for more complicated environment such those seen in many real cases. Also, several studies have shown that several jobs, if not all, have time-dependent setup times. So, decision makers must therefore organize the jobs scheduling on this single machine by trying to minimize downtime while respecting the different deadlines. On the other hand, several resolution methods have been developed in literature to solve this kind of problem using approaching methods like metaheuristics or hybrid methods integrating exact methods. This thesis is in this direction. In this research, we propose several effective approaches to solve the single machine scheduling problem with sequence-dependent setup times with the objective to minimize total tardiness of the jobs. First, a new crossover operator in a genetic algorithm is proposed. This algorithm shows its ability to be competitive with the best approaches found in the literature on benchmark problems. We thus highlight the importance of incorporating specific problem knowledge into genetic operators, even if classical genetic operators could be used. However, the results obtained show that the proposed genetic algorithm is less efficient in comparison to the best resolution method found in the literature. Indeed, we noticed that the proposed algorithm lacks an intensification process for enhancing its. To overcome this shortcoming, we explore resolution class methods that showed promising prospects in the last decade. Even, the hybrid algorithms have achieved very good results in a wide variety of problems. In recent years, several researchers have introduced the hybridization of metaheuristics with exact methods. In fact, this kind of hybridization can be an attractive alternative because both methods have very different characteristics that can be combined to produce better results. Thus we explore the design of hybrid genetic algorithms improving the intensification process of the proposed genetic algorithm by integrating an exact method and mechanisms from other resolution methods to improve the performance. However, we find few approaches hybridizing metaheuristics and constraint programming in the literature to solve scheduling problems. In a second step, we propose a modeling for the total tardiness single machine with setup-dependent times scheduling problem using the constraint-based scheduling and the ILOG C++ API. The related experiments show that algorithm efficiency improves with specific tree search strategies and heuristic scheduling variables. Under prohibitive time computing, the proposed approach results are generally far from the best known solutions. However, we got interesting performance on small instances to consider allowing its hybridization with other resolution methods. In a third step, we introduce a hybrid approach based on the integration between genetic algorithm and constraint based scheduling approaches. This latter approach is integrated in the reproduction and the intensification processes of a genetic algorithm autonomously. Finally, we propose a hybrid algorithm which integrates several concepts from constraint programming, multi-objective optimization and ant colonies optimization in a crossover operator into a genetic algorithm. The proposed hybrid algorithms show excellent performance on benchmark problems from the literature by improving many of the best known solutions for some of them.

In real world manufacturing systems there are many combinatorial optimization problems (COP) imposing on more complex issues, such as complex structure, nonlinear constraints, and multiple objectives to be handled simultaneously. Manufacturing scheduling is one of the important and complex COP models, where it can have a major impact on the productivity of a production process. Moreover, the COP models make the problem intractable to the traditional optimization techniques because most of scheduling problems fall into the class of NP-hard combinatorial problems. In order to develop effective and efficient solution algorithms that are in a sense good, i.e., whose computational time is small as within 3 min, or at least reasonable for NP-hard combinatorial problems met in practice, we have to consider: quality of solution, computational time and effectiveness of the nondominated solutions for multiobjective optimization problem (MOP). When solving any NP-hard problem, a genetic algorithm (GA) based on principles from evolution theory is most powerful metaheuristics. In this paper, we concern with the design of hybrid genetic algorithms (HGA) and multiobjective HGA (Mo-HGA) to solve manufacturing scheduling problems. Firstly we introduce typical models in manufacturing scheduling systems such as parallel machines scheduling (PMS), flexible job-shop scheduling problem (FJSP) and assembly line balancing (ALB) problem. Secondly to solve NP-hard COP models, we introduce design scheme of HGA combined with fuzzy logic controller (FLC) and multiobjective HGA (Mo-HGA) with several fitness assignment mechanisms. For demonstrating computational experiments by HGA and Mo-HGA, the effectiveness of the HGA for the HDD (hard disc device) and Mo-HGA for TFT-LCD (thin-film transistor-liquid crystal display) module assembly problems as a practical manufacturing model, respectively is demonstrated in the concatenated paper Part II.

Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed-Muller (MPRM) logic circuits, few studies have taken into account the polarity conversion sequence. In order to improve the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimization approach (FPOA) considering the polarity conversion sequence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set waiting for evaluation is obtained by using the proposed hybrid genetic algorithm (HGA); secondly, each of polarity in the polarity set is converted according to the best polarity conversion sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been presented for MCNC benchmark circuits. The experimental results show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity optimization of MPRM logic circuits compared with the traditional polarity optimization approach which neglects the polarity conversion sequence and the improved polarity optimization approach with heuristic technique.

Constraint Satisfaction Problems (CSP) provide a modelling framework for many computer aided decision making problems. Many of these problems are associated to an optimization criterion. Solving a CSP consists in finding an assignment of values to the variables that satisfies the constraints and optimizes a given objective function (in case of an optimization problem). In this paper, we extend our framework for genetic algorithms (GA) as suggested by the reviewers of our previous ICLP paper [5]. Our purpose is not to solve efficiently the Balanced Academic Curriculum Problem (BACP) [2] but to combine a genetic algorithm with constraint programming techniques and to propose a general modelling framework to precisely design such hybrid resolution process and highlight their characteristics and properties.

Immune genetic algorithm has the disadvantage of insufficient local search capability when solving the job-shop scheduling problem, and it is difficult to solve large-scale job-shop scheduling problems well. Aiming at this problem, this paper proposes a hybrid immune genetic algorithm which combines neighborhood search with immune genetic algorithm. After the genetic operation, in addition to the vaccination operation of the poorer individuals in the population, the better individuals will be selected for neighborhood search to improve the local search ability of the algorithm. The neighborhood search operator selects the N5 neighborhood search operator, which can perform a neighborhood search on key paths of high-quality individuals to find a better neighborhood solution. The experimental results show that the hybrid immune genetic algorithm with the neighborhood search method can obtain more standard case optimal solutions.