Chiung Moon

An efficient genetic algorithm for the traveling salesman problem with precedence constraints

Abstract The traveling salesman problem with precedence constraints (TSPPC) is one of the most difficult combinatorial optimization problems. In this paper, an efficient genetic algorithm (GA) to solve the TSPPC is presented. The key concept of the proposed GA is a topological sort (TS), which is defined as an ordering of vertices in a directed graph. Also, a new crossover operation is developed for the proposed GA. The results of numerical experiments show that the proposed GA produces an optimal solution and shows superior performance compared to the traditional algorithms.

Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain

In this paper, we propose an integrated process planning and scheduling (IPPS) model for the multi-plant supply chain (MSC), which behaves like a single company through strong coordination and cooperation toward mutual goals. The IPPS problem is one of the most important issues for supporting the global objectives, because the function takes part in the assignment of factory resources to production tasks. The problem is formulated as a mathematical model considering alternative machines and sequences, sequence-dependent setup, and distinct due dates. The objective of the model is to decide the schedules for minimizing total tardiness through analysis of the alternative machine selection and the operation sequences in MSC. In order to obtain good approximate solutions, genetic algorithm-based heuristic approach is developed. Numerical experiments are carried out to demonstrate the efficiency of the proposed approach.

Evolutionary algorithm for advanced process planning and scheduling in a multi-plant

Integration of process planning and scheduling is one of the most important functions to support flexible planning in a multi-plant. The planning and scheduling are actually interrelated and should be solved simultaneously. In this paper, we propose an advanced process planning and scheduling model for the multi-plant. The objective of the model is to decide the schedules for minimizing makespan and operation sequences with machine selections considering precedence constraints, flexible sequences, and alternative machines. The problem is formulated as a mathematical model, and an evolutionary algorithm is developed to solve the model. Numerous experiments are carried out to demonstrate the efficiency of the proposed approach.

Integrated process planning and scheduling in a supply chain

This paper deals with the integration of process planning and scheduling, which is one of the most important functions in a supply chain to achieve high quality products at lower cost, lower inventory, and high level of performance. Solving the problem is essential for the generation of flexible process sequences with resource selection and for the decision of the operation schedules that can minimize makespan. We formulate a mixed integer programming model to solve this problem of integration. This model considers alternative resources: sequences and precedence constraints. To solve the model, we develop a new evolutionary search approach based on a topological sort. We use the topological sort to generate a set of feasible sequences in the model within a reasonable computing time. Since precedence constraints between operations are handled by the topological sort, the developed evolutionary search approach produces only feasible solutions. The experimental results using various sizes of problems provide a way to demonstrate the efficiency of the developed evolutionary search approach.

Advanced planning and scheduling based on precedence and resource constraints for e-plant chains

The e-plant chain is an extension of the integration beyond a production site by means of improved distribution management, electronic data interchange and coordination of multiple plants. The present paper proposes an advanced planning and scheduling model for the e-plant chain. The advanced planning and scheduling is the most important function when supporting flexible planning and scheduling in the e-plant chain. The problem is formulated as a mixed integer-programming model. The model includes the main features of the system including flexible operations’ sequences, resource requirements and alternative schedules. Since the problem is NP-hard, an intelligent search approach based on a genetic algorithm is developed. Numerical experiments show the proposed approach is satisfactory in its accuracy and efficiency.

Integrated machine tool selection and operation sequencing with capacity and precedence constraints using genetic algorithm

In this paper, an integrated machine tool selection and sequencing model is proposed. The model determines machine visiting sequences for all part types, such that the total production time for the production order is minimized and workloads among machine tools are balanced. The model is formulated as a 0-1 integer programming. To solve the model, a genetic algorithm approach based on a topological sort technique is developed. To demonstrate the efficiency of the proposed GA approach on the integrated machine tool selection and sequencing problem, a number of numerical experiments using various size problems are carried out. The numerical experiments show that the proposed GA approach is efficient to this problems.

Adaptive genetic algorithm for advanced planning in manufacturing supply chain

A main function for supporting global objectives in a manufacturing supply chain is planning and scheduling. This is considered such an important function because it is involved in the assignment of factory resources to production tasks. In this paper, an advanced planning model that simultaneously decides process plans and schedules was proposed for the manufacturing supply chain (MSC). The model was formulated with mixed integer programming, which considered alternative resources and sequences, a sequence-dependent setup and transportation times.The objective of the model was to analyze alternative resources and sequences to determine the schedules and operation sequences that minimize makespan. A new adaptive genetic algorithm approach was developed to solve the model. Numerical experiments were carried out to demonstrate the efficiency of the developed approach.

Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems

The optimal design of supply chain (SC) is a difficult task, if it is composed of the complicated multistage structures with component plants, assembly plants, distribution centers, retail stores and so on. It is mainly because that the multistage-based SC with complicated routes may not be solved using conventional optimization methods. In this study, we propose a genetic algorithm (GA) approach with adaptive local search scheme to effectively solve the multistage-based SC problems. The proposed algorithm has an adaptive local search scheme which automatically determines whether local search technique is used in GA loop or not. In numerical example, two multistage-based SC problems are suggested and tested using the proposed algorithm and other competing algorithms. The results obtained show that the proposed algorithm outperforms the other competing algorithms.

Hybrid genetic algorithm approach for precedence-constrained sequencing problem

The objective of precedence-constrained sequencing problem (PCSP) is to locate the optimal sequence with the shortest traveling time among all feasible sequences. Various methods for effectively solving the PCSP have been suggested. This paper proposes a new concept of hybrid genetic algorithm (HGA) with adaptive local search scheme in order that the PCSP should be effectively solved. By the use of the adaptive local search scheme, the local search is automatically adapted into the loop of genetic algorithm. Two types of the PCSP are presented and analyzed to compare the efficiency among the proposed HGA approach and other competing conventional approaches. Finally, it is proved that the proposed HGA approach outperforms the other competing conventional approaches.

Genetic algorithm approach for precedence-constrained sequencing problems

In this paper we propose a genetic algorithm (GA) approach based on a topological sort (TS)-based representation procedure for effectively solving precedence-constrained sequencing problems (PCSPs). The TS-based representation procedure used in the proposed GA approach can generate feasible sequences in PCSPs. By applying the proposed GA approach, the sequence determination problems with precedence constraints can be easily solved. Experimental results show that the proposed GA approach is a good alternative in locating optimal sequence for various types of PCSPs.