Coordination of planning among several companies has been widely received much attention from the viewpoint of global supply chain management. In a practical situations, a plausible plan for several companies should be created by the negotiation and coordination among the companies without sharing the confidential information such as inventory costs, set up costs and due date penalties for each company. In this paper, we propose a framework of a distributed optimization of supply chain planning using a new augmented Lagrangian relaxation method. The feature of the proposed method is that a feasible solution can be derived without using the procedure of heuristic generation of feasible solutions. From the computational experiments, it has been shown that the average gap between the solution derived by the proposed method and an optimal solution is within 1% of the performance index even though only the local information is used to derive a solution for each company.
In this paper, we propose a column generation heuristics to a continuous time model of conflict-free pickup and delivery vehicle routing problems. The transportation system is divided into several regions with regular intervals. A network model of conflict-free multi-vehicle routing problem with acceleration and deceleration motions is developed. A column generation heuristics is used to find a near-optimal solution. The pricing problem for each vehicle routing problem is formulated as a resource constrained shortest path problem, which is effectively solved by a labeling algorithm. Computational results demonstrate the effectiveness of the proposed method.
In the conventional Lagrangian relaxation approach, the scheduling problems are decomposed into job-level sub-problems or operation-level sub-problems. However, these approaches are not applicable to the flowshop problems with the changeover cost which depends on the sequence of operations. In this paper, we propose a machine-oriented decomposition method for the flowshop problems using the Lagrangian decomposition and coordination technique. In the proposed method, each sub-problem for single machine is solved by the simulated annealing method. The solutions of the sub-problems are used to generate a feasible schedule by a heuristic procedure. The effectiveness of the proposed method is verified by comparing the results of the example problems solved by the proposed method with those solved by the conventional method. Furthermore, it has been shown that the proposed approach is easily applicable to the flow hop problem with resource constrains minimizing the changeover costs and due date penalties.
Planning coordination for multiple companies has received much attention from viewpoints of global supply chain management. In practical situations, a plausible plan for multiple companies should be created by mutual negotiation and coordination without sharing such confidential information as inventory costs, setup costs, and due date penalties for each company. In this paper, we propose a framework for distributed optimization of supply chain planning using an augmented Lagrangian decomposition and coordination approach. A feature of the proposed method is that it can derive a near-optimal solution without requiring all of the information. The proposed method is applied to supply chain planning problems for a petroleum complex, and a midterm planning problem for multiple companies. Computational experiments demonstrate that the average gap between a solution derived by the proposed method and the optimal solution is within 3% of the performance index, even though only local information is used to derive a solution for each company.
Particle swarm optimization (PSO) is an efficient optimization algorithm and has been applied to solve various real-world problems. However, the performance of PSO on a specific problem highly depends on the velocity updating strategy. For a real-world engineering problem, the function landscapes are usually very complex and problem-specific knowledge is sometimes unavailable. To respond to this challenge, we propose a multipopulation ensemble particle swarm optimizer (MPEPSO). The proposed algorithm consists of three existing efficient and simple PSO searching strategies. The particles are divided into four subpopulations including three indicator subpopulations and one reward subpopulation. Particles in the three indicator subpopulations update their velocities by different strategies. During every learning period, the improved function values of the three strategies are recorded. At the end of a learning period, the reward subpopulation is allocated to the best-performed strategy. Therefore, the appropriate PSO searching strategy can have more computational expense. The performance of MPEPSO is evaluated by the CEC 2014 test suite and compared with six other efficient PSO variants. These results suggest that MPEPSO ranks the first among these algorithms. Moreover, MPEPSO is applied to solve four engineering design problems. The results show the advantages of MPEPSO. The MATLAB source codes of MPEPSO are available at https://github.com/zi-ang-liu/MPEPSO.
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