Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains

Discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion

With consideration of uncertainty in the distributed manufacturing systems, this paper addresses a multi-objective fuzzy distributed hybrid flow shop scheduling problem with fuzzy processing times and fuzzy due dates. To optimize the fuzzy total tardiness and robustness simultaneously, a cooperative coevolution algorithm with problem-specific strategies is proposed by reasonably combining the estimation of distribution algorithm (EDA) and the iterated greedy (IG) search. In the EDA-mode search, a problem-specific probability model is established to reduce the solution space and a sample mechanism is proposed to generate new individuals. To enhance exploitation, a specific local search is designed to improve performance of non-dominated solutions. Moreover, destruction and reconstruction methods in the IG-mode search are employed for further exploiting better solutions. To balance exploration and exploitation capabilities, a cooperation scheme for mode switching is designed based on the information entropy and the diversity of elite solutions. The effect of the key parameters on the performances of the proposed algorithm is investigated by Taguchi design of experiment method. Comparative results and statistical analysis demonstrate the effectiveness of the proposed algorithm in solving the problem.

A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop

Intelligent scheduling covers various tools and techniques for successfully and efficiently solving the scheduling problems. In this paper, we provide a survey of intelligent scheduling systems by categorizing them into five major techniques containing fuzzy logic, expert systems, machine learning, stochastic local search optimization algorithms and constraint programming. We also review the application case studies of these techniques.

A state of the art review of intelligent scheduling

Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.

Energy-Efficient Scheduling of Distributed Flow Shop With Heterogeneous Factories: A Real-World Case From Automobile Industry in China

Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling

Display Omitted A set of fuzzy constraint satisfaction problems associated with supply chain planning and scheduling problem.This work proposes an agent-based fuzzy constraint-directed negotiation (AFCN).The proposed AFCN by iteratively exchanging offers/counteroffers with limited information sharing.AFCN model is sufficiently flexible to incorporate three negotiation strategies including competitive, win-win, and collaborative strategies. Supply chain planning and scheduling problems require manufacturers (project managers) to determine product configurations, select suppliers (contractors) for task allocation, and schedule project tasks while considering various production constraints among manufacturers and suppliers. Most relevant studies have focused on finding an optimal solution based on complete information provided by each enterprise in a supply chain. However, the practical implementation of complete information sharing is difficult, if not impossible, because of the fully distributed nature of the supply chain process. This work proposes an agent-based fuzzy constraint-directed negotiation (AFCN) model to solve problems associated with supply chain planning and scheduling, which are modeled as a set of fuzzy constraint satisfaction problems (FCSPs) that are interlinked via inter-agent constraints. To accommodate the perspectives and interests of each enterprise in a supply chain, conflicts among FCSPs are resolved using the AFCN protocol through the iterative exchange of offers/counter-offers with limited information sharing and without privacy breaches. A proposed offer/counter-offer represents not only a set of acceptable solutions and preferences for an operational task but also the possibility of conflict in this area. For each FCSP, the incremental process of offer/counter-offer evaluation eliminates redundant and infeasible solutions. The sharing of limited non-strategic sensitive information among agents enables them to elucidate their opponents' intentions through iterative negotiations, such that the agents can reach an agreement while ensuring that the solution to a project planning and scheduling problem is satisfactory. The AFCN model is also sufficiently flexible to incorporate different negotiation strategies such as competitive, win-win, and collaborative strategies, for various production environments. Herein, a numerical study was conducted to examine the practical viability and effectiveness of the proposed AFCN model. The experimental results show that the proposed AFCN model not only can generate a schedule that is comparable to a near-optimal solution but also is time-efficient. This indicates that the proposed AFCN is a practical and effective method for solving supply chain planning and scheduling problems in fully distributed environments.

Van Lam Ho

Papers

Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling

An agent-based fuzzy constraint-directed negotiation model for solving supply chain planning and scheduling problems