DISTRIBUSI KEUNTUNGAN YANG ADIL ANTAR AKTOR RANTAI PASOK AGROINDUSTRI SAGU DI KABUPATEN KEPULAUAN MERANTI, RIAU

The distribution of benefits among supply chain actors is complex and full of challenges because various factors, including uncertainty, influence it. This study aims to solve the problem of profit distribution to produce a fair profit distribution among supply chain actors by incorporating elements of uncertainty, risk, and value-added. The model of fair profit distribution is made using the cooperative game theory approach with fuzzy Shapley values, which incorporates the elements of uncertainty in profit, risk, and added value. The fair profit distribution between supply chain actors is validated in the sago agro-industry supply chain in the Meranti Islands Regency. The risks of each supply chain actor were obtained using the fuzzy analytical hierarchy process technique, with risk values of 0.52, 0.23, 0.2, and 0.29 for farmers, traders, wet starch agro-industries, and dry starch agro-industries, respectively. While the value-added ratio of each supply chain actor is 12%, 35.92%, 13.9%, and 15.1%, respectively, as obtained by the Hayami method. The model validation results show that the fair profit distribution to farmers is 17.77%, to traders it is 29.69%, to the wet starch agro-industry it is 9.91%, and to the dry starch agro-industry it is 42.63% of the total supply chain profits. This result is more proportional than the current profit distribution, which is respectively 10.03%, 15.29%, 1.7%, and 72.98%. These results are considered fairer and more proportional because they take into account the uncertainty of the benefits, risks, and added value of each actor in the sago agro-industry supply chain. Keywords: fair profit distribution, fuzzy Shapley value, sago agro-industry, supply chain, uncertaint

Decentralized Coalition Formation with Agent-based Combinatorial Heuristics

A steadily growing pervasion of the energy distribution grid with communication technology is widely seen as an enabler for new computational coordination techniques for renewable, distributed generation as well as for bundling with controllable consumers. Smart markets will foster a decentralized grid management. One important task as prerequisite to decentralized management is the ability to group together in order to jointly gain enough suitable flexibility and capacity to assume responsibility for a specific control task in the grid. In self-organized smart grid scenarios, grouping or coalition formation has to be achieved in a decentralized and situation aware way based on individual capabilities. We present a fully decentralized coalition formation approach based on an established agent-based heuristics for predictive scheduling with the additional advantage of keeping all information about local decision base and local operational constraints private. Two closely interlocked optimization processes orchestrate an overall procedure that adapts a coalition structure to best suit a given set of energy products. The approach is evaluated in several simulation scenarios with different type of established models for integrating distributed energy resources and is also extended to the induced use case of surplus distribution using basically the same algorithm