An ERNSGA‐III algorithm for the production and distribution planning problem in the multiagent supply chain

Abstract

Nowadays, new issues have been emerged in industry by increasing the supply chain boundaries, especially when some supply chain members seek their own interests. In the literature, this case is referred to as a multiagent problem in which each agent has his/her own set of jobs and objectives. Here, an integrated production scheduling and distribution problem in a multisite supply chain is investigated from the three‐agent perspective of the manufacturer, distributor, and customer. Due to the complexity of the problem, a novel evolutionary‐based reference point determination of the third version of the non‐dominated sorting genetic algorithm (ERNSGA‐III) is proposed. In this algorithm, some modifications to the classic version of NSGA‐III have been made and a novel reference point determination method based on Bees algorithm is used in the selection operation. Some statistical tests are performed to compare the proposed algorithm with three other algorithms in the literature. The results show that the proposed algorithm is more effective than other algorithms.

Coordinated optimized scheduling of locks and transshipment in inland waterway transportation using binary NSGA‐II

“…Due to the properties of multiobjective problems, multiple performance indices should be used to compare the performances of different methods on finding Pareto‐optimal set. In our experiments, the following performance indexes are used (Yuan et al., ; Gharaei and Jolai, ): Size of space covered (S‐Metric): given a set of m decision vectors (), S‐Metric gives the volume enclosed by the union of the polytopes , where each P i is formed by the intersections of the following hyperplanes arising out of x i , along with the axes: for each axis in the objective space, there exists a hyperplane perpendicular to the axis and passing through its objective value. Here, since the LWTC model we presented above is a minimization problem, we choose the largest objective values among the results obtained by all methods in 20 trials as the reference point (i.e., largest tardiness, largest costs).…”

Solving the Sustainable Automobile Production-Distribution Joint Optimization in the Physical Internet-Enabled Hyperconnected Order-to-Delivery System by I-NSGAIII

“…In recent years, a lot of approaches have been developed to address IPDS, such as the exact method [24] and heuristic algorithms (e.g. adaptive genetic algorithm [10], memetic algorithm [12] and NSGAIII [25]). Exact methods are not undeniably feasible when the problem is scaled up to a certain level.…”

“…Constraints (24) state that order a cannot be painted on time slot nt if colour e of order a is not currently in the spray package. Constraints (25), as shown at the bottom of the next page, indicate that an order must be painted and assembled in the same plant. Constraints ( 26) -( 29), as shown at the bottom of the next page, describe the production capacity for PT in each time period t, where the total time period T can also be counted by time slot nt.…”

Heuristic Search for Rank Aggregation with Application to Label Ranking

“…Rank aggregation is a classical problem in voting theory, where each voter provides a preference ranking on a set of alternatives, and the system aggregates these rankings into a single consensus preference order to rank the alternatives. Rank aggregation plays a critical role in a variety of applications such as collaborative filtering [1], [2], multiagent planning [3], information retrieval [4], and label ranking [5]- [7]. As a result, this problem has been widely studied, particularly in social choice theory and artificial intelligence.…”