Bioenergy supply chains can offer social benefits. In most related research, the total number of created jobs is used as the indicator of social benefits. Only a few of them quantify social benefits considering the different impact of economic activities in different locations. In this paper, a new method of measuring the social benefits of bioethanol supply chains is proposed that considers job creation, biomass purchase, and the different impacts of economic activities in different locations. A multi-objective mixed integer linear programming (MILP) model is developed to address the optimal design of a bioethanol supply chain that maximizes both economic and social benefits. The ε-constraint method is employed to solve the model and a set of Pareto-optimal solutions is obtained that shows the relationship between the two objectives. The developed model is applied to case studies in Liaoning Province in Northeast China. Actual data are collected as practical as possible for the feasibility and effectiveness of the results. The results show that the bioethanol supply chain can bring about both economic and social benefits in the given area and offers governments a better and more efficient way to create social benefits. The effect of the government subsidy on enterprises’ decisions about economic and social benefits is discussed.

This article considers the minmax common due-window assignment in group scheduling with resource allocation on a single machine. In this problem, jobs are partitioned into groups. In a group, all jobs are processed contiguously, and a common due-window is assigned to them. First, a case with constant processing times is examined. The objective is to minimize the sum of the maximal costs arising from the common due-window among the jobs in each group. This problem is proved to be polynomially solvable. Next, the problem is extended to the case that includes resource allocation. The processing time of each job is considered as either a linear or a convex function of the quantity of resource allocation. The objective function considers the total resource-consumption cost and sum of the maximal costs. For the special case of group-position-dependent penalties, polynomial-time solutions are presented.

Recently, due to the growth in machine learning and data mining, for scheduling applications in China’s industrial intelligence, we are quite fortunate to witness a paradigm of evolutionary scheduling via learning, which includes a new tool of evolutionary transfer optimization (ETO). As a new subset in ETO, single-objective to multi-objective/many-objective optimization (SMO) acts as a powerful, abstract and general framework with wide industrial applications like shop scheduling and vehicle routing. In this paper, we focus on the general mechanism of selection that selects or gathers elite and high potential solutions towards gathering/transferring strength from single-objective problems, or gathering/transferring storms of knowledge from solved tasks. Extensive studies in vehicle routing problems with time windows (VRPTW) on well-studied benchmarks validate the great universality of the SMO framework. Our investigations (1) contribute to a deep understanding of SMO, (2) enrich the classical and fundamental theory of building blocks for genetic algorithms and memetic algorithms, and (3) provide a completive and potential solution for VRPTW.

The reheating furnace is the key piece of equipment in the hot rolling process of steel production. In order to fully exploit all of the data recorded from the production process representing different information, this paper designs a process monitoring algorithm with multisource information fusion by integrating multiple information to comprehensively monitor the operating state of the reheating furnace. Multisource information fusion combines process variable data of the reheating furnace and heating process data of the slab. To overcome the challenge of fusion of heterogeneous data due to different sampling patterns, univariate time series and multivariate time series data are fused by a transformer. In the fusion scheme, univariate time series data are represented by bidirectional gated recurrent unit for one-dimensional temporal representation, multivariate time series data are represented by temporal convolutional network for two-dimensional temporal representation, and multivariate time series data are represented by eigenvalue decomposition for correlation representation between variables. To evaluate the performance of the proposed method, computational experiments based on actual data are carried out. In univariate and multivariate time series representations, the highest predictions are obtained for bidirectional gated recurrent unit and temporal convolutional network by comparison with different regression algorithms, respectively. By comparing with fusing different fusion objects and different fusion schemes, the proposed algorithm achieves the highest accuracy (91.33%), precision (91.46%), and recall (92.59%), proving the effectiveness of the fusion approach. The process monitoring performance is compared with multivariate statistical process monitoring algorithms, which achieve the highest accuracy (95%), precision (93.45%), and recall (97.08%).

This paper studies a multi-stage multi-product production and inventory planning problem with random yield derived from the cold rolling process in the steel industry. The cold rolling process has multiple stages, and intermediate inventory buffers are kept between stages to ensure continuous operation. Switching products during the cold rolling process is typically very costly. Backorder costs are incurred for unsatisfied demand while inventory holding costs are incurred for excess inventory. The process also experiences random yield. The objective of the production and inventory planning problem is to minimize the total cost including the switching costs, inventory holding costs, and backorder costs. We propose a stochastic formulation with a nonlinear objective function. Two lower bounds are proposed, which are based on full information relaxation and Jensen’s inequality, respectively. Then, we develop two heuristics from the proposed lower bounds. In addition, we propose a two-stage procedure motivated by newsvendor logic. To verify the performance of the proposed bounds and heuristics, computational tests are conducted on synthetic instances. The results show the efficiency of the proposed bounds and heuristics.