This note studies a unrelated parallel-machine scheduling problem with controllable processing times and job-dependent learning effects, where the objective function is to minimize the weighted sum of total completion time, total load, and total  compression cost. We show that the problem can be solved in $O(n^{m+2})$ time, where $m$ and $n$ are the numbers of machines and jobs. We also show how to apply the technique to several single-machine scheduling problems with total criteria.

This paper studies single-machine due-window assignment scheduling problems with truncated learning effect and resource allocation simultaneously. Linear and convex resource allocation functions under common due-window (CONW) assignment are considered. The goal is to find the optimal due-window starting (finishing) time, resource allocations and job sequence that minimize a weighted sum function of earliness and tardiness, due window starting time, due window size, and total resource consumption cost, where the weight is position-dependent weight. Optimality properties and polynomial time algorithms are proposed to solve these problems.

With the rapid development of computer networking technology, people pay more and more attention to the role of online reviews in management decision making. The existing methods of online reviews fusion are limited to rational decision-making behavior, which does not accord with the characteristics of evaluators’ behavior characteristics in the real environment. In order to solve the online reviews fusion problem based on bounded rational behavior which is closer to the reality of property service quality evaluation, the multi-index and multi-scale (MIMS) method is extended into the generalized form, the online reviews are quantified by using the adverb structure scaling method, and an online reviews fusion method based on the improved TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) model is proposed. The feasibility and effectiveness of the proposed method are verified by an example analysis of property service quality evaluation. The research results are as follows: the adverb structure scaling method is suitable for a large number of online reviews processing, the proposed method improves the efficiency of online reviews information fusion, and it is feasible and effective to evaluate property service quality based on the bounded rationality of evaluator’s behavior.

This paper considers single machine due-window assignment scheduling problems with position-dependent weights. Under the learning and deterioration effects of jobs processing times, our goal is to minimize the weighted sum of earliness-tardiness, starting time of due-window, and due-window size, where the weights only depends on their position in a sequence (i.e., positiondependent weights). Under common due-window (CONW), slack due-window (SLKW) and different due-window (DIFW) assignments, we show that these problems remain polynomial-time solvable.