This article considers two scheduling problems on parallel identical machines with deteriorating jobs, in which the processing time of job is proportional function of its starting times to be processed. Namely, for job j, p _j = b _j t, where b _j > 0 is its deterioration rate, t ≥ t ₀ > 0 is its starting time and t ₀ is a given constant. The criteria of optimality are minimising the makespan and maximising the minimum machine completion time. It has been proved that the first problem is NP-hard; we prove that the second problem is also NP-hard. For both problems heuristic algorithms are constructed and their performance is analysed.

Rescheduling in production planning means to schedule the sequenced jobs again together with a set of new arrived jobs so as to generate a new feasible schedule, which creates disruptions to any job between the original and adjusted position. In this paper, we study rescheduling problems with learning effect under disruption constraints to minimize several classical objectives, where learning effect means that the workers gain experience during the process of operation and make the actual processing time of jobs shorter than their normal processing time. The objectives are to find optimal sequences to minimize the makespan and the total completion time under a limit of the disruptions from the original schedule. For the considered objectives under a single disruption constraint or a disruption cost constraint, we propose polynomial-time algorithms and pseudo-polynomial time algorithms, respectively.

With the background of implementing carbon peaking and carbon neutralization, identifying methods to realize energy-saving and carbon reduction effectively has become an important issue in the intelligent energy-conservation manufacturing industry. During the process of achieving this goal, determining an optimal location for a low-carbon and intelligent manufacturing industrial park is a foremost decision-making problem for manufacturing corporations’ energy-efficient development. The article established a multi-criteria decision framework to assist manufacturing companies when selecting suitable industrial park sites. To begin with, an evaluation criteria framework is confirmed by literature search. Then, a fuzzy optimization model, which combines the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is presented, where fuzzy TOPSIS is used to determine the decision-maker criteria weights. Then, criteria weights are calculated by the optimization model with construction of a Lagrange function. Moreover, the fuzzy VIKOR method is applied to sort alternatives and choose the best alternative location. In addition, five alternative sites for a manufacturing company are evaluated and ranked according to the values of the ranking index as a numerical case to demonstrate the proposed framework’s application. Finally, a comprehensive analysis of diverse methods and sensitivity analyses for the volatility in criteria weights and decision-maker weights is illustrated to confirm that the framework is practicable for the problem of intelligent and sustainable manufacturing industrial park-site selection.

Live-streaming commerce has become a critical information channel through which consumers evaluate products and make purchase decisions, yet brand manufacturers face substantial uncertainty when selecting live-streaming partners. This study investigates how streamer influence and consumer sensitivity to live-streaming service quality jointly shape optimal cooperation structures in platform-based commerce. We develop a game-theoretic decision framework comparing three cooperation modes—Nash negotiation, manufacturer-led, and streamer-led—and derive closed-form equilibria for pricing, service quality, and profit allocation. The results show that manufacturer-led cooperation consistently maximizes brand profit by preserving incentives to provide service quality. In contrast, streamer-led cooperation can reduce overall efficiency when dominant streamers lack motivation to improve service quality. To corroborate our theoretical predictions, we employ live-stream sales data from the Douyin (TikTok) platform, covering multiple streamer tiers and two cosmetic brands, and find empirical support for the model’s key insights. The findings contribute to the literature on information processing and incentive alignment in platform-based live-streaming commerce, offering actionable guidance for manufacturers’ partnership strategies.

Live-streaming commerce has become a critical information channel through which consumers evaluate products and make purchase decisions, yet brand manufacturers face substantial uncertainty when selecting live-streaming partners. This study investigates how streamer influence and consumer sensitivity to live-streaming service quality jointly shape optimal cooperation structures in platform-based commerce. We develop a game-theoretic decision framework comparing three cooperation modes—Nash negotiation, manufacturer-led, and streamer-led, and derive closed-form equilibria for pricing, service quality and profit allocation. The results show that manufacturer-led cooperation consistently maximizes brand profit by preserving incentives for service quality provision, while streamer-led cooperation can reduce overall efficiency when dominant streamers lack motivation to improve service quality. Using live-stream sales data from the Douyin (TikTok) platform covering multiple streamer tiers and two cosmetic brands, we empirically validate the model's predictions. The findings contribute to research on information processing and incentive alignment in platform-based live-streaming commerce.