This paper studies the pricing and green promotion effort decisions in a retailer-owned dual-channel supply chain consisting of multiple manufacturers one retailer. Specifically, we consider general version problem where inter-correlated products are sold through both online offline retail channels, customer demands jointly affected by product prices efforts. With objective maximizing members' profits, game theoretic models built to obtain optimal on wholesale prices, price markups efforts...

In this paper, we propose a novel reinforcement learning algorithm for inventory management of newly launched products with no or limited historical demand information. The follows the classic Dyna-$Q$ structure, balancing model-based and model-free approaches, while accelerating training process mitigating model discrepancy generated by feedback. Warm-start information from data existing similar can be incorporated into to further stabilize early-stage reduce variance estimated optimal...

Purpose This study explores optimizing high-speed railway (HSR) meal services, a unique logistical challenge requiring precise alignment with train departure times. Unlike standard delivery systems, HSR services demand strict on-time delivery, balancing the conflicting costs of earliness and tardiness while accounting for stochastic nature preparation processes. Design/methodology/approach A single-machine scheduling model is developed to minimize expected in delivery. The problem formulated...

In many real-world scenarios, reward signal for agents are exceedingly sparse, making it challenging to learn an effective function shaping. To address this issue, our approach performs shaping not only by utilizing non-zero-reward transitions but also employing the Semi-Supervised Learning (SSL) technique combined with a novel data augmentation trajectory space representations from majority of transitions, zero-reward thereby improving efficacy Experimental results in Atari and robotic...

We are interested in developing reinforcement learning algorithm to tackle risk-aware sequential decision making problems. The model we investigate is a discounted infinite-horizon Markov processes with finite state and action spaces. Our based on estimating general minimax function stochastic approximation, show that several risk measures fall within this form. derive finite-time bounds for by combining approximation the theories of dynamic programming. Finally, present extensions...

Electric vehicles (EVs) are now widely acknowledged as ideal means of transportation in future, terms energy conservation and environmental protection for urban governance. EV lease service is an effective measure to promote the use EVs has gained support many countries. In this article, a comprehensive pricing scheme developed optimize annual operations profit based on mileage-based (MBP), where defined revenue subtracted by maintenance cost. Different from traditional MBP vehicle service,...

Problem definition: Social influenced emotions of pride and guilt have been identified by the environmental psychology (EP) literature as crucial drivers impacting recycling behavior, but they mostly overlooked in operations management (OM) research. In contrast, EP studies often ignore firms’ operational decisions. We analyze impacts both social influence decisions to provide a comprehensive understanding consumers’ behaviors, which is essential for realizing remanufacturing’s full...

A new method for the vulnerability assessment of large-scale power grid based on small-world topological model is proposed to study intrinsic spreading mechanism chain failure in a this paper. It assesses by network topology modeling, basic characteristic parameters, fault simulation and calculation maximum supply area grid. The results practical large show that structural closely related structure, which verifies reasonable effective.

Electric vehicles (EVs) acknowledged as potential means of transportation tools in the near future are widely accepted development trend automobile industry due to its environment protection and fuel energy saving properties. As a product containing new technologies concepts with little market experience consumer identification, studying life cycle operation modes will help figure out an optimal approach conduct EVs manufacturing service. The two common modes, lease sale EVs, have their own...

Previous research on sales prediction has always used a single model. However, no model can perform the best for all kinds of merchandise. Accurate results just one commodity are meaningless to sellers. A general commodities is needed. This paper illustrates novel trigger system that match certain with give better different commodities. We find some related factors classification. Several classical models included as basic compared other models. The show accuracy than implications business...

We develop a stochastic approximation-type algorithm to solve finite state/action, infinite-horizon, risk-aware Markov decision processes. Our has two loops. The inner loop computes the risk by solving saddle-point problem. outer performs Q- learning compute an optimal policy. Several widely investigated measures (e.g., conditional value-at-risk, optimized certainty equivalent, and absolute semideviation) are covered our algorithm. Almost sure convergence rate of established. For error...

Nuclear power plants (NPPs) has their special characteristics, which both on product safety and the life cycle of supplier, thus evaluation selection equipment supplier requires independent index system, based establishing an framework is proposed to address problem in NPP application improved technique for order preference by similarity ideal solution (TOPSIS) weighted analytical hierarchy process (AHP) entropy value, where it calculates all indexespsila weights distances between find each...

In this paper, we consider decision-making problems where the decision maker's (DM's) utility/risk preferences are ambiguous but can be described by a general class of choice functions defined over space cumulative distribution (CDFs) random prospects. These assumed to satisfy two basic properties: (i) monotonicity w.r.t. order on CDFs and (ii) quasiconcavity. We propose maximin preference robust optimization (PRO) model optimal is based function from set elicited available information DM's...

China has experienced fascinating economic growth trajectory with huge energy consumption resulting therefrom over the past two decades. Meanwhile, environmental problem and sustainable development view been increasingly attracting emphasis by Chinese policy makers. The aim of this thesis is to find equilibrium satisfy economic, environmental, social demand. In paper, authors properly develop external cost into whole life cycle (LCC) for electric power generation, make preliminary analysis...

Decision maker's preferences are often captured by some choice functions which used to rank prospects. In this paper, we consider ambiguity in over a multi-attribute prospect space. Our main result is robust preference model where the optimal decision based on worst-case function from an set constructed through elicitation with pairwise comparisons of Differing existing works area, our focus quasi-concave rather than concave and enables us cover wide range utility/risk problems including...

The absence of transparency and explainability hinders the clinical adoption Machine learning (ML) algorithms. Although various methods explainable artificial intelligence (XAI) have been suggested, there is a lack literature that delves into their practicality assesses them based on criteria could foster trust in environments. To address this gap study evaluates two popular XAI used for explaining predictive models healthcare context terms whether they (i) generate domain-appropriate...