Capacity Requirements Planning for Production Companies Using Deep Reinforcement Learning

Part 7: Deep Learning - Convolutional ANNInternational audienceIn recent years, deep reinforcement learning has proven an impressive success in the area of games, without explicit knowledge about the rules and strategies of the games itself, like Backgammon, Checkers, Go, Atari video games, for instance [1]. Deep reinforcement learning combines reinforcement-learning algorithms with deep neural networks. In principle, reinforcement-learning applications learn an appropriate policy automatically, which maximizes an objective function in order to win a game. In this paper, a universal methodology is proposed on how to create a deep reinforcement learning application for a business planning process systematically, named Deep Planning Methodology (DPM). This methodology is applied to the business process domain of capacity requirements planning. Therefore, this planning process was designed as a Markov decision process [2]. The proposed deep neuronal network learns a policy choosing the best shift schedule, which provides the required capacity for producing orders in time, with high capacity utilization, minimized stock and a short throughput time. The deep learning framework TensorFlowTM [3] was used to implement the capacity requirements planning application for a production company

Value chain management for commodities: a case study from the chemical industry

We present a planning model for chemical commodities related to an industry case. Commodities are standard chemicals characterized by sales and supply volatility in volume and value. Increasing and volatile prices of crude oil-dependent raw materials require coordination of sales and supply decisions by volume and value throughout the value chain to ensure profitability. Contract and spot demand differentiation with volatile and uncertain spot prices, spot sales quantity flexibility, spot sales price¿3quantity functions and variable raw material consumption rates in production are problem specifics to be considered. Existing chemical industry planning models are limited to production and distribution decisions to minimize costs or makespan. Demand-oriented models focus on uncertainty in demand quantities not in prices.We develop an integrated model to optimize profit by coordinating sales quantity, price and supply decisions throughout the value chain. A two-phase optimization approach supports robust planning ensuring minimum profitability even in case of worst-case spot sales price scenarios. Model evaluations with industry case data demonstrate the impact of elasticities, variable raw material consumption rates and price uncertainties on planned profit and volume