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This work presents a novel approach that addresses the management of chemical supply chains (SCs) under demand uncertainty. One of the main objectives is to overcome the numerical difficulties associated with solving the underlying large-scale mixed integer nonlinear problem (MINLP). The approach that is proposed relies on a simulation-based optimization strategy that uses a discrete-event system to model the SC. Within this framework, each SC entity is represented as an agent whose activity is described by a collection of states and transitions. The overall system is coupled with an optimization algorithm that is designed to improve its operation. This strategy is a very attractive alternative in the field of decision-making processes under uncertainty, the advantages of which are highlighted with some cases of SC networks that are composed of several plants, warehouses, distribution centers, and retailers.

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In this article the scheduling of batch plants is integrated with pricing decisions. The proposed integrated model simultaneously provides the optimal prices and schedule as opposed to earlier models where prices are usually considered as input data. The main advantages of such formulation are highlighted through a case study where comparison with the traditional approach is carried out. A two-stage stochastic mathematical model is also developed in order to address the uncertainty associated to the demand curve. Finally, financial risk management is discussed.

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In this work, we present a novel approach that provides decision support in making optimal offer proposals during the negotiation process between customers and suppliers that takes place in chemical industry supply chains (SC). The proposed approach takes into account the tradeoff between the quality of the offers made to customers, i.e., the level of satisfaction perceived by the client, and the expected profit to be achieved in the short-term operation of the SC. Therefore, a two-stage stochastic formula is derived that considers the uncertainty associated with reactions to future demand in an attempt to compute a set of Pareto-optimal solutions to the proposed problem. Each of these solutions comprises an SC schedule and a set of values for the parameters of the offers. Through comparison of the Pareto curve and the solution that would be obtained without negotiation, a set of offers representing contracts that are desirable from the supplier's perspective is obtained. This set of values may be offered by the supplier to reach an agreement with the customer during the negotiation procedure. The proposed approach facilitates a rational negotiation, in the sense that it enables the negotiator to simultaneously process many more data related to production and transport plans and customer preferences, thus avoiding having to rely exclusively on the negotiator's beliefs and interests.