A multiperiod optimization model is proposed for addressing the supply chain optimization in continuous flexible process networks. The main feature of this study is that detailed operational decisions are considered over a short time horizon ranging from 1 week to 1 month. For given flexible process networks where dedicated and flexible processes coexist, we take into account the supply chain for sales, intermittent deliveries, production shortfalls, delivery delays, inventory profiles, and job changeovers. The proposed optimization model requires efficient solution strategies to reduce the computational expense. We describe a bilevel decomposition algorithm that involves a relaxed problem (RP) and a subproblem (SP) for the original supply chain problem. Decisions for purchasing raw materials are made in the RP in which the changeover constraints are relaxed, yielding an upper bound to the profit. In the SP, fixing the delivery predicted in RP, the supply chain optimization is performed with job changeovers, yielding a lower bound. As will be shown in the examples, the algorithm achieves significant reduction in CPU time for the larger problems.

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An efficient short-term scheduling, mixed integer programming model for a multipurpose pipeless plant over a continuous-time domain is addressed. In contrast to the conventional scheduling models for the pipeless plants with discrete time representation requiring a large number of 0−1 variables, the continuous-time model where each product has deterministic processing stage blocks with duration for unit allocation and each processing unit has a corresponding time slot is built. Jobshop features such as re-entrant production flows and diverse processing directions are effectively modeled by identifying the stage number of products in an invariant way regardless of processing sequence or recipe. The performances of the model and the solution method are illustrated through two examples.

Mixed integer linear programming based methods have been widely employed but have been limited to problems with simple recipes or small-scale scheduling problems. This paper proposes the sequence branch algorithm (SBA) that can handle large-scale complex scheduling problems for batch processes under unlimited intermediate storage policy. The SBA generates the schedule tree in which all feasible schedules are included. A heuristic function is employed to accelerate the searching speed and it causes the results somewhat deviated from the optimal solution. The efficiency of the proposed algorithm is illustrated through complex and large-scale problems including a case study of a semiconductor fabrication process.