Inspired by the industries such as food and beverage, metal and steel, as well as petroleum and petrochemical ones, the current study addresses a joint order acceptance and scheduling, lot streaming in a flexible flow shop and batch delivery problem. For maximizing a profit objective function with trading off between the revenue of the accepted orders and the costs incurred, a novel mixed integer linear programming is proposed. This paper develops a hybrid metaheuristic algorithm based on the Genetic Algorithm. In the developed algorithm, (1) a heuristic, (2) a local search, and (3) a restart phase is proposed. To set the appropriate parameters of the algorithms, Taguchi experimental design was applied. The obtained results reveal the appropriate performance of the hybrid genetic algorithm.

The way orders are accepted or rejected is the most important factor in customer satisfaction and success of make-to-order systems. The incoming orders to such organizations have certain delivery date in which the customer expects the order to be fulfilled and delivered. In some cases, unexpectedly increased orders exceed the existing capacity for on time fulfillment. In addition to rejection of order, as a typical choice, other options like outsourcing and capacity expansion are available to compensate for capacity shortage and deliver incoming orders according to schedule. However, each of the proposed options is superior in one or more criteria and so selecting the best one is not simply possible. The main goal of this study is to provide managers with a comprehensive, systematic, and applicable approach to evaluate and select the best of the existing options. For this purpose, a model comprised of some multi-criteria techniques is delivered. Our proposed model is a blend of FAHP and FTOPSIS methods. In this model, FAHP is first used to determine the weight of criteria and then Fuzzy-TOPSIS (FTOPSIS) is employed to rank the options. Finally, the proposed model is applied on an actual case to assess and examine its efficiency.

Decisions related to acceptance or rejection of orders play an important role in companies engaged in make-to-order production. The incoming orders have a specific delivery date by which the customer expects the due date to be met and the order delivered. In some cases the level of input orders exceeds beyond the existing capacity. In such situations the main concern is to decide which orders must be accepted and which ones rejected taking into account the available production capacity. This paper prioritises the input orders according to a comprehensive and systematic multi criteria decision making (MCDM) model. It then proceeds with making decisions to either accept or reject orders according to the calculated prioritises and production constraints. Ultimately the optimum list of orders for acceptance is determined. The proposed model is a combination of two techniques of Fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this model FAHP is used to determine the weights of criteria and TOPSIS is used for prioritizing the orders. Finally the proposed model is tested for its efficiency by application to a real case.