Yomna Hossam Eldin Gaber

In serial multi-stage manufacturing systems (SMMS), optimization of part quality inspection planning (PQIP), buffer allocation problem (BAP), and preventive maintenance (PM), individually and jointly, is attracting researchers’ attention. The model formulation for complicated manufacturing systems and the previously mentioned joint decisions is very beneficial given the interdependencies between the various manufacturing functions. As a result, this paper evaluates the literature on joint optimization of the multi-stage serial production system. The literature is classified based on the decision variables basis to represent each manufacturing function [inspection sample size and allocation (PQIP), buffer sizing and allocation (BAP), and preventive maintenance scheduling (PM)], and a general example model is presented in each classification, with a summary of recent studies, solution methods, research gaps, and future research recommendations. In the integrated models, almost all the studies considered only two functions, with that it is worth noting that research into the optimization of over two functions is still in its beginning. Furthermore, most studies neglected many of the real industrial settings that should also be integrated into the model. And finally, there was no specific solution technique recommended in the literature, yet a general simulation optimization method was used to generate and evaluate the combinatorial complex joint models.

Establishment of aggregation hubs in a supply chain network (SCN) is typically a facility location-allocation (FLA) decision, which is known to be a NP-hard optimization problem. Considering the flow of heterogeneous perishable products, like fresh produce, with different spoilage rates, further increases the complexity of such a problem. This is due to the effect of transportation time and conditions, services provided in the hub, and hub proximity to supply sources, on the quality and quantity of products eventually reaching the demand destinations, and hence on the location-allocation decision. In this paper, this problem is formulated as a mixed integer linear programming (MILP) model that considers a number of problem characteristics simultaneously for the first time, to minimize the transportation, spoilage, processing, and capacity-based hub establishment costs. Due to its complexity, two hybrid algorithms that combine a meta-heuristic with a perishability-modified transportation algorithm, are proposed to solve the problem. The algorithms are based on binary particle swarm optimization (BPSO) and simulated annealing (SA). Taguchi analysis is used to tune the significant parameters of both algorithms considering different problem sizes. Computational analysis is further conducted to evaluate and compare the performances of the algorithms using randomly generated test instances and exact solutions obtained using CPLEX. Results show that while both algorithms are capable of obtaining optimum solutions for most instances, the hybrid BPSO slightly outperforms the hybrid SA in terms of consistency and solution time.