Printed circuit board (PCB) assembly lines are significant to produce a variety of electronic products. PCB manufacturing industries tend to move towards automated and complex manufacturing system due to an increase in the customer demand for more sophisticated products. PCB assembly process involves highly interrelated levels of planning and scheduling problems. Therefore, the current research investigates multi-level planning and scheduling of PCB assembly lines, which include line assignment to the PCB models, component allocations to machines, and component placement sequencing by machines on PCB boards. A mixed integer-programming model is developed to integrate the planning and scheduling problem of parallel PCB assembly lines to maximize the net profit. A hybrid spider monkey optimization (HSMO) algorithm is proposed to solve the multi-level planning and scheduling problem. The performance of the proposed HSMO algorithm is compared to artificial bee colonial (ABC), genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms. Moreover, the proposed HSMO algorithm is validated against ABC, GA, PSO, and SA algorithms with the case problem taken from well-reputed PCB manufacturing industry in China. The computation experiments indicate that the proposed HSMO algorithm can achieve good near-optimal solutions when compared with the other-mentioned four algorithms based on performance and efficiency for benchmark problems and real case problem.

In recent years, environmental concerns have increased the need for design and optimisation of closed-loop supply chain (CLSC) networks. Majority of the existing research papers consider the CLSC network designing and line balancing decisions separately. However, this approach may lead to sub-optimal designs due to the interdependency of these decisions. To this end, this paper investigates a CLSC network designing problem integrated with assembly and disassembly line balancing under demand and return uncertainty. The proposed CLSC network contains manufacturers, remanufacturers, assembly centres, intermediate centres (where disassembly lines are located), and customer centres. A new mixed integer non-linear programming model for the proposed problem is developed. Furthermore, an enhanced decomposition approach is developed to solve the proposed model. Computational results, based on randomly generated problem instances, show the efficiency of proposed enhanced decomposition approach. Specifically, results shows that the proposed enhanced decomposition approach leads to significantly smaller running times in comparison with an existing decomposition approach. Results also highlight the importance of integrating supply chain network designing and line balancing decisions.

Today’s dynamic environment and increasing demand for highly customized products have significantly increased the number of companies operating in the project environment. Project planning and scheduling are one of the major problems faced by managers due to resource constraints. Enterprises have to execute several projects simultaneously while sharing limited resources (i.e., human resources, equipment, and budget) among the projects to effectively meet the deadlines. Therefore, this work investigates the integrated planning and scheduling problem of multiple projects with different release dates and execution modes while considering the renewable and non-renewable resource constraints. Moreover, the raccoon family optimization (RFO) algorithm is proposed to maximize the net profit while considering the early completion bonus, penalty cost, and resource costs. In the proposed RFO algorithm, greedy search and modified genetic operators are introduced to enhance the performance and efficiency. Effectiveness of the proposed RFO algorithm is compared with the genetic algorithm (GA), raccoon optimization algorithm (ROA), and artificial bee colonial (ABC) algorithm on test cases as well as an industrial case study. The results indicate that the proposed RFO algorithm outperforms the other compared algorithms, both in terms of effectiveness and efficiency.

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to “learning effect”. The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

Operating room planning and scheduling significantly affect all hospital areas, including the intensive care unit and downstream wards. Planning and scheduling operating rooms integrated with intensive care units and downstream wards can lead to more stable plans and schedules that are less prone to cancellations. Thus, this study considers the operating room’s capacity and downstream units while making surgery-related decisions. A mixed integer linear programming model for integrated planning consisting of two stages is proposed. The first stage model maximizes the scheduled surgical time of all operating rooms. In contrast, the second stage model aims to minimize the makespan of patients in operating rooms by incorporating sequence-dependent setup time and the capacity constraints of all resources under consideration at both stages. A two-stage genetic artificial bee colony algorithm (TGABC) hybrid of genetic algorithm and artificial bee colony algorithm is proposed to solve the model. Taguchi design of experiments is employed to fine-tune the parameters of the proposed TGABC algorithm. Experiments are designed to evaluate the performance of the proposed TGABC algorithm with generated instances mimicking real data for different-sized problems, and results are presented. The proposed method is compared with the exact method and three standard metaheuristics. It provides near-optimal results in comparatively shorter CPU time. Moreover, it outperforms the genetic algorithm, artificial bee colony algorithm, and simulated annealing in terms of solution quality compared on the considered test instances.