Because of the fundamental role of production flow lines in modern industry, considerable attention has paid to how improve their efficiency. A particular performance measure indicating efficiency is throughput. Throughput is the number of completed units of work per time period in steady state. In general, the throughput of a production line increases, or at least remains constant, as the buffer capacities increase. A larger throughput is usually the consequence of a larger buffer configuration, which, in turn, yields a larger inventory accumulation. One of the main problems with designing a production flow line is to determine the appropriate amount of buffers between stations in order to achieve the goals. This is known as a Buffer Allocation problem (BAP). The BAP is a nonlinear, multi-objective one in integer variables. The purpose of this paper is to present a heuristic algorithm to find the optimal Allocation of buffers that maximizes throughput. The main idea is to find the amount of buffers under which the system operates, as a system with infinite buffer capacity, so the stations will be independent of each other and throughput becomes maximum. Numerical results are used to demonstrate the accuracy of the proposed algorithm. The algorithm finds the Allocation where it's throughput is maximum, or only slightly less than maximum. But where it's total buffer capacity is considerably less than optimal Allocation.

We develop a bicriteria model for the resource Allocation problem in PERT networks, in which the total direct costs of the project as the first objective, and the mean of project completion time as the second objective are minimized. The activity durations are assumed to be independent random variables with either exponential or Erlang distributions, in which the mean of each activity duration is a non-increasing function of the amount of resource allocated to it. The direct cost of each activity is assumed to be a non-decreasing function of the amount of resource allocated to it. Finally, we use the goat attainment method to solve the related bicriteria optimal control problem numerically, by converting this problem to a related bicriteria nonlinear programming, and obtain the optimal values of the resources allocated to the activities.

The Redundancy Allocation problem (RAP) aims to optimize system reliability or cost by selecting redundant components under given constraints. While traditional RAP studies focus on reliability or cost alone, real-world systems, particularly queueing networks, require a balance between redundancy Allocation and operational performance. This paper investigates the RAP for a tandem queueing network with repairable subsystems, where queueing costs and repair costs are jointly minimized. Unlike prior works, our model integrates queueing dynamics into redundancy optimization, ensuring both system reliability and operational efficiency. To solve this NP-hard problem, we propose a hybrid simulation-PSO algorithm, combining simulation for performance evaluation and Particle Swarm Optimization (PSO) for efficient solution search. Extensive numerical experiments demonstrate that our approach effectively minimizes total system costs while maintaining reliability. The results validate the applicability of our model in real-world service and manufacturing systems, such as healthcare, assembly lines, and logistics networks.

In this paper, we develop a capacitated location-covering model considering interval values for demand and service parameters. We also consider flexibility on distance standard for covering demand nodes by the servers. We use the satisfaction degree to represent the constraint of service capacity. The proposed model belongs to the class of mixed integer programming models. Our model can be reduced to the p-median problem in polynomial time so it is NP-Hard. A genetic algorithm is proposed to solve the developed model and experimental results of solving the model are presented.

Container terminals are places where cargo is transported from point to point using special equipment. One of the most important issues in planning coastal operations at container terminals is the issue of the planning of the embankment or quay, which has a significant impact on crane, yard, warehouse and truck routing programs. This research involved a basin planning model with a multi-pier simultaneous planning approach in a port. An improved chemical reaction algorithm was used to solve this model. The CRO algorithm is an effective algorithm for searching for optimal or near optimal solutions. This algorithm has a variable population that sometimes slows down the algorithm or renders some of its operators unusable. In this study, it is attempted to keep the population of the algorithm in proper range. To evaluate the proposed method, we investigate the scheduling results of the proposed method with three algorithms DE, GA and PSO. By reducing ship expectations, the port in question can be more successful in competing with nearby ports by increasing the satisfaction of shipping companies and delivering higher productivity and profitability.