Process Planning, Scheduling, and Layout Optimization for Multi-Unit Mass-Customized Products in Sustainable Reconfigurable Manufacturing System

Currently, manufacturers seek to provide customized and sustainable products, requiring flexible manufacturing systems and advanced production management to cope with customization complexity and improve environmental performance. The reconfigurable manufacturing system (RMS) is expected to provide cost-effective customization in high responsiveness. However, reconfiguration optimization to produce sustainable mass-customized products in RMS is a complex problem requiring multi-criteria decision making. It is related to three problems, process planning, scheduling, and layout optimization, which should be integrated to optimize the RMS performance. This paper aims at integrating the above three problems and developing an effective approach to solving them concurrently. It formulates a multi-objective mathematical model simultaneously optimizing process planning, job-shop scheduling, and open-field layout problem to improve RMS sustainability. The penalty for product tardiness, the total manufacturing cost, the hazardous waste, and the greenhouse gases emissions are minimized. Economic and environmental indicators are defined to modify the Pareto efficiency when searching the Pareto-optimal solutions. Exact Pareto-optimal solutions are obtained by brute-force search and compared with those of the non-environmental indicator model. NSGA-III is adopted to obtain the approximate Pareto-optimal solutions in high effectiveness and efficiency. A small numerical example is applied to validate the mathematical model and resolution methods.

MODELING THE PLAN OF THE TRANSPORTATION PROCESS DURING THE MORNING RUSH HOUR ON THE CLOSED BOLSHAYA KOLTSEVAYA LINE OF THE MOSCOW METRO

The article analyzes the characteristics of the construction of the Bolshaya Koltsevaya line (Large Circle Line) of the Moscow Metro from the point of view of the transportation process planning. The authors have studied the characteristics of all operating depots of the Moscow Metro that justify the plans for the construction of two new depots (Aminievsky and Nizhegorodsky), as well as the reconstruction of one of the existing depots (Zamoskvoretsky), located within the city limits of Moscow – inside the Moscow Automobile Ring Road (MKAD), in the vicinity of the Third Transport Ring and beyond. For the needs of modeling, the station-to-station length of the closed Bolshaya Koltsevaya line was measured, subject to the transfer of a train traffi c section from the Delovoy Tsentr station to the Khoroshevskaya station of the projected Rublyovo-Arkhangelskaya metro line. Based on the measurement fi ndings, the station-to-station travel times along the closed Bolshaya Koltsevaya line were calculated, which subsequently made it possible to determine the number of electric rolling stock units required to service the Bolshaya Koltsevaya line in the morning rush hour

A Hybrid Grey Wolf Optimizer for Process Planning Optimization with Precedence Constraints

Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.

Modeling Of Multi-Objective Process Plan, Its Optimization using Linear Modeling Technique

The Process planning is the procedure to opt for and schedule manufacturing procedure so as to attain one or more organizational goals and suit with a set of constraints. More specifically a Process planning in the reconfigurable manufacturing setup engages a sequence of all activities from raw material storage into the finished manufactured yield. In the current study a manufacturing setup of automotive industry is discussed. At the outset of papers, a basic process plan is modeled that includes design requirements after that it is mathematically modeled. Mathematically modeled process plan is then optimized in order to find optimal solution. Research then search the potential of linear programming optimization technique in handling optimization of process plan.

Devolpment and Modeling of an Industrial Process Plan, Its Optimization using stochastic search Optimization Technique

Industrial process planning is principally an association between design and development or final production and has vital function in the manufacturing systems. In this paper the under research industry is security vehicle manufacturing industry in Pakistan. First of all a fundamental process plan is developed and then modeled mathematically using progressive closed loop approach. Mathematically modeled process plan is then optimized in order to find optimal or sub optimal solutions. Research then investigates the capability of an innovative optimization technique called stochastic search in handling optimization of manufacturing process plan. This new technique of stochastic, searches the best approximate process planning solution. Finally the research examines the convergence of optimization techniques to an optimal solution for a manufacturing framework.