Designing a manufacturing cell system by assigning workforce

Purpose: In this paper, we have proposed a new model for designing a Cellular Manufacturing System (CMS) for minimizing the costs regarding a limited number of cells to be formed by assigning workforce. Design/methodology/approach: Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used. A small randomly generated test problem with real-world dimensions has been solved using simulated annealing and particle swarm algorithms. Findings: The quality of the two algorithms has been compared. The results showed that PSO algorithm provides more satisfactory solutions than SA algorithm in designing a CMS under uncertainty demands regarding the workforce allocation. Originality/value: In the most of the previous research, cell production has been considered under certainty production or demand conditions, while in practice production and demand are in a dynamic situations and in the real settings, cell production problems require variables and active constraints for each different time periods to achieve better design, so modeling such a problem in dynamic structure leads to more complexity while getting more applicability. The contribution of this paper is providing a new model by considering dynamic production times and uncertainty demands in designing cells.Peer Reviewe

Optimization of a network of compressors in parallel: Operational and maintenance planning – The air separation plant case

A general mathematical framework for the optimization of compressors operations in air separation plants that considers operating constraints for compressors, several types of maintenance policies and managerial aspects is presented. The proposed approach can be used in a rolling horizon scheme. The operating status, the power consumption, the startup and the shutdown costs for compressors, the compressor-to-header assignments as well as the outlet mass flow rates for compressed air and distillation products are optimized under full demand satisfaction. The power consumption in the compressors is expressed by regression functions that have been derived using technical and historical data. Several case studies of an industrial air separation plant are solved. The results demonstrate that the simultaneous optimization of maintenance and operational tasks of the compressors favor the generation of better solutions in terms of total costs

Joint optimization of dynamic lot-sizing and condition-based maintenance

This study investigates the dynamic lot-sizing problem integrated with Condition-based maintenance (CBM) for a stochastically deteriorating production system. The main difference of this work and the previous literature on the joint optimization of lot-sizing and CBM is the relaxation of the constant demand assumption. In addition, the influence of the lot-size quantity on the evolution of the equipment degradation is considered. To optimally integrate production and maintenance, a stochastic dynamic programming model is developed that optimizes the total expected production and maintenance cost including production setup cost, inventory holding cost, lost sales cost, preventive maintenance cost and corrective maintenance cost. The algorithm is run on a set of instances and the results show that the joint optimization model provides considerable cost savings compared to the separate optimization of lot-sizing and CBM

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing : an empirical study

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. (C) 2017 Elsevier Ltd. All rights reserved

Integrated condition-based planning of production and utility systems under uncertainty

A general rolling horizon optimization framework for the integrated condition-based operational and maintenance planning of production and utility systems in process industries is presented. In brief, the proposed optimization framework considers for the production and utility units: (i) improved unit performance degradation and recovery models that depend on both the cumulative time of operation and the unit operating levels deviation of units; (ii) modified operating capacities under online cleaning periods; (iii) different types of cleaning tasks (flexible time-window and online or offline condition-based); (iv) alternative options for offline cleaning tasks; (v) limited availability of cleaning resources; (vi) the initial state of the overall system at the beginning of each planning horizon; and (vii) terminal constraints for the rolling horizon problem. Total cost constitutes the objective function of the resulting problem and includes unit operating costs, cleaning costs, energy consumption costs and resource purchases costs. The case studies solved show that when compared to solutions obtained by sequential approaches the proposed integrated approach provides significantly better solutions in terms of total costs (reduction from 5%-32%), and especially in cost terms related to utility units operation, energy consumption, cleaning and startup/shutdown operations. Unnecessary cleanings and purchases of resources can be avoided by the proposed integrated approach. Overall, the significant reduction in total costs is a direct result of the enhanced energy efficiency of the overall system through the efficient generation and use of energy, the improved utilization of energy and material resources resulting in a more sustainable and cleaner production practices

A lean assessment tool based on systems dynamics

Lean manufacturing is synonymous with a set of practices used in the identification and elimination of waste related with the manufacturing system, and focusing on what creates value for the customer. Lean assessment tools enable an overall audit of the performance of lean practices, and so are able to identify lean improvements. The interactions between lean practices and their improvements are often latent and need to be investigated: a systems approach can be used to disclose these hidden interactions. In this article, system dynamics is used as a lean assessment tool to assess and improve lean performance for a print packaging manufacturing system

Preventive maintenance task balancing with spare parts optimisation via big-bang big-crunch algorithm

Work balancing increasingly plays an important role in both the production and maintenance functions. However, the literature on work balancing problems in transfer line manufacturing systems provides little information on the contributions of maintenance technicians and spare parts with a focus on penalty, technicians’ costs and incentives for staff. Unlike existing reports, the current investigation attempts to solve the maintenance task balancing problem. It combines preventive maintenance technicians’ assignments with product demand and spares utilisation in a transfer line manufacturing system. It uses an optimisation framework that measures the success of post-line balancing solution performance in a system from a holistic perspective. The novelty of the approach lies in the integration of technicians and spare parts theory and the introduction of penalty, technicians’ costs and incentive for staff. The proposed optimisation method was applied to a case study for detergent manufacturing system as a means of testing the effectiveness and robustness of the approach. The results show that the proposed model appears to be effective. Some simulations were also carried out to complement practical result

The Effects of JIT on the Development of Productivity Norms

Low inventory, or just-in-time (JIT) manufacturing systems, enjoy increasing application worldwide, yet the behavioral effects of such systems remain largely unexplored. Operations Research (OR) models of low inventory systems typically make a simplifying assumption that individual worker processing times are independent random variables. This leads to predictions that low-inventory systems will exhibit production interruptions. Yet empirical results suggest that low-inventory systems do not exhibit the predicted productivity losses. This paper develops a model integrating feedback, goal-setting, group cohesiveness, task norms, and peer pressure to predict how individual behavior may adjust to alleviate production interruptions in low-inventory systems. In doing so we integrate previous research on the development of task norms. Findings suggest that low-inventory systems induce individual and group responses that cause behavioral changes that mitigate production interruptions