A bi-objective model in sustainable dynamic cell formation problem with skill-based worker assignment

Abstract

The most recent revolution in industry (Industrial Revolution 4.0) requires increased flexibility, agility and efficiency in the use of production equipment. The Dynamic Cellular Manufacturing System (DCMS) is one of the best production systems to meet such requirements. In addition, the increasing importance of environmental and social issues, along with recent laws, is forcing manufacturers and managers to take account of sustainability when designing and configuring manufacturing systems. This paper proposes a new bi-objective mathematical model of the Dynamic Cell Formation Problem (DCFP), in which both the worker's assignment and environmental and social criteria are considered. The first objective in this model is to minimize both production and labor costs, and total waste (e.g., energy, chemical material, raw material, CO2 emissions, etc.). Social criteria are represented as constraints. Due to the NP-hardness of this problem, we propose a new resolution approach called NSGA II-MOSA, that merges an efficient hybrid meta-heuristic based on the Non-dominated Sorting Genetic Algorithm (NSGA-II), with Multi-Objective Simulated Annealing (MOSA). Randomlygenerated test problems demonstrate the performance of our algorithm. Literature reviewThis section gives an overview of the most prominent research on DCMS. Due to the large number of investigations in this area, we focus mainly on recent studies. First, Rheault et al. [3] introduced the concept of a dynamic environment in CFP. Schaller et al. [4] integrated CFP with inventory aspects, then showed the performance of their model on multiple heuristics and evaluated several alternative lower bounding methods. Chen and Cao [5] proposed a mathematical model for multi-period Cellular Manufacturing Systems (Dynamic CMS) minimizing the total cost, which includes: inter-cell material handling, inventory holding and the setting up of cells. They also developed a Tabu Search (TS) method to obtain good solutions and show the efficiency of their model. Next, these authors [6] generated a robust system configuration by integrating cell formation and part allocation. They also proposed a twostage TS to find the optimal or near optimal solutions. Tavakkoli-Moghaddam et al. [7] presented a nonlinear integer model of DCMS with machine capacity limitation, machine replication, inter-cell movements and production in batches. They used constant and variable costs as well as reconfiguration and inter-cell movement costs to formulate their objective function. Some of these authors [8] applied a Memetic Algorithm (MA) to solve their DCMS model. Defersha and Chen [9] formulated a comprehensive model containing dynamic cell configuration, alternative routings, lot splitting, sequence of operations and workload balancing. They also considered machine adjacency and cell size capacity as constraints. Moreover, Defersha and Chen [10] also proposed a two-phase GA-based heuristic to solve DCFP with alternative routings. Safaei et al. [11],[12] presented a DCMS mathematical model with unc...

Mathematical model for dynamic cell formation in fast fashion apparel manufacturing stage

“…Based on the production requirements and desired design attributes, CMS can be broadly categorized into two segments as Static Cellular Manufacturing System (SCMS) and Dynamic Cellular Manufacturing System (DCMS) (Niakan et al 2016;Khannan et al 2016).…”

“…As stated by Houshyar et al (2014), presently the low volume-high variety products with volatile demand and shorter lead-times are popular in most of the industries. Optimal cells of a particular period may not be optimal for other periods due to possible variations of production requirements of different product mixes (Niakan et al 2016;Deep and Singh 2016). According to Niakan et al (2016), static cells are beneficial if the same product mix is manufactured for entire planning horizon or the new products are perfectly matched with existing product families being manufactured in static cells.…”

“…Optimal cells of a particular period may not be optimal for other periods due to possible variations of production requirements of different product mixes (Niakan et al 2016;Deep and Singh 2016). According to Niakan et al (2016), static cells are beneficial if the same product mix is manufactured for entire planning horizon or the new products are perfectly matched with existing product families being manufactured in static cells. Pillai et al (2011), Modrák (2011) and, Marsh et al (1997 argued that the static cells are inflexible for introduction of completely new product mix.…”

“…Incorporating flexible layouts that can accommodate dynamic production environments while ensuring minimum manufacturing cost is vital to be competitive in & Gayathri Perera upamali28@gmail.com Vijitha Ratnayake vijithar@uom.lk volatile market conditions (De Carlo et al 2013;Hamedi et al 2012). Niakan et al (2016) and Nouri (2016) suggested Dynamic Cellular Manufacturing System (DCMS) as the most suitable approach in achieving high degree of flexibility and agility to manage changes in product mix.…”

Dynamic cellular manufacturing system considering machine failure and workload balance

“…Cellular manufacturing (CM) is one of the most important applications of GT, which takes advantage of the link between the flow-shop system (high rate of production) and job-shop systems (manufacturing flexibility and diversity of products). Cellular manufacturing system (CMS) enables us to produce various products with low waste (Niakan et al 2016), so it is used in most major manufacturing centers with a relatively high diversity of products and also multipurpose facilities. As defined, CM means processing a set of similar parts on certain groups of machines or manufacturing processes (Mungwattana 2000).…”