Manufacturing cell formation with flexible processing capabilities and worker assignment: Comparison of constraint programming and integer programming approaches

Abstract

Cell formation deals with grouping of machines and parts in manufacturing systems according to their compatibility. Manufacturing processes are surrounded with an abundance of complex constraints which should be considered carefully and represented clearly for obtaining high efficiency and productivity. Constraint programming is a new approach to combinatorial optimization and provides a rich language to represent complex constraints easily. However, the cell formation problems are well suited to be solved by constraint programming approach since the problem has many constraints such as part-machine requirements, availabilities in the system in terms of capacity, machine and worker abilities. In this study, the cell formation problem is modeled using machine, part processing and worker flexibilities via resource element–based representation. Resource elements define the processing requirements of parts and processing capabilities of machines and workers, which are resource-independent capability units. A total of 12 case problems are generated, and different search phases of constraint programming are defined for the solution procedure. The cell formation problem is modeled in both constraint programming and integer programming, and a comparative analysis of constraint programming and integer programming model solutions is done. The results indicate that both the models are effective and efficient in the solution of the cell formation problem.

Big Data-Driven Assessment of Proposals to Improve Enterprise Flexibility Through Control Options Untested in Practice

“…The interactions between the technical and social aspects of production are covered in many studies (Baykasog ˇlu et al, 2016;Doyle-Kent & Kopacek, 2020;Eidelwein et al, 2018;Kluz & Antosz, 2019;Slavin, 2000). These studies show that professional and social skills have an impact on productivity, as well as that, the more interested workers are in interacting together in a coordinated way, the more their productivity grows.…”

Integrating multi-dynamic virtual cellular manufacturing systems into multi-market allocation and production planning

“…Development of the model under other stochastic parameters such as costs, machine failure, machine and labour availability. 21…”

“…As a direction for future research, it would be interesting to investigate the following directions: Development of the model under other stochastic parameters such as costs, machine failure, machine and labour availability. 21 Applying the multi-objective approach to solve the considered problem in which the CFP decisions could be optimized in one objective while production planning is optimized in the other. Aggregating other production aspects like layout problem considerations and scheduling into the proposed model.…”

“…Human issues include worker assignment, team work, describing roles of workers, skill identification, training and reward/ compensation system. Considering that human issues increase the model complexity, 21 they make it practically difficult to solve the workforce planning models within a reasonable time. Numerous researchers have developed heuristic algorithms for solving these problems.…”

A Literature Survey on Various Factors in Dynamic Cellular Manufacturing System

“…Resource elements define the processing requirements of parts and processing capabilities of machines and workers, which are resource-independent capability units. [16] UK  Papaioannou G et al, : A static environment is related to the traditional CMS where no changes in the demand are taken into account whereas in dynamic environment, the part demand volume and part mix will change. Most previous articles considered the CMS under the static condition and just formed cells in a single-time period.…”