The Multi-Objective Line Balancing (MOLB) game is a spreadsheet-based game to teach assembly line balancing as a common topic of discussion in operations research, operations management, supply chain management, or management science courses at the undergraduate or graduate level. The MOLB game was designed based on the triple bottom line framework, in which the economic, social, and environmental aspects of line-balancing decisions are simultaneously taken into account. The MOLB game can be played in teams of three or four students. First, each team receives unique information for balancing an assembly line. Each team should find as many feasible balances as possible in a collaborative form and then send the Pareto solution set and the best-found solution to a peer team. In the second round of the game, the teams assess the results of a peer team first by trying to find infeasible or non-Pareto solutions and second by attempting to improve on the provided solutions. Finally, the reviewer team presents the results of the peer-review process to the entire class. The main learning objectives of the game are to provide students with a learning experience in

• Balancing a production line w.r.t a set of constraints,
• Resolving a Multi-Objective Decision Making (MCDM) scenario,
• Team effort in discovering and advancing a solution for implementation,
• Peer critique of the suitability and appeal of a recommended solution or set of solutions.

Each team receives the same 30-task precedence diagram containing dedicated processing time and tool requirements per task. Tasks are the elements of work that have to be performed to assemble the product. The precedence diagram contains nodes and arrows that partially specify the sequence of tasks that has to be considered to be able to perform tasks. For instance, as shown in the diagram, both Tasks 4 and 5 have to be completed before Task 8 can be started. Performing each task requires some time called task processing time. Also, each task might need a specific type of cordless power tool that is necessary to perform the corresponding task. The processing time and tool requirement per task are given below and above the corresponding task number box in the diagram, respectively. There are three different types of cordless power tools labeled M1, M2, and M3. For instance, Task 5 does not need a tool; however, Tasks 9 and 13 require tool type M1 to be performed by the operator.

An assembly line comprises a sequence of working areas known as workstations (WSs). Each workstation continually perform a fixed set of tasks on consecutive product units moving along the assembly line at a constant speed. For instance, Tasks 1,2,3,4,6, and 7 with the same tool typle M3 have been allocated to WS1 whose workload is equal to the Tack time of 60. In a pull production system, the speed of the assembly line is determined by the pace of the custimer demand. To do so, the workload of each workstation should not suprpase a predetermined Takt time. One over the takt time determines the throughput rate of the assembly line

In the MOLB game, the effectiveness of a balancing solution is evaluated based on the TBL framework by taking the economic, social, and environmental aspects into account. The economic objective can be achieved by minimizing the number of workstations (i.e., a fewer number of workstations is translated into less labor and space costs), the social objective can be achieved by maximizing operators’ workload smoothness (i.e., a higher operator workload smoothness translated into more fairness and fewer ergonomic risks), and the environmental objective can be achieved by minimizing the total number of cordless power tools required for the entire assembly line (i.e., fewer tools are translated into less hazardous materials, such as cadmium and mercury used in the batteries that can contaminate soil and groundwater). The economic, social, and environmental objective values have different scales. Therefore, we first need to normalize them on a scale of [0, 1], where 0 shows no achievement and 1 shows a full achievement of the corresponding objective. The weighted-sum value corresponding to a balance decision can be calculated by incorporating the decision maker’s preferences and three normalized objectives.