Applied Soft Computing 12 (2012) 1359–1370 Contents lists available at SciVerse ScienceDirect Applied Soft Computing j ourna l ho mepage: www.elsevier.com/locate/asoc Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines H. Mosadegh a , M. Zandieh b , S.M.T. Fatemi Ghomi a,∗ a Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, 1591634311, Tehran, Iran b Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, G.C., Tehran, Iran a r t i c l e i n f o Article history: Received 10 February 2011 Received in revised form 3 October 2011 Accepted 23 November 2011 Available online 16 December 2011 Keywords: Mixed-model assembly line Sequencing Balancing Mixed-integer linear programming Genetic algorithm Simulated annealing Taguchi a b s t r a c t In spite of many studies, investigating balancing and sequencing problems in Mixed-Model Assembly Line (MMAL) individually, this paper solves them simultaneously aiming to minimize total utility work. A new Mixed-Integer Linear Programming (MILP) model is developed to provide the exact solution of the problem with station-dependent assembly times. Because of NP-hardness, a Simulated Annealing (SA) is applied and compared to the Co-evolutionary Genetic Algorithm (Co-GA) from the literature. To strengthen the search process, two main hypotheses, namely simultaneous search and feasible search, are developed contrasting Co-GA. Various parameters of SA are reviewed to calibrate the algorithm by means of Taguchi design of experiments. Numerical results statistically show the efficiency and effectiveness of the proposed SA in terms of both the quality of solution and the time of achieving the best solution. Finally, the contribution of each hypothesis in this superiority is analyzed. © 2011 Elsevier B.V. All rights reserved. 1. Introduction Nowadays, the new pattern of customer demand has increas- ingly made modern methods of manufacturing to be invented. One of the most important challenges of manufacturing companies is assembling various common-based products (called models) in one line. This type of assembly line, widely used in Just-In-Time (JIT) systems, is called Mixed-Model Assembly Line (MMAL). In MMAL, results of solving two main problems, i.e. balancing and sequenc- ing problems, are used for strategic and tactical planning of the assembly line respectively. There are many studies, solving afore- mentioned problems separately or hierarchically, in the literature, but it suffers from lack of a comprehensive simultaneous consider- ation, especially with more realistic assumptions. For each of balancing or sequencing problems, there are spe- cific surveys, presented by Boysen et al. [1,2] and Becker and Scholl [3]. Also, many researchers have investigated both problems sepa- rately, hierarchically or simultaneously such as Dar-El and Nadivi [4], Sawik [5], Kim et al. [6,7], Miltenburg [8], Bock et al. [9], Kim et al. [10], Simaria and Vilarinho [11], KarabatI and SayIn [12], Man- souri [13], Sabuncuoglu et al. [14] and Hwang and Katayama [15]. ∗ Corresponding author. Tel.: +98 21 64545381; fax: +98 21 66954569. E-mail address: fatemi@aut.ac.ir (S.M.T.F. Ghomi). The differences and contributions of this paper comparing with MMAL’s literature can be summarized as follows. 1) A new problem is defined. Depending on established equipment, the processing time of each task might differ from station to station. Hence, the problem with station-dependent assembly times is studied minimizing total utility work. 2) A new Mixed-Integer Linear Programming (MILP) model is developed which simultaneously considers both balancing and sequencing problems with station-dependent assembly times. The exact solution of the problem is provided by solving the MILP model. 3) A Simulated Annealing (SA), originated by Kirkpatrick [16], is applied to deal with the problem. The proposed SA includes two new hypotheses, simultaneous search and feasible search, which have been developed contrasting Co-evolutionary Genetic Algo- rithm [6]. In addition, it uses the advantage of a new feature of the problem, called selection mechanism. 4) Some new test problems are created. Some of test problems are originally from [17] and the others (very larger than [17]) are generated by authors which can be used for future studies. Despite new versions of SA [18], this paper focuses on strength- ening search process using a simple SA instead of complicated algorithms, e.g. Co-GA. Furthermore, to examine the capability of 1568-4946/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.asoc.2011.11.027