Real Time Scheduling of Nonidentical Multiple Batch Processors with Machine Eligibility Restriction

This study addresses a new problem on scheduling of nonidentical multiple diffusion furnaces (NMDF) with machine eligibility restriction (MER) along with important real-life problem characteristics such as multiple and incompatible job-families, different release time, different due-date, non-agreeable release time & due-date, and occurrences of randomly and dynamically generated real time events (jobs and/or resources related) to minimize total weighted tardiness. Due to the computational difficulty in obtaining an optimal solution for large real-life sized scheduling of NMDF, an alternate solution procedure: heuristic algorithm based on due-date based dispatching rule, called due-date based heuristic algorithm (DDHA) is constructed in this study. Though there has been extensive scheduling research relating to the use of existing dispatching rules in semiconductor manufacturing along with/without new dispatching rules, there is no comparative analysis of all due-date based dispatching rules for real time scheduling (RTS) of NMDF-MER. Hence, this study proposes 20 variants of DDHA, considering various due-date based dispatching rules, for RTS-NMDF-MER and compares their performance. A suitable experimental design is developed and randomly generated 270 instances to represent the research problem considered in this study. From the empirical and statistical analysis carried out in this study, the better performing DDHA(s), for RTS-NMDF-MER, are identified.

Bi-objective unrelated parallel machines scheduling problem with worker allocation and sequence dependent setup times considering machine eligibility and precedence constraints

<p style='text-indent:20px;'>In today's competitive world, scheduling problems are one of the most important and vital issues. In this study, a bi-objective unrelated parallel machine scheduling problem with worker allocation, sequence dependent setup times, precedence constraints, and machine eligibility is presented. The objective functions are to minimize the costs of tardiness and hiring workers. In order to formulate the proposed problem, a mixed-integer quadratic programming model is presented. A strategy called repair is also proposed to implement the precedence constraints. Because the problem is NP-hard, two metaheuristic algorithms, a multi-objective tabu search (MOTS) and a multi-objective simulated annealing (MOSA), are presented to tackle the problem. Furthermore, a hybrid metaheuristic algorithm is also developed. Finally, computational experiments are carried out to evaluate different test problems, and analysis of variance is done to compare the performance of the proposed algorithms. The results show that MOTS is doing better in terms of objective values and mean ideal distance (MID) metric, while the proposed hybrid algorithm outperforms in most cases, considering other employed comparison metrics.</p>

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

This paper studies the order acceptance and scheduling problem on unrelated parallel machines with machine eligibility constraints. Two objectives are considered to maximize total net profit and minimize the makespan, and the mathematical model of this problem is formulated as multiobjective mixed integer linear programming. Some properties with respect to the objectives are analysed, and then a classic list scheduling (LS) rule named the first available machine rule is extended, and three new LS rules are presented, which focus on the maximization of the net profit, the minimization of the makespan, and the trade-off between the two objectives, respectively. Furthermore, a list-scheduling-based multiobjective parthenogenetic algorithm (LS-MPGA) is presented with parthenogenetic operators and Pareto-ranking and selection method. Computational experiments on randomly generated instances are carried out to assess the effectiveness and efficiency of the four LS rules under the framework of LS-MPGA and discuss their application environments. Results demonstrate that the performance of the LS-MPGA developed for trade-off is superior to the other three algorithms.