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A Survey of Scheduling Problems with Setup Times or Costs
"... The first comprehensive survey paper on scheduling problems with separate setup times or costs was conducted by Allahverdi et al. (1999), who reviewed the literature since the mid1960s. Since the appearance of that survey paper, there has been an increasing interest in scheduling problems with setu ..."
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Cited by 104 (5 self)
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The first comprehensive survey paper on scheduling problems with separate setup times or costs was conducted by Allahverdi et al. (1999), who reviewed the literature since the mid1960s. Since the appearance of that survey paper, there has been an increasing interest in scheduling problems with setup times (costs) with an average of more than 40 papers per year being added to the literature. The objective of this paper is to provide an extensive review of the scheduling literature on models with setup times (costs) from then to date covering more than 300 papers. Given that so many papers have appeared in a short time, there are cases where different researchers addressed the same problem independently, and sometimes by using even the same technique, e.g., genetic algorithm. Throughout the paper we identify such areas where independently developed techniques need to be compared. The paper classifies scheduling problems into those with batching and nonbatching considerations, and with sequenceindependent and sequencedependent setup times. It further categorizes the literature according to shop environments, including singlemachine, parallel machines, flow shop, nowait flow shop, flexible flow shop, job shop, open shop, and others.
Lot splitting to minimize average flowtime in a twomachine flowshop
, 1999
"... Lot splitting is a technique for accelerating the flow of work by splitting job lots into sublots. In this paper we investigate the lot splitting scheduling problem in a twomachine flowshop environment with detached setups and with batch availability. The performance measure considered is the aver ..."
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Cited by 3 (1 self)
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Lot splitting is a technique for accelerating the flow of work by splitting job lots into sublots. In this paper we investigate the lot splitting scheduling problem in a twomachine flowshop environment with detached setups and with batch availability. The performance measure considered is the average flowtime which is indicative of the increasingly important manufacturing leadtime. Our contribution is both theoretic and practical for the case of general (not necessarily equal) sublots. We identify properties of the optimal solution and develop a solution procedure to solve the problem. We then present a computational study which indicates that our solution technique is very efficient. 1. Introduction and
MIPBased Approaches for Solving Scheduling Problems with Batch Processing Machines
"... Abstract In this paper, we address the scheduling problem involving batch processing machines. The presented mixed integer programming formulation first provides an elegant model for the problem under study. Furthermore, it enables solutions to the problem instances beyond the capability of exact me ..."
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Abstract In this paper, we address the scheduling problem involving batch processing machines. The presented mixed integer programming formulation first provides an elegant model for the problem under study. Furthermore, it enables solutions to the problem instances beyond the capability of exact methods developed so far. In order to alleviate computational burden, we propose MIPbased approaches which balance solution quality and computing time.
Analysis of gap times in a twostage stochastic flowshop with
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"... Abstract: We consider in this paper a twomachine flowshop scheduling problem in which the first machine processes jobs individually while the second machine processes jobs in batches. The forming of each batch on the second machine incurs a constant setup time. The objective is to minimize the make ..."
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Abstract: We consider in this paper a twomachine flowshop scheduling problem in which the first machine processes jobs individually while the second machine processes jobs in batches. The forming of each batch on the second machine incurs a constant setup time. The objective is to minimize the makespan. This problem was previously shown to be NPhard in the ordinary sense. In this paper, we first present a strong NPhardness result of the problem. We also identify a polynomially solvable case with either anticipatory or nonanticipatory setups. We then establish a property that an optimal solution for the special case is a lower bound for the general problem. To obtain nearoptimal solutions for the general problem, we devise some heuristics. The lower bound is used to evaluate the quality of the heuristic solutions. Results of computational experiments reveal that the heuristics produce solutions with small error ratios. They also suggest that the lower bound is close to the optimal solution.
Scheduling in an AssemblyType Production Chain with Batch Transfer
"... This paper addresses a threemachine assemblytype flowshop scheduling problem, which frequently arises from manufacturing process management as well as from supply chain management. Machines one and two are arranged in parallel for producing component parts individually, and machine three is an as ..."
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This paper addresses a threemachine assemblytype flowshop scheduling problem, which frequently arises from manufacturing process management as well as from supply chain management. Machines one and two are arranged in parallel for producing component parts individually, and machine three is an assembly line arranged as the secondstage of a flowshop for processing the component parts in batches. Whenever a batch is formed on the secondstage machine, a constant setup time is required. The objective is to minimize the makespan. In this study we establish the strong NPhardness of the problem for the case where all the jobs have the same processing time on the secondstage machine. We then explore a useful property, based upon which a special case can be optimally solved in polynomial time. We also study several heuristic algorithms to generate quality approximate solutions for the general problem. Computational experiments are conducted to evaluate the effectiveness of the algorithms.