Scheduling in an assembly-type production chain with batch transfer

Abstract

This paper addresses a three-machine assembly-type 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 second-stage of a flowshop for processing the component parts in batches. Whenever a batch is formed on the second-stage machine, a constant setup time is required. The objective is to minimize the makespan. In this study we establish the strong NP-hardness of the problem for the case where all the jobs have the same processing time on the second-stage 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.

Optimization of delay time and environmental pollution in scheduling of production and transportation system: a novel multi-society genetic algorithm approach

“…They have tried postponement strategy as an innovative strategy for maintaining a supply chain efficient and agile by reducing end-product inventory which boost flexibility while lead to final cost reduction without the clear positive impact on reducing gas emission in distribution network. Deeping in the core problem, production and transportation scheduling, specifically are investigated by researchers with focus of modeling a combination of complex integral programming and simulation models in multi-site manufacturing systems (Gnoni et al, 2003), scheduling of a two-stage supply chain with the objective of minimizing the maximum completion time of the works (Lin et al, 2007;Chauhan et al, 2007), scheduling with the objective of minimizing the total inventory and shipping costs Cheng, 2009a, 2009b), the transport integration in a single-site and two-stage supply chain, taking into account the allocation of tasks to suppliers and geographic areas with dividing the suppliers into geographic areas (Zegordi and Beheshti Nia, 2009), on-line scheduling with the objective of minimizing total flow time and total delivery cost with the separated transportation system in a two-stage supply chain with several customers with an estimated on-line algorithm (Averbakh and Baysan, 2013), the relationship between timing and selecting suppliers with a probabilistic programming model with the tow group of suppliers (inside the zone of manufacturing hub and outside) (Sawik, 2014), a hybrid optimization for the post-crisis transportation system using intermediate warehouses which is optimized by the dynamic genetic algorithm (Beheshti Nia and Moghimi, 2017), a novel order allocation model considering geographic zoning and exclusive suppliers concurrently (Khatibi et al, 2018) and minimization of total tardiness and earliness of orders in an integrated production and transportation scheduling problem in a two-stage supply chain (Taheri and Beheshtinia, 2019). In terms of algorithms which are implemented on the models to satisfy the objective functions, genetic algorithm and its improvements have attracted the researchers.…”

Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures

“…Note that this problem can be seen as a particular case of the DP 2 → 1|r j |C max assuming that the release date of the component that is not purchased is zero for all products, and that the processing time of the purchased component is zero for all products, a problem addressed by Komaki and Kayvanfar (2015) for m machines that is discussed below. The same problem, assuming that the assembly machine processes batches of specific jobs, and that a (non-sequence dependent) setup is required every time a batch is formed, is addressed by Lin et al (2006), i.e.…”

Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company)

“…Agnetis et al (2006) studied scheduling problem in a two-stage supply chain with one supplier in Stage 1 and several companies in Stage 2. Lin et al (2007) considered scheduling in a two-stage supply chain with the aim of minimizing the maximum jobs’ completion time. They assumed that there are two suppliers in Stage 1 and one manufacturer that assembles the parts produced by suppliers in Stage 2.…”