Sustainable Production Scheduling and Supply Chain Management under Resource Constraints and Factory Eligibility

Dear Colleagues,

In today's competitive environment, manufacturing industries are facing complex challenges such as energy consumption, pollution, production time, and operational costs. With the intensification of market competition and the general trend of economic globalization, production systems with more than one production center are becoming more and more common. For example, instead of producing all the components needed for the final product, more and more enterprises prefer to receive them from eligible suppliers and assemble them in the main factory. This outsourcing strategy in production is widely used in automobiles and electronics. Many companies such as Dell, Lucent, Cisco, and HP outsource most of their components and single-factory firms are less common, with multi-plant companies being more commonplace. Therefore, suppliers are taking on a more important role in practice to achieve lower production costs, reduced energy consumption, carbon emissions and pollution, higher product quality, and lower management risks. This type of production system, the so-called distributed manufacturing system, can reduce production costs while keeping or increasing product quality. 
Under such aforementioned conditions, production planning and control plays a vital role in achieving the ideal result. Shop floor production planning and control, which is well known as a scheduling problem and efforts to schedule jobs and assign them to processing machines so that a given criterion is optimized are essential. Concurrent-type scheduling models and solution approaches for the processing, operation and assembly stages have many applications in real-world conditions, and so are receiving increasing attention in the field of academic research and manufacturing enterprises. There are different approaches to achieve this aim depending on the problem’s features and its restrictions. A distributed flow shop scheduling problem followed by the assembly (DAFSP) shop is a generalization of the assembly flow shop scheduling problem (AFSP), which provides a closer approximation to a range of problems encountered in real manufacturing systems. A more constrained version of this problem is the distributed assembly permutation flow shop scheduling problem (DAPFSP), wherein the same sequence of parts is maintained for all machines in each factory.

This study aims to address a distributed assembly permutation flow shop scheduling problem (DAPFSP) with non-identical factories considering budget constraints. Suppose that several products of different kinds are ordered to be produced. Each product consists of a set of parts that are processed through several factories. All of these factories have a flow shop with different technology levels, and so their performance is different in terms of processing time, energy consumption, pollution, and operational costs. Moreover, we have budget constraints, so we can assign limited parts to the high-tech factories. The second stage is the assembly stage, wherein there is a work station to assemble the ready parts into the products. Many worldwide companies have needed to alter their outsourcing and manufacturing activities to deal with the supply-chain disruptions and uncertainty in demand. Many companies have implemented these new measures well in response to the disruptions through digital technology application, supply chain digitization, green supply chain operations and business model innovations. 

This Special Issue will concentration on analyzing the new practices of production and job shop scheduling logistics, supply chain management, humanitarian supply chain, and logistics production management.
We encourage and welcome high-quality applied and methodological articles on such topics as those listed below, but not limited to:

• job shop scheduling
• green production technology
• production planning and scheduling with budget constrains
• humanitarian supply chain and logistics production planning
• big data analysis in job shop scheduling problems
• optimum methods of operations to improve the 3Rs (responsiveness, resilience, and restoration) in supply chain
• soft computing techniques to job scheduling under ergonomic constraints in the manufacturing industry
• inventory control in production planning and job shop scheduling
• hybrid flow shop scheduling problem with setup time and assembly operations
• maintenance operations and access restrictions to machines
• multi-objective scheduling problems
• JIT scheduling problem in the machine flow shop
• leanness concept and system dynamics approach in manufacturing systems
• goal programming approach to optimizes sustainable operations management

Dr. Shib Sankar Sana
Dr. Subrata Saha
Guest Editors

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