In this research integrated scheduling of machines and automated guided vehicles (AGVs) in a flexible job shop environment is addressed. The scheduling literature generally ignores the transportation of jobs between the machines and when considered typically assumes an unlimited number of AGVs. In order to comply with Industry 4.0 requirements, today’s manufacturing systems make use of AGVs to transport jobs between the machines. The addressed problem involves simultaneous assignment of operations to one of the alternative machines, determining the sequence of operations on each machine and assignment of transportation operations between machines to an available AGV. We present a Microsoft Excel® spreadsheet-based solution for the problem. Evolver®, a proprietary GA is used for the optimization. The GA routine works as an add-in to the spreadsheet environment. The flexible job shop model is developed in Microsoft Excel® spreadsheet. The assignment of AGV is independent of the GA routine and is done by the spreadsheet model while the GA finds the assignment of operations to the machines and then finds the best sequence of operations on each machine. Computational analysis demonstrates that the proposed method can effectively and efficiently solve a wide range of problems with reasonable accuracy. Benchmark problems from the literature are used to highlight the effectiveness and efficiency of the proposed implementation. In most of the cases the proposed implementation can find the best-known solution found by previous studies.

Hub location is crucial for resilient and uninterrupted supply chain operations, particularly during disruptions or unforeseen events. In this paper, we propose a resilience hub location framework for Third Party Logistics (3PL) companies with two key objectives: optimizing demand flows and establishing a resilient network capable of with-standing sudden disruptions. The study aims to identify the key criteria that contribute to the successful implementation of the resilient center. The proposed structure utilizes a two-phase decision-making methodology. The first phase presents a new Multi-Criteria Decision-Making (MCDM) approach called SWARA-EDAS method that evaluates and ranks potential locations based on resiliency criteria. The second phase proposes an optimization model to determine the optimal hub location. To illustrate the approach, a real-world case study of a 3PL company in Tehran is included. Due to the absence of precise demand data in the case study, a novel clustering approach is proposed to estimate the demand flow. Each cluster can be considered as a distinct demand point, and a clustering analysis involving 122 regions within Tehran is conducted, taking into account various factors such as population, economic index, accessibility to the Internet, and the number of business units. To enhance the resiliency of the network, mobile distribution centers are also deployed. These mobile centers not only provide flexibility but also serve as backup capabilities in the event of a disruption or failure at the fixed hub. The proposed structure offers practical in-sights for 3PL companies seeking to implement a resilient network structure.

In this paper, we extend the multi-skilled resource-constrained multi-project scheduling problem (MSRCMPSP) by introducing the concept of multiple project types (MSRCMPMTSP). The study considers two categories of projects: investment projects, which generate positive net present values (NPVs), and mandatory projects, which result in negative NPVs but are required to be executed. To solve this problem, we employ a priority rule-based heuristic approach. Specifically, forward scheduling is applied to projects expected to yield positive NPVs, whether they are optional or mandatory. In contrast, backward scheduling is used for projects with negative NPVs, as this strategy minimizes the impact of excessive negative NPVs. The dataset for this study is constructed using the design of experiments (DoE) methodology, enabling a comprehensive evaluation of the proposed heuristic. We compare the performance of our approach against randomly generated schedules through extensive simulations. The results indicate that the heuristic is effective in addressing the MSRCMPMTSP.