This study addresses the minimization of total tardiness in a hybrid flow shop scheduling problem with sequence-dependent setup times and blocking constraints. Each production stage includes multiple machines, and there are no buffers between the stages. The setup time required to process a job depends on the previously processed job. Two mixed-integer linear programming models are developed to formulate the problem. Moreover, an iterative local search algorithm and hybrid genetic algorithms are proposed to have quality solutions with minimal computational efforts. Several computational tests are conducted to tune the heuristic parameters for better performance. Computational experiments are carried out to evaluate the performance of solution methodologies in terms of quality and time. The results indicate that while mixed-integer programming models can solve small-size problem instances, they are not capable of solving large-sized instances. However, the proposed heuristic algorithms find quality solutions for all instances in a very short time.

In recent years, various issues such as industrial waste and emissions of greenhouse gases have led to serious environmental pollution. Industrial managers nowadays need to regard cutting carbon emissions as one of their principal responsibilities in relation to the environment, as industry is a major source of carbon emissions. Two prominent regulatory approaches to reducing carbon emissions from operations are the carbon tax and the cap-and-trade system. The existing literature on inventory studies has often considered the market-expanding effects of greening efforts. Nevertheless, a number of additional factors exert influence on greening efforts, with the cost reduction effect representing a critical one. This paper develops an inventory system in which each time a lot of items is received, a proportion of items are found to be of imperfect quality; to identify these, the retailer carries out a 100% inspection of goods received. Following this inspection, the saleable items are added to the inventory in the warehouse in batches of equal size, rather than one by one, and the retailer allows backordering to meet demand. Carbon emissions are incurred at every stage, including ordering, purchasing, repairing, transporting, and holding, so advanced green technology is employed to reduce them. Imperfect products can be sold to a second-hand market or sent to a repair shop. The model discussed in this paper calculates, for both options, the most cost-effective lot size for orders, shortage quantity, scale of green investment and number of batches.

Sustainable production is not limited to environmental concerns only; It also provides economic benefits for businesses. Businesses that adopt sustainability principles can gain advantages in matters such as cost savings, competitive advantage, risk management, legal compliance and corporate reputation. Therefore, sustainability is no longer an option but a strategic imperative for businesses. For this reason, studies on energy-sensitive scheduling have started to increase recently. Another important factor in sustainable manufacturing is the reduction of scrap. Rework operations are required to reduce scrap. In this study, the multi-objective flexible job shop scheduling problem (MO-FJSP) that considers energy efficiency is discussed. The created model aims to minimize the energy consumption, total machine workload and makespan. In this study, new job arrivals are considered as dynamic events. Another dynamic event added to the model is the addition of rework processes between operations to reduce the scrap rate when a scrap decision is made during the production stages. The enhanced NSGA II algorithm was applied to solve this problem. The enhanced NSGA II algorithm was applied to test instances and its performance was compared using some of the multi-objective performance indicators. These experimental results prove the effectiveness of the proposed solution method.

The Covid-19 pandemic has significantly impacted consumer behavior and commerce, prompting a shift towards online goods and services. The surge in demand has led to inefficiencies and disruptions, especially in the last-mile delivery (LMD) process. Because of the LMD, the final stage of the supply chain, plays a crucial role in transporting goods from businesses to consumers, challenges such as the cost inefficiencies of direct home delivery have underscored the need for innovative solutions. In this study, the collection delivery points (CDPs) approach was adopted instead of direct home delivery. It focuses on addressing these challenges by adopting service points as dynamic CDPs and handling the problem as a dynamic location routing problem (DLRP). Two solutions approaches are proposed, to select candidate depots strategically and determine efficient route configurations, to aim to minimize travel distance. One of them is a two-phased hierarchical method that starts with clustering and continues with an Ant Colony Optimization (ACO) based-hybrid algorithm, and the other one is based solely on an ACO-based hybrid algorithm. The performance of these approaches is evaluated on modified benchmark instances from the literature. It has been observed that the ACO based-hybrid algorithm is more successful in terms of total travel distance, and if an evaluation is made in terms of the number of routes, it is recommended that the results of the two-phased hierarchical method should also be considered. Furthermore, a real word case study was conducted with the proposed methods and the results were compared from different perspectives. The results corroborate the findings regarding benchmark instances, thereby providing additional validation to the results obtained.

Order picking (OP) is a critical yet time-consuming and labor-intensive warehouse operation within the supply chain. In picker-to-part systems with high demand, pickers are exposed to fatigue due to the excessive repetition of picking activities, which results in high human energy expenditure. The literature indicates that energy expenditure depends on the picking activity and the worker’s attributes, such as pickers’ weight, gender, and age. Studies have shown that as the weights of individuals increase, the energy consumed for the same task increases. This study proposes a two-stage stochastic programming model that minimizes assignment and overtime costs while avoiding excessive fatigue levels for pickers by incorporating rest allowance into the picking tour time. In the first stage, the number of pickers required is decided. In the second stage, orders are assigned to pickers considering uncertain energy expenditure. The two-stage stochastic programming model is solved by the sample average approximation algorithm. Results show that both OP cost and the number of pickers required to fulfill an order increase when the picker’s weight exceeds 80kg. In allocating orders, pickers weighing less than 80kg should be assigned to orders with more items, such as those containing 4- or 5-items. Conversely, pickers weighing more than 80kg should be assigned to orders with fewer items, like those containing 2- or 3-items, to avoid fatigue side effects.

Most of today's production systems are working with parallel production resources to increase throughput rate due to the increase in high variability in demand and product mix. Effective control and performance evaluation of such systems is of paramount importance to minimize production and inventory-related costs. We examine a production-inventory system featuring parallel production resources capable of producing various products. In many industries such as automotive, white goods, electronics, and paint, multiple/parallel production resources are widely used to produce the ideal amount and satisfy incoming demands for distinct products. In this study, shortage cost is not restricted to only one type and both lost sales and backordering cases are analyzed. In order to analyze the optimal production policies' behavior, we initially formulate dynamic programming models for both lost sales and backordering systems, treating them as Markov Decision Processes. Subsequently, we solve these models using the value iteration algorithm. Given the challenges posed by the curse of dimensionality in the value iteration algorithm, we suggest alternative heuristic production policies. These policies extend the existing ones described for multi-item single-resource make-to-stock (MTS) systems to accommodate multiple resources. We construct simulation models to assess the efficacy of the heuristic policies, conducting comparisons of their performance against both the optimal policy and among one another. To the best of our knowledge, there has been no exploration of scenarios involving multiple production resources concurrently manufacturing distinct products in a MTS environment. Hence, this study serves as an extension to the examination of multi-item, multi-production resource MTS systems.

Timely and cost-effective supply of spare parts is the main purpose of spare parts inventory management and substitution is an effective way to fulfill demand on time. However, direct substitution of spare parts is not suitable for the high-tech industries due to the ever-changing nature of the product structures. Hence, parts should be transformed to be used as substitutes. This paper provides a novel spare parts inventory control model for the high-tech industries. In the proposed model, part transformation-based substitution is considered and the near-optimal values of spare part inventory levels (s, S) that minimize total cost are determined by using a simulated annealing-based simulation optimization approach. Computational analyses are performed for a hypothetical inventory system by considering transformation and no-transformation cases. The results reveal that transformation is very useful for the companies who endure long production lead times and high penalty costs associated with backorders.

The main focus of this research paper is to shed light on the challenges encountered by the chair production department employees when it comes to identifying and locating glue residue after the assembly process. This issue arises due to the transparent nature of the glue, making it difficult to detect and subsequently negatively impacting the overall quality of the chairs in the polishing department. To tackle this problem, various experiments and tests were conducted, which are thoroughly discussed in this paper. The findings of this study are not only applicable to the specific manufacturing company under investigation but also to other companies within the industry that face similar difficulties in detecting defects caused by transparent glue. As a result, a new method for detecting glue defects is proposed, which can be adopted by various industries encountering similar challenges.

Metaheuristic algorithms are easy, flexible and nature-inspired algorithms used to optimize functions. To make metaheuristic algorithms better, multiple algorithms are combined and hybridized. In this context, a hybrid algorithm (HSSAOA) was developed by adapting the exploration phase of the arithmetic optimization algorithm (AOA) to the position update part of the salp swarm algorithm (SSA) of the leader salps/salps. And also, there have also been a few new additions to the SSA. The proposed HSSAOA was tested in three different groups using 22 benchmark functions and compared with 7 well-known algorithms. HSSAOA optimized the best results in a total of 16 benchmark functions in each group. In addition, a statistically significant difference was obtained compared to other algorithms.

This paper considers the general lot sizing and scheduling problem (GLSP) in single level capacitated environments with sequence dependent item changeovers. The proposed model simultaneously determines the production sequence of multiple items with capacity-constrained dynamic demand and lot size to minimize overall costs. First, a mixed-integer programming (MIP) model for the GLSP is developed in order to solve smaller size problems. Afterwards, a matheuristic algorithm that integrates Simulated Annealing (SA) algorithm and the proposed MIP model is devised for solving larger size problems. The proposed matheuristic approach decomposes the GLSP into sub-problems. The proposed SA algorithm plays the controller role. It guides the search process by determining values for some of the decision variables and calls the MIP model to identify the optimal values for the remaining decision variables at each iteration. Extensive numerical experiments on randomly generated test instances are performed in order to evaluate the performance of the proposed matheuristic method. It is observed that the proposed matheuristic based solution method outperforms the MIP and SA, if they are used alone for solving the present GLSP.