Many uncertainties arise during the manufacturing process, such as changes in the working environment, traffic transportation delays, machine breakdowns, and worker performance instabilities. These factors can cause job processing times and ready times to change. In this study, we address a scheduling model for a single machine where both job release dates and processing times are scenario dependent. The objective is to minimize the total completion time across the worst-case scenarios. Even without the uncertainty factor, this problem is NP-hard. To solve it, we derive several properties and a lower bound used in a branch-and-bound method to find an optimal solution. We propose nine heuristics based on a linear combination of scenario-dependent processing times and release times for approximate solutions. Additionally, we offer an iterated greedy population-based algorithm that efficiently solves this problem by taking advantage of the diversity of solutions. We evaluate the performance of the proposed nine heuristics and the iterated greedy population-based algorithm.

Facing current client expectations for high quality, timely order response, and multiple shipments of various needed merchandise, today’s producers must simultaneously satisfy external requirements and operate internally with minimum overall expenses and capacity constrained. Aiming to help present-day producers achieve the operational goals mentioned above, this work develops a decisional scheme to determine the best manufacturing-delivery policy for a multi-item economic production quantity (EPQ) system with multi-shipment, quality assurance, overtime, postponement, and external source. Combining a production postponement strategy in our multi-item batch fabricating procedures intends to first make all required standard/common parts for various client-needed merchandise and make finished goods in the 2nd phase. Two fabricating-uptime-shortening strategies are adopted: contracting out a proportion of the standard part’s batch and overtime-making of finished goods. We include screening and rework tasks in fabricating procedures to help us remove the identified scraps and correct the repairable faulty items. The quality-assured finished batches are divided into multiple equal-amount shipments transported to meet client requests. The overall manufacturing-transportation relevant expenses, including quality and uptime-expedited costs, are mathematically modeled and minimized using optimization methodology to help derive the best manufacturing-delivery operating policy. Moreover, we offer an illustration to validate the results and our research scheme’s capability numerically. This work mainly contributes to the literature by presenting a practical decision-making model. It enables the producers to expose numerous crucial problem-related managerial insights to facilitate producers in deciding the most appropriate manufacturing-delivery policy to meet clients’ multi-criteria demands.

Assembly lines, generally speaking, can reduce production costs, shorten cycle times, and achieve higher quality levels. Since the current market is characterized by increasing product variability, mixed-model assembly lines, in which similar product models can be assembled simultaneously, are more suitable to respond to varied market demands than traditional single-model assembly lines. In addition, in an assembly line, tasks often differ in processing requirements, and workers may have different qualification levels. This study, therefore, aims to construct models for the multi-objective mixed-model assembly line balancing problem with hierarchical worker assignment (MO-MALBP-HW). The goal is to generate a suitable plan for a mixed-model assembly line balancing problem considering the constraint of a hierarchical workforce, the cost of a hierarchical workforce, and production cycle time. When the problem is simple, it can be solved by a mixed integer programming (MIP) model. When the problem becomes complex, it can be solved by a multi-objective genetic algorithm (MOGA) and a non-dominated sorting genetic algorithm II (NSGA-II) to obtain a near-optimal solution. The implementation of this model can effectively manage the multi-objective mixed-model assembly line balancing plan, thereby improving plant efficiency and reducing cost.

The study examines the influence of different carbon policies and the incorporation of green technologies in a two-echelon supply chain, with a focus on carbon emissions generated during transportation, production, and storage phases. The study evaluates three strategies for controlling carbon emissions: setting a maximum limit on total emissions, implementing carbon-taxation, and adopting a cap-and-trade framework. The proposed model assists businesses determine the optimal production and delivery volumes, as well as calculate the most effective investment in green technologies to reduce costs in the context of different carbon emission regulations. Furthermore, this study offers practical guidance for policymakers, highlighting the importance of balancing environmental sustainability with economic growth. Results indicate that companies are more inclined to pursue advanced green technology solutions under a carbon tax policy. The analysis highlights that carbon emissions per unit of production and transportation distance significantly impact overall emissions. The imposed emission cap has a stronger influence than the emission reduction potential of green technologies. The study recommends that governments establish realistic emission limits in cap-and-trade schemes to prevent excessive trading of emission allowances by suppliers.

This study aims to optimize a hybrid multi-item fabricating-shipping integrated system incorporating scrap, adjustable rate, and postponement. In present-day competitive market environments, there is a clear client demand trend for various goods, shorter lead time, and expected quality. To satisfy the client’s needs, the management of manufacturing firms requires an effective and efficient plan to fabricate various high-quality goods in an expedited period, under limited capacity, and with minimal operating expenses. Inspired by facilitating production management to determine the best fabricating scheme/plan to achieve their operational goals, this work proposes an exploratory postponement model with quality assurance and uptime reduction strategies for their decision-making. By employing a two-phase making scheme, the required standard components are first made in the 1st phase, and multiple finished merchandise is fabricated in the 2nd phase. The study suggests strategies of contracting out a part of the common parts’ batch and adopting an adjusted/expedited making rate in the 2nd phase to considerably reduce both phases’ production uptimes. During both fabricating processes, the screening tasks identify/remove scrapped/faulty goods to ensure each finished batch’s quality. Equal-amount multiple shipments of end merchandise are transported to the clients in fixed time-interval. Optimization methodology and mathematical analyses support us in deriving the model’s expected annual operating cost and deciding the optimal production-transportation policy. A numerical illustration helps verify our model’s applicability and reveals important managerial insights into the studied problem to facilitate management in decision-making.

This research delves into the software testing process and its environmental factors to uncover the core elements influencing software reliability. Specifically, it focuses on the learning and negligent aspects of the software reliability growth model. The learning factor accelerates reliability growth, leading to an S-shaped curve in the mean value function, while the negligent factor highlights the occurrence of imperfect debugging. The study also uses Brownian motion and stochastic differential equations to establish statistical confidence intervals for reliability and costs. These intervals aid software managers in assessing potential release risks at various confidence levels, allowing them to make informed decisions considering resource constraints and desired system reliability levels across different scenarios.

When making a batch production decision for a buyer-vendor coordination system, the management must simultaneously consider the operating expenses incurred in in-house manufacturing and inventory, finished goods’ shipping, and stock holding at the retailer end. Achieving the operational goals of desirable quality, minimal production disruption, and shortening fabrication time help minimize overall in-house operating costs and maximize customer satisfaction. This work builds an operating cost minimization model for buyer-vendor coordination batch system with scrap, breakdowns, overtime, multi-shipment, and an external source to assist the management in optimizing their production-delivery plan. Removing inevitable scrap items ensures product quality, and correction action on stochastic equipment breakdown prevents unacceptable production delays. Implementing partial overtime and adopting an external source expedites in-house manufacturing time. Model construction and cost analysis enable us to decide the operating expense function. Then, we verify the function’s convexity and decide our model’s best manufacturing runtime with the differential calculus and a proposed algorithm. Furthermore, the numerical demonstrations are used to exhibit our work’s applicability and show what kinds of crucial in-depth information can be disclosed and made accessible to the production planners for their decision-making.

In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale.

The present research explores the collective influence of quality assurance and postponement on a hybrid multiproduct replenishing-delivery decision-making. Assume the required multiproduct has a standard (common) component, and our replenishing-delivery model has incorporated a two-phase postponement strategy. The first phase makes all standard components and hires an external supplier to partially provide the required parts to cut short the needed uptime. In contrast, the second phase fabricates the finished multiproduct in sequence. To ensure the desired merchandise quality, we apply a quality-assurance action to the in-house processes to screen and remove scrap items and rework the repairable defects in both stages. Upon completing each merchandise, these products are transported to the customer in n fixed-quantity shipment in fixed-time intervals. We employ math modeling and formulating approaches to gain the overall supply-chain operating expenses comprising subcontracting, fabricating, stock holding, transportation, and customer holding costs. By minimizing system operating expenses, this research determines the optimal replenishing-delivery policy. Lastly, we give a numerical example to demonstrate our study’s applicability and usefulness/capability for facilitating managerial decision-making.

In this work, a probabilistic bid generation problem with the pricing of a bundle of lanes and carrier’s vehicle routing is considered as it is an importation in transportation service procurement. Depending on the network of the vehicle, there exist multiple lanes for traveling between two locations. To solve the bid generation problem efficiently, a two-phase method approach is presented. At the core of the procedure a feasible vehicle routing problem on a multidigraph is solved by an exhaustive search algorithm to enumerate all routes concerning routing constraints and treat each route as a decision variable in the set partitioning formulation. We examine our model both analytically and empirically using a simulation-based analysis.