Minimizing the internal supply chains’ operating expenses is a crucial management goal in current transnational enterprises, where manufacturing and retailing often operate independently. Still, managers must periodically evaluate the consolidated operating expenses and performance. The operational goals in the manufacturing units consist of lowering quality- and reliability-relevant costs, avoiding manufacturing delays due to random breakdowns, and meeting order due dates through expediting strategies, such as partial subcontracting and accelerating the fabrication plan. Inspired by efforts to optimize batch runtime for the mentioned intra-supply chains, this study investigates the combined impact of quality-surety actions (including defect removal and rework), correction of breakdowns, multi-delivery, adjustable-rate, and outsourcing on a coordinated producer–retailer system. This study presents a research scheme comprising: (1) model development for the mentioned internal supply-chain features; and (2) optimization approaches for determining the cycle time decision that minimizes the overall system operating expenses. To conclude our work, we validate the research scheme, procedure, and results through numerical demonstration and show that it can effectively support management’s decision-making with various exploratory and crucial information.

The rapid advancement of the Internet has significantly reshaped traditional business models, enabling firms to leverage both online and offline channels to enhance competitiveness and profitability. This study explores the interplay between bundling and pricing strategies within a dual-channel retail system that integrates a physical store and an online shop, utilizing a game-theoretic approach while accounting for network externalities. A two-stage model is proposed: in stage 1, a manufacturer supplies two products with differing network externalities to a retailer, who must decide whether to sell them individually or as a bundle. In stage 2, the manufacturer considers launching its online channel to complement the existing physical channel. Four distinct scenarios are analyzed, examining the bundling and pricing strategies employed by both the manufacturer and the retailer across both channels to maximize their profits. The findings indicate that the integration of physical and online channels is mutually beneficial for both parties, resulting in increased profits that grow alongside stronger network externalities. Moreover, the optimal bundling strategy is contingent upon the nature of the products and their respective externalities. Specifically, when both products demonstrate high network externalities, the manufacturer should implement a mixed strategy—offering the products as a bundle online while selling them individually in physical stores. Numerical analysis emphasizes the importance of network externalities in shaping bundling decisions and profit outcomes, providing actionable insights for firms operating in multi-channel retail environments.

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.

Variable selection is an important step in data envelopment analysis (DEA) when the number of decision making units (DMUs) is insufficient. This research thus proposes a two-stage variable selection method integrating a well-known supervised machine learning technique, the random forest algorithm. In the first stage, a baseline DEA model with full input and output variables is implemented and each DMU can be determined as being efficient or inefficient. In the second stage, a random forest is trained to learn how to classify efficient or inefficient DMUs well in high dimensions. Accordingly, the importance of each variable can be calculated based on permutation importance indices in random forest. This paper further discusses two issues about data augmentation in order to improve the robustness of permutation importance when the number of DMUs is quite small.

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.