In the context of multichannel retailing, the phenomenon of web-rooming, where consumers research online and purchase offline, has become widespread. Therefore, we consider supply chain consisting of a manufacturer, an e-commerce platform, and an offline retailer, we study the impact of consumer web-rooming effect on the three sales modes of online channels, which are directly operated by the manufacturer, resold by the e-commerce platform, or co-exist with direct operation and resale, and comparatively analyze the pricing, demand, and profit of each channel under the three modes. The conditions for optimal pricing decisions are further explored through numerical simulation. It is found that profit always increases whether the online channel is opened by the manufacturer or the e-commerce company. The coexistence of direct sales and resale does not always increase profit for offline retailers, which must be discussed in the context of the sales model before channel expansion. The existence of web-rooming not only affects the decision-making of manufacturers and e-commerce platforms, but also always harms the interests of e-commerce platforms, and as the intensity of web-rooming deepens, the revenue of e-commerce platforms becomes smaller. Offline retailers benefit from web-rooming and experience a slowdown in profit growth as the intensity of the phenomenon increases. For manufacturers, the impact of changes in the intensity of the web-rooming is analyzed in relation to platform commission rates and online retail prices.

This paper considers a dual-channel supply chain with two members, comprising a manufacturer and an online platform. We mainly investigate the influence of various key system variables on manufacturer encroachment strategy and all members’ optimal decisions through Stackelberg game models. Our findings show that, regardless of the size of each parameter, the encroachment strategy is always optimal to the manufacturer; the manufacturer may be motivated to choose the direct selling channel and the platform may opt for the agency selling channel due to a high commission rate. Moreover, when the inter-channel substitution rate is high, the encroachment strategy has a diminishing positive effect on the manufacturer and an increasing negative effect on the platform so that the platform may temporarily benefit from the manufacturer encroachment; in cases where the inter-channel substitution rate is not high, the encroachment strategy always yields advantages for the manufacturer while causing disadvantages for the platform. In addition, if the elasticity coefficient is large, both the manufacturer and the platform are inclined to the reselling channel, that is, if the platform service cost is high, it is advisable for the platform to reduce its investment of service to avoid negative effect.

This research introduces a comprehensive scheme to tackle the Mixed-Model Assembly Line Balancing Problem (MALBPLW) within manufacturing contexts. The primary aim is to optimize assembly line task assignments by integrating both the learning effect and worker prerequisites. The learning effect recognizes the enhanced efficiency of workers over time due to learning and experience. A novel mathematical model and solution approach are proposed, encompassing factors like cycle time, task interdependencies, worker classifications, and the learning effect. The model endeavors to minimize the overall costs related to both workers and workstations while simultaneously maximizing production efficiency. Experimental assessments are conducted to evaluate the efficacy of this proposed approach. Diverse manufacturing scenarios are inspected, comparing and analyzing cost reductions and production efficiency. The outcomes highlight the effectiveness of this approach in achieving enhanced cost-effectiveness and resource utilization in contrast to conventional methods. This study contributes significantly to advancing assembly line balancing and production planning techniques by presenting a pragmatic framework for optimizing resource usage and reducing costs in manufacturing environments. The knowledge extracted from these discoveries can significantly assist professionals in the industry seeking to improve manufacturing processes and strengthen competitiveness.

The distributed permutation flowshop scheduling is a critical issue in various industries, involving jobs allocation and scheduling among multiple flowshops. This paper extends the research to explore the Distributed Mixed No-Wait Permutation Flowshop Scheduling Problems (DMNWPFSP) with minimizing makespan. The innovation lies in an optimized mathematical model, hybrid heuristic algorithms, an improved iterated greedy algorithm (IIG), and high-quality solutions. Extensive experimental results demonstrate the effectiveness and superiority of the proposed IIG in terms of scheduling quality, computational efficiency, and robustness compared to existing approaches. The outcomes of this work contribute to the field of distributed flowshop scheduling, providing valuable insights for practitioners seeking to enhance production efficiency and competitiveness.

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.

Competition among seaports has been becoming more and more fierce in current times, which has extended to the contest between transportation chains including seaports and their inland ports. Against this background, this paper studies competitive inland port location and pricing problem for an entering seaport under the condition that the incumbent competitive seaport has construct-ed inland transportation chains inside their overlapping hinterland. Specifically, this paper formulates a mixed-integer nonlinear program for the considered problem, in which we take packaged price and service time as influence factors for the inland transportation chains competition and characterize inland ports choice behaviors for shippers based on logit model. Additionally, this paper designs a hybrid heuristic method by integrating a genetic algorithm and an analytical method to solve location and pricing subproblems, respectively. Based on the computational results and sensitivity analysis, this paper provides some valuable suggestions on how to locate in-land ports and make price decisions for the new entering seaport.

The purpose of this research is to investigate the influence of green recruitment and selection, green training, and green intellectual capital on employee performance in Yogyakarta, Indonesia. The data collection method used in this research is to look for observation, interviews, and the distribution of questionnaires. Structural Equation Modeling is used to be an analysis method with SmartPLS assistance. Within the SEM model, this research predominantly employs the Partial Least Squares (PLS) technique, a component-based or variance-based structural equation modeling approach. PLS enables the testing of theories or theory development for predictive purposes based on experimental results. Based on the results of this study, green recruitment and selection have a significant and positive impact on employee performance. However, green training does not have any statistically significant positive effect on employee performance in the same region. On the other hand, green intellectual capital shows a significant and positive influence on employee performance. These results contribute to understanding the relationship between green human resource practices and employee performance.

A simple and effective multi-attribute decision-making method, named as BHARAT method, is proposed in Part-1 of this paper and the same method is used now as a multi- and many-objective decision-making method for evaluating the Pareto optimal solutions. The proposed BHARAT method is used to identify the best compromise Pareto solution. Based on their importance for the given optimization problem, the objectives are ranked, and the weights are assigned. The weights of the objectives and the normalized values of the objectives for different Pareto optimal solutions are used to compute the total scores. The total scores are used to differentiate the alternative optimal solutions and an alternative solution that gets the highest total score is suggested as the best compromise solution. Three case studies are presented to illustrate and validate the proposed BHARAT method. The case study 1 is a multi-objective optimization problem related to cloud manufacturing with 3 objectives and 20 alternative solutions; case study 2 is a many-objective optimization problem of electro-discharge machining process with 4 objectives and 50 alternative solutions; case study 3 is a many-objective optimization of milling process parameters with 4 objectives and 100 alternative solutions. The outcomes of the suggested BHARAT method are compared with those of the other popular decision-making approaches for each of the three case studies considered. The suggested simple and more logical BHARAT method can be used in multi- and many-objective optimization problems to select the best compromise solution.

Greenhouse gases (GHGs) have caused extreme temperature changes. In January 2023, temperatures were 0.1°C higher than the normal 30-year monthly average. Construction, especially high-rise offices, which occupy 42% of Jakarta, contributes significantly through energy con-Sumption. To reduce carbon emissions, Indonesia has started to implement green retrofits as part of the Net Zero Emission 2050. Due to high costs and lack of public education on new and existing green buildings, the implementation of green retrofits is inhibited, and owners prefer conventional buildings. This research aims to analyze the feasibility and investment risk of implementing green retrofits in high-rise office buildings using the life cycle cost method and the Minister of Public Works and Public Housing Regulation No. 21 of 2021 to generate a feasible and safe in-vestment. It has been proven with cost savings in energy and water consumption of up to 15% compared to conventional office buildings. Profits have also been achieved by providing 9 benefits to the building owner, building manager and building occupants. Therefore, this research has the potential to accelerate the green revolution through feasible and safe green retrofit investments in Jakarta's office buildings.

A simple and effective multi-criteria decision-making methodology named as “Best Holistic Adaptable Ranking of Attributes Technique (BHARAT)” is proposed that can be used in single- as well as group decision-making scenarios of the industrial environment. The attributes data for various alternatives can be quantitative or qualitative (i.e., expressed in linguistic terms). This paper proposes to transform the qualitative attributes into quantitative attributes by means of simple linear scales rather than complex fuzzy scales. The proposed BHARAT method normalizes the data with reference to the “best” alternative corresponding to an attribute and the normalization procedure is repeated for all the attributes to get the normalized data. A group of decision-makers or a decision-maker assigns ranks to the attributes according to how important they are deemed to be, and these ranks are then transformed into the proper weights. The total scores of the alternatives are calculated by multiplying the weights of the attributes by the corresponding normalized data of the attributes for different alternatives. Four industrial case studies are presented to illustrate the potential of the suggested BHARAT method. The first case study deals with the problem of an automated warehouse selection for a large industrial plant involving a single decision-maker, 13 attributes, and 4 alternative warehouses; the second case study deals with the problem of sustainable maintenance service provider selection for a large petrochemical plant involving fuzzy group decision-making with 5 decision-makers, 9 attributes, and 4 alternative maintenance service providers; the third case study deals with the problem of alternative strategy selection for implementation of a make-to-order system for passenger car manufacturers involving 6 factors, 18 sub-factors, and 3 alternative strategies; and the fourth case study deals with the problem of process parameters selection in a sustainable high speed turning operation involving 4 attributes and 9 alternative sets of experimental conditions. The results of the proposed decision-making method and its second version are compared with the other popular decision-making methods. The proposed method and its another version are proved simple, effective, powerful, flexible, easy to apply, do not require the use of fuzzy logic, offer logical and consistent procedures to assign weights to the attributes, and are applicable to different decision-making scenarios of the industries. Part-1 of this paper describes the applications of the BHARAT method to multi-attribute decision-making problems and Part-2 describes the evaluation of Pareto solutions using the BHARAT method in multiple objective decision-making problems.