Search results

Different external supply scenarios faced by the retailers will affect their choice of strategy when supply is disrupted and becomes far less than demand, urgently. This study focuses on analyzing both demand and supply side response strategies to meet customer demand and reduce the impact of the shortage during supply disruptions.

According to the quantity of products that the external market can provide, the external supply scenarios were divided into sufficient-type external supply and learning-type external supply. A two-echelon perishable goods supply chain was analyzed, and three kinds of contingency strategy models for downstream retailers were investigated. First, in the sufficient external supply scenario, the optimal price and transshipment quantity to maximize retailer's profits is discussed. Second, in the scenario of learning-type external supply, this study analyzes the optimal decision in three mechanisms of the hybrid strategy and their application: price priority mechanism, quantity priority mechanism and price–quantity balance mechanism. Furthermore, the influence of penalty cost and supply on the priority orders of different mechanisms was studied.

Results show that comparing the two pure strategies (pricing strategy and transshipment strategy)it was noted that the hybrid strategy produces the best results in sufficient-type external supply scenario. In the learning-type external supply scenario, a numerical study has shown the existence of three areas in case of penalty cost and supplier's capacity, and each areas has different priority orders of the three mechanisms. Under the situation of learning external supply, the retailer's optimal strategy is affected by parameters such as penalty cost and supply volume.

The main innovation of the work lies in the following: First; the external supply situation was divided into sufficiency type and learning type, which improves the external situation faced by retailers after the outbreak of emergencies, helps retailers understand the external situation, conforms to the actual situation and has certain practical application value. Second; in the context of learning external supply, there are three coping strategies for retailers, including: Price priority mechanism, Quantity priority mechanism and Pricing and transshipment balance mechanism. This will help retailers make strategic choices, make more scientific management decisions and improve the supply chain emergency management theory. Third; the demand side response was managed through the change of external supply during supply side recovery period and supply disruption. The proposed model enables managing and analyzing supply disruption efficiently and effectively via handling uncertainty by considering all aspects of decision-making process. The proposed model can be applied in various fields such as vegetable and fruit, fresh food, etc.

To describe how the well‐known Shapley Value concept from cooperative game theory can be used as an approach to solve the transshipment problem.

This paper provides a numerical example for a three‐person game in a non‐cooperative environment, a selected cooperative environment, and a fully cooperative environment. The well‐known Shapely Value concept from cooperative game theory is used as an approach to solve the transshipment problem and show “how” the pooling coalitions should be formed. The appurtenance of the Shapley Value is verified to ensure that a stable solution exists.

The primary focus of this paper is to describe how the well‐known Shapley Value concept from cooperative game theory can be used as an approach to solve the transshipment problem and show “how” the pooling coalitions should be formed.

While pooling coalitions are presented, this paper does not discuss revenue sharing.

This paper helps students understand “how to” make the right decisions about where they want to position themselves with respect to supply chain strategies.

The principal contribution of this approach is for establishing a pooling coalition in order to provide a more stable and cooperative solution. This paper also presents application to recent supply chain management business scenarios found in industry.

This study aims to focus on building a conceptual closed-loop vaccine supply chain (CLVSC) to decrease vaccine wastage and counterfeit/fake vaccines.

Through a focused literature review, the framework for the CLVSC is described, and the system dynamics (SD) research methodology is used to build a causal loop diagram (CLD) of the proposed model.

In the battle against COVID-19, waste management systems have become overwhelmed, which has created negative environmental and extremely hazardous societal impacts. A key contributing factor is unused vaccine doses, shown as a source for counterfeit/fake vaccines. The findings identify a CLVSC design and transshipment operations to decrease vaccine wastage and the potential for vaccine theft.

This study contributes to establishing a pandemic-specific VSC structure. The proposed model informs the current COVID-19 pandemic as well as potential future pandemics.

A large part of the negative impact of counterfeit/fake vaccines is on human well-being, and this can be avoided with proper CLVSC.

This study develops a novel overarching SD CLD by integrating the epidemic model of disease transmission, VSC and closed-loop structure. This study enhances the policymakers’ understanding of the importance of vaccine waste collection, proper handling and threats to the public, which are born through illicit activities that rely on stolen vaccine doses.

When making sourcing decisions, both cost optimization and customer demand fulfillment are equally important for firm competitiveness. The purpose of this paper is to develop a stochastic search technique, hybrid genetic algorithm (HGA), for cost-optimized decision making in wholesaler inventory management in a supply chain network of wholesalers, retailers and suppliers.

This study develops a HGA by using a mixture of greedy-based and randomly generated solutions in the initial population and a local search method (hill climbing) applied to individuals selected for performing crossover before crossover is implemented and to the best individual in the population at the end of HGA as well as gene slice and integration.

The application of the proposed HGA is illustrated by considering multiple scenarios and comparing with the other commonly adopted methods of standard genetic algorithm, simulated annealing and tabu search. The simulation results demonstrate the capability of the proposed approach in producing more effective solutions.

The pragmatic importance of this method is for the inventory management of wholesaler operations and this can be scalable to address real contexts with multiple wholesalers and multiple suppliers with variable lead times.

The proposed stochastic-based search techniques have the capability in producing good-quality optimal or suboptimal solutions for large-scale problems within a reasonable time using ordinary computing resources available in firms.

The purpose of this research is to develop a holistic approach to maximize the customer service level while minimizing the logistics cost by using an integrated multiple criteria decision making (MCDM) method for the contemporary transshipment problem. Unlike the prevalent optimization techniques, this paper proposes an integrated approach which considers both quantitative and qualitative factors in order to maximize the benefits of service deliverers and customers under uncertain environments.

This paper proposes a fuzzy‐based integer linear programming model, based on the existing literature and validated with an example case. The model integrates the developed fuzzy modification of the analytic hierarchy process (FAHP), and solves the multi‐criteria transshipment problem.

This paper provides several novel insights about how to transform a company from a cost‐based model to a service‐dominated model by using an integrated MCDM method. It suggests that the contemporary customer‐driven supply chain remains and increases its competitiveness from two aspects: optimizing the cost and providing the best service simultaneously.

This research used one illustrative industry case to exemplify the developed method. Considering the generalization of the research findings and the complexity of the transshipment service network, more cases across multiple industries are necessary to further enhance the validity of the research output.

The paper includes implications for the evaluation and selection of transshipment service suppliers, the construction of optimal transshipment network as well as managing the network.

The major advantages of this generic approach are that both quantitative and qualitative factors under fuzzy environment are considered simultaneously and also the viewpoints of service deliverers and customers are focused. Therefore, it is believed that it is useful and applicable for the transshipment service network design.

Facing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an important prerequisite to control energy consumption. This paper aims to present a bi-objective green in-house transportation scheduling and fleet size determination problem (BOGIHTS&FSDP) in automobile assembly line to schedule the material delivery tasks, which jointly take the energy consumption into consideration as well.

This research proposes an optimal method for material handling in automobile assembly line. To solve the problem, several properties and definitions are proposed to solve the model more efficiently. Because of the non-deterministic polynomial-time-hard nature of the proposed problem, a Multi-objective Discrete Differential Evolution Algorithm with Variable Neighborhood Search (VNS-MDDE) is developed to solve the multi-objective problem.

The performances of VNS-MDDE are evaluated in simulation and the results indicate that the proposed algorithm is effective and efficient in solving BOGIHTS&FSDP problem.

This study is the first to take advantage of the robot's interactive functions for part supply in automobile assembly lines, which is both the challenge and trend of future intelligent logistics under the pressure of energy and resource. To solve the problem, a VNS-MDDE is developed to solve the multi-objective problem.

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

The aim of this chapter is to identify factors that have been affecting the increase of transhipment (T/S) cargoes of the port of Busan and to explore how these factors can be utilised more effectively to attract and increase T/S cargoes through the development of a port choice model using the technique of system dynamics (SD). To clarify the reason why T/S cargoes have increased in the port of Busan, several steps have been taken as follows.

The first step was to design a quantitative model to explain the development of T/S cargoes during the last decade. The second step was to define dependent and independent variables for multiple regressions after testing variable significance. For this, data collection and the accuracy of validation have been achieved by using the direct interview procedure involving experienced experts from both Korean and foreign shipping companies. After validating the model with the collected data, the final step was to find variables that confidently explain the model.

It is found that two variables are clearly identified as core factors that describe well the development of T/S cargoes in the port of Busan: ‘Mohring effect’ and total cost. Further, it is strongly recommended, through this empirical study, that an incentive scheme be changed to the way in which more feeder vessels rather than mother vessels can reduce their direct costs to call at the port of Busan.

Based on regression analysis, it is also found that the sensitivity model for transhipment cargo is useful for dynamic forecasting in changing the cost factor and Mohring factor with time-series technique.

The expansion of the Panama Canal that is completed in 2016 provides container carriers with new opportunities to redeploy global oceangoing trunk routes. The purpose of this paper is to examine the cargo sources and geographical locations of three trunk routes, the departure points of which are all in East Asia.

The operating conditions of various shipping practices were used to simulate trunk route deployment after canal expansion. Subsequently, a clean-line strategy featuring liquefied natural gas (LNG) as a replacement for heavy oil is proposed to explore the effects that container carriers have on energy savings and emission reductions.

The results showed that the unit emissions of ships traveling trunk routes in East Coast North America and East Coast South America did not differ significantly regardless of whether the container carrier employed a conventional method or the new deployment plan following the expansion of the Panama Canal. By contrast, the adoption of a new method for sailing through the canal yields significant emission reductions for Far East/Europe routes. In addition, the slow-steam strategy adopted by carriers and the more costly clean-line strategy of LNG-fueled ships are both effective when applied to trunk routes.

The results of this study provide a reference to container carriers deploying route structures and the International Maritime Organization when promoting emission-reduction policies.

This paper aimed to introduce a systematic body of knowledge via a scientometric review, guiding the sustainable transition from conventional construction to prefabricated construction. The construction industry currently faces a challenge to balance sustainable development and the construction of new buildings. In this context, one of the most recent debates is prefabricated construction. As an emerging construction approach, although existing knowledge makes contributions to the implementation of prefabricated construction, there is a lack of a comprehensive and in-depth overview of the critical knowledge themes and gaps.

This study uses the scientometric analysis to review the state-of-the-art knowledge of prefabricated construction. It retrieved data from the Web of Science core collection database. CiteSpace software was used to conduct the analysis and visualization; three analysis methods identify the knowledge hotspots, knowledge domains and knowledge topics. Finally, according to integrating the hidden connections among results, a body of knowledge for prefabricated construction application can be inferred.

The results show that 120 knowledge hotspots, five critical knowledge domains and five prominent knowledge topics are vital for promoting implementation of prefabricated construction. Based on the afore analysis, a body of knowledge for prefabricated construction that can systematically cover a broad knowledge of prefabricated construction-related research and activities are integrated and proposed in this paper.

Body of knowledge systematically covers a broad knowledge of prefabricated construction applications and is vital to guide researchers and practitioners to conduct related research and activities, thereby promoting the sustainable transition to prefabricated construction implementation.