This study presents a novel approach to the vehicle routing problem by focusing on greenhouse gas emissions and fuel consumption aiming to mitigate adverse environmental effects of transportation. A time-dependent model with time windows is developed to incorporate speed and schedule in transportation. The model considers speed limits for different times of the day in a realistic delivery context. Due to the complexity of solving the model, a simulated annealing algorithm is proposed to find solutions with high quality in a timely manner. Our method can be used in practice to lower fuel consumption and greenhouse gas emissions while total route cost is also controlled to some extent. The capability of method is depicted by numerical examples productively solved within 3.5% to the exact optimal for small and mid-sized problems.Moreover, comparatively appropriate solutions are obtained for large problems in averagely one tenth of the exact method restricted computation time.

This study proposes a supply chain resilience assessment framework at the network (i.e. structural) level based on quantifying supply chain networks' structural factors and their relationships to different resilience strategies, by using a hybrid DEMATEL-ANP approach. DEMATEL is used to quantify interdependencies between the structural resilience factors, and between the resilience strategies. ANP is then used to quantify the outer-dependencies among these elements and to construct the limit supermatrix from which the global weights of all the decision network's elements are estimated. To create the structural resilience factors, different network factors are selected and adopted from the social network analysis and supply chain resilience literatures. A case study is then performed to assess the performance of the proposed approach and to derive important observations to support future decision making. According to the results, the proposed approach can suitably measure the resilience performance of a supply chain network and help decision makers plan for more effective resilience improvement actions.

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In this paper the capacitated hub location problem is formulated by a minimax regret model, which takes into account uncertain setup cost and demand. We focus on hub location with multiple allocations as a strategic problem requiring one definite solution. Investigating how deterministic models may lead to sub-optimal solutions, we provide an efficient formulation method for the problem.A computational analysis is performed to investigate the impact of uncertainty on the location of hubs. The suggested model is also compared with an alternative method, seasonal optimization, in terms of efficiency and practicability. The results indicate the importance of incorporating stochasticity and variability of parameters in solving practical hub location problems. Applying our method to a case study derived from an industrial food production company, we solve a logistical problem involving seasonal demand and uncertainty. The solution yields a definite hub network configuration to be implemented throughout the planning horizon.

PurposeThis paper presents a contingency analysis of additive manufacturing's (AM) impacts, proposes a novel form of AM-enabled competitive capabilities and explores manufacturing contexts (including product-operation-organization-related factors) influencing those capabilities.Design/methodology/approachA theoretical model incorporating manufacturing competitive capabilities and contingency concepts is developed and validated using an empirical study on 105 manufacturing firms using AM. Structural equation modeling is applied for statistical data analysis.FindingsThe results indicate that the production volume and material type have contingency effects on AM-enabled product quality, cost reduction and green capabilities. Besides, it has been demonstrated that the degree of a country's economic development and the firm's experience have contingency impacts on AM's capabilities as well.Research limitations/implicationsThe contextual settings employed in this study are limited. A future contingency analysis requires further exploration of other factors (e.g. different AM technologies or application sectors) through in-depth case studies. Future studies can also be built upon the proposed framework to generalize the model for analyzing other emerging manufacturing technologies.Practical implicationsUncertainties around AM implementation and its consequences place the context of evaluation as an essential facet. The derived insights aid practitioners in aligning the firm's internal characteristics (i.e. manufacturing and organizational contexts) with AM's promising competitive capabilities.Originality/valueThe study is among the first analysis to empirically and rigorously establishes the contingency effects of manufacturing and organizational factors on competitive capabilities related to AM, using a representative sample of manufacturers spanning different countries, firm sizes and other investigated manufacturing contexts.