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In today's volatile global marketplace co-developing of new products is increasingly important, owing to the uncertainties of developing and launching a new product. The collaborative new product development process however, presents a new challenge for the partner firms due to the sharing of information, resources and technology. Literature has not adequately addressed the issues associated with collaborative decision making that has to be robust in an environment with varying performance, quality and timing uncertainties, despite the growing need to collaborate. In our model, we consider a product development company and a technology development company with different but symbiotic capabilities. We analyze various scenarios, assessing the benefits and downsides of demand forecast information sharing on each company and the supply chain as a whole. The scenarios analyzed vary according to the level of technology, innovation and resources shared between the firms.

This paper models a humanitarian relief chain that includes a relief goods supply chain and an evacuation chain in case of a natural disaster. Optimum network flow is studied for both the chains by considering three conflicting objectives, namely demand satisfaction in relief chain, demand satisfaction in evacuation chain and overall logistics cost.The relief goods supply chain consists of three echelons: suppliers, relief camps and affected areas. The evacuation chain consists of two echelons: evacuation camps and affected areas. The model has been made more resilient by considering multiple paths between any two locations and disruption of camps and paths due to natural factors. The Mixed Integer Programming problem has been solved using NSGA-III and results have been compared to those from benchmark algorithms. The model has been successfully tested on generated real-life-like data.

The optimization of machining parameters is critical to the quality of machined products and the production rate. This paper aims to optimize the surface roughness of aluminium-2014 alloy by adjusting the machining parameters of computer numerical control (CNC) turning, including, cutting speed, depth of cut and feed rate. According to L9 orthogonal array, a total of nine experiments were conducted according to Taguchi method with different parameter settings. The surface roughness of the machined products was measured by a roughness tester, and evaluated by signal-to-noise ratio (SNR). The analysis of variance (ANOVA) was conducted to find the optimal parameter settings for surface roughness. The results show that the cutting speed is the most influential parameter (67.28 %) on surface roughness, followed by feed rate (32.28 %) and depth of cut (0.33 %) for surface roughness. Hence, the surface roughness can be optimized by minimizing the feed rate and depth of cut.

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