Artificial intelligence for decision support systems in the field of operations research: review and future scope of research

Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

     

Conductivity studies of plasticized proton conducting PVA–PVIM blend doped with NH4BF4

The proton conducting solid-state polymer electrolyte comprising blend of poly(vinyl alcohol) (PVA) and poly(N-vinylimidazole) (PVIM), ammonium tetrafluoroborate (NH₄BF₄) as salt, and polyethylene glycol (PEG) (molecular weight 300 and 600) as plasticizer is prepared at various compositions by solution cast technique. The prepared films are characterized by Fourier transform infrared spectroscopy, differential scanning calorimetry, X-ray diffraction, and scanning electron microscopy analysis. The conductivity–temperature plots are found to follow an Arrhenius nature. The conductivity of solid polymer electrolytes is found to depend on salt and plasticizer content and also on the dielectric constant value and molecular weight of the plasticizer. Maximum ionic conductivity values of 2.20?×?10^?4 and 1.28?×?10^?4?S?cm^?1 at 30 °C are obtained for the system (PVA–PVIM)?+?20 wt.% NH₄BF₄?+?150 wt.% PEG300 and (PVA–PVIM)?+?20 wt.% NH₄BF₄?+?150 wt.% PEG300, respectively. The blended polymer, complexed with salt and plasticizer, is shown to be a predominantly ionic conductor. The proton transport in the system may be expected to follow Grotthuss-type mechanism.     

Measurement of ionic conductivity of poly (<Emphasis Type="Italic">N</Emphasis>-vinylimidazole) and its fluoroborate salt in solid state

The conductivity of poly(N-vinylimidazole) (PVIM) and its fluoroborate salt (PVIM–HBF₄) are reported here. N-vinylimidazole is polymerized by free radical method and PVIM–HBF₄ is prepared by acidification of PVIM with HBF₄. The polyelectrolyte so formed has been characterized by infrared, hydrogen-1 nuclear magnetic resonance, thermogravimetric analyzer, and differential scanning calorimetry. Frequency and temperature dependence of AC conductivity has been studied to learn about the electrical conduction behavior in the materials. The electrical conductivity of the new material is found to be in the range of 10⁻⁵ to 10⁻⁶ S cm⁻¹.There is about 10²- to 10³-fold increase in conductivity of the polyelectrolyte. The material is shown to be a predominantly ionic conductor with t _ion ≈ 0.88. Apparent activation energies are found to be 0.397 and 0.250 eV for the polymer and the polyelectrolyte, respectively.